Ms. Lora W. Johnson, CMC July 19, 2019 Page 2 of 2 Should you have any questions regarding the above, I may be reached at (504) 576-2984. Thank you for your assistance with this matter. Sincerely, Harry M. Barton HMB/bkd Enclosures cc: Official Service List (Public Version via email) 2018 Integrated Resource Plan July 2019 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION Contents Section 1. Executive Summary .........................................................................................4 2. Planning Objectives and Principles ...............................................................................8 2.1 Planning Objectives .................................................................................................................. 8 2.2 Planning Principles ................................................................................................................... 9 3. ENO Generation, Transmission, and Distribution .......................................................10 Generation .................................................................................................................................... 10 3.1 Current Fleet ........................................................................................................................... 10 3.2 Existing Fleet Deactivation Assumptions ............................................................................... 12 3.2.1 Union Power Block 1 ................................................................................................................................. 13 3.2.2 Affiliate PPAs............................................................................................................................................. 13 3.3 Load Forecasting Methodology .............................................................................................. 13 3.3.1 Sales Forecasting....................................................................................................................................... 15 3.3.2 Load Forecasting ....................................................................................................................................... 19 3.4 Resource Portfolio Needs........................................................................................................ 19 3.4.1 Long-term Capacity Considerations .......................................................................................................... 19 3.4.2 ENO’s Expected Energy Coverage ............................................................................................................ 20 3.4.3 Resource Life Assumptions ........................................................................................................................ 21 3.5 Summary of Types of Resources Needed ............................................................................... 21 Transmission ................................................................................................................................. 22 3.6 Participation in MISO ............................................................................................................. 22 3.7 Transmission Planning ............................................................................................................ 23 3.7.1 Integration of Transmission and Resource Planning................................................................................. 24 3.8 Resource Adequacy and Planning Reserve Requirements ...................................................... 25 3.8.1 MISO Resource Adequacy Requirements .................................................................................................. 25 3.8.2 Long-Term Planning Reserve Margin Targets .......................................................................................... 25 3.9 Distribution ............................................................................................................................. 26 3.9.1 DER/Distribution Planning Requirements ................................................................................................. 26 3.9.2 Company Work to Develop DER Planning Capabilities ........................................................................... 26 3.9.3 Grid Infrastructure .................................................................................................................................... 26 3.9.4 Smart Infrastructure & Software Systems .................................................................................................. 27 3.9.5 Advanced Planning .................................................................................................................................... 30 3.10 DOE Grant Proposal—DERadio........................................................................................... 32 4. Planning Assumptions, Inputs, and Considerations .....................................................33 4.1 Evolving Customer Preferences .............................................................................................. 33 Integrated Resource Plan ................................................................................................34 2 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION 4.2 Assessing Alternatives to Meet Customer Resource Needs ................................................... 35 4.2.1 Technology Assessment.............................................................................................................................. 35 4.2.2 Renewables (Solar PV and Wind) .............................................................................................................. 37 4.2.3 Energy Storage Systems ............................................................................................................................. 41 4.2.4 Demand-Side Management ........................................................................................................................ 42 4.2.5 Energy Efficiency ....................................................................................................................................... 44 4.2.6 Demand Response ...................................................................................................................................... 47 4.3 Natural Gas Price Forecast...................................................................................................... 50 4.4 CO2 Price Assumptions ........................................................................................................... 51 5. Portfolio Design Analytics ........................................................................................53 5.1 Planning Scenarios .................................................................................................................. 53 5.1.1 Scenario 1: ................................................................................................................................................. 54 5.1.2 Scenario 2: ................................................................................................................................................. 54 5.1.3 Scenario 3: ................................................................................................................................................. 54 5.2 Planning Strategies Overview ................................................................................................. 55 5.2.1 Strategy 1:.................................................................................................................................................. 55 5.2.2 Strategy 2:.................................................................................................................................................. 55 5.2.3 Strategy 3:.................................................................................................................................................. 55 5.2.4 Strategy 4:.................................................................................................................................................. 55 5.2.5 Strategy 5:.................................................................................................................................................. 56 5.3 Market Modeling .................................................................................................................... 56 5.4 Capacity Optimization and Results ......................................................................................... 57 5.4.1 Solar Capacity Credit Modeling:............................................................................................................... 60 5.5 Total Relevant Supply Cost Results......................................................................................... 62 5.6 Stochastic Assessment of Risks............................................................................................... 64 5.7 Scorecard Metrics and Results ............................................................................................... 66 6. Action Plan ................................................................................................................69 6.1 Recap of 2015 IRP Action Plan ................................................................................................ 69 6.2 2018 IRP Action Plan ............................................................................................................... 71 Appendix A: Rules Compliance Matrix ......................................................................73 Appendix B: Actual Historic Load and Load Forecast ..............................................99 Appendix C: Total Relevant Supply Costs – Detail .................................................. 114 3 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION Section 1 1. Executive Summary Productive Collaboration The process leading up to this 2018 Integrated Resource Plan (“IRP”) report has undoubtedly been the most collaborative to date. Working under the new IRP Rules developed through the Council for the City of New Orleans’s (“Council’s”) 2017 rulemaking,1 the parties have engaged in a series of constructive discussions at four technical meetings over the last 18 months about the inputs and analysis required to develop the IRP.2 The result is a report that meets the goal expressed in the preamble to the new Rules: “It is the Council’s desire that a comprehensive IRP conducted in accordance with these IRP Rules provide a full picture of all reasonably available resource options in light of current and expected market conditions and technology trends, and generate an informed understanding of the economic, reliability, and risk evaluation of utility resource planning as well as associated social and environmental impacts [emphasis added].” The following summaries provide additional context on these key elements: • 1 2 3 A full picture—This IRP provides a broad view of options for meeting customers’ electrical needs across the 20-year planning period from 2019-38 in light of current and expected market conditions and technology trends. The analysis was built on three different planning Scenarios that varied a number of key assumptions about future market conditions outside New Orleans and five different planning Strategies that assessed policy and planning objectives within the city. The parameters of these Scenarios and Strategies were discussed and agreed upon by all parties during the stakeholder process outlined in the new IRP Rules. An important variable among the five Strategies involved the assumed potential savings from and costs of Demand Side Management (“DSM”) programs over the 20-year period. These assumptions came from two DSM Potential Studies—the study prepared by Optimal Energy and the one prepared by Navigant Consulting. The parties agreed on assignments of DSM input cases from one study or the other to each of the five Strategies for use in the analysis. A discussion of the Scenarios and Strategies can be found in Section 5.1 and Section 5.2.3 See, Council Docket No. UD-17-01. Technical Meeting #1 was held on January 22, 2018, Technical Meeting #2 on September 14, 2018, Technical Meeting #3 on November 28, 2018, and Technical Meeting #4 on May 1, 2019. While the inputs and Council policies to be considered in this 2018 IRP analysis were finalized prior to the initiation of the Council’s Renewable Portfolio Standard (“RPS”) Rulemaking in Docket UD-19-01, the Company recognizes that future IRP proceedings will clearly be affected by any policies resulting from that rulemaking. To that end, ENO has filed two 4 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION • All reasonably available resource options—Each Strategy was analyzed in the context of each Scenario to identify an optimized Portfolio of resources to serve customers’ needs under that combination of assumptions. Given the combination of three Scenarios times five Strategies, this resulted in an initial set of 15 Optimized Portfolios. The parties reviewed this initial set and agreed on a representative subset of five Portfolios to carry through the remainder of the detailed total relevant supply cost analysis. These Portfolios include different combinations of renewables, battery storage, combustion turbines, and DSM programs depending on their particular assumptions. A discussion of the down-selected set of five Portfolios can be found in Section 5.5. • Economic, reliability, and risk evaluation—The total relevant supply cost analysis, which represents the cost to serve customers’ resource needs reliably under the assumptions of that Portfolio through the planning horizon, used cross-testing to identify a 20-year revenue requirement for each of the five Portfolios in all three Scenarios. In order to work within schedule and resource constraints, the parties agreed to a framework under which stochastic analysis was conducted on four of the five Portfolios to evaluate their sensitivity to changes in two main input assumptions—natural gas price and CO2 price. Information on the total relevant supply cost and risk analysis can be found in Section 5.5 and Section 5.6. • Social and environmental impacts—The new IRP Rules required the development of a scorecard to assist the Council in assessing the IRP based on several aspects of the Resource Portfolios, including social and environmental impacts, some of which are only able to be evaluated on a subjective basis. Given the difficulties inherent in trying to compare Portfolios developed under different assumptions across subjective and objective characteristics, the parties agreed on a framework for this initial scorecard, found in Section 5.7. Practical Considerations There are two important points to bear in mind while reviewing this IRP report. First, the analysis conducted here shows that Entergy New Orleans, LLC (“ENO” or the “Company”) will not have a capacity need for new resources until 2033 (the year Union 1 is assumed to deactivate) based on current assumptions. With the removal of the requirement that a preferred Portfolio be identified, the value of the IRP as a general planning and strategic study has been emphasized. Thus, there are two main uses for this IRP—as the long-term planning tool contemplated in the preamble to the Rules that can inform the Council and ENO about a wide range of possibilities for serving customer needs in the future, and as a near-term source to inform the implementation of Energy Smart DSM programs in the city over the next few years. The second main point is that the use of DSM input cases from two different potential studies greatly rounds of comments presenting various analyses of the relative benefit to customers of a Clean Energy Standard that recognizes the value of existing nuclear generation, electrification efforts, demand-side management, and flexible means of compliance as compared to a Renewable Portfolio Standard that mandates compliance through prescribed amounts, types, and locations of particular renewable technologies. 5 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION increased both the complexity of the analysis and the difficulty in comparing Portfolios and total relevant supply costs. The use of two DSM potential studies, as contemplated by the new IRP Rules, presented a novel issue for consideration among the parties and highlighted associated challenges in conducting the IRP analysis. This may, in fact, be the first time that two different potential studies have been used in the same IRP analysis in any jurisdiction. Given the requirements of the Rules and the discussions among the parties that led to the identification of the subset of five Portfolios, the Resource Portfolios include a mix of DSM cases —three Portfolios using three different Navigant cases (Base, 2%, and High) and two using the Optimal Program Achievable case. Despite starting from the same set of data provided by ENO, historical Energy Smart results, and current implementation information, Navigant and Optimal drew very different conclusions about the achievable DSM potential and the costs to implement programs to capture it. The net effect for the IRP is that the two Portfolios using Optimal cases show generally lower total relevant supply costs due mainly to the large amounts of kWh and kW savings assumed over the 20-year period that Optimal believes can be achieved at a, perhaps unrealistically, low cost. It is important to note that the total costs of Portfolios incorporating DSM inputs from the two different studies cannot be directly compared. However, the costs of any Portfolio can be considered for the Scenario in which it was initially optimized, compared to the other Scenarios in which it was cross-tested, and to a similar degree compared to other Portfolios created using DSM inputs from the same potential study. One key takeaway from the Portfolio data is that over the 20-year planning period, the spread between the lowest and highest total relevant supply costs is relatively small. Looking at the total costs for the five selected Portfolios in the Scenarios where they were initially optimized, the difference from the lowest (Portfolio 5 with the Optimal Program Achievable case) to the highest (Portfolio 2 with the Navigant 2% case) is about 18%. In other words, the comparative value of this IRP report comes from considering the different inputs, assumptions, and risk sensitivities of each Portfolio as a guide for the future, not from focusing on the costs of one Portfolio versus another, particularly given the use of different DSM inputs and the fact that actual costs in the future will be driven by resource certifications and DSM implementations that rely on then-current, actual market costs. The difference between the two potential studies raises another, more immediate issue regarding their suitability as foundations for actual DSM program implementation plans. As will be discussed further in this report, ENO has significant doubts about the possibility of developing a viable implementation plan based solely on Optimal’s study given several factors, including but not limited to: (i) its assumption that administrative costs could be held to less than 25% when experience with Energy Smart and programs in other jurisdictions comparable to New Orleans4 has shown that implementation costs are generally higher 4 The Optimal study notes, at page 74, that data concerning administrative costs of program measures was “sourced from recent program performance in New England, the MidAtlantic states, and Minnesota.” The study also notes that data from these sampled jurisdictions shows that the “average administrative costs for the various program types range from 25 percent to 37 percent.” Optimal provided no explanation as to why its study avoided including data from Southeastern utilities, or 6 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION than what Optimal’s study assumes, (ii) its overall assumptions about the cost of programs necessary to achieve the high level of savings projected in the study, and (iii) its assumptions around measure-level savings that in some cases appear overly aggressive for New Orleans based on actual experience and results with Energy Smart, as confirmed through independent Evaluation, Measurement, and Verification (“EM&V”). That said, the inclusion of two DSM Potential Studies in the IRP process has allowed for varied and diverse perspectives to inform views on demand-side resources and ENO intends to develop its Implementation Plan drawing on both studies. Additional Focus Areas The new Rules require that ENO discuss its progress towards developing the capability to optimize the value of distributed energy resources on the distribution grid. Several ongoing efforts are key to developing this capability, including the implementation of Automated Metering Infrastructure (“AMI”) and its associated software systems, the execution of Grid Modernization projects, the implementation of the LoadSEER application, which, when coupled with AMI, its associated software, and resulting data, can enable bottom-up capacity analysis at the feeder level, and the utilization of additional functionality in existing software applications. Section 3.9 of this report includes more detail on these important efforts. Section 3.6 and Section 3.7 provides an overview of ENO’s transmission planning activities as a market participant in Midcontinent Independent System Operator, Inc. (“MISO”) and the relationship of those activities to resource planning, as required by Section 6 of the new IRP Rules. While the new IRP Rules no longer mandate identification of a preferred Portfolio, there are numerous ongoing and planned activities that are important to supporting Council goals and Company initiatives in the short term. Some of these include filing the Implementation Plan for the next few years of Energy Smart programs in the city, continuing to support the Council’s Smart Cities initiative, and completing the AMI and Grid Modernization work necessary to support critical goals and policies in the future. The Action Plan for pursuing these efforts is found in Section 6.2. In conclusion, ENO greatly appreciates the collaborative efforts of the Council, its Advisors, Intervenors, and the public that resulted in this 2018 IRP report. As the first effort under the Council’s new IRP Rules, the result is an instructive view of resource options under a range of possible future Scenarios that should be useful in ongoing discussions about meeting the electricity needs of ENO’s customers. why Optimal used cost inputs at the absolute lowest level supported by its sample set. Optimal also admitted in discovery that it made no adjustments to account for changes to economies of scale attributable to the fact that its selected samples were from jurisdictions with state-wide programs, whereas Energy Smart is only available to customers in New Orleans. 7 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION Section 2 2. Planning Objectives and Principles Under the Council’s new IRP Rules, the planning process seeks to identify Portfolios of supply- and demand-side resources that could reliably meet customer power needs across a range of possible future Scenarios at the lowest reasonable supply cost, while considering risk. This work is particularly relevant given the ongoing evolution of the electric utility industry, and ENO’s continued focus on meeting its customers’ needs and expectations. 2.1 Planning Objectives While the utility environment may be changing, ENO strives to achieve a balance between providing customers sustainably-sourced, reliable power, at the lowest reasonable supply cost, while considering risk. The ENO IRP was developed consistent with these objectives and in accordance with the following objectives articulated in Section 3 of the Council’s IRP Rules: 1. Optimize the integration of supply-side resources and demand-side resources, while taking into account transmission and distribution, to provide New Orleans ratepayers with reliable electricity at the lowest practicable cost given an acceptable level of risk; 2. Maintain the Utility's financial integrity; 3. Anticipate and mitigate risks associated with fuel and market prices, environmental compliance costs, and other economic factors; 4. Support the resiliency and sustainability of the Utility's systems in New Orleans; 5. Comply with local, state and federal regulatory requirements and known policies (including such policies identified in the Initiating Resolution) established by the Council; 6. Evaluate the appropriateness of incorporating advances in technology, including, but not limited to, renewable energy, storage, and distributed energy resources (“DERs”), among others; 7. Achieve a range of acceptable risk in the trade-off between cost and risk; and 8. Maintain transparency and engagement with stakeholders throughout the IRP process by conducting technical conferences and providing for stakeholder feedback regarding the Planning Scenarios, Planning Strategies, input parameters, and assumptions. 8 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION 2.2 Planning Principles In designing Portfolios to achieve the planning objectives listed above, the planning process is guided by the following principles: • • • • • • • Capacity - Provide adequate capacity to meet customer needs measured by non-coincident peak load plus a long-term planning reserve margin, accounting for impacts from DSM programs. Base Load Production Cost - Provide resources to economically meet base load requirements at reasonably stable prices. Load Following Production Cost - Provide economically dispatchable resources capable of responding to the varying needs of customers as driven by such factors as hourly demands, weather, and the integration of renewable generation. Modern Portfolio - Leverage modern, efficient supply alternatives Price Stability - Mitigate exposure to price volatility associated with uncertainties in fuel and purchased power costs. Supply Diversity - Mitigate exposure to risks that that may occur through concentration of Portfolio attributes such as technology, location, large capital commitments, or supply channels. In-Region Resources - Avoid overreliance on remote resources; provide adequate amounts and types of in-region resources to meet area needs reliably at a reasonable cost. 9 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION Section 3 3. ENO Generation, Transmission, and Distribution Generation 3.1 Current Fleet As shown in Figure 1, below, which accounts for approved and planned resource additions, ENO has been successful in transforming its portfolio with reliable, efficient gas-fired generation, renewables generation, and load modifying resources to meet its supply needs. Figure 1: ENO's Evolving Portfolio ENO currently controls about 1.2 GW of generating capacity either through direct ownership or contracts with affiliate Entergy Operating Companies and other counterparties. Table 1 below shows ENO’s supply resources by fuel type measured in installed capacity with percentages of the overall Portfolio, taking into account existing units and planned additions. 10 PUBLIC VERSION Entergy New Orleans, LLC, 2018 Integrated Resource Plan Table 1: ENO’s 2021 Resource Portfolio – Fuel Mix ENO’s Resource Portfolio: Fuel Type MW % Coal 33 2% Nuclear 422 30% CCGT 623 45% CT/ RICE 129 9% Legacy Gas 58 4% Solar 100 7% Load Modifying Resources 22 2% Third Party PPAs 11 1% 1,399 100% Total ENO’s Portfolio by unit is shown in the table below. Table 2: ENO’s 2021 Resource Portfolio by Unit Plant ANO ANO Acadia Grand Gulf ENMP Grand Gulf ELMP Grand Gulf EAMP Independence Little Gypsy Little Gypsy Ninemile Ninemile Ninemile Perryville Perryville Riverbend 30 Unit MW Fuel Typical Operating Role 1 2 23 27 7 216 3 32 7 8 10 13 14 118 2 1 99 Nuclear Nuclear Natural Gas Nuclear Nuclear Nuclear Coal Natural Gas Natural Gas Natural Gas Natural Gas Natural Gas Natural Gas Natural Gas Nuclear Base Load/ Load Following Base Load/ Load Following Base Load/ Load Following Base Load/ Load Following Base Load/ Load Following Base Load/ Load Following Base Load/ Load Following Seasonal Load Following Seasonal Load Following Seasonal Load Following Seasonal Load Following Base Load/ Load Following Base Load/ Load Following Peaking Base Load/ Load Following 1 2 3 4 5 6 1 2 Operation Date 1974 1980 2002 1985 1985 1985 1983 1966 1969 1971 1973 2015 2002 2001 1986 11 PUBLIC VERSION Entergy New Orleans, LLC, 2018 Integrated Resource Plan Waterford Waterford Waterford Waterford White Bluff White Bluff Sterlington 7A Union PB LMR (Load Modifying Resource) Third Party PPAs New Orleans Power Station ENO Rooftop Solar Project ENO Renewables RFP 2 ENO Renewables RFP 3 ENO Renewables RFP 4 ENOI Solar Total 1 2 3 4 1 2 7 7 22 1 12 13 1 496 Natural Gas Natural Gas Nuclear Natural Gas Coal Coal Natural Gas Natural Gas Seasonal Load Following Seasonal Load Following Base Load/ Load Following Peaking Base Load/ Load Following Base Load/ Load Following Peaking/ Reserves Base Load/ Load Following 1975 1975 1985 2009 1980 1981 1974 2016 22 N/A Peaking/ Reserves - 11 N/A N/A - 1 128 Natural Gas Peaking/ Reserves 2020 1 5 Solar Peaking/ Reserves 2019 1 1 1 1 - 50 20 20 5 1,399 Solar Solar Solar Solar Peaking/ Reserves Peaking/ Reserves Peaking/ Reserves Peaking/ Reserves 2021 2021 2021 2021 1 3.2 Existing Fleet Deactivation Assumptions The IRP includes deactivation assumptions for existing generation in order to plan for and evaluate the best options for replacing that capacity over the planning horizon. Based on current planning assumptions, during the planning period, the total net reduction in ENO’s generating capacity from the anticipated unit deactivations is expected to be approximately 650 MWs. Generally, current planning assumptions reflect generic deactivation assumptions for the generation fleet: 60 years for coal and legacy gas resources, and 30 years for combustion turbine (“CT”) technology (CTs and combined cycle gas turbines (“CCGTs”)). As resources age and assumed deactivation dates near, as equipment failures occur, or as operating performance diminishes, cross-functional teams are assembled within the Company to evaluate whether to keep a particular unit in service for an additional length of time at an acceptable level of cost and reliability. These deactivation assumptions do not constitute a definitive deactivation schedule but are based upon the best available information and are used as planning tools to help prompt cross-functional reviews and recommendations.5 It is not unusual for these assumptions to change over time, given the dynamic use and 5 In Council Resolution No. R-17-332, adopting the new IRP Rules, the Council found that the IRP should not be used to evaluate resource deactivation decisions. See R-17-332 at pg. 26. (“[T]he Council agrees that requiring the type of analysis 12 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION operating characteristics of generating resources. ENO’s unit deactivation assumptions for the 2018 IRP are outlined below.6 3.2.1 Union Power Block 1 Deactivation currently assumed for Union 1 is 2033. This is a generic planning assumption only and does not reflect unit-specific analysis or decisions. As stated above, this resource will be reevaluated as it ages and operating conditions change. As shown in Table 2, above, Union 1 accounts for approximately 495 MW of capacity for ENO. 3.2.2 Affiliate PPAs ENO receives allocations of several units through affiliate life-of-unit Purchased Power Agreements (“PPAs”) that could deactivate during the planning period. These resource deactivations are assumed to total approximately 150 MW of capacity for ENO. 3.3 Load Forecasting Methodology A wide range of factors will affect electric load over the planning horizon, including: • Levels of economic activity and growth; • The potential for technological change to affect the efficiency of electricity consumption; • Potential changes in the purposes for which customers use electricity (e.g., replacement of vehicles that operate using internal combustion engines with vehicles that operate using electric motors); • The potential adoption of end-use (behind-the-meter) self-generation technologies (e.g., rooftop solar panels); and • The level of energy efficiency, conservation measures, and distributed generation adopted by customers. Such factors may affect both the levels and patterns of electricity consumption in the future. Peak loads may be higher or lower than projected levels. Uncertainties in load levels and patterns may affect both the amount and type of resources required to efficiently meet customer needs in the future. The long-term load forecast is an hour-by-hour, 20-year forecast of MW consumption. The preparation of the long-term load forecast involves two distinct and sequential processes: (1) electric sales forecasting and (2) load forecasting. In the first process, the monthly sales are forecasted assuming normal weather across the forecast horizon. The second process takes the monthly sales forecast and develops monthly peaks and allocates the monthly MWh to individual hourly MW based on hourly consumption profiles or shapes. These processes are discussed in more detail below. 6 performed for resource retirement proceedings to be performed for every portfolio considered in an IRP process would be unduly burdensome and create a proceeding that would consume an unreasonable amount of time and resources.”) It should be noted that actual deactivation decisions are confidential due to the commercial sensitivity of the decision. 13 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION For the 2018 IRP three load forecasts were produced as part of the analytical framework. As described further in Section 5: Portfolio Design Analytics, the IRP relies on a number of assumptions to assess Resource Portfolios across a range of economic outcomes. This includes sensitivities around the load forecast impacting both ENO, specifically, and the broader MISO market.7 Table 3: Forecast Sensitivities Sensitivity Drivers • Low Reference 7 Residential and Commercial customer growth rates decreased by 15% and 25% respectively o Job growth does not materialize in the area o Brick and mortar retail stores continue closing in the face of online competition o Residential and Commercial Energy Efficiency increases 25% o Energy efficient appliance technology continues to advance o LED light bulbs continue to get cheaper with higher adoption o Commercial electricity prices increase by 10% with elasticity of -0.2 • Industrial o Fewer new projects come online as well as reduced output from existing customers o Large and Small Industrial growth rates decreased by 20%  o Customers add more cogeneration and solar to offset power consumption • Louisiana’s natural resources and tax structure create opportunities for new large and small industrial sales • Increases in heating and cooling efficiency • LED lighting becoming more affordable and common • Use per customer declines in Residential and Commercial, partially offset by growth in customer counts Pursuant to Council Resolution R-19-78, ENO and the Sewerage & Water Board of New Orleans (“S&WB”) have formed a Joint Reliability Team (“JRT”) to collaborate in developing solutions to help ensure the reliability of electric service to S&WB facilities, and to facilitate the transition of S&WB from relying on aging and inefficient generation at its Carrollton plant, to ENO as the primary source of reliable and economic power. The team has been engaged in discussions regarding the construction of a new transmission voltage substation adjacent to S&WB’s Carrollton plant that would enable ENO to serve the S&WB’s electric loads related to drainage pumping and water purification. It should be noted that the load forecast used in the IRP analysis does not include any assumption around load that might be added as a result of this long-term solution. 14 Entergy New Orleans, LLC, 2018 Integrated Resource Plan • High • • PUBLIC VERSION Residential and Commercial customer count growth rate increased by 25% and 10% respectively o Residential appliance energy efficiency decreased by 25% o LED light bulb penetration weaker than anticipated Department of Energy discontinues Energy Star program used to incentivize businesses to create more efficient appliances Large and small industrial sales growth rates increased by 10% and realization of speculative projects 3.3.1 Sales Forecasting The sales forecast is developed using a bottom-up approach by customer class – residential, commercial, large industrial, small industrial, and governmental. The High and the Low scenarios are sensitivities based on the Reference Case, which is the same as was used in the Company’s 2019 Business Plan or “BP19”. The Reference Case forecast was developed using historical sales volumes and customer counts, as well as historical and estimated normal weather, economic, and energy efficiency measures. In addition, the forecast includes estimates for future growth in large industrial usage as well as estimates of future growth from electric vehicles and declines due to future rooftop solar adoption. For each sensitivity, the monthly sales forecasts are converted to hourly load forecasts using historical hourly load shapes and specific shapes for the daytime effects of rooftop solar. Because many of the drivers of the load forecast are assembled to first develop the underlying sales forecast in terms of annual MWh, many of the explanations below refer to the sales forecasts. Overall, the compound annual growth rate (“CAGR”) for 2019-2038 for the Reference Case forecast is 0.22%/year. This growth is primarily driven by growth for the industrial class of customers and is offset somewhat by slight growth for the residential, commercial, and governmental classes. Those forecasts are discussed further below. Methodology: The sales forecasts for the residential, commercial, small industrial, and governmental classes are developed individually using statistical regression software and a mix of historical data and forward-looking data. The historical data primarily includes monthly sales volumes by class and temperature data expressed as cooling degree days (“CDDs”) and heating degree days (“HDDs”). Some of the forecasts also use historical indices for elements such as population, employment, and levels of enduse consumption for things such as heating/cooling, refrigeration, and lighting. These historical data are used in econometric forecasting software called Metrix ND, which is licensed from Itron. This software is used to develop statistical relationships between historical consumption levels and explanatory variables such as weather, economic factors, and/or time periods and those relationships are applied going forward to estimates of normal weather, economic factors, and/or time periods to develop the forecast. Autoregressive and moving average variables are also included in the models to account for time series effects when significant. Explanatory variables are included in each forecast model if the significance is greater than 95%. The sales forecasts assume weather to be “normal.” For this purpose, normal weather is defined as a 20- 15 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION year average of temperatures by month. The use of 20 years strikes a reasonable balance between longer periods (30 years), which may take longer to pick up changing weather trends and shorter periods (10 years), which may not provide enough data points to smooth out volatility. The 20-year averages are built from hourly temperatures and are allocated to each calendar month based on the billing cycles for each month to ensure that the resulting averages appropriately consider the temperatures on the days when the power was consumed. Residential: Growth in residential sales is expected to be relatively flat through 2038 with a forecast CAGR of 0.3%/year for 2019-2038 due to several factors. By 2021, residential sales are assumed to decline by 1.5% due to ENO’s installation of the AMI metering and the accompanying consumption information that will be available to customers to help them manage their usage. The 1.5% expected reduction is the combination of a 1.75% reduction in electricity consumption offset by a 0.25% increase in billed sales related to unaccounted for energy. The decrement is phased in over three years starting in 2019. In addition, the forecast assumes future levels of energy efficiency putting downward pressure on electricity consumption. The energy efficiency is expected to come primarily from cooling and lighting and is based on future consumption estimates from the Energy Information Agency (“EIA”) and is separate from company-sponsored DSM discussed further below. Overall, average annual kWh consumption per household is expected to decline by 0.3%/year for 2019 – 2038. The monthly model for residential use per customer, taking into account expected efficiency, is: Residential use per customer per day = Heating Degree Daysm * Heating efficiency indexm * Heating coefficientm + Cooling Degree Daysm * Cooling efficiency indexm * Cooling coefficientm + other use coefficient * other use efficiency indexm Forecasting use per billing day increases the monthly forecast accuracy because the days in a billing cycle vary from month to month. Monthly heating and cooling coefficients are used in the regression because generally a degree day in August has more effect than a degree day in May. Actual historical weather is used in the regression model. The twenty-year normal weather is used for forecasting normal sales. Offsetting declines in use per customer are expectations for customer count growth. Based on historical growth in customer counts as well as expected future growth in the population and numbers of households in New Orleans, ENO has forecasted residential customer growth of 0.6%/year for 2019-2038. The combined effect of lower usage per customer (“UPC”) (resulting from AMI, energy efficiency, etc.) and increasing customer count growth leads to a net forecasted CAGR in residential electricity sales of 0.3%/year for 2019-2038. See Table 4 showing the breakdown for the 2019-2038 CAGRs in Residential energy, customer counts, and household counts. 16 PUBLIC VERSION Entergy New Orleans, LLC, 2018 Integrated Resource Plan Table 4: YoY Growth Residential 2019-2038 CAGR Energy 0.3% Customers 0.6% UPC -0.3% Commercial Forecast: Commercial sales are forecasted to have very modest growth for 2019-2038 with a CAGR of 0.1%/yr. This is being driven by forecasted customer count growth of 0.4% per year offset by commercial UPC declines of 0.3%/year. The explanations for the commercial class are very similar to those for the residential class in that the commercial forecast includes a net decrement of 1.5% by 2021 (phased-in starting in 2019) for the AMI installations and related customer information that will be available to help customers manage their electricity usage. In addition, the commercial forecast accounts for increased energy efficiency, primarily from HVAC, lighting, and refrigeration, that is separate from company-sponsored DSM discussed further below. Monthly commercial sales are forecasted in total rather than by use per customer because of the diversity of commercial customers. Commercial Salesm= Heating Degree Days * Heating efficiency index * Heating coefficientm + Cooling Degree Days * Cooling efficiency index * Cooling coefficientm + other use coefficient * other use efficiency indexm See Table 5 showing the breakdown for the 2019-2038 CAGRs for commercial sales, commercial customer counts, and average use per customer. Table 5: YoY Growth Commercial 2019-2038 CAGR Energy 0.1% Customers 0.4% UPC -0.3% 17 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION DSM: The forecast for ENO also considers the effects of company-sponsored DSM programs. Historical levels of DSM effects are added back to historical sales to produce an initial forecast as if there had been no DSM. The estimated future levels of DSM are then subtracted from the forecasted levels based on the accumulated and carry-forward effects of historical programs as well as budgeted estimates for future DSM savings. For example, a program from two years prior to encourage conversion of incandescent lighting to LED lighting is still expected to lower consumption this year and beyond as the newer, more efficient lighting continues to operate. As such, DSM programs have useful lives that extend beyond the first measure year of the program. The DSM effects are calculated at the class level for residential and commercial sales and reduce the forecasted load based on the residential and commercial load shapes and the expected future DSM volumes. ENO’s DSM programs are expected to reduce Residential and Commercial sales by 2% and 3.5%, respectively, for 2019 and by almost 4% and 9%, respectively, by 2038. Electric Vehicles (“EVs”) and Solar: Forecasts for incremental EVs and rooftop solar are included in the base residential and commercial forecasts. The EV forecast is based on the estimated historical EV adoption rates in Louisiana and allocated to New Orleans based on population since vehicles are registered at the state level and not at the parish level. Future levels of EV adoption and related electricity consumption are projected based on a long-term adoption curve that assumes 95% of all light-duty vehicles will be powered by electricity by the year 2100. By 2038, EVs are expected to add just under 2% to ENO’s residential and commercial electricity consumption. The rooftop solar forecast is based on historical adoption levels as well as future estimates for the installed costs of rooftop panels, tax incentives, and electricity prices. In the base forecast, future levels of rooftop solar adoption are relatively low due to the low electricity prices in New Orleans and the end of state tax credits. By 2038, incremental rooftop solar additions are expected to reduce electricity consumption by less than 2%. Industrial Growth: The industrial class of customers is divided into two groups, small and large. The customers in the large industrial class are forecasted individually and are the main growth driver in the forecast overall. The 2019-2038 CAGR for ENO large industrial sales is 0.95%/year. Forecasts for new or prospective large industrials are based on information from the new/prospective customer and ENO’s Economic Development team as to their expected MW size, operating profile, and ramping schedule. The forecasts are also risk-adjusted based on the status of the customer along the path of signing an electric service agreement and progress towards achieving commercial operations. Existing industrial customers are forecasted based on historical usage, planned future outages, expansions or contractions. The small industrial forecast includes industrial sales that are not forecast individually in the large industrial forecast described above. Forecasts are based on historical trends and IHS economic indices for labor force, refining, and chemicals. Small industrial sales can be volatile and are generally not temperature related. See Table 6 for the forecasted year-over-year growth in sales to industrials. 18 PUBLIC VERSION Entergy New Orleans, LLC, 2018 Integrated Resource Plan Table 6: Industrial Growth YoY Growth in: 2019 - 2025 CAGR Large Industrial Energy Sales 0.95% Small Industrial Energy Sales 0.05% Total Industrial Energy Sales 0.68% 3.3.2 Load Forecasting The long-term hourly load forecast is the result of the calibration of a monthly peak forecast, the monthly sales forecast, and estimated load shapes for each customer class. Like the process used for the sales forecast, twenty years of normal weather data is used to convert historical load shapes into “normal load shapes.” This adjusts the historical consumption profiles by month and hour for year-over-year changes in days of the week, holiday schedules, and temperatures. For example, if the actual sales for ENO’s residential customers occurred during very hot weather conditions, the normal load shape would flatten the historic load shape. If the actual weather were mild, the normal load shape would raise the historic load shape. Each customer class reacts differently to weather, so each has its own weather response function. The peak forecast is developed using historical calendarized sales, historical peaks, and degree days to develop relationships between peaks and energy. Those relationships are applied to the forecasted energy and use normal weather for the future forecast period. As mentioned previously, the forecasted energy, the forecasted peaks, and the forecasted hourly profiles are calibrated together to ensure that all the forecasted energy is accounted for while maintaining, as closely as possible, the forecasted peaks and shapes. Typical load shapes for incremental solar and electric vehicle consumption are used to allocate reduced or increased consumption to the appropriate month, day, and hour of electricity use. The final load forecasts are grossed up to include transmission and distribution losses, which are computed by class. The resulting forecast is for estimated hourly load at the generator. 3.4 Resource Portfolio Needs 3.4.1 Long-term Capacity Considerations Consistent with its planning guidelines, ENO plans to meet its projected peak load requirement plus a 12 percent planning reserve margin based on installed capacity for conventional generation and effective capacity for renewable generation. The requirements shown below reflect this assumption and are adjusted to account for ENO’s current Resource Portfolio reflected in Table 1 and Table 2 above. The requirements evolve over time as forecasted energy use changes and resources are assumed to deactivate. The Low, Medium, and High load sensitivities attempt to bookend the 19 PUBLIC VERSION Entergy New Orleans, LLC, 2018 Integrated Resource Plan effect that changes to customer use patterns could have on ENO’s energy and peak requirements. MW 900 800 700 600 500 400 300 200 100 0 ENO Rooftop Solar Project ENO Renewables RFP 3 Medium ENO Renewables RFP 2 ENO Renewables RFP 4 Low NOPS ENOI Solar High Figure 2: ENO’s Projected Long-Term Resource Requirements Given approved and planned resource additions, across each forecasted load sensitivity ENO expects that around 600 MW of replacement capacity is necessary to account for deactivating generation and load growth over the 20-year planning horizon. 3.4.2 ENO’s Expected Energy Coverage Shown below is ENO’s annual projected energy generation based on the expected commitment and dispatch of its total allocated share of resources in MISO’s energy market. This is compared to the total amount of ENO’s forecasted annual energy requirements. Any gap between generation and load on an annual basis indicates net purchases from the MISO market, and as such, is an indication of the magnitude of customer energy price exposure. 20 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION TWh 10 PPA ENO Solar 8 NOPS 6 CCGT 4 Coal 2 Nuclear 0 ENO Energy Requirement 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 Figure 3: ENO’s Expected Energy Coverage ENO is expected to remain a net seller in MISO’s energy markets during the planning horizon. This energy position provides price stability for ENO’s customers relative to MISO’s day ahead and real time energy markets for economic energy consistent with ENO’s guiding principles of maintaining Base Load Production Costs and Price Stability. 3.4.3 Resource Life Assumptions ENO must make assumptions regarding the longevity of generating assets to conduct Portfolio analytics. For CT and CCGT technology, consistent with guidance from the Electric Power Research Institute (“EPRI”), ENO assumes a 30-year useful life unless unit-specific information is available to support a different assumption. ENO maintains generic useful life assumptions of 30 years and 25 years for tracking solar and onshore wind respectively due to its lack of experience with renewables having operated to the end of their useful lives and deactivated. As more information on useful life of renewables becomes available ENO will look to update these assumptions. As with all assets in its Portfolio, ENO evaluates whether to make investments necessary to keep a particular unit in service for an additional period of time at an acceptable level of operational reliability as the unit nears the end of its assumed useful life, as equipment failures at the unit occur, or as unit operating performance diminishes. 3.5 Summary of Types of Resources Needed As discussed in Section 4.2, there are a number of supply-side and demand-side alternatives available to address ENO’s long-term resource needs. These include incremental long-term resource additions from selfsupply alternatives, acquisitions, and long-term PPAs. Demand-side alternatives including Energy 21 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION Efficiency, Demand Response, and customer-focused products and services can also provide solutions to meet long-term needs. The Portfolio design analytics outlined in more detail below explore the value of renewables, dispatchable supply-side alternatives, and demand-side measures. As the solar industry matures and the capital costs associated with these resources continue to decline, solar is anticipated to become increasingly feasible as a utility-scale supply solution. As intermittent generating additions increase, and ENO’s legacy fleet deactivates, ENO will require additional flexible and quick-start capability. ENO will continue to assess this need over the long-term planning horizon. Transmission 3.6 Participation in MISO ENO has been a market participant in the MISO Regional Transmission Organization (“RTO”) since December 19, 2013. MISO is a non-profit, member-based organization, which exists to provide an independent platform for efficient regional energy markets. MISO conducts transmission planning and manages buying and selling of wholesale electricity across 15 U.S. states and the Canadian province of Manitoba. As shown below, ENO is located within Local Resource Zone (“LRZ”) 9 of the MISO footprint. Figure 4: LRZ 9 within MISO As a MISO member, ENO has access to a large, structured market that enhances the resource alternatives available to meet customers’ near-term power needs. Over the long term, risk associated with the 22 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION availability and price of power in the MISO market affects ENO’s resource planning. Additionally, ENO retains responsibility for providing safe and reliable service to its customers. Thus, the 2018 ENO IRP is designed to help ensure development of a long-term integrated resource plan for ENO that reflects that responsibility and balances the objective of minimizing the cost of service while considering factors that affect risk and reliability. 3.7 Transmission Planning The Company’s transmission planning ensures that its transmission system: (1) remains compliant with applicable North American Electric Reliability Corporation (“NERC”) standards and the Company’s related local planning criteria, and (2) is designed to efficiently deliver energy to end use customers at a reasonable cost. Since joining MISO, ENO also plans its transmission system in accordance with the MISO Tariff. Expansion of, and enhancements to, transmission facilities must be planned well in advance of the need for such improvements given that regulatory approvals, right-of-way acquisition, and construction can take years to complete. Advanced planning requires that computer models be used to evaluate the transmission system in future years, taking into account the planned uses of the system, generation and load forecasts, and planned transmission facilities. On an annual basis, the Company’s Transmission Planning Group performs analyses to determine the reliability and economic performance needs of ENO’s portion of the interconnected transmission system. The projects developed are included in the Long Term Transmission Plan (“LTTP”) for submission to the MISO Transmission Expansion Planning (“MTEP”) process as part of a bottom up planning process for MISO’s consideration and review. The LTTP consists of transmission projects planned to be in service in an ensuing 10‐year planning period. The projects included in the LTTP serve several purposes: to address specific customer needs, to provide economic benefit to customers, to meet NERC transmission planning reliability standards, to facilitate incremental load additions, and to enable transmission service to be sold and generators to interconnect to the electric grid. A key aspect of ENO’s engagement with MISO is its active participation in MISO’s bottom up reliability planning process. This process is designed to ensure that the performance of the ENO transmission system continues to meet reliability standards and is also a key input into MISO’s Market Congestion Planning Study (“MCPS”) process. Through the MCPS, transmission system efficiency is ensured by monitoring and eliminating congestion when benefits outweigh costs. Reliability projects submitted by Transmission Owners are reviewed to determine if potential synergies exist between congestion relief and reliability. In the case of ENO’s transmission system, the baseline reliability plan that includes both transmission upgrades and the New Orleans Power Station currently under construction is expected to ensure compliance with NERC requirements for reliability of the bulk electric system by reducing the risk of cascading outages in New Orleans. In addition, that reliability plan is expected to eliminate much of the congestion in the ENO footprint. The levels of congestion remaining in the ENO footprint once those projects are placed in service are projected to be minimal and thus do not justify additional transmission projects at this time. ENO has identified various reliability projects through the MISO process since 2013. 23 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION Recent transmission projects resulting in reliability and congestion benefits in the ENO service area include the Ninemile to Derbigny and Ninemile – Napoleon 230 kV line upgrades (completed in 2016) and the Paterson to Pontchartrain Park 115 kV Line Reconductor (projected to be completed at the end of 2019). Details of the ENO LTTP projects can be found in the current and past MISO MTEP reports.8 3.7.1 Integration of Transmission and Resource Planning The availability and location of current and future generation on the transmission system can have a significant impact on the long-term transmission plan, the requirements for meeting NERC reliability standards, and efficiently delivering energy to customers at a reasonable cost. Like transmission, new generation must be planned well in advance, and due to the interrelationship of generation and transmission planning, looking far enough into the future and addressing potential supply needs is critical in meeting ENO’s planning objectives of cost, reliability, and risk. As part of its ongoing planning process, ENO considers transmission and capacity requirements and the impacts of generation siting on transmission reliability and voltage support. Inverter-based technology, including solar PV, can produce significant energy benefits and fill an important role as part of ENO’s resource mix. However, consideration must be given to the increased role that dispatchable resources may need to play in maintaining regional reliability as reliance on inverterbased resources increase. First, it is important to note that the load in the region just after sunset is often only slightly less than the peak load for that day. In fact, there are times when the daily peak for the city of New Orleans actually occurs at night. Thus, conventional resources must be capable of quickly ramping up to offset the loss of solar PV energy as the sun sets. Second, inverter-based resources do not contribute to system inertia, which is produced by the rotating mass of conventional resources and which allows the entire electrical system to resist changes to system frequency and maintain stable operating characteristics. Going forward, as the role of renewables increases in ENO’s resource portfolio, it will be important to consider transmission projects and the need for supportive dispatchable generation to ensure reliability and economic planning principles are met. The Resource Portfolios identified through the IRP analysis are designed based on energy import/export capability between MISO South and North. These Portfolios are designed primarily to meet projected capacity and energy needs as prescribed by ENO’s guiding principles and Council policies. While the implementation of a sound transmission plan is necessary to ensure reliability and can facilitate the efficient flow of energy within a system, it does not address capacity needs. Other analyses, which are part of ongoing planning processes, such as for the siting of specific future generation resources, will take into account transmission planning by applying the transmission topology, including approved MISO MTEP projects. 8 https://www.misoenergy.org/planning/planning/ 24 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION 3.8 Resource Adequacy and Planning Reserve Requirements As a load serving entity (“LSE”) within MISO, ENO is responsible for planning and maintaining a Resource Portfolio to meet its customers’ power needs. To meet its customers’ needs, ENO must maintain the right type and amount of capacity in its Portfolio. With respect to the amount of capacity, two considerations are relevant: 1) MISO Resource Adequacy Requirements; and 2) Long-Term Planning Reserve Margin Targets. 3.8.1 MISO Resource Adequacy Requirements Resource Adequacy is the process by which MISO obligates participating LSEs to procure sufficient capacity, through the procurement of zonal resource credits (“ZRC”) equal to their Planning Reserve Margin Requirement (“PRMR”) in order to ensure regional reliability. ZRCs are provided by both supplyside generation and demand side alternatives. An LSE’s PRMR is based on its forecasted peak load coincident with MISO’s forecasted peak load, plus a planning reserve margin established by MISO annually for the MISO footprint. Under MISO’s Resource Adequacy process, the planning reserve margin is determined annually by November 1st prior to the upcoming planning year (June - May). Additionally, through MISO’s annual Resource Adequacy process, MISO determines the annual capacity needs for a particular region or LRZ based on load requirements, capability of the existing generation, and import capability of the LRZ. Those generation needs are articulated through a Local Clearing Requirement for the LRZ for each Planning Year. At present, the MISO Resource Adequacy process is a short-term construct. Requirements are set annually and apply only to the next year. Similarly, the cost of zonal resource credits, as determined annually through the MISO auction process, apply only to the forthcoming year. Both the level of required ZRCs and the cost of those ZRCs are subject to change from year to year. In particular, the cost of ZRCs can change quickly as a result of, among other things, changes in bidding strategy of market participants, the availability of generation within MISO and a specific LRZ, and an LRZ’s Local Clearing Requirement. As a result, although the MISO Resource Adequacy process establishes minimum requirements that must be met in the short term and are reviewed regularly as part of the resource planning process, it does not provide an appropriate basis for determining ENO’s long-term resource needs. In other words, and as the Council has previously acknowledged, relying on the short-term market for ZRCs to meet customers’ long-term power needs involves significant risk. A more stable basis for long-term planning is needed if ENO is to meet its long-term planning objectives. 3.8.2 Long-Term Planning Reserve Margin Targets ENO plans to meet its projected peak load, plus a 12 percent planning reserve margin, based on installed capacity. The long-term planning reserve margin is intended as a generation supply safety margin to maintain reliable service during unplanned events, like generating unit outages and extreme weather, over the long-term planning horizon, while still benefitting from participation in MISO’s broader energy markets. This long-term planning approach (as opposed to relying heavily on MISO’s short-term capacity 25 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION and energy markets) not only helps reduce unnecessary reliability and economic risk to customers but also allows ENO to be more agile in serving customers’ needs. 3.9 Distribution 3.9.1 DER/Distribution Planning Requirements Section 6.E. of the Council’s new IRP Rules requires that ENO evaluate the extent to which reliability of the distribution system can be improved through the strategic location of distributed energy resources or other resources identified as part of the IRP planning process. To the extent ENO does not currently have the capability to meet this requirement, it is required to demonstrate progress toward developing this capability in its IRP report. In response to this requirement, the following section explains in detail various steps being undertaken to implement foundational systems, software, and processes that will be necessary for ENO to develop the ability to evaluate locational and reliability benefits and impacts of DERs in the future. 3.9.2 Company Work to Develop DER Planning Capabilities The Company discussed the three pillars of its plan for grid modernization in the Grid Modernization and Smart Cities Report filed April 10, 2018, in Docket UD-18-01: 1) Upgrading existing Grid Infrastructure with newer assets to improve reliability and support technologically advanced options for meeting customers’ needs, 2) Deploying Grid Technology to collect, analyze, and deliver information for real time decision making and automation, and 3) Advanced Planning processes that will leverage the data received from the modern grid technologies to enable the Company to meet customer demands for interconnection of DERs while improving reliability and resiliency. 9 3.9.3 Grid Infrastructure The first pillar, upgrading existing grid infrastructure, is being addressed through reliability work the Company has identified in filings to the Council in Docket UD-17-04.10 That work is ongoing and continues to be the subject of periodic progress reports. This pillar is also being addressed through the Grid Modernization projects reported in Dockets UD-18-01 and UD-18-07. Specifically, Docket UD-1801 is considering how certain Smart City functionalities can be enabled by Grid Modernization and how those applications can benefit all residents of New Orleans in an equitable manner. In Docket UD-18-07, ENO has proposed for the Council’s review and approval: (i) a process for reviewing the planning and execution of all Grid Modernization projects for ENO’s distribution grid, and (ii) a cost recovery mechanism (the Distribution Grid Modernization Rider, or Rider DGM) that would enable ENO to modernize the distribution grid in New Orleans in the most cost-efficient and timely manner possible. 9 10 Grid Modernization and Smart Cities Report, at 5-6. See, ENO Reliability Plan (November 11, 2017), Quanta Assessment (October 31, 2018), and ENO 2019 Reliability Plan (January 18, 2019) 26 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION 3.9.4 Smart Infrastructure & Software Systems The second pillar, deploying grid technology, is being pursued through several deployments of smart infrastructure and software systems. The foundational investment of AMI, specifically implementation of the communications network, head-end system, and advanced meter installations as approved through Docket UD-16-04, will enable enhanced sensing and awareness of the distribution grid. The advanced meters act as smart sensors on the distribution grid to inform other systems on the status of the grid. This information will be integrated with other data sources such as customer phone calls and input from ENO’s Supervisory Control and Data Acquisition (“SCADA”) system into a new Distribution Management and Outage Management (“DMS/OMS”) system. The distribution management system (“DMS”) is a software platform that supports the full suite of distribution management and the optimization of the distribution grid. The DMS platform utilizes all available information collected from AMI meters, Distribution Automation (“DA”) enabled devices, asset topology, and SCADA to perform load flow modeling. The DMS enables several smart grid capabilities including fault location, isolation, and restoration (“FLISR”), volt/volt-ampere (var) optimization, and integration of distributed resources. The ability to monitor and actively manage the distribution grid with real time sensing and analysis is foundational to enable future safe and reliable operation for all of ENO’s customers. An outage management system (“OMS”) is a utility network management software application that models network topology for safe and efficient field operations related to outage restoration. The OMS tightly integrates with the call centers to provide timely, accurate, customer-specific outage information, and with the SCADA system for real-time confirmed switching and breaker operations. These systems track, group, and display outages to safely and efficiently manage service restoration activities. Combined, the DMS and OMS systems will deliver several important grid management capabilities complementary to AMI: • • Distribution Grid Analysis: The ability to perform distribution planning, as well as operational analysis to support strong engineering and quality improvement. This will enable engineers to visualize how the distribution system should be built and set the state for optimization by placing electrical equipment at appropriate locations along the feeders, and Distribution Grid restoration: This capability will provide maximum value when major events, such as hurricanes, ice storms, and wind storms, stress system operations. Operations change significantly during major events, so dedicated outage management software is required. With this capability, utilities can model network topology and correlate incidents with customers, assets, and crews. The OMS system is expected to enable predictive analysis for fault location during outage events, improved estimation of restoration times, and analysis for management of crews assisting in restoration and calculation of manpower needed in outage events. The OMS system models how each customer is connected to the network and can provide critical information necessary to calculate network performance indices. The DMS/OMS deployment is coordinated with the deployment of the AMI meters and is expected to be fully operational in 2020. 27 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION In September 2018, ENO replaced and consolidated its transmission and distribution data historian systems to allow users easier access to SCADA information and increase the rate of data retrieval. The new SCADA historian, Pi Historian, enables users to graphically view analog data, recognize trends, and customize displays. The Pi Historian is a foundational investment to collect and analyze important grid information that will be utilized in the advanced planning process. The costs associated with DMS/OMS, which includes Pi Historian, were approved by the Council in conjunction with approval of the deployment of AMI. ENO continues to submit quarterly reports to the Council and Advisors concerning the progress with and cost of AMI deployment, including the integration of these foundational components, which are necessary to achieve the goal of optimizing the distribution grid as described in the IRP Rules. To help facilitate transition to the new Distribution Management system, additional important functionality is being deployed through the Enterprise Asset Management (“EAM”) project. EAM seeks to install an integrated system to manage the asset, maintenance, renewal, and replacement records of all distribution, transmission, gas, and transportation fleet assets. The combination of technology and the redesign of processes will modernize and significantly improve the way ENO and the other Entergy Operating Companies manage the assets used to serve customers, while also providing functionalities required to execute on the goal describe in the Rules. Integration of work processes and systems allows a more streamlined approach to, and greater ability to track, work across field operations, customer contact centers, and back office operations to provide an improved overall customer experience. The EAM project consists of the: 1) EAM System, 2) a modernized Workforce Management System (“WFMS”), 3) Field Mobility Devices, 4) verification, collection, and correction of the current asset records, 5) an advanced Geospatial Information System (“GIS”), and 6) Intelligent Electronic Device Management System (“IEDMS”). As ENO and the other Entergy Operating Companies continue to move through the EAM project, ENO will be able to develop and disclose a more refined cost estimate of these initiatives. However, at present time, a high-level estimate of the costs associated with the EAM project for ENO is approximately $17 million. More information on the components of the EAM project are below. EAM System: The EAM system, which will use IBM’s Maximo Asset Management software (“Maximo”), will enable more proactive asset maintenance, renewal, and replacement policies and practices, as well as more complete and more accurate asset history records. Using an EAM System like Maximo creates the opportunity to have a single platform to record asset information (e.g., manufacturer, date installed, and model number), track asset performance (installation and retirement dates, any available asset health information from testing if available) and derive asset-centric analytics for a variety of utility assets. WFMS & Field Mobility Devices: Integrated into the EAM system, a modernized WFMS will empower field employees to make real-time, asset-driven maintenance, renewal, and replacement decisions and provide them the 28 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION latest technology devices. A new WFMS uses advanced work, schedule, and route optimization algorithms to facilitate more efficient job routing and completion. Asset Data Inventory: The EAM project includes analysis and validation of existing asset data in addition to the capture and inventory of new asset data because data quality is critical to the success of any system implementation. Geographic Information System (“GIS”): To properly calculate and model real time conditions of the distribution grid, all changes must be captured and incorporated into the model on a timely basis. ENO is currently making enhancements to its business processes to update its GIS with the necessary frequency. ENO’s system architecture syncs all relevant asset data into a network-connected model that serves as the basis for the DMS/OMS analysis. An accurate network model is critical to distribution grid management especially with the addition of distributed energy resources that can change the potential load flows on the distribution system. This project seeks to implement a new map layer or land-base inside a new GIS system so additional layers of data such as transmission and distribution line routes can be easily located on a map. The application included in the project estimate is an enterprise license for ESRI ArcGIS. The ArcGIS enterprise application allows multiple sources of data to be viewed in a single web-based platform. Intelligent Electronic Device Management System (“IEDMS”): An IEDMS is a collection of software packages that act as a data warehouse for settings and configurations of relays and smart devices for both transmission and distribution. Other smart grid technologies being deployed are DA devices that are installed on the distribution grid and communicate the status and configuration of the distribution grid through the AMI integrated communication network to the DMS/OMS. The DA devices work in conjunction with the AMI meter data and the DMS/OMS system to automatically reconfigure the path of power to isolate any outage conditions and restore power to unaffected customers. The DA devices will provide additional monitoring of the system and introduce control of the distribution grid. DA devices are another foundational technology required to safely and reliably incorporate distributed resources on to the distribution grid. DA enabled devices also facilitate the mitigation of line losses through the active management of reactive power (vars) that can result in reduced fuel costs for customers. DA-enabled devices require the AMI communication network for status and control functions and are being deployed as a part of the Grid Modernization projects proposed in Docket UD-18-07. ENO provided the cost estimates associated with the first five Grid Modernization projects, including the DA-related components, in Docket UD-18-07 and intends to continue submitting cost estimates of new Grid Modernization projects to the Council in conjunction with the process ENO has proposed for Council review and approval of such future projects. As demonstrated by testimony submitted in Docket UD-18-07, the Council’s approval of this proposed 29 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION process, as well as the proposed Rider DGM, will enable execution and completion of Grid Modernization work in the most timely and cost-efficient manner possible. Additionally, ENO is actively monitoring the commercial availability of products and components for a Distributed Energy Management System (“DERMS”). A DERMS is a system that integrates with ENO’s other new technologies to enable the monitoring and control of distributed energy resources on the ENO distribution grid. ENO will continue to develop the IT system architecture to support the enhanced capabilities of a DERMS, but the foundational Grid Modernization investments described above must be in place before a full-scale DERMS will be viable. 3.9.5 Advanced Planning The third pillar, developing advanced planning processes, is focused on providing planning, engineering, and related technical services to support adoption of both customer-owned and Company-owned DERs. As noted above, in Docket UD-18-07, ENO has proposed for the Council’s review and approval a process for reviewing the planning and execution of all Grid Modernization projects for ENO’s distribution grid. The Grid Modernization projects were developed through a planning approach that begins with the customer experience. The new planning criteria consider upgrades to the distribution system that enable the integration of DERs in addition to minimizing the likelihood that customers in particular areas will experience interruptions. The new planning criteria include analysis to consider loading from traditional growth, DER penetration, and potential for electrification. The Grid Modernization projects represent ENO’s efforts to take concrete and immediate steps to prepare ENO’s distribution system for new capabilities. The addition of smart infrastructure and technology as described above will continue to improve the data available for distribution planners to evaluate projects for ENO. Currently, the distribution planning organization supports the system impact studies for customers requesting interconnection of DERs to ENO’s distribution grid. As these requests increase, ENO will require resources to conduct the additional analysis and design potential infrastructure projects necessary to support the addition of distributed resources. Efforts are being made to identify personnel, knowledge, and skills that will be needed to accommodate higher penetrations of DERs on the distribution grid. This includes reviewing how best to utilize existing tools, what new tools or analysis will be needed, how to work with transmission planning, and how to train engineers in these new areas. It is important to create effective interconnection processes and standards that use data to understand the effects and impacts of DERs on the grid. Customers today are adopting DERs without the Company fully understanding the operational and/or grid impacts. To address this issue, Entergy Corporation’s Transformation organization is leading two efforts to improve the distribution planning process, one focused on improving the studies performed today in the Company’s current engineering software, and the other focused on implementing new tools with advanced analytics to better forecast adoption impacts to the distribution system. In the first effort, ENO is working with Quanta Technologies to develop a process to model and study various DER program impacts on the distribution system using our existing engineering software. These 30 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION studies utilize customer load and generation shapes, along with projected adoption rates, to demonstrate the impacts of DER programs on a distribution feeder. This study will allow ENO to better plan for increased penetrations of DER programs and demonstrate their value as Non-Wire Alternatives (“NWAs”). Following completion of the studies and planning methodology by Quanta, Company engineers will be fully briefed and trained in the use of any new processes. Additionally, the Transformation organization is working with Integral Analytics to deploy additional software to support the analysis of DER penetration. ENO is implementing a new software package, LoadSEER, a spatial load forecasting tool which can integrate with the current planning analysis software, SynerGi. Together, along with AMI, its associated software applications, and resulting data, these tools will enable the Company to prioritize distribution grid needs in light of planned DER and DSM projects, perform locational analyses, and develop traditional (i.e., distribution asset) or alternative (i.e., non-wires) solutions to address grid needs. The upfront license and implementation cost for LoadSEER is approximately $395K, with an ongoing annual maintenance cost of about $80K. Another Transformation organization workstream is addressing the need for formalized DER standards to help avoid grid reliability and safety concerns. The team, alongside distribution planning, is working with the distribution and transmission design basis groups to develop technical specifications, requirements, and standards for better integration of customer- and Entergy-owned DERs. The joint effort is also developing a streamlined interconnection study process to reduce time required to perform studies for DER interconnection applications, while using industry-standard practices to ensure adequacy of results. Figure 5, below, depicts the systems and processes being addressed through the Transformation organization efforts: Figure 5: New Planning Tools in the Interconnect and Analysis Process 31 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION In summary, the investments being made over the next two years to implement the smart infrastructure of AMI and DA-enabled devices and the smart systems of DMS/OMS and LoadSEER will provide essential components of the foundation for ENO to develop the capability of evaluating DERs for safe and reliable integration into the ENO grid in the future. Grid Modernization constitutes another broader, yet necessary component, required to enable optimal integration of DERs and other advanced functionalities into the distribution grid. 3.10 DOE Grant Proposal—DERadio In addition to the workstreams and implementations described above, the Company also recently pursued a DOE grant for functionality that could facilitate more robust integration and operation of DERs in the future. The project, known as DERadio (Distributed Energy Resources Analytics, Dispatch, Interoperability and Optimization), would have built a foundational element of a robust, secure, and interoperable infrastructure utilizing utility-scale solar PV, energy storage systems, residential solar PV and controllable loads on the ENO system. The project would have sought to develop and demonstrate a novel, vendor-agnostic platform at the grid edge to enhance situational awareness of DERs and strengthen the cyber and physical security and resilience for the ENO distribution grid and its critical customer infrastructure. This solution would have addressed several industry core needs such as enhanced monitoring and analysis of solar energy systems, distributed control at the grid edge, dynamic energy dispatch for both normal grid operations and emergent outage operations, and facilitated interoperability of a diverse array of DERs to provide reliable, secure and resilient electricity for critical infrastructure. While this grant proposal was not selected, the Company plans to continue looking for innovative approaches to develop and monitor the distribution grid in New Orleans. 32 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION Section 4 4. Planning Assumptions, Inputs, and Considerations The IRP analysis is built on a variety of planning assumptions and inputs. As required by the IRP Rules and Initiating Resolution, these assumptions and inputs were the focus of presentations and discussions among ENO, the Advisors, and the Intervenors at Technical Meetings held as part of the Stakeholder process. This 2018 IRP uses the assumptions and inputs developed in the Company’s 2019 Business Plan, which was the most current as of the December 7, 2018 procedural deadline for finalizing the inputs. Also, as stipulated in the procedural schedule, this IRP incorporates the policies articulated by the Council as of October 31, 2018.11 4.1 Evolving Customer Preferences In developing this 2018 IRP, ENO recognized that customer expectations of the utility are changing due to advances in technology and customers’ own evolving priorities. Customers increasingly want and rely on technology and innovations that give them more control over parts of their daily lives that historically were overseen or provided by third parties. The available mix of technologies used to generate and control electricity in homes, factories, and businesses has changed, impacting the way customers think about electricity. Additionally, customers are increasingly interested in the use of cleaner, more sustainable energy sources. 11 With Council Resolution No. R-19-109, dated March 28, 2019, the Council opened a rulemaking to consider, among other things, whether to establish a Renewable Portfolio Standard (“RPS”) or Clean Energy Standard (“CES”) for ENO. The Council’s rulemaking is ongoing. Future IRP cycles would be able to incorporate any policy goals established as a result of this proceeding; but this IRP cycle has not taken any possible results of this ongoing proceeding into account, nor is this IRP report a substitute for the kinds of analyses that should inform the Council’s decision in that ongoing rulemaking. 33 PUBLIC VERSION Entergy New Orleans, LLC, 2018 Integrated Resource Plan Customer Preferences Advancing Technology • Increased desire for more options • Interested in the use of cleaner more sustainable energy sources • Changes to expected types of services and products • Expanding list of viable supply and demand -side alternatives, including an Integrated Grid, battery storage, commercial scale solar, and demand response Integrated Resource Plan • Understand changing customer needs • Develop experience with new technologies • Design Portfolios to meet customer needs and wants • Continue to account for affordability and reliability Increased Customer Value • Increased available services and products • Diversity of supply alternatives • Decreased risk to supply cost • Increased reliability • Modernization of the Resource Portfolio Figure 6: Changes and Opportunities within the Utility Industry An expanding and changing selection of available supply alternatives and technologies has provided more ways to address planning objectives and to meet customer needs reliably and affordably. Additionally, new and emerging technologies that may be able to support integrated grids, such as battery storage and utilityscale solar, may enable the delivery of more sustainable energy that can help serve customers’ evolving needs while also addressing ENO’s long-term planning objectives. Figure 7: An Integrated Grid By combining an understanding of what its customers want with sound and comprehensive planning, ENO can ensure that it continues to deliver the types of products and services its customers expect without losing sight of the traditional and critical planning objectives of low cost, high reliability, and risk mitigation. A diverse resource portfolio mitigates exposure to price volatility associated with uncertainties in fuel and 34 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION purchased power costs, and other risks that may arise through unbalanced concentrations of portfolio attributes such as technology type, location, capital expenditures, or uncertain supply channels. 4.2 Assessing Alternatives to Meet Customer Resource Needs 4.2.1 Technology Assessment The IRP process considers a range of alternatives available to meet the planning objectives, including the existing fleet of generating units, new demand-side management programs, and supply-side resource alternatives. As part of this process, a Technology Assessment was conducted to identify a wide range of potential supply-side resource alternatives that merit more detailed analysis due to their potential to meet ENO’s planning objectives of balancing reliability, cost, and risk. Alternatives evaluated are technologically mature and could reasonably be expected to be operational in or around the ENO service territory. As discussed in the Technical Meetings, the technologies selected for further, more detailed evaluation in the IRP included: Figure 8: Technology Screening Curves Illustration I. Natural Gas Fired Technologies a. Combustion Turbine (CT) b. Combined Cycle Gas Turbine (CCGT) c. Aeroderivative CT d. Internal combustion engine (“ICE”) or reciprocating internal combustion engine (“RICE”) II. Renewable Technologies a. Solar Photovoltaic (“PV”) (Tracking) b. Wind (Onshore) III. Energy Storage a. Battery storage technologies 35 PUBLIC VERSION Entergy New Orleans, LLC, 2018 Integrated Resource Plan Each of these technologies has advantages and disadvantages to consider when designing a Resource Portfolio to meet customers’ capacity needs. The information below summarizes some of those various considerations which were utilized in the Portfolio analyses discussed later. Table 7: Gas-Fired Technology Considerations CT CCGT Aeroderivative CT RICE Description Frame CTs are a mature technology. Low gas prices and continual heat rate and capacity improvements have made CTs the industry’s technology of choice for peaking applications. CTs can also help integrate renewables by providing quickstart (~10 minutes) backup power. Modern combined cycle facilities provide efficiencies, moderate flexibility, and improved CO2 emissions relative to coal plants, making them suitable for a variety of supply roles (baseload, load-following, limited peaking). CCGT efficiency and flexibility is expected to continue to improve. Aeroderivative CTs trade increased cost for greater flexibility (start time, ramp times), lower heat rates, and higher reliability relative to frame CTs. RICEs are useful for applications requiring heavy cycling and ramping, as they incur lower O&M penalties when operated in this manner relative to other conventional peaker technologies. As renewable penetration increases, this technology will likely see increased deployment in North American power markets due to its flexibility and efficiency. Advantages -Low capital and staffing costs -Lowest heat rates -Higher flexibility -Low heat rates -Moderate capital cost -Moderate heat rates -Highest flexibility -High reliability -No gas compression needed -Existing operating expertise -Flexible, quick start capability Disadvantages -Higher heat rates -Difficult to neatly match need (blocky -Synergies with existing and planned fleet (e.g., parts, staff) -Increases reliance on natural gas -Modular additions -Moderate capital cost -Moderate capital cost -High gas pressure -High variable 36 PUBLIC VERSION Entergy New Orleans, LLC, 2018 Integrated Resource Plan additions) -Blocky additions requirements operating cost -High gas pressure requirements -High gas pressure requirements -Less experience with technology -Less experience with technology In addition to the qualitative factors considered above, the table below summarizes the cost information from the Technology Assessment for gas-fired generation. Table 8: Gas-Fired Resource Assumptions Fixed Capital O&M Summer [2017$/ Capacity Cost [MW] [2017$/kW] kW-yr] Technology Variable O&M Expected [2017$/ Heat Rate Capacity MWh] [Btu/kWh] Factor [%] 1x1 501JAC 605 $1,244 $16.70 $3.14 6,300 80% 501JAC 346 $809 $2.37 $13.35 9,400 10% Aeroderivative LMS100PA CT 102 $1,543 $5.86 $2.90 9,400 20% 7x Wartsila 18V50SG 128 $1,545 $31.94 $7.30 8,400 30% CT / CCGT RICE 4.2.2 Renewables (Solar PV and Wind) In the last decade, the renewable energy industry has experienced substantial growth, driven in large part by cost declines, technological improvements, and environmental concerns. As shown in Figure 9, renewables’ capital cost declines are particularly evident in utility-scale solar installations within the U.S. over the past five years. Among all technologically feasible renewable energy options, solar and onshore wind resources are the most cost effective, commercially-available alternatives to meet ENO’s capacity and energy needs. 37 PUBLIC VERSION Entergy New Orleans, LLC, 2018 Integrated Resource Plan 2018$/w-DC $6.00 $4.00 $2.00 $2010 2011 2012 2013 2014 Single-Axis Tracking 2015 2016 Fixed-Tilt 2017 2018 Figure 9: Historical Utility-Scale Solar Capital Costs12 The costs of renewable generation have declined significantly in the previous five years, and this trend is expected to continue. As depicted below, installed costs of utility-scale renewables (wind and solar) in real dollars are expected to decline throughout the planning horizon. Installed Cost (Real 2019 $/MWh) $55 $50 $45 $40 $35 $30 2019 2021 2023 2025 2027 Tracking Solar 2029 2031 2033 2035 2037 Onshore wind Figure 10: Projected Installed Costs of Renewable Alternatives 12 Data adapted from NREL U.S. Solar Photovoltaic System Cost Benchmark, Q1 2017. 38 PUBLIC VERSION Entergy New Orleans, LLC, 2018 Integrated Resource Plan The table below details the opportunities presented by solar and wind generation. In general, advantages of renewables include zero emissions and fuel costs, which decrease reliance on fuel commodities. Disadvantages are related to land use compared with traditional alternatives as well as relative capacity contribution due to the intermittent nature of these energy sources. Table 9: Renewable Technology Considerations Solar Wind Description Solar capital costs have fallen dramatically in the last decade and continue to decline as the industry matures. Solar production aligns partially with customer load patterns, but grid flexibility and quickstart backup generation are necessary to ensure reliability in the absence of large-scale, economic energy storage alternatives. The industry will continue to mature and utility scale solar energy is expected to continue to compete with gas-fired generation within the planning horizon, constrained mainly by site-specific performance and market conditions (e.g., construction cost, energy value). The wind industry is mature relative to the solar industry. Current research focuses more on improving performance, rather than cost, through larger, taller turbines and improved control technologies (e.g., turbine alignment sensors, integrated battery storage). Wind is not likely to see extensive local deployment within the MISO South region but could play a role in the region’s energy mix if storage economics improve or significant high voltage direct current (“HVDC”) projects are completed. Advantages • • • • • • • • • Disadvantages • Relative capacity value to traditional generation • Land-intensive • Integration requirements (responsive, quickstart generation is necessary to integrate large amounts of solar PV) • Site-specific performance Zero Emissions No fuel cost Capital costs continue to decline Federal investment tax credits (ITCs) Predictable energy curve Zero Emissions No fuel cost Federal production tax credits Efficiency continues to increase • Relative capacity value to traditional generation • Land-intensive • Integration requirements (responsive, quickstart generation is necessary to integrate large amounts of wind) • MISO South not ideal for wind without incurring transmission or congestion costs 39 PUBLIC VERSION Entergy New Orleans, LLC, 2018 Integrated Resource Plan Additional benefits associated with renewable generation are summarized below. Table 10: Additional Benefits of Renewables Additional Benefits of Renewables Diversity Renewables add fuel diversity and provide a hedge within gascentric Resource Portfolios as ENO’s ability to rely on coal for fuel diversity becomes uncertain Infrastructure Reduced infrastructure requirements (e.g., gas pipelines, water supply) increase siting flexibility Scalability Deployment can be scaled up or down to meet capacity needs more easily relative to conventional alternatives Carbon and other emissions Renewables offer customers protection against uncertainty related to potential CO2 costs and the increasing stringency of other emissions regulations Gaining experience with renewables can help ENO take advantage of opportunities such as community solar, deployment of DERs, and opportunities provided by the integration of AMI Customer Engagement The table below provides a summary of operational costs and performance assumptions for solar and wind technology used in the 2018 IRP, as discussed during the Technical Meetings. Table 11: Renewable Modeling Assumptions Solar Wind Fixed O&M (2017$/kW-yr) $15.78 $36.01 Useful Life (yr) 30 25 Capacity Factor 26% 34% Capacity Value 50% 15.6% Tracking Type Single Axis N/A 40 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION 4.2.3 Energy Storage Systems Energy storage, particularly in the case of battery-enabled storage, provides a range of attributes that differ from traditional supply-side options discussed previously, such as: 1. The ability to store limited amounts of energy for later commitment and dispatch; 2. Ability to discharge in milliseconds and fast ramping capability; 3. Rapid construction (on the order of months); 4. Modular deployment; 5. Portability and capability to be redeployed in different areas; 6. Small footprint (typically less than an acre), allowing for flexible siting; and 7. Low round-trip losses compared to other storage technologies (such as compressed air). Battery storage system benefits lie in the attributes highlighted above and the ability to offer stacked values through multiple revenue streams to benefit customers. Battery storage has the potential to effectively enable an intra-day temporal shift between energy production and energy use. Energy can be absorbed and stored during off-peak/low cost hours and discharged during on-peak/high cost hours. The spread (i.e., cost difference) between the time periods creates cost savings for customers and may produce a reduction in emissions. In addition to energy market attributes, battery storage systems qualify in some markets for various ancillary service applications such as regulation, reserves, and voltage regulation, and qualify for MISO’s capacity market, given sufficient discharge duration. Lastly, energy storage may, depending on location and characteristics, offer the capability of transmission and distribution cost deferral. Given the current higher installed cost, energy storage faces challenges for high-deployment potential. The typical on-peak/off-peak cost spread remains low in MISO South, which may limit arbitrage potential. Additionally, MISO’s ancillary services market is limited today and fully met with existing resources but continues to evolve. ENO will continue to monitor MISO’s energy and ancillary market conditions to identify energy storage potential. At the time of this report, the fixed costs of energy storage today remain above a new build CT, as depicted below. 41 PUBLIC VERSION Entergy New Orleans, LLC, 2018 Integrated Resource Plan Levelized Real $/kWYr Fixed Costs $180 ILLUSTRATIVE $160 $140 $120 $100 $80 $60 $40 Battery Storage CT $20 $0 2019 2021 2023 2025 2027 2029 2031 2033 2035 2037 Figure 11: Storage and CT Cost Comparison For storage, the key to achieving positive net benefits today is identifying the right location-specific usecase that can off-set transmission or distribution investment. For example, battery storage can provide avoided cost benefits by preventing distribution investments required due to line overloads. In addition to these peak-shaving applications, energy storage sited in location-specific areas provide voltage support, which mitigates the effects of electrical anomalies and disturbances. However, if sited and/or operated suboptimally, storage can increase congestion and could drive otherwise unnecessary transmission or distribution improvements. Also, charge and discharge cycles must be optimized so as not to conflict with reliability and/or any potential economic benefits. Similar to what has been seen in recent years within the solar industry, it is expected that battery storage costs will decline within the planning horizon. Therefore, while limited deployment may make sense today for ENO’s customers, this technology will continue to evolve, and additional applications could present themselves in the future. 4.2.4 Demand-Side Management For the 2018 IRP, ENO engaged the services of Navigant Consulting to perform a DSM potential study to assess the long-term potential for reducing ENO customer energy consumption through energy efficiency (“EE”) and peak load reduction measures and improving end-user behaviors. Navigant utilized its DSMSimTM model to calculate various levels of EE savings potential across the ENO service area and its DRSimTM model to estimate various levels of demand response (“DR”) potential. Navigant analyzed both EE and DR under Low, Base, and High cases, and EE under a “2% case” constructed to comply with the Council’s goal of increasing EE savings .2% annually until a total 2% of sales reduction was achieved. In total, the Navigant Base case identified cost-effective potential of ~1,100 GWh of EE savings at a cost of about $390 million over 20 years, with an associated demand reduction of 220 MW. Navigant also 42 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION estimated ~35 MWs of cumulative peak reduction from DR by 2038. Additionally, the Council engaged Optimal Energy to perform a DSM potential study to assess potential for energy savings and peak demand reduction in the city through utility-run energy efficiency, peak demand, and rate design programs. Optimal provided ENO with a 20-year forecast of energy and demand savings in the form of energy efficiency and demand response programs. The energy efficiency potential study included results for a Program Achievable case and a Max Achievable case. In total, the Optimal Program Achievable case identified potential of ~2,335 GWh of EE savings at a cost of about $434 million over 20 years with an associated demand reduction of 243 MW. Thus, Optimal’s Program Achievable case identified a 112% greater EE potential than the Navigant Base case at only a ~10% greater cost. In addition, Optimal’s demand response potential study analyzed two cases, identified by Optimal as “Scenario One” and “Scenario Two,” which utilized low and high participation and identified 53 MW and 81 MW of potential, respectively over the 20-year horizon.13 Navigant and Optimal received the same sets of data from ENO, relied on the New Orleans Technical Resource Manual (“NOTRM”) as a source document for measure information, and considered the historical results and current implementation plans for the Energy Smart programs. However, the significant divergence between identified EE potential and costs to achieve savings indicate fundamental differences in assumptions. An obvious difference is Optimal’s use of a 25% administrative cost / 75% incentive cost split where Navigant used a 50/50 split in its Base case. Under Energy Smart, administrative costs have historically run closer to the 50% level, although in Program Year 9 administrative costs are tracking closer to 40% due to increased experience on the part of ENO’s third-party implementer and improvements in program delivery. An additional point of comparison is Entergy Arkansas’s experience in its most recent program year, 2018, for which it reported approximately 40% administrative costs to implement a much larger program than Energy Smart. Additionally, Optimal’s Study notes, at page 74, that data concerning administrative costs was “sourced from recent program performance in New England, the MidAtlantic states, and Minnesota.” The study also notes that data from these sampled jurisdictions shows that the “average administrative costs for the various program types range from 25 percent to 37 percent.” Optimal provided no explanation as to why its study avoided including data from Southeastern utilities, or why Optimal chose to assume costs at the absolute lowest level supported by its sample set. Optimal also admitted in discovery that it made no adjustments to account for changes to economies of scale attributable to the fact that its selected samples were from jurisdictions with state-wide programs, whereas Energy Smart is only available to customers in New Orleans. Considering these facts, the actual 13 Both the Navigant and Optimal studies assessed potential over the 20-year period 2018-2037. To align the studies with the IRP analysis that focused on 2019-2038, the Navigant potential study results were extrapolated to include the year 2038. For the Optimal potential study ENO was advised by Optimal to change the start date of results to 2019 while keeping all costs constant to cover the 2019-2038 study timeline. 43 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION results of the Energy Smart program in New Orleans, and the long-running Entergy Arkansas programs, a 25% assumption for administrative costs seems overly aggressive and unrealistic. Given Optimal’s general conclusions that significantly more kWh savings can be achieved at a lower cost per kWh than Navigant projects, it seems likely that Optimal may have more aggressive assumptions about measure costs, initial measure saturation levels, and adoption rates as well.14 These, coupled with Optimal’s position on administrative costs, would explain, at least in part, the substantial differences in identified EE potential. Further, Optimal identifies significant potential associated with low-income programs using a definition that seems to assume 49% of the residential class is low-income.15 This assumption seems high as compared to ENO’s experience with Energy Smart which uses a more restrictive definition of low income (i.e., 200% of the Federal Poverty Standard)16 The differences highlighted here do not mean that one study is “right” and the other “wrong.” However, in the context of an IRP analysis, Portfolios built using input cases from these divergent studies will present fundamentally different points of view about the resources required to serve customer needs over the 20-year planning horizon, and therefore the related total supply costs. And more immediately, in the context of short-term DSM implementation planning, ENO must consider the different perspectives offered by the studies as it designs an Energy Smart Implementation Plan that it believes is reasonable, cost-effective, and achievable for the Council to review. To that end, ENO intends to develop the Energy Smart Implementation Plan by drawing on information from both studies. 4.2.5 Energy Efficiency The International Energy Agency defines Energy Efficiency as, “achieving the same services with less energy.” This ensures an opportunity for ENO to serve its customers by providing energy savings. The method utilized by Navigant for determining EE is summarized below. 14 15 16 One measure-level comparison between the studies is worth noting. Low-flow showerheads accounted for the largest percentage of savings among residential measures in Optimal’s Study at 14%. Low-flow showerheads have been a direct install measure in the Energy Smart program for several years but have had a very low acceptance rate. Navigant’s study includes low-flow showerheads but at a more reasonable 3.6% of residential savings. See, Appendix E, Optimal DSM Potential Study, Table 47. https://aspe.hhs.gov/poverty-guidelines 44 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION Figure 12: Energy Efficiency study approach Navigant’s energy efficiency modeling included 4 potential cases: • • • • Base Case: Reflects current program spend targets with incentives on average at 50% of incremental measure cost Low Case: Uses the same inputs as the base case except incentives are at 25% of incremental measure cost. High Case: Uses the same inputs as the base case except incentives are at 75% of incremental measure cost. 2% Case: Achieves a 2% reduction during the forecast period with a 0.2% ramp year over year starting in the first modeled year (2018). To achieve 2% Navigant modified model parameters: o Increased marketing factor through 2022 o Increased incentive percent of incremental measure cost from 50% in 2019 then ramping up to 100% in 2025 (and maintaining 100% in remaining years) o Ramped down TRC ratio threshold from 1 in 2018 to 0.87 in 2023 and remaining years. The total potential of each Navigant EE case is outlined in Figure 13. 45 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION Figure 13: Current and Expanded EE Program Potential The method utilized by Optimal for determining EE potential is summarized below. Optimal used the following major steps to conduct the energy efficiency potential study: 1. Develop energy use forecasts 2. Disaggregate energy forecasts by sector (e.g., residential vs. commercial), and end uses (e.g., lighting, cooling, refrigeration) 3. Characterize efficiency measures 4. Screen measures and programs for cost-effectiveness 5. Develop measure penetrations for “achievable” scenarios 6. Determine scenario potential and develop outputs In addition to the steps listed above, Optimal utilized a “top-down” methodology. Optimal began with the entirety of ENO’s electric sales and broke the electric sales down into separate groups representing consumption by customer and building type. Optimal applied energy efficiency measures to the applicable distinct groups of customers and building types. Optimal’s energy efficiency potential analysis included three levels of potential, defined in its Potential Study as follows: • Economic: Everything that is cost-effective and technically feasible, assuming no market barriers. A measure is considered to be cost-effective if the net present value of the avoided energy and capacity costs over its effective useful life is equal to or greater than the net present value of the measure cost. 46 Entergy New Orleans, LLC, 2018 Integrated Resource Plan • • PUBLIC VERSION Maximum Achievable: The maximum level of program activity and savings that is possible given the market barriers to adoption of energy efficient technologies, with no limits on incentive payment, but including administrative costs necessary to implement programs. Program Achievable: Optimal’s view of a feasible and practical level of achievable savings given a specific set of programs targeting specific markets, with Optimal’s estimates of realistic estimates of incentive payments. Administrative costs are again included. The total potential energy savings of the Optimal cases are outlined in Figure 134. Figure 14: Optimal Cumulative Current and Expanded EE Program Potential Savings Relative to Sales Forecast 4.2.6 Demand Response Additionally, Navigant and Optimal performed DR studies. DR offerings which Navigant found to be cost effective using the Total Resource Cost test are shown below, and Optimal’s DR offerings follow. 47 PUBLIC VERSION Entergy New Orleans, LLC, 2018 Integrated Resource Plan Table 12: Navigant Cost Effective DR DR Option C&I Curtailment Eligible Customer Class Residential and Small C&I Large C&I Dynamic Pricing All Customer Classes DLC Measure Thermostat-HVAC Switch- HVAC Manual HVAC Control Dynamic Pricing w/o Enabling Technology Dynamic Pricing w/ Enabling Technology These programs were made available to the AURORA model for a Base, Low, and High case, differing in terms of pricing signals and adoption rates. The total annual MW savings made available for selection is illustrated below, representing approximately 34 MW in the medium case and 35 MW in the high case. Figure 15: Navigant Cumulative Achievable DR DR program costs utilized in the IRP include program start-up cost, marketing and recruitment costs, metering cost, program administration, incentives paid to participants, and program delivery costs. Program delivery costs are a fixed contracted payment for the third-party delivery of the DR programs. The program results reflect an assumption that over the planning horizon, customers will re-opt measures at the end of the program life at the same level of program efficiency. In other words, customers who opt-in are assumed to remain in the program through the remainder of the IRP planning period at the same level of savings. The DR offerings Optimal found to be cost effective using the Total Resource Cost test are as follows. 48 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION Table 13: Optimal Cost Effective DR DR Option Residential Large Customer Measure Direct Load Control and Automated Demand Response Peak Time Rebate Pricing with and without AMI technology Critical Peak Pricing with and without AMI Technology Standard Offer Program Standard Offer Program plus a Direct Load Control/Automated Demand Response offering These programs were assumed to be economic in the AURORA model for two Scenarios, differing in terms of participation rate and program pricing type. The total annual MW savings made available for selection is illustrated below, representing approximately 50 MW in Optimal Scenario 1 and 80 MW in Optimal Scenario 2. Figure 16: Optimal Scenario 1 Achievable DR Savings Potential 49 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION Figure 17: Optimal Scenario 2 Achievable DR Savings Potential DR program costs utilized in the IRP include program start-up costs, incentive cost, administrative cost, marketing, and program operations and maintenance costs. Program rates are a fixed estimate over the delivery life of the DR programs. The study did not attempt to project the future changes in codes that are not currently planned, nor changes in costs and savings from current technologies over time. Due to this assumption, future DSM program goals and implementation plans should consider this data constraint. 4.3 Natural Gas Price Forecast The near-term portion (first year) of the natural gas price forecast is based on NYMEX Henry Hub forward prices, which are market future prices as of July 2018. Because the NYMEX futures market becomes increasingly illiquid as the time horizon increases, NYMEX forward prices are not a reliable predictor of future prices in the long term. Due to this limitation, the long-term point of view regarding future natural gas prices utilizes a consensus average of several expert independent, third-party consultant forecasts. The long-term natural gas price forecast used in the IRP also includes cases for high and low gas prices to support analysis across a range of future Scenarios. In levelized 2019 dollars per MMBtu through the IRP period (2019-2038), the natural gas price forecast in the medium case is $3.61, in the low case is $2.53, and in the high case is $4.86, as discussed in the Technical Meetings. Each gas price sensitivity is illustrated below and is described in more detail later in this section. Each of the IRP Planning Scenarios assumes one of the three natural gas price forecast sensitivities as agreed to by the parties. 50 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION Figure 18: Natural Gas Price Forecast 4.4 CO2 Price Assumptions ENO’s point of view is that national carbon regulation for the power generation sector will occur; however, the timing, design, and outcome of any carbon-control program remain uncertain. The CO2 pricing forecasted and utilized in the IRP analysis is based on the following three cases: 1. Low Case - A $0/ton CO2 price, representing either no program or a program that requires “insidethe-fence” measures at generating facilities, such as efficiency improvements, that do not result in a tradable CO2 price. This Scenario is basically consistent with the Affordable Clean Energy (“ACE”) rule proposed by the EPA in August 2018. 2. Medium Case - A “CPP Delay” case reflects a 6-year delay in the implementation of the Clean Power Plan or similar national regulation and represents a regional mass-based cap consistent with achieving the final CPP requirements but delayed by approximately 4-6 years due to the federal administration change in 2017 and consistent with the President’s executive order in March 2017; and 3. High Case - A “National Cap and Trade” High Case assumes a national cap and trade program that begins in 2028 and targets an approximately 80 percent reduction from 2005 sector Emissions by 2050. This case is generally consistent with the 2030 and 2050 emission reduction targets developed by the Intergovernmental Panel on Climate Change and anticipated by the Paris Agreement. For the purposes of modeling in Planning Scenario 3, the start year is moved up to 51 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION 2022 in accordance with the consensus reached by the parties. Figure 19: CO2 Price Forecast 52 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION Section 5 5. Portfolio Design Analytics To support the development of a broad range of Resource Portfolios, ENO, the Advisors, and the Intervenors agreed to use three planning Scenarios representing a range of market drivers and possible futures. Additionally, the parties came to consensus on five planning Strategies that informed or constrained the Portfolio development process consistent with defined objectives or policies. Using the AURORA Capacity Expansion Model, fifteen Portfolios were developed based on a combination of each Scenario and Strategy. 5.1 Planning Scenarios For the 2018 IRP, ENO utilized a set of three Scenarios which vary based on economic, policy, and customer behavior assumptions that impact market prices, including: • • • • • Peak load and energy growth Natural gas prices Coal and legacy gas generation deactivations Renewable penetration CO2 prices The three Scenarios utilized by ENO for the 2018 IRP are given below. 53 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION Table 14: Overview of Scenarios Each Scenario represents a set of key market drivers as described below. 5.1.1 Scenario 1: The market experiences flat to declining electric UPC in residential and commercial sectors due to increases in energy efficiency. This trend is partially offset by industrial growth and growth in residential and commercial customer counts. In the MISO region outside of ENO’s service area, renewables and gas play balanced roles in replacing retiring capacity to promote fuel diversity in long-term resource planning. 5.1.2 Scenario 2: Residential and commercial customer growth rates increase due to economic development and decreased energy efficiency gains driven by a shift in public policy. Combined with increased industrial sales growth due to realization of lower-probability projects, this results in high peak and energy load growth. Sustained low gas prices accelerate legacy gas and coal retirements due to economic pressure. Sustained low gas pricing, a low (zero) CO2 price, and a shift in public policy lead to gas-fired generation comprising the majority of capacity additions in the MISO region outside of ENO’s service area, complemented by some renewables. 5.1.3 Scenario 3: Residential, commercial, and industrial growth rates are decreased due to strong customer preferences for energy efficiency and distributed energy resources, resulting in a low (compared to medium) energy and peak load growth. High CO2 cost starting in 2022 and gas prices drive coal and legacy gas plants to retire earlier than anticipated. In the MISO region outside of ENO’s service area, the capacity and energy are replaced by a high penetration of renewables complemented by gas-fired generation. 54 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION 5.2 Planning Strategies Overview The Strategies were developed to support a range of potential planning objectives, DSM policies, and clean energy priorities. The details provided in Table 15 below were used to constrain the capacity expansion modeling to conform to the objectives defined by each Strategy. Table 15: Strategy Overview 5.2.1 Strategy 1: Strategy 1, using a 12% long-term Planning Reserve Margin, focuses on least cost alternatives to meet planning needs as required by Section 7.D.1. of the new IRP Rules. Demand and supply side alternatives are selected based solely on need and cost. Strategy 1 utilizes the Navigant Base case EE and DR program penetration and costs and allows the AURORA model to select only the economic EE programs, whereas all DR programs are assumed to be selected. 5.2.2 Strategy 2: Strategy 2 is focused on meeting the Council’s stated 2% DSM savings goal as required by Section 7.D.3 of the new IRP Rules. The Strategy utilizes the Navigant 2% case EE programs and Navigant Medium DR programs and forces the selection of all EE and DR programs to meet the 2% goal. 5.2.3 Strategy 3: Strategy 3 optimized Portfolios utilizing a 12% Planning Reserve Margin, Optimal Program Achievable EE, and Optimal Scenario 1 DR inputs. The EE and DR programs are assumed to be economic within this Strategy, so all programs are selected in the optimized Portfolios. 5.2.4 Strategy 4: Strategy 4 aims to meet a 12% Planning Reserve Margin using the Navigant High case DR and EE programs. Both the EE and DR programs are assumed to be economic within Strategy 4, so all programs 55 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION are included in all Strategy 4 optimized Portfolios. 5.2.5 Strategy 5: Strategy 5 is similar to Strategy 3, but represents a Stakeholder Strategy as contemplated by Section 7.D.2. of the new IRP Rules and optimized Portfolios utilizing a 12% Planning Reserve Margin, Optimal Program Achievable EE, and Optimal Scenario 1 DR inputs. However, resources available for selection were limited to DSM, renewable, and energy storage resources. In Strategy 5 the EE programs were optimized in AURORA and only economic programs were selected, whereas all DR programs are assumed to be economic within this Strategy, so all programs were selected in the optimized Portfolios. 5.3 Market Modeling The first step within the market modeling process is to utilize the AURORA17 production cost model to develop a projection of the future market supply based on the specific characteristics of each Scenario. The energy market simulation results in hourly energy prices (Locational Marginal Prices, or “LMPs”) for each of the three Scenarios. This projection encompasses the power market for the entire MISO footprint (excluding ENO). Projected LMPs for MISO-South (excluding ENO) were extracted to assess potential Portfolios for ENO within each Scenario. Figure 20 below represents projected annual MISO (excluding ENO) power prices for each Scenario. 17 The AURORA model is the primary production cost tool used to perform MISO market modeling and long-term variable supply cost planning for ENO. AURORA supports a variety of resource planning activities and is well suited for Scenario modeling and risk assessment modeling through hourly simulation of the MISO market. It is widely used by a range of organizations, including large investor-owned utilities, small publicly-owned utilities, regulators, planning authorities, independent power producers and developers, research institutions, and electric industry consultants. 56 PUBLIC VERSION Entergy New Orleans, LLC, 2018 Integrated Resource Plan $/MWh 120 100 80 60 40 20 0 Scenario 1 Scenario 2 Scenario 3 Figure 20: Average Annual MISO South Non-ENO LMP 5.4 Capacity Optimization and Results Following the market modeling process, the AURORA Capacity Expansion Model was used to identify economic type, amount, and timing of demand-side and supply-side resources needed to meet reserve margin requirements subject to the constraints imposed by each Strategy. The result of this process was a set of Portfolios of resources (“optimized Portfolios”) that produce the lowest total supply cost to meet the identified need within the constraints defined in each of the Strategy and Scenario combinations. Table 16 below depicts the optimized Portfolios that resulted from each Scenario and Strategy combination. The stars in the table depict the selected Portfolios that the parties agreed would be moved through the total supply cost evaluation as described in Section 5.4, below. 57 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION Table 16: ENO Optimized Portfolios18 Consistent with the scope of the Strategies, Strategies 2-4 include all programs identified in the DSM input case assigned to the particular Strategy and each program contributes towards meeting ENO’s resource needs. Table 17 and Table 18 below provide a summary of the DSM results for Strategies 1 and 5. 18 As noted above, Strategy 3, Scenario 3 Portfolio is identical to Strategy 5, Scenario 3 Portfolio, expressed by the outlined star seen in Table 16. 58 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION Table 17: Strategy 1 DSM Selections Table 18: Strategy 5 DSM Selections In Strategy 1, under all Scenarios, all DSM was selected in either the first year of the study or subsequently 59 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION in 203319. In Strategy 5, all DSM programs were selected under Scenario 3, and the majority under Scenario 1 and 2. The Residential Lighting program was not available in 2033 in Scenarios 1 and 2 and the Efficient New Homes program was not selected in either Scenario 1 or 2; Residential HVAC was not selected under Scenario 2. Scenario 1 Portfolio Results: As discussed above, Scenario 1 is defined by Medium assumptions and a 1/3 to 2/3 split of renewables to gas for incremental market additions. With the moderate market generation mix and energy prices, the capacity optimization, under Strategies 1-4, resulted in a balance of DSM, Solar, CT generation, and a minimal amount of battery storage. Strategy 5 did not select a CT due to Strategy characteristics that limited new generation technologies to renewables and battery storage resources, so the Portfolio selection resulted in 400 MWs of solar generation and 300 MWs of battery storage. Scenario 2 Portfolio Results: Scenario 2 is defined by high load growth, low gas prices, and a 1/4 to 3/4 split of renewables to gas for incremental market additions. In Scenario 2, due to low energy prices and zero CO2 pricing, the optimized Portfolios mostly consisted of a CT addition and battery storage to fill the remaining capacity need. Strategy 5 was restricted to only allow for renewable and battery storage additions, so the Portfolio did not select a CT, but instead covered all the capacity need utilizing battery storage. Scenario 3 Portfolio Results: Scenario 3 is defined by high energy prices due to gas and CO2 assumptions. Additionally, the 50/50 renewables to gas incremental market additions mix lead to volatile LMPs over the planning horizon. Due to these characteristics, Portfolios optimized in Scenario 3 saw high penetrations of wind generation and battery storage, complementing the high solar penetration assumed within the MISO South non-ENO market. With the exception of Scenario 3/ Strategy 2, no CT resources were selected in the optimized Portfolios. With the combination of low energy needs and high DSM, Strategy 2 selected a peaking CT to meet the capacity need most economically. 5.4.1 Solar Capacity Credit Modeling: For the 2018 IRP, ENO sought to take into account integration considerations of intermittent generation. In order to reasonably account for the diminishing contribution of solar towards capacity and energy requirements as the level of solar penetration increases, it was assumed for modeling purposes that the capacity contribution of solar diminished as a function of the amount of incremental solar added in the ENO footprint consistent with the curve MISO studied in the Renewable Integration Impact Assessment (“RIIA”). The concept that increasing amounts of solar provide diminishing returns in capacity and energy 19 During the Technical Meetings, the parties agreed that, if the capacity expansion did not select a particular program in year 1, additional inputs would be provided to allow the model to consider selecting the program in 2033, the first year with an identified capacity need. This approach increased the chances that DSM would be selected to serve customer needs. 60 PUBLIC VERSION Entergy New Orleans, LLC, 2018 Integrated Resource Plan value is a relatively recent notion that has been further explored in detailed work by CAISO20and MISO21 and generally is due to solar production shifting a load serving entity’s net peak such that every incremental unit of solar provides less value in supporting reliability needs. As discussed during the Technical Meetings,22 for the purposes of capacity expansion within the IRP, ENO used the following capacity credit for solar in AURORA when making Portfolio selections through capacity expansion. 70% Solar Capacity Credit 60% MISO RIIA 50% ENOL solar credit, by tranche 40% 30% 20% 10% 0% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Nameplate Solar % of Entity Peak Figure 21: Solar Credit Step-Down as Penetration23 Increases This is a heuristic approach which utilizes a step-down from 50% credit (the current first year capacity credit a solar resource is granted in MISO) to attempt to capture the diminishing returns solar has for designing Portfolios to meet capacity needs. This assumption was applied only to the AURORA capacity expansion; for the purpose of computing the total supply cost to customers of a Portfolio that includes solar, ENO defaulted to the 50% credit consistent with current MISO practice. 20 https://www.nrel.gov/docs/fy16osti/65023.pdf https://cdn.misoenergy.org/20180605%20RIIA%20Workshop%20Presentation213125.pdf 22 See, Appendix G. 23 “Solar Penetration” is defined as nameplate capacity of installed solar as a percentage of peak demand. 21 61 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION 5.5 Total Relevant Supply Cost Results Figure 22: Five Portfolios Selected for Total Relevant Supply Cost Analysis Through discussions at Technical Meeting #4, ENO, the Intervenors, and the Advisors agreed upon a representative subset of five of the fifteen optimized Portfolios to be run through a Total Relevant Supply Cost Analysis. The Total Relevant Supply Cost (“TRSC”) for each of the five selected Portfolios shown in Figure 22, above, was calculated in each of the three planning Scenarios. The TRSC is calculated using: • • • • Variable Supply Cost - The variable output from the AURORA model for each Portfolio in each of the futures, which includes fuel costs, variable O&M, CO2 emission costs, startup costs, energy revenue, and uplift revenue. Levelized Real Non-Fuel Fixed Costs - Return of and on capital investment, fixed O&M, and property tax for the incremental resource additions in each Portfolio. Demand Side Management (DSM) Costs Capacity Purchases/(Sales) - The capacity surplus (or deficit) in each Portfolio multiplied by the assumed capacity price. It is important to note that, given the significant differences in program cost and savings assumptions between the two DSM potential studies, there is no meaningful way to compare the total relevant supply costs of Portfolios constructed using DSM input cases from different studies. It is, however, possible to compare Portfolios developed using different input cases from the same DSM potential study. Therefore, the two tables immediately below present the Navigant-based Portfolios and Optimal-based Portfolios separately. Figure 23 shows the present value of the total relevant supply cost for each Navigant-based Portfolio by Scenario. The red boxes indicate the Scenario in which the Portfolio was initially optimized under the applicable Strategy. 62 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION Figure 23: Total Relevant Supply Cost Results (2019$ NPV) for Navigant-Based Portfolios In addition, the present value of the total relevant supply cost for each Optimal-based Portfolio by Scenario is shown in the following figure. Figure 24: Total Relevant Supply Cost Results (2019$ NPV) for Optimal-Based Portfolios To reiterate, ENO separated the total relevant supply costs for the Navigant and Optimal Portfolios due to the major differences in DSM program assumptions of the two vendors. While the spread from the lowest (Portfolio 5 with the Optimal program achievable case) to the highest (Portfolio 2 with the Navigant 2% case) is relatively small at about 18%, the use of DSM input cases from different studies prevents a direct comparison of the total relevant supply costs of those Portfolios. The comparative value of the analyses 63 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION comes from considering the different inputs, assumptions, and risk sensitivities of each Portfolio as a guide for the future, not from focusing on the costs of one Portfolio versus another, particularly given that actual costs in the future will be driven by resource certifications and DSM implementations that rely on thencurrent, actual market costs. 5.6 Stochastic Assessment of Risks The stochastic risk assessment gives an indication of the variability of a Portfolio’s costs as underlying assumptions change (e.g., gas, CO2). Given schedule and resource constraints, the parties agreed at Technical Meeting #4 to run the stochastic assessment for the following four optimized Portfolios. Figure 25: Portfolios Analyzed in Stochastic Risk Assessment The sensitivity of a Portfolio’s performance was assessed relative to changes in assumptions for natural gas prices and CO2 emission prices through stochastic analysis. Distributions of potential prices for each variable were developed that were lower-bounded by zero and positively skewed toward higher prices, which is consistent with the expectation that commodity prices would not be less than zero and would have some potential for high price spikes. In total, 400 production cost simulations were performed for the four Portfolios using the same set of the 200 gas price outcomes and 200 CO2 price outcomes and a resulting total relevant supply cost per MWh was determined for each price variant, as described by the following box plot charts. 64 PUBLIC VERSION Entergy New Orleans, LLC, 2018 Integrated Resource Plan Navigant-Based Optimal-Based Figure 26: Natural Gas Price Stochastic Results The 10th, 25th, 50th, 75th, and 90th percentiles are denoted by the vertical markers for each Portfolio. The natural gas price is described by the distribution shown to the right. The variance of total relevant supply cost for each Portfolio indicates the sensitivity of that Portfolio to natural gas prices; however, as discussed above, direct comparison of TRSC results across Portfolios that incorporate DSM input cases from different studies is not possible because of the varying cost and performance assumptions for the DSM programs. Navigant-Based Optimal-Based Figure 27: CO2 Price Stochastic Results The 10th, 25th, 50th, 75th, and 90th percentiles are denoted by the vertical markers for each Portfolio. The CO2 price is described by the distribution shown to the right. The variance of total relevant supply cost for each Portfolio indicates the sensitivity of that Portfolio to CO2 prices; however, as discussed above, direct comparison of TRSC results across Portfolios that incorporate DSM input cases from different studies is 65 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION not possible because of the varying cost and performance assumptions for the DSM programs. Due to the makeup of the Portfolios and the assumed start date of carbon regulation farther out in the future, the Portfolios are more sensitive to natural gas price variance (which can occur throughout the planning period) than CO2 price variance. 5.7 Scorecard Metrics and Results As required by the IRP Rules, ENO, with the help of the Advisors and Intervenors, developed a scorecard to attempt to assist the Council in assessing the IRP based on the Resource Portfolios. The reasons discussed above regarding the difficulty in comparing the total relevant supply costs of Portfolios designed with input cases from different DSM potential studies apply as well to the comparisons attempted through the scorecard. Thus, it must be noted that any comparison across Portfolios does not provide a complete view of ranking, risk, or total benefit given the fundamental difference in DSM inputs. The metrics discussed at Technical Meetings #3 and #4 and utilized in the scorecard are stated below. Table 19: Scorecard Metrics Metric Description Measure Expected Value The average total relevant supply cost of Portfolios across Scenarios and relative to other optimized Portfolios (all Scenarios are weighted equally) 1-10 Grading Scale Net Present Value The Total Relevant Supply Cost of the Portfolio in the Scenario it was optimized in 1-10 Grading Scale Nominal Portfolio Value A sum of the initial 5 years of the planning period 1-10 Grading Scale Distribution of Potential Utility Costs The standard deviation of total relevant supply cost across Scenarios divided by the expected value to get to a coefficient of variation 1-10 Grading Scale Range of Potential Utility Costs The sum of the total relevant supply cost upside and downside risk of Portfolios 1-10 Grading Scale Probability of High CO2 Intensity Probability of high CO2 intensity in the initial 5 years of the planning period 1-100% Grading Scale Probability of High Groundwater Usage Probability of high groundwater usage in the initial 5 1-100% Grading years of the planning period Scale 66 PUBLIC VERSION Entergy New Orleans, LLC, 2018 Integrated Resource Plan Flexible Resources The total MW of ramp available in the final year of the planning period 1-10 Grading Scale Quick-Start Resources The total MW of quick start available in the final year of the planning period 1-10 Grading Scale UCAP/ICAP Ratio The total UCAP/ICAP ratio in the final year of the planning period 1-10 Grading Scale CO2 Intensity The cumulative tons of CO2/GWh over the planning period 1-10 Grading Scale Groundwater Usage The cumulative percentage of energy generated by resources that use ground water 1-100% Grading Scale Climate Action Plan100% Low Carbon The cumulative percentage of Carbon free energy from new resources over the planning period 1-100% Grading Scale Climate Action Plan- 255 MW Solar Additions The total MWs of solar additions over the planning period 0-255 MW Grading Scale Climate Action Plan3.3% Annual Energy Savings The compound annual growth rate (CAGR) of DSM 0-3.3% Grading over the planning period Scale Macroeconomic Factors DSM spending represents only quantifiable macroeconomic impact at this time. Future ability to N/A evaluate/model DERs could provide additional basis for comparison. Based on the metrics discussed above the Portfolios were given a grade determined by how the Portfolio performed in relation to the other Portfolios. Again, due to differing Scenario and Strategy characteristics the grades of the scorecard should not be relied upon at face value without considering the inherent compositional differences among the Portfolios. Metrics that consider costs inclusive of DSM program cost (e.g. Net Present Value) cannot be utilized to compare Portfolios that were optimized using different vendor DSM programs and costs; thus, the grade is not representative of ranking based on equal testing criteria. Acknowledging such, the results of the scorecard are described in Table 20 below. 67 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION Table 20: Scorecard Results Notes: 1. Except as otherwise noted, A is the top quartile of Portfolios, B is the second quartile, C is the third quartile and D is the bottom quartile. 2. Quick-Start includes supply and demand side dispatchable resources 3. Carbon-free resources include Energy Efficiency 4. DSM spending represents only quantifiable macroeconomic impact at this time. Future ability to evaluate/model DERs could provide additional basis for comparison. 68 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION Section 6 6. Action Plan 6.1 Recap of 2015 IRP Action Plan The Company committed to, and followed through with, several actions as part of the 2015 IRP Action Plan as shown below. Description Deactivation of Michoud Units 2 and 3 Action to be Taken -Confirmed Attachment Y deactivation request complete for Michoud 2 and 3 pursuant to the MISO tariff. Resolution -Deactivation completed June 1, 2016. -Units 2 and 3 will be deactivated June 1, 2016 subject to completion of necessary transmission upgrades as required by Attachment Y. Union Power Station -Obtained council approval on November 19, 2015 for ENO purchase of Union Power Block 1. -Unit purchase transaction closed in 2016. -Transaction scheduled to close in early 2016. ENO Solar Pilot -Construction to begin 1st quarter 2016. -Target in service date Summer 2016. In-region Peaking Generation -Continue development activities and finalize preliminary design and site location. -A.B. Paterson 1 MW Solar + .5 MWh battery storage project (“New Orleans Solar Pilot Project,” or “NOSPP”) commenced operation in June 2016. -New Orleans Power Station (“NOPS”) 128 MW RICE alternative approved in Council Resolutions R-18-65 and R-19-78. -File for Council approval in a timely manner. -Target 2019 in service date. -Project under construction; expected to achieve commercial operation in 2Q2020. Clean Power Plan -Continue to monitor pending litigation of the rule and the status of Louisiana Department of Environmental Quality plan to comply. -Continue to monitor situation given Trump administration’s decision to halt implementation at federal level. DSM -Continue implementation and performance monitoring of Council approved programs for -Continue implementation and performance monitoring of Council approved programs for 69 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION Energy Smart Years 5 and 6 through March 2017. Energy Smart Years 7-9 through December 2019. Resource Needs -Continue to monitor resource needs (load, customer count, net metering, resource deactivations) and adjust near-term action plan accordingly. -The Company continues to monitor its resource needs and report to the Council through the IRP process. Renewable RFP -Conduct a Renewable RFP to obtain actionable information on the cost and deliverability of renewable resources. -Approval of 90 MW portfolio of solar resources (discussed above) selected from the Company’s 2016 Renewables RFP was requested through an Application filed in Docket UD-18-06; an Agreement in Principle (“AIP”) was filed on June 28, 2019, representing a settlement among the Company, Advisors, and Intervenors. Council Utility Committee approved AIP on July 17, 2019 -Council approved construction of 5 MW Distributed-Generation-scale solar project in Docket No. UD-17-05; construction is underway. Distributed Generation -Evaluate alternative methods for the treatment of DG in the integrated resource planning process for opportunities for improvement. -As discussed in Section 3.9, above, the Company is taking numerous steps to develop its capabilities to analyze the impacts of DERs on the distribution system as contemplated by the Council’s updated IRP Rules. AMI -Entergy is currently considering various future investments to modernize the distribution grid and more fully utilize new technologies. -The Council approved the Company’s application to implement AMI throughout the city in Resolution R-18-37. -AMI continues to be analyzed and ENO plans to talk further with the City Council and the Advisors regarding potential future AMI investments. -Accelerated implementation is ongoing and is expected to be complete in late 2020. 70 PUBLIC VERSION Entergy New Orleans, LLC, 2018 Integrated Resource Plan 6.2 2018 IRP Action Plan ENO has identified a number of actions it will pursue in the near term given the analysis of the Portfolios identified under the various planning Scenarios and Strategies. Description Action to be Taken 90 MW Portfolio Implementation Upon final approval of the Company’s Application in Docket UD-1806, undertake construction of New Orleans Solar Station (“NOSS”) project at NASA Michoud and monitor counterparty efforts to bring projects underlying the St. James and Iris solar PPAs online in accordance with contractual deadlines. Commercial Rooftop Program Complete installation of 5 MWAC rooftop solar projects approved in Resolution R-18-222. Report on project outcome to Council and consider whether requesting expansion of program beyond 5 MW limit is warranted. Community Solar Program Implementation Continue building internal resources and processes to support administration of Council’s Community Solar program under rules approved in Docket UD-18-03. Distribution Planning Capabilities As discussed in Section 3.9, above, the Company is taking numerous steps to develop its capabilities to analyze the impacts of DERs on the distribution system as contemplated by the Council’s updated IRP Rules. DSM/DR Implementation File Implementation Plan for Energy Smart Program Years 10-12 as required under Resolution R-17-430. Grid Modernization Implementation Continue implementing Grid Modernization as outlined in plans submitted in Docket UD-18-01. One Hundred Homes Rooftop Solar Initiative Complete implementation of rooftop solar pilot program with up to 100 low income residential customers in 2019. Smart Cities Implementation Continue working with Advisors and other stakeholders in Docket UD-18-01 to support implementation of Smart Cities technologies and EV charging infrastructure solutions. 71 Entergy New Orleans, LLC, 2018 Integrated Resource Plan Exhibits · · · · · · · Appendix A—Rules Compliance Matrix Appendix B—Actual Historic Load and Load Forecast (HSPM in part) Appendix C—Total Resource Supply Costs - Detail (HSPM in part) Appendix D—Navigant Potential Study Appendix E—Optimal Potential Study Appendix F—Macro Inputs Workbook (HSPM) Appendix G—Technical Meeting Materials for Meetings 1-4 72 PUBLIC VERSION Entergy New Orleans, LLC, 2018 Integrated Resource Plan Appendix A: Rules Compliance Matrix Requirement No. Section No. Page No. Key phrase or Issue Excerpt Response and/or Citation to IRP Report 1 1.C. 1 Rules Matrix Each Utility IRP shall include a matrix of these rules, the corresponding section of the IRP responsive to that rule, and a brief description of how the Utility complied with the rules. Appendix A 2 3.A. 4 Specific Objectives The Utility shall state and support specific objectives to be accomplished in the IRP planning process, which include but are not limited to the following: Pg 8: Planning Objectives 3 3.A.1. 4 Integration of Supply Side and Demand Side Resources optimize the integration of supplyside resources and demand-side resources, while taking into account transmission and distribution, to provide New Orleans ratepayers with reliable electricity at the lowest practicable cost given an acceptable level of risk; Pg 8: Planning Objectives Pg 22 Transmission Pg 26 Distribution Section 5 Portfolio Analytics 4 3.A.2. 4 Maintain Financial Integrity maintain the Utility's financial integrity; Pg 8: Planning Objectives 5 3.A.3. 4 Mitigate Risks anticipate and mitigate risks associated with fuel and market prices, environmental compliance Pg. 63: Stochastic Assessment of Risk 73 PUBLIC VERSION Entergy New Orleans, LLC, 2018 Integrated Resource Plan costs, and other economic factors; 6 3.A.4. 4 Support Resiliency and Sustainability support the resiliency and sustainability of the Utility's systems in New Orleans; Pg 22: Transmission Pg 26: Distribution Pg 65: Scorecard Metrics and Results 7 3.A.5. 4 Comply with Requirements and Council Policies comply with local, state and federal regulatory requirements and regulatory requirements and known policies (including such policies identified in the Initiating Resolution) established by the Council; Pg 54: Planning Strategy Overview Pg 65: Scorecard Metrics and Results 8 3.A.6. 4 Evaluate Incorporation of new technology evaluate the appropriateness of incorporating advances in technology, including, but not limited to, renewable energy, storage, and DERs, among others; Pg 34: Assessing Alternatives to Meet ENO Resource Needs 9 3.A.7. 4 Acceptable Risk achieve a range of acceptable risk in the trade-off between cost and risk; Pg. 63: Stochastic Assessment of Risk 10 3.A.8. 4 Transparency and Engagement maintain transparency and engagement with stakeholders throughout the IRP process by conducting technical conferences and providing for stakeholder feedback regarding the Planning Scenarios, Planning Strategies, input parameters, and assumptions. Technical Meeting #1: 1/22/18 Technical Meeting #2: 9/14/18 Technical Meeting #3: 11/28/18 Technical Meeting #4: 5/1/19 74 PUBLIC VERSION Entergy New Orleans, LLC, 2018 Integrated Resource Plan 11 3.B. 4 Efforts to Achieve Objectives In the IRP Report, the Utility shall discuss its efforts to achieve the objectives identified in Section 3A and any additional specific objectives identified in the Initiating Resolution. Pg 8: Planning Objectives Section 5, Portfolio Design Analytics 12 4.A. 5 Reference Load Forecasts and alternatives The Utility shall develop a reference case Load Forecast and at least two alternative Load Forecasts applicable to the Planning Period which are consistent with the Planning Scenarios identified in Section 7C. The following data shall be supplied in support of each Load Forecast: Pg 12: Load Forecasting Methodology 13 4.A.1. 5 Forecast of Demand and Energy by Customer Class The Utility's forecast of demand and energy usage by customer class for the Planning Period; Pg 12: Load Forecasting Methodology 14 4.A.2. 5 Methodology A detailed discussion of the forecasting methodology and a list of independent variables and their reference sources that were utilized in the development of the Load Forecast, including assumptions and econometrically evaluated estimates. The details of the Load Forecast should identify the energy and demand impacts of customer-owned DERs and then existing Utility-sponsored DSM programs; Pg 12: Load Forecasting Methodology 75 PUBLIC VERSION Entergy New Orleans, LLC, 2018 Integrated Resource Plan 15 4.A.3. 5 Independent Variables Forecasts of the independent variables for the Planning Period, including their probability distributions and statistical significance; Pg 12: Load Forecasting Methodology 16 4.A.4. 5 Expected Value of forecast The expected value of the Load Forecast as well as the probability distributions (uncertainty ranges) around the expected value of the Load Forecast; Pg 12: Load Forecasting Methodology; Appendix B 17 4.A.5. 5 Line Losses A discussion of the extent to which line losses have been incorporated in the Load Forecast. Pg 12: Load Forecasting Methodology 18 4.B. 5 Composite Customer Hourly Load Profiles The Utility shall construct composite customer hourly load profiles based on the forecasted demand and energy usage by customer class and relevant load research data, including the factors which determine future load levels and shape. Pg 12: Load Forecasting Methodology and Appendix B 19 4.C. 5 Demand and Energy data for 5 preceding years Concurrent with the presentation of the Load Forecasts to the Advisors, CURO, and stakeholders, the Utility shall provide historical demand and energy data for the five (5) years immediately preceding the Planning Period. At a minimum, the following data shall be provided: Pg 12: Load Forecasting Methodology and Appendix B 76 PUBLIC VERSION Entergy New Orleans, LLC, 2018 Integrated Resource Plan 20 4.C.1. 5 Monthly energy consumption by class monthly energy consumption for the Utility in total and for each customer class; Pg 12: Load Forecasting Methodology and Appendix B 21 4.C.2. 5 Monthly CP for utility and classes monthly coincident peak demand for the Utility and estimates of the monthly coincident peak demand for each customer class; Pg 12: Load Forecasting Methodology and Appendix B 22 4.C.3. 5 Monthly peak demand by class estimates of the monthly peak demand for each customer class; Pg 12: Load Forecasting Methodology and Appendix B 23 4.D. 5 Section 4 data in attachment The data and discussions developed pursuant to Section 4A and Section 4B, and Section 4C shall be provided as an attachment to the IRP report and summarized in the IRP report. Pg 12: Load Forecasting Methodology and Appendix B 24 4.E. 6 Known cogen and >300kW DER resources The Utility shall also provide a list of any known co-generation resources and DERs larger than 300 kW existing on the Utility’s system, including resources maintained by the City of New Orleans for city/parish purposes, (e.g. Sewerage and Water Board, Orleans Levee District, or by independent agencies or entities such as universities, etc.). Paterson Solar + Storage Pilot; Sites constructed under Commercial Rooftop Project (UD17-05): TCI and Dwyer Rd. 77 PUBLIC VERSION Entergy New Orleans, LLC, 2018 Integrated Resource Plan 25 5.A. 6 Identification of resource options Identification of resource options. The Utility shall identify and evaluate all existing supply-side and demand-side resources and identify a variety of potential supply-side and demand-side resources which can be reasonably expected to meet the Utility’s projected resource needs during the Planning Period. Appendix D and E: Optimal and Navigant Studies Pg 34: Assessing Alternatives to Meet ENO Resource Needs 26 5.A.1. 6 Existing supply side resource costs Existing supply-side resources. For existing supply-side resources, the Utility should incorporate all fixed and variable costs necessary to continue to utilize the resource as part of a Resource Portfolio. Costs shall include the costs of any anticipated renewal and replacement projects as well as the cost of regulatory mandated current and future emission controls. Appendix C-Variable Supply Cost reflects the optimized run time of existing units 27 5.A.1.a. 6 Changes to resource mix The Utility shall identify important changes to the Utility’s resource mix that occurred since the last IRP including large capital projects, resource procurements, changes in fuel types, and actual or expected operational changes regardless of cause. Pg 10: Figure 2 78 PUBLIC VERSION Entergy New Orleans, LLC, 2018 Integrated Resource Plan 28 5.A.1.b. 6 Supply side resource info Data supplied as part of the Utility’s IRP filing should include a list of the Utility’s existing supply-side resources including: the resource name, fuel type, capacity rating at time of summer and winter peak, and typical operating role (e.g. base, intermediate, peaking). Pg. 11: Table 2 29 5.A.2. 6 Load reductions from existing DSM resources For existing demand-side resources, the Utility should account for load reductions attributable to the then-existing demand-side resources in each year of the Planning Period. Each existing demand-side resource will be identified as either a specific energy efficiency program or DR program with an individual program lifetime and estimated energy and demand reductions applicable to the Planning Period, or as a then-existing Utility owned or Utility-managed distributed generation resource with energy and demand impacts that are estimated for applicable years of the Planning Period. Data supplied as part of the Utility’s IRP filing should include: Pg 12: Load Forecasting Methodology Pg 42: Demand-Side Management Pg 68: Action Plan 79 PUBLIC VERSION Entergy New Orleans, LLC, 2018 Integrated Resource Plan 30 5.A.2.a. 6 Projected reductions Details of projected kWh/kW reductions from existing DSM programs based on quantifiable results and other credible support derived from Energy Smart New Orleans, or any successor program, using verified data available to the Utility from prior DSM program implementation years. Appendix B 31 5.A.2.b. 6 Existing DSM resources A list categorizing the Utility’s existing demand-side resources including anticipated capacity at time of summer and winter peak. Pg 42: Demand-Side Management Pg 68: Action Plan 32 5.A.3. 6 Potential SS resources With respect to potential supplyside resources, the Utility shall consider: Utility-owned and purchased power resources; conventional and new generating technologies including technologies expected to become commercially viable during the Planning Period; technologies utilizing renewable fuels; energy storage technologies; cogeneration resources; and Distributed Energy Resources, among others. Pg 34: Technology Assessment 33 5.A.3.a. 7 Incorporate known policy goals The Utility should incorporate any known Council policy goals (including such policy goals identified in the Initiating Resolution) with respect to Section V, Portfolio Design Analytics Pg 68: Action Plan Pg 25: Distribution 80 PUBLIC VERSION Entergy New Orleans, LLC, 2018 Integrated Resource Plan resource acquisition, including, but not limited to, renewable resources, energy storage technologies, and DERs. 34 5.A.3.b. 7 Required data for resources Data supplied as part of the Utility’s IRP filing should include: a description of each potential supply-side resource including a technology description, operating characteristics, capital cost or demand charge, fixed operation and maintenance costs, variable charges, variable operation and maintenance costs, earliest date available to provide supply, expected life or contractual term of resource, and fuel type with reference to fuel forecast. Pg 34: Assessing Alternatives to Meet ENO Resource Needs 35 5.A.4. 7 Potential DSM Resources Potential demand-side resources. With respect to potential demandside resources, the Utility should consider and identify all costeffective demand-side resources through the development of a DSM potential study. All DSM measures with a Total Resource Cost Test value of 1.0 or greater shall be considered cost effective for DSM measure screening purposes. Appendix D and E-Navigant and Optimal Potential Studies 36 5.A.4.a. 7 DSM Potential Study The DSM potential study shall include, but not be limited to: identification of eligible measures, measure life expectancies, Appendix D and E-Navigant and Optimal Potential Studies 81 PUBLIC VERSION Entergy New Orleans, LLC, 2018 Integrated Resource Plan baseline standards, load reduction profiles, incremental capacity and energy savings, measure and program cost assumptions, participant adoption rates, market development, and avoided energy and capacity costs for DSM measure and program screening purposes. 37 5.A.4.b. 7 N.O. TRM The principal reference document for the DSM potential study shall be the New Orleans Technical Reference Manual. Appendix D and E-Navigant and Optimal Potential Studies 38 5.A.4.c. 7 CA Standard Practice Tests In the development of the DSM potential study, all four California Standard Practice Tests (i.e. TRC, PACT, RIM and PCT) will be calculated for the DSM measures and programs considered. Appendix D and E-Navigant and Optimal Potential Studies 39 5.A.4.d. 7 Known policy goals re: DSM The Utility should incorporate any known Council policy goals or targets (including such policy goals or targets identified in the Initiating Resolution) with respect to demand-side resources. Pg 54: Planning Strategy Overview Pg 65: Scorecard Metrics and Results 40 5.A.4.e. 7 Cost effective DR programs The cost-effective DR programs should include consideration of those programs enabled by the deployment of Advanced Meter Infrastructure, including both direct load control and DR pricing programs for both Residential and Appendix D and E-Navigant and Optimal Potential Studies 82 PUBLIC VERSION Entergy New Orleans, LLC, 2018 Integrated Resource Plan Commercial customer classes. 41 5.A.4.f. 8 Required data for DSM analysis Data supplied as part of the Utility’s IRP filing should include: a description of each potential demand-side resource considered, including a description of the resource or program; expected penetration levels by planning year; hourly load reduction profiles for each DSM program utilized in the IRP process; and results of appropriate cost-benefit analyses and acceptance tests, as part of the planning assumptions utilized within the IRP planning process. Appendix D and E-Navigant and Optimal Studies Pg 42: Demand-Side Management Pg 44: Energy Efficiency Pg 47: Demand Response 42 5.B. 8 Stakeholder process Through the Stakeholder Process, Consensus among the Utility shall strive to develop a parties reached at position agreed to by the Utility, Technical Meeting #3 the Advisors, and a majority of the Intervenors regarding the potential supply-side and potential demand-side resources and their associated defining characteristics (e.g., capital cost, operating and maintenance costs, emissions, DSM supply curve, etc.). 83 PUBLIC VERSION Entergy New Orleans, LLC, 2018 Integrated Resource Plan 43 5.B.1. 8 Reference Planning Strategy To the extent such a consensus can Consensus among be achieved among the Utility, the parties reached at Advisors, and a majority of the Technical Meeting #3 Intervenors, the resulting collection of potential supply-side and demand-side resources and their associated defining characteristics will be utilized in the reference Planning Strategy developed pursuant to Section 7D. 44 5.B.2. 8 Stakeholder Strategy To the extent such a consensus cannot be achieved, the Utility shall model, in coordination with the requirements in Section 7D, two distinct Planning Strategies: a reference Planning Strategy and a stakeholder Planning Strategy. The reference Planning Strategy will be based on the Utility’s assessment of the collection of potential supply-side and demandside resources and their associated defining characteristics. The stakeholder Planning Strategy will be determined by a majority of the Intervenors and modeled by the Utility based on inputs provided to the Utility describing the collection of potential supply-side and demand-side resources and their associated defining characteristics. To maintain consistency in the modeling process, the Advisors will work Consensus among parties reached at Technical Meeting #3 84 PUBLIC VERSION Entergy New Orleans, LLC, 2018 Integrated Resource Plan with the Intervenors and the Utility to ensure that input that is provided for the stakeholder Planning Strategy can be accommodated within the framework of the existing model and software. 45 6.A. 8 Integration of T&D planning into IRP The Utility shall explain how the Utility’s current transmission system, and any planned transmission system expansions (including regional transmission system expansion planned by the RTO in which the Utility participates) and the Utility's distribution system are integrated into the overall resource planning process to optimize the Utility's resource portfolio and provide New Orleans ratepayers with reliable electricity at the lowest practicable cost. Pg 22: Transmission Pg 26: Distribution 46 6.B. 9 Planned transmission topology Models developed for the Pg 22: Transmission integrated resource planning process should incorporate the planned configuration of the Utility’s transmission system and the interconnected RTO during the Planning Period. 85 PUBLIC VERSION Entergy New Orleans, LLC, 2018 Integrated Resource Plan 47 6.C. 9 Major changes to T&D systems To the extent major changes in the operation or planning of the transmission system and/or distribution system (including changes to accommodate the expansion of DERs) are contemplated in the Planning Period, the Utility should describe the anticipated changes and provide an assessment of the cost and benefits to the Utility and its customers. Pg 22: Transmission Pg 26: Distribution 48 6.D. 9 Transmission solutions for reliability To the extent that new resource Pg 22: Transmission additions are selected by the Utility for a Resource Portfolio based on reliability needs rather than as a result of the optimized development of a Resource Portfolio, the Utility shall identify reasonable transmission solutions that can be employed to either reduce the size, delay, or eliminate the need for the new reliabilitydriven resource additions and provide economic analyses demonstrating why the new reliability-driven resource addition was selected in lieu of the transmission solutions identified. 86 PUBLIC VERSION Entergy New Orleans, LLC, 2018 Integrated Resource Plan 49 6.E. 9 Evaluation of DERs It is the Council's intent that, as part of the IRP, the Utility shall evaluate the extent to which reliability of the distribution system can be improved through the strategic location of DERs or other resources identified as part of the IRP planning process. The Utility should provide an analysis, discussion, and quantification of the costs and benefits as part of the evaluation. To the extent the Utility does not currently have the capability to meet this requirement, the utility shall demonstrate progress toward accomplishing this requirement until such time as it acquires the capability. Pg 26: Distribution 50 7.A. 9 IRP Modeling parameters The integrated resource planning process should include modeling of specific parameters and their relationships consistent with market fundamentals, and as appropriate for long-term Portfolio planning. This overall modeling approach is an accepted analytic approach used in resource planning considering the range of both supply-side and demand-side options as well as uncertainty surrounding market pricing. To represent and account for the different characteristics of alternative types of resource Section 5, Portfolio Design Analytics 87 PUBLIC VERSION Entergy New Orleans, LLC, 2018 Integrated Resource Plan options, mathematical methods such as a linear programming formulation should be used to optimize resource decisions. 51 7.B. 9 External Capacity sales The optimization process shall be constrained to mitigate the overreliance on forecasted revenues from external capacity market sales and external energy market sales driving the selection of resources. Pg 56: Market Modeling Pg 56: Capacity Optimization and Results 52 7.C. 9 Planning Scenarios The Utility shall develop three to four Planning Scenarios that incorporate different economic and environmental circumstances and national and regional regulatory and legislative policies. Consensus among parties reached at Technical Meeting #3 53 7.C.1. 10 Reference and Alternative The Planning Scenarios should Scenarios include a reference Planning Scenario that represents the Utility’s point of view on the most likely future circumstances and policies, as well as two alternative Planning Scenarios that account for alternative circumstances and policies. Consensus among parties reached at Technical Meeting #3 54 7.C.2. 10 Scenario Assumptions In the development of the Planning Consensus among Scenarios, the Utility should seek parties reached at to develop a position agreed to by Technical Meeting #3 the Utility, Advisors, and a 88 PUBLIC VERSION Entergy New Orleans, LLC, 2018 Integrated Resource Plan majority of Intervenors regarding the assumptions surrounding each of the Planning Scenarios. To the extent such a consensus is not reasonably attainable regarding the Planning Scenarios, the Utility shall model a fourth Planning Scenario which is based upon input agreed to by a majority of the Intervenors. 55 7.C.3. 10 Data for Scenarios For each IRP Planning Scenario, data supplied as part of the Utility’s IRP filing should include: 56 7.C.3.a. 10 Fuel Price Forecast a fuel price forecast for each fuel considered for utilization in any existing or potential supply-side resource; Pg 50: Natural Gas Price Forecast 57 7.C.3.b. 10 Hourly Market Price Forecast for Energy an hourly market price forecast for energy (e.g. locational marginal prices); Pg 56: Market Modeling 58 7.C.3.c. 10 Annual Capacity Price Forecast an annual capacity price forecast for both a short-term capacity purchase (e.g. bilateral contract or Planning Resource Credit) and a long-term capacity purchase (e.g. long-run marginal cost of a new replacement gas combustion turbine); Appendix F--Macro Inputs Workbook 59 7.C.3.d. 10 Other Price Components forecasts of price for any other price related components that are defined by the Planning Scenario Pg 51: CO2 Price forecast 89 PUBLIC VERSION Entergy New Orleans, LLC, 2018 Integrated Resource Plan (e.g. CO2 price forecast, etc.). 60 7.D. 10 Strategies Distinct from the Planning Scenarios, the Utility shall identify two to four Planning Strategies which constrain the optimization process to achieve particular goals, regulatory policies and/or business decisions over which the Council, the Utility, or stakeholders have control. Consensus among the parties reached at Technical Meeting #3 61 7.D.1. 10 Lowest Cost Strategy The Utility shall develop a Pg 54: Planning Planning Strategy that allows the Strategy Overview optimization process to identify the lowest cost option for meeting the needs identified in the IRP process. 62 7.D.2. 10 Reference Strategy The Utility shall develop a Consensus among the reference Planning Strategy parties reached at agreed to by the Utility, Advisors, Technical Meeting #3 and a majority of the Intervenors. To the extent such a consensus cannot be reasonably achieved, the reference Planning Strategy shall reflect the Utility’s point of view on resource input parameters and constraints, and the Utility shall model a separate stakeholder Planning Strategy based upon input determined by a majority of the Intervenors. 90 PUBLIC VERSION Entergy New Orleans, LLC, 2018 Integrated Resource Plan 63 7.D.3. 11 Alternate Strategies As necessary, the Utility shall develop alternate Planning Strategies to reflect known utility regulatory policy goals of the Council (including such policy goals or targets identified in the Initiating Resolution) as established no later than 30 days prior to the date the Planning Strategy inputs must be finalized. Consensus among the parties reached at Technical Meeting #3 64 7.E. 11 Finalization of Scenario and Strategy Parameters Prior to the development of optimized Resource Portfolios, the parameters developed for the Planning Scenarios and Planning Strategies shall be set, considered finalized, and not subject for alteration during the remainder of the IRP planning cycle. The IRP Report shall describe the parameters of each Planning Scenario and each Planning Strategy, including all artificial constraints utilized in the optimization modeling. Section 5, Portfolio Design Analytics 65 7.F. 11 Portfolio Optimization Resource Portfolios shall be developed through optimization utilizing the Utility’s modeling software. The Utility shall identify the least-cost Resource Portfolio for each Planning Scenario and Planning Strategy combination, based on total cost. Resource Portfolios shall consist of optimized combinations of supply- Pg 56: Capacity Optimization and Results 91 PUBLIC VERSION Entergy New Orleans, LLC, 2018 Integrated Resource Plan side and demand-side resources, while recognizing constraints including transmission and distribution. 66 7.G. 11 Results of Scenario&Strategy combinations The Utility shall provide a Pg 61: Total relevant discussion and presentation of supply Cost Results; results for each Planning Appendix C Scenario/Planning Strategy combination, the annual total demand related costs, energy related costs, and total supply costs associated with each leastcost Resource Portfolio identified under each Planning Scenario/Planning Strategy combination, a load and capability table indicating the total load requirements and identifying all supply-side and demand-side resources included in the Resource Portfolio (including identifying the impacts of existing demand-side resources on the total load requirements), and a description of the supply-side and demand-side resources that are planned and, if applicable, their principal rationale for selection (i.e., supply peak demand, supply non-peak demand or operational constraints, achieve more economical production of energy, etc.). 92 PUBLIC VERSION Entergy New Orleans, LLC, 2018 Integrated Resource Plan 67 7.G.1. 11 Annual and Cumulative portfolio costs Data supplied as part of the Utility’s IRP filing shall include a cumulative present worth summary of the results as well as the annual estimates of costs that result in the cumulative present worth to enable the Council to understand the timing of costs and savings of each least-cost Resource Portfolio. Pg 61: Total relevant supply Cost Results; Appendix C 68 7.H. 11 Discussion of Portfolio Results The IRP report’s discussion and presentation of results for each Resource Portfolio should identify key characteristics of that Resource Portfolio and significant factors that drive the ultimate cost of that Resource Portfolio such that the Council may understand which factors could ultimately and significantly affect the preference of a Resource Portfolio by the Council. Pg 61: Total Relevant Supply Cost Results 69 7.I. 11 Scorecard template The Utility will develop and include a scorecard template or set of quantitative and qualitative metrics to assist the Council in assessing the IRP based on the Resource Portfolios. The scorecard should rank the resource portfolios by how well each portfolio achieves each metric. Such metrics should include but not necessarily be limited to: cost; impact on the Pg 65: Scorecard Metrics and Results 93 PUBLIC VERSION Entergy New Orleans, LLC, 2018 Integrated Resource Plan Utility's revenue requirements; risk; flexibility of resource options; reasonably quantifiable environmental impacts (such as national average emissions for the technologies chosen, amount of groundwater consumed, etc.); consistency with established, published city policies, such as the City's sustainability plan; and macroeconomic impacts in New Orleans. 70 8.A. 12 Cost/Risk Analysis The Utility shall develop a cost/risk analysis which balances quantifiable costs with quantifiable risks of the identified least-cost Resource Portfolios. The risk assessment must be presented in the IRP to allow the Council to comprehend the robustness of each Resource Portfolio across the cost/risk range of possible Resource Portfolios. Pg 63: Stochastic Assessment of Risk 71 8.A.1. 12 Assessment of social and environmental costs In quantifying Resource Portfolio costs/risks, the IRP shall assess any social and environmental effects of the Resource Portfolios to the extent that: 1) those effects can be quantified and have been modeled for a Resource Portfolio, including the applicable Planning Period years and ranges of uncertainty surrounding each Pg 65: Scorecard Metrics and Results 94 PUBLIC VERSION Entergy New Orleans, LLC, 2018 Integrated Resource Plan externality cost, and 2) each quantified cost must be clearly identified by the portion which relates to the Utility’s revenue requirements or cost of providing service to the Utility’s customers under the Resource Portfolio. 72 8.A.2. 12 Probabilities of outcomes It is the Council's intent that, as Pg 63: Stochastic part of the IRP, a risk assessment Assessment of Risk be conducted to evaluate both the expected outcome of potential costs as well as the distribution and potential range and associated probabilities of outcomes. To the extent the Utility believes the risk assessment described herein is beyond the current modeling capabilities of the Utility or that the risk assessment cannot be accomplished within the procedural schedule set forth in the Initiating Resolution, the Utility shall so inform the Council and meet with the Intervenors and Advisors to agree upon an alternative form of risk analysis to recommend to the Council. 73 8.A.2.a. 12 Cost/MWh in future years The risk assessment shall include the expected cost per MWh of the Resource Portfolios in selected future years, along with the range of annual average costs foreseen for the 10th and 90th percentiles Pg 63: Stochastic Assessment of Risk 95 PUBLIC VERSION Entergy New Orleans, LLC, 2018 Integrated Resource Plan of simulated possible outcomes. 74 8.A.2.b. 12 Supporting Methodology Included The supporting methodology shall be included, such as the iterations or simulations performed for the selected years, in which the possible outcomes are drawn from distributions that describe market expectations and volatility as of the current filing date. 75 9.A. 12 IRP Process Requirements At a minimum, the IRP process shall include, but not be limited to, the following elements: 76 9.A.1. 12 Collaboration on IRP inputs The opportunity for Intervenors to participate in the concurrent development of inputs and assumptions for the major components of the IRP in collaboration with the Utility within the confines of the IRP timeline and procedural schedule. Pg 63: Stochastic Assessment of Risk Stakeholder process conducted in accordance with IRP Rules and Initiating Resolution 96 PUBLIC VERSION Entergy New Orleans, LLC, 2018 Integrated Resource Plan 77 9.A.2. 12 Four Technical Meetings At least four technical meetings attended by the parties in the Docket focused on major IRP components that include the Utility, Intervenors, CURO, and the Advisors with structured comment deadlines so that meeting participants have the opportunity to present inputs and assumptions and provide comments, and attempt to reach consensus while remaining mindful of the procedural schedule established in the Initiating Resolution. Technical Meeting #1: 1/22/18 Technical Meeting #2: 9/14/18 Technical Meeting #3: 11/28/18 Technical Meeting #4: 5/1/19 Technical Meeting #5: TBD 78 9.A.3. 13 Three Public Meetings At least 3 public engagement technical conferences advertised through multiple media channels at a minimum of 30 days prior to the public technical conference. Public Meeting #1: 9/25/17 Public Meeting #2: 8/9/19 Public Meeting #3: TBD 79 10.A. 13 Public Review of IRP The Utility shall make its IRP Public IRP Available available for public review subject on ENO IRP Website to the provisions of the Council Resolution initiating the current IRP planning cycle and referenced in Section 1B. 80 10.B. 13 Filing of IRP The Utility shall file its IRP with the Council consistent with and subject to the provisions of the Council Resolution initiating the current IRP planning cycle IRP Filed: 7/19/19 97 PUBLIC VERSION Entergy New Orleans, LLC, 2018 Integrated Resource Plan referenced in Section 1B. 81 10.C. 13 Discussion of Stakeholder The IRP report should discuss the engagement stakeholders’ engagement throughout the IRP process; the access to data inputs and specific modeling results by all parties; the consensus reached regarding all demand-side and supply-side resource inputs and assumptions; specific descriptions of unresolved issues regarding inputs, assumptions, or methodology; the formulation of the stakeholder Planning Scenario and/or stakeholder Planning Strategy as needed; and recommendations to improve the transparency and efficiency of the IRP process for prospective IRP cycles. 82 10.D. 13 Action Plan The IRP shall include an action plan and timeline discussing any steps or actions the Utility may propose to take as a result of the IRP, understanding that the Council’s acceptance of the filing of the Utility’s IRP would not operate as approval of any such proposed steps or actions. Pg 4: Executive Summary; Pg 53: Portfolio Design Analytics Pg 68: Action Plan 98 PUBLIC VERSION Entergy New Orleans, LLC, 2018 Integrated Resource Plan Appendix B: Actual Historic Load and Load Forecast Historic Peak Demand and Energy Table 1: Annual Billed Sales at the Meter (GWh) Residential Commercial Industrial 2010 2011 2012 2013 2014 2015 2016 2017 2018 1,858 1,888 1,772 1,867 1,963 2,104 2,231 2,155 2,401 1,899 1,939 1,968 1,998 2,046 2,167 2,268 2,248 2,270 503 498 484 481 452 461 441 429 448 Governmental Total 810 795 785 758 768 814 794 790 795 5,069 5,120 5,009 5,105 5,230 5,547 5,733 5,621 5,914 Table 2: Summer and Winter Historical Peaks with Distribution Losses (MW) Summer Winter 2010 2011 2012 2013 2014 2015 2016 2017 2018 1,101 1,115 1,104 1,104 1,066 1,161 1,142 1,118 1,150 975 993 830 903 1,056 1,008 952 1,023 1,181 Table 3: Historic Monthly Billed Sales at the Meter (MWh) Residential Commercial Industrial Governmental 1/1/2010 2/1/2010 3/1/2010 4/1/2010 5/1/2010 6/1/2010 7/1/2010 8/1/2010 9/1/2010 10/1/2010 11/1/2010 12/1/2010 179,921 159,381 146,460 92,298 114,665 172,176 199,176 216,973 191,740 147,993 110,358 127,019 151,178 142,735 134,268 135,186 151,184 171,779 186,908 188,679 179,188 161,356 153,488 142,588 40,363 32,322 35,021 43,730 41,015 49,094 46,230 50,137 42,450 42,863 41,678 38,240 65,903 59,204 57,458 57,566 63,780 69,876 77,750 77,149 76,541 76,771 65,151 62,425 Total 437,366 393,643 373,206 328,780 370,645 462,925 510,064 532,938 489,920 428,983 370,676 370,273 99 PUBLIC VERSION Entergy New Orleans, LLC, 2018 Integrated Resource Plan 1/1/2011 2/1/2011 3/1/2011 4/1/2011 5/1/2011 6/1/2011 7/1/2011 8/1/2011 9/1/2011 10/1/2011 11/1/2011 12/1/2011 1/1/2012 2/1/2012 3/1/2012 4/1/2012 5/1/2012 6/1/2012 7/1/2012 8/1/2012 9/1/2012 10/1/2012 11/1/2012 12/1/2012 1/1/2013 2/1/2013 3/1/2013 4/1/2013 5/1/2013 6/1/2013 7/1/2013 8/1/2013 9/1/2013 10/1/2013 11/1/2013 12/1/2013 1/1/2014 2/1/2014 3/1/2014 4/1/2014 5/1/2014 181,190 164,921 120,894 107,134 128,907 187,998 207,021 207,089 206,174 147,396 103,867 125,248 146,027 120,258 117,043 110,747 130,405 194,937 207,621 196,602 174,737 145,664 113,255 114,992 161,718 140,035 130,082 109,798 106,279 176,880 199,988 206,422 206,555 172,771 112,254 144,472 203,822 199,387 137,747 106,718 117,880 153,844 139,287 145,897 147,743 154,333 177,707 188,637 186,587 186,007 169,136 147,240 142,290 151,302 144,784 150,577 151,841 163,704 191,287 191,295 187,542 174,459 168,165 150,617 142,360 156,576 149,482 144,781 141,019 150,277 183,333 189,754 190,508 196,753 185,164 155,326 155,452 163,569 159,754 148,471 152,772 154,766 35,871 38,053 37,792 41,150 41,538 46,731 45,380 47,720 46,512 41,381 41,280 34,472 37,679 37,216 36,108 37,289 40,159 46,755 43,023 43,944 42,683 44,742 36,138 38,576 33,536 34,265 35,598 37,511 33,565 44,523 45,683 45,739 47,547 43,988 41,032 38,258 39,652 30,515 35,494 36,419 37,176 63,459 58,554 60,941 62,692 63,959 69,557 74,520 74,318 74,375 70,540 61,653 60,837 60,852 59,637 60,944 63,109 62,845 71,588 72,967 72,930 72,773 66,937 61,995 57,998 59,472 62,904 59,970 57,269 59,552 65,513 67,921 67,432 69,604 68,988 61,036 58,608 59,589 57,316 57,741 57,670 58,727 434,365 400,815 365,524 358,718 388,736 481,993 515,558 515,715 513,068 428,453 354,041 362,847 395,860 361,897 364,672 362,986 397,112 504,567 514,906 501,018 464,651 425,508 362,005 353,925 411,303 386,685 370,430 345,597 349,673 470,249 503,347 510,101 520,459 470,911 369,648 396,790 466,633 446,972 379,453 353,580 368,549 100 PUBLIC VERSION Entergy New Orleans, LLC, 2018 Integrated Resource Plan 6/1/2014 7/1/2014 8/1/2014 9/1/2014 10/1/2014 11/1/2014 12/1/2014 1/1/2015 2/1/2015 3/1/2015 4/1/2015 5/1/2015 6/1/2015 7/1/2015 8/1/2015 9/1/2015 10/1/2015 11/1/2015 12/1/2015 1/1/2016 2/1/2016 3/1/2016 4/1/2016 5/1/2016 6/1/2016 7/1/2016 8/1/2016 9/1/2016 10/1/2016 11/1/2016 12/1/2016 1/1/2017 2/1/2017 3/1/2017 4/1/2017 5/1/2017 6/1/2017 7/1/2017 8/1/2017 9/1/2017 10/1/2017 169,678 198,382 211,035 204,812 152,295 127,234 134,386 168,087 176,838 148,446 118,379 133,556 175,745 225,248 249,885 242,074 187,021 139,019 139,562 178,568 175,616 145,066 119,352 135,321 204,623 264,987 239,623 247,790 220,888 156,298 142,745 177,349 144,210 134,177 135,116 149,105 183,982 227,517 249,650 233,404 210,577 183,369 194,327 198,126 196,301 173,345 168,444 153,250 162,304 159,758 153,380 162,760 169,522 183,660 211,817 210,776 211,902 195,552 175,382 170,363 177,522 160,036 172,416 165,316 171,054 201,329 223,156 209,788 219,512 209,712 186,334 171,370 179,242 166,961 168,723 170,949 178,925 191,567 208,816 216,178 208,945 206,058 40,333 40,870 41,264 41,964 38,716 36,104 33,975 35,337 33,355 33,656 38,132 34,485 42,760 44,721 43,165 44,023 40,933 35,927 34,742 36,821 31,585 32,223 34,945 34,929 37,081 42,085 40,528 42,709 38,250 36,451 33,001 31,260 35,949 31,116 34,094 33,880 36,783 39,083 39,204 40,375 38,924 64,815 72,084 70,154 77,161 67,667 65,619 59,297 59,914 59,578 62,515 61,054 67,526 65,792 71,322 83,999 76,832 70,740 68,433 66,596 62,336 55,476 60,035 59,261 62,566 67,746 73,904 75,202 75,363 72,836 66,449 63,157 62,288 62,623 58,862 59,930 60,373 67,370 71,921 71,035 73,969 70,943 458,195 505,662 520,580 520,238 432,022 397,400 380,907 425,642 429,530 397,997 380,325 405,088 467,957 553,108 587,825 574,830 494,247 418,760 411,264 455,247 422,711 409,740 378,873 403,871 510,780 604,133 565,141 585,375 541,685 445,532 410,273 450,139 409,744 392,878 400,089 422,282 479,702 547,337 576,068 556,693 526,502 101 PUBLIC VERSION Entergy New Orleans, LLC, 2018 Integrated Resource Plan 11/1/2017 12/1/2017 1/1/2018 2/1/2018 3/1/2018 4/1/2018 5/1/2018 6/1/2018 7/1/2018 8/1/2018 9/1/2018 10/1/2018 11/1/2018 12/1/2018 153,747 155,809 237,027 206,863 133,384 121,577 138,072 229,864 261,418 267,772 249,569 225,794 160,357 169,266 178,674 172,821 183,430 174,067 166,744 156,580 166,998 202,967 226,463 213,686 220,494 211,439 184,564 162,711 34,209 33,989 33,687 31,683 33,404 34,884 35,024 41,466 41,675 43,081 43,389 40,343 35,107 33,942 66,347 64,397 62,394 59,377 59,355 58,840 58,485 67,743 72,711 75,663 76,821 74,443 68,619 60,528 432,976 427,016 516,539 471,991 392,887 371,882 398,579 542,040 602,266 600,201 590,274 552,019 448,647 426,447 Evaluation of Previous IRP Load Forecast Table 4: Peak Forecasted vs Actual (Includes T&D Losses) Peak (MW) Previous IRP Peak Forecast (BP15)* Weather Normalized Actual Peak Deviation % Deviation 2016 1,125 1,116 9 1% 2017 1,136 1,152 -16 -1% 2018 1,143 1,153 -10 -1% *From ENO’s 2015 IRP Final Report, Table 28, Stakeholder Input Case 2018 IRP Load Forecast Table 5: Annual Energy Forecasts (GWh) (Includes T&D Losses) 102 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION Table 6: Monthly Energy Forecasts (GWh) (Includes T&D Losses) 103 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION 104 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION 105 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION 106 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION 107 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION 108 PUBLIC VERSION Entergy New Orleans, LLC, 2018 Integrated Resource Plan Table 7: Annual Coincident Peaks (MW) Forecast Date 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 Res Com Ind Gov Company Use Total 539 532 530 532 529 528 523 523 527 527 545 531 547 537 544 556 559 561 564 555 433 438 435 435 436 434 434 431 429 426 415 425 416 426 425 415 414 416 416 426 63 64 65 65 65 65 66 66 67 66 64 67 65 68 68 65 65 66 66 70 139 139 143 143 143 143 143 142 142 142 138 141 138 141 140 137 136 136 136 139 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1,175 1,174 1,174 1,176 1,172 1,171 1,166 1,164 1,166 1,162 1,162 1,165 1,166 1,172 1,179 1,173 1,175 1,180 1,182 1,191 Table 8: Annual Load Factor Forecast Res Com Ind Date 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 49% 50% 50% 49% 50% 50% 50% 50% 50% 50% 48% 50% 49% 50% 49% 48% 62% 63% 64% 64% 65% 65% 65% 65% 65% 65% 67% 65% 67% 65% 66% 67% 78% 77% 78% 79% 80% 79% 79% 79% 79% 80% 83% 79% 83% 79% 79% 84% Gov Total 69% 71% 68% 70% 72% 72% 72% 72% 72% 72% 74% 72% 74% 72% 73% 75% 58% 59% 59% 59% 59% 60% 60% 60% 60% 60% 60% 60% 60% 60% 60% 60% 109 PUBLIC VERSION Entergy New Orleans, LLC, 2018 Integrated Resource Plan 2035 2036 2037 2038 49% 49% 49% 50% 68% 67% 68% 66% 84% 84% 84% 80% 75% 75% 75% 73% 60% 60% 60% 60% 110 PUBLIC VERSION Entergy New Orleans, LLC, 2018 Integrated Resource Plan 2018 IRP DSM Summary Table 9: Annual DSM by Program Year (kWh) (Historic and Forecast) Note: DSM kWh broken out by program year incremental impact as well as carryover effects from previous Energy Smart years. Carryover effects are calculated using a straight-line depreciation based on individual program life assumptions. PY1 PY2 PY3 PY4 PY5 PY6 PY7 PY8 PY9 11,538,009 14,818,896 14,360,232 15,604,754 20,092,094 23,433,485 19,948,677 46,099,307 53,894,394 10,435,749 13,350,334 12,869,828 13,937,714 17,984,619 16,932,189 18,393,567 37,863,514 9,333,490 11,881,772 11,379,424 12,270,674 12,705,293 18,805,230 16,838,457 8,231,230 10,413,210 9,971,333 8,232,390 14,239,122 16,221,994 7,128,970 8,944,648 6,647,226 9,698,826 12,304,397 6,044,813 5,996,857 7,920,342 8,371,647 4,137,336 6,437,738 6,780,614 4,226,307 5,057,272 3,209,906 Total 11,538,009 25,254,646 37,044,056 48,587,583 62,951,411 78,649,571 74,599,969 125,820,439 160,542,194 111 PUBLIC VERSION Entergy New Orleans, LLC, 2018 Integrated Resource Plan PY10 PY11 PY12 PY13 PY14 PY15 PY16 PY17 PY18 PY19 PY20 53,894,394 53,894,394 53,894,394 53,894,394 53,894,394 53,894,394 53,894,394 53,894,394 53,894,394 53,894,394 53,894,394 42,330,649 42,330,649 42,330,649 42,330,649 42,330,649 42,330,649 42,330,649 42,330,649 42,330,649 42,330,649 42,330,649 34,627,721 38,766,904 38,766,904 38,766,904 38,766,904 38,766,904 38,766,904 38,766,904 38,766,904 38,766,904 38,766,904 15,283,347 31,391,928 35,203,160 35,203,160 35,203,160 35,203,160 35,203,160 35,203,160 35,203,160 35,203,160 35,203,160 13,842,463 13,728,237 28,156,135 31,639,415 31,639,415 31,639,415 31,639,415 31,639,415 31,639,415 31,639,415 31,639,415 10,369,672 11,462,933 12,222,909 24,986,717 28,075,670 28,075,670 28,075,670 28,075,670 28,075,670 28,075,670 28,075,670 7,113,114 8,586,900 9,556,180 10,717,580 21,817,298 24,511,926 24,511,926 24,511,926 24,511,926 24,511,926 24,511,926 5,640,886 5,854,581 6,804,129 7,649,427 9,212,251 18,647,880 20,948,181 20,948,181 20,948,181 20,948,181 20,948,181 3,676,805 4,501,159 4,596,048 5,021,358 5,742,673 7,706,923 15,478,462 17,384,437 17,384,437 17,384,437 17,384,437 2,193,504 2,296,338 3,361,431 3,337,515 3,238,587 3,835,920 6,201,594 12,309,044 13,820,692 13,820,692 13,820,692 1,177,103 915,871 2,221,703 2,078,982 1,455,816 1,929,167 4,696,265 9,139,626 10,256,947 10,256,947 941,682 732,697 1,777,363 1,663,186 1,164,653 1,543,334 3,202,606 5,997,431 6,720,425 706,262 549,522 1,333,022 1,247,389 873,489 1,157,500 1,724,974 2,892,622 470,841 366,348 888,681 831,593 582,326 771,667 859,035 235,421 183,174 444,341 415,796 291,163 385,833 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 188,972,555 213,991,125 236,749,490 257,207,779 274,797,710 289,666,633 302,463,420 313,452,802 321,073,283 325,617,610 327,690,291 112 PUBLIC VERSION Entergy New Orleans, LLC, 2018 Integrated Resource Plan PY21 53,894,394 42,330,649 38,766,904 35,203,160 31,639,415 28,075,670 24,511,926 20,948,181 17,384,437 13,820,692 10,256,947 6,720,425 3,221,290 1,236,848 379,716 0 0 0 0 0 0 PY22 53,894,394 42,330,649 38,766,904 35,203,160 31,639,415 28,075,670 24,511,926 20,948,181 17,384,437 13,820,692 10,256,947 6,720,425 3,221,290 1,379,360 584,572 112,084 0 0 0 0 0 0 PY23 53,894,394 42,330,649 38,766,904 35,203,160 31,639,415 28,075,670 24,511,926 20,948,181 17,384,437 13,820,692 10,256,947 6,720,425 3,221,290 1,379,360 659,800 166,852 0 0 0 0 0 0 0 PY24 53,894,394 42,330,649 38,766,904 35,203,160 31,639,415 28,075,670 24,511,926 20,948,181 17,384,437 13,820,692 10,256,947 6,720,425 3,221,290 1,379,360 659,800 173,763 0 0 0 0 0 0 0 0 PY25 53,894,394 42,330,649 38,766,904 35,203,160 31,639,415 28,075,670 24,511,926 20,948,181 17,384,437 13,820,692 10,256,947 6,720,425 3,221,290 1,379,360 659,800 173,763 0 0 0 0 0 0 0 0 0 PY26 53,894,394 42,330,649 38,766,904 35,203,160 31,639,415 28,075,670 24,511,926 20,948,181 17,384,437 13,820,692 10,256,947 6,720,425 3,221,290 1,379,360 659,800 173,763 0 0 0 0 0 0 0 0 0 0 PY27 53,894,394 42,330,649 38,766,904 35,203,160 31,639,415 28,075,670 24,511,926 20,948,181 17,384,437 13,820,692 10,256,947 6,720,425 3,221,290 1,379,360 659,800 173,763 0 0 0 0 0 0 0 0 0 0 0 PY28 53,894,394 42,330,649 38,766,904 35,203,160 31,639,415 28,075,670 24,511,926 20,948,181 17,384,437 13,820,692 10,256,947 6,720,425 3,221,290 1,379,360 659,800 173,763 0 0 0 0 0 0 0 0 0 0 0 0 328,390,655 328,850,106 328,980,102 328,987,013 328,987,013 328,987,013 328,987,013 328,987,013 113 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION Appendix C: Total Relevant Supply Costs – Detail Scenario 1 – Present Value (2019$) of Total Relevant Supply Costs Note: Fixed costs are calculated on a levelized real basis for all futures Portfolio titles denoted by red font were optimized in the above Scenario Strategy 1 - Total Relevant Supply Cost PV 2019$ [2019-2038] Variable Supply Cost [$MM] $1,114 Resource Additions -Fixed Costs [$MM] $134 Capacity Purchases / (Sales) [$MM] ($35) DSM - Fixed Costs [$MM] $198 Total Supply Cost [$MM] $1,411 Strategy 2 - Total Relevant Supply Cost PV 2019$ [2019-2038] Variable Supply Cost [$MM] $961 Resource Additions - Fixed Costs [$MM] $121 Capacity Purchases / (Sales) [$MM] ($46) DSM - Fixed Costs [$MM] $542 Total Supply Cost [$MM] $1,577 Strategy 3 - Total Relevant Supply Cost PV 2019$ [2019-2038] Variable Supply Cost [$MM] $986 Resource Additions - Fixed Costs [$MM] $114 Capacity Purchases / (Sales) [$MM] ($47) DSM - Fixed Costs [$MM] $258 Total Supply Cost [$MM] $1,311 Strategy 4 - Total Relevant Supply Cost PV 2019$ [2019-2038] Variable Supply Cost [$MM] $1,104 Resource Additions - Fixed Costs [$MM] $97 Capacity Purchases / (Sales) [$MM] ($33) DSM - Fixed Costs [$MM] $333 Total Supply Cost [$MM] $1,501 Strategy 5 - Total Relevant Supply Cost PV 2019$ [2019-2038] Variable Supply Cost [$MM] $942 Resource Additions - Fixed Costs [$MM] $181 Capacity Purchases / (Sales) [$MM] ($75) DSM - Fixed Costs [$MM] $247 Total Supply Cost [$MM] $1,295 114 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION Scenario 1 – Annual Total Relevant Supply Costs 115 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION Scenario 2 – Present Value (2019$) of Total Relevant Supply Costs Note: Fixed costs are calculated on a levelized real basis for all futures Portfolio titles denoted by red font were optimized in the above Scenario Strategy 1 - Total Relevant Supply Cost PV 2019$ [2019-2038] Variable Supply Cost [$MM] $912 Resource Additions -Fixed Costs [$MM] $134 Capacity Purchases / (Sales) [$MM] ($28) DSM - Fixed Costs [$MM] $198 Total Supply Cost [$MM] $1,217 Strategy 2 - Total Relevant Supply Cost PV 2019$ [2019-2038] Variable Supply Cost [$MM] $799 Resource Additions - Fixed Costs [$MM] $121 Capacity Purchases / (Sales) [$MM] ($38) DSM - Fixed Costs [$MM] $542 Total Supply Cost [$MM] $1,423 Strategy 3 - Total Relevant Supply Cost PV 2019$ [2019-2038] Variable Supply Cost [$MM] $814 Resource Additions - Fixed Costs [$MM] $114 Capacity Purchases / (Sales) [$MM] ($39) DSM - Fixed Costs [$MM] $258 Total Supply Cost [$MM] $1,147 Strategy 4 - Total Relevant Supply Cost PV 2019$ [2019-2038] Variable Supply Cost [$MM] $902 Resource Additions - Fixed Costs [$MM] $97 Capacity Purchases / (Sales) [$MM] ($25) DSM - Fixed Costs [$MM] $333 Total Supply Cost [$MM] $1,307 Strategy 5 - Total Relevant Supply Cost PV 2019$ [2019-2038] Variable Supply Cost [$MM] $784 Resource Additions - Fixed Costs [$MM] $181 Capacity Purchases / (Sales) [$MM] ($67) DSM - Fixed Costs [$MM] $247 Total Supply Cost [$MM] $1,146 116 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION Scenario 2 – Annual Total Relevant Supply Costs 117 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION Scenario 3 – Present Value (2019$) of Total Relevant Supply Costs Note: Fixed costs are calculated on a levelized real basis for all futures Portfolio titles denoted by red font were optimized in the above Scenario Strategy 1 - Total Relevant Supply Cost PV 2019$ [2019-2038] Variable Supply Cost [$MM] $1,248 Resource Additions -Fixed Costs [$MM] $134 Capacity Purchases / (Sales) [$MM] ($59) DSM - Fixed Costs [$MM] $198 Total Supply Cost [$MM] $1,521 Strategy 2 - Total Relevant Supply Cost PV 2019$ [2019-2038] Variable Supply Cost [$MM] $991 Resource Additions - Fixed Costs [$MM] $121 Capacity Purchases / (Sales) [$MM] ($69) DSM - Fixed Costs [$MM] $542 Total Supply Cost [$MM] $1,584 Strategy 3 - Total Relevant Supply Cost PV 2019$ [2019-2038] Variable Supply Cost [$MM] $1,030 Resource Additions - Fixed Costs [$MM] $114 Capacity Purchases / (Sales) [$MM] ($70) DSM - Fixed Costs [$MM] $258 Total Supply Cost [$MM] $1,331 Strategy 4 - Total Relevant Supply Cost PV 2019$ [2019-2038] Variable Supply Cost [$MM] $1,224 Resource Additions - Fixed Costs [$MM] $97 Capacity Purchases / (Sales) [$MM] ($56) DSM - Fixed Costs [$MM] $333 Total Supply Cost [$MM] $1,597 Strategy 5 - Total Relevant Supply Cost PV 2019$ [2019-2038] Variable Supply Cost [$MM] $964 Resource Additions - Fixed Costs [$MM] $181 Capacity Purchases / (Sales) [$MM] ($98) DSM - Fixed Costs [$MM] $247 Total Supply Cost [$MM] $1,294 118 Entergy New Orleans, LLC, 2018 Integrated Resource Plan PUBLIC VERSION Scenario 3 – Annual Total Relevant Supply Costs 119 BEFORE THE COUNCIL OF THE CITY OF NEW ORLEANS EX PARTE: IN RE: 2018 TRIENNIAL INTEGRATED RESOURCE PLAN OF ENTERGY NEW ORLEANS, INC. ) ) ) ) DOCKET NO. UD-17-03 APPENDIX D Demand Side Management Potential Study prepared by Navigant Consulting, Inc. JULY 2019 Entergy New Orleans, LLC 2018 Integrated Resource Plan DSM Potential Study FINAL REPORT Prepared for: Submitted by: Navigant Consulting, Inc. 101 California Street Suite 4100 San Francisco, CA 94111 navigant.com Reference No.: 198692 August 31, 2018 Confidential and Proprietary ©2018 Navigant Consulting, Inc. DSM Potential Study Table of Contents Executive Summary .................................................................................................................................... x Introduction ................................................................................................................................................ x Study Objectives ....................................................................................................................................... x Energy Efficiency...................................................................................................................................... xi Demand Response.................................................................................................................................. xx Conclusions and Next Steps ............................................................................................................... xxviii 1. Introduction ............................................................................................................................................. 1 1.1 Context and Study Goals .................................................................................................................... 1 1.1.1 Study Objectives .......................................................................................................................... 1 1.2 Organization of the Report .................................................................................................................. 2 1.3 Caveats and Limitations ...................................................................................................................... 3 1.3.1 Forecasting Limitations ................................................................................................................ 3 1.3.2 Segmentation ............................................................................................................................... 3 1.3.3 Measure Characterization ............................................................................................................ 3 1.3.4 Measure Interactive Effects ......................................................................................................... 4 1.3.5 Measure-Level Results ................................................................................................................ 5 1.3.6 Gross Savings Study ................................................................................................................... 5 2. Study Approach and Data ...................................................................................................................... 6 2.1 Energy Efficiency ................................................................................................................................ 6 2.1.1 Market Characterization ............................................................................................................... 7 2.1.2 Reference Case Forecast .......................................................................................................... 16 2.1.3 Energy Efficiency Measure Characterization ............................................................................. 23 2.1.4 Potential Estimation Approach ................................................................................................... 30 2.2 Demand Response ........................................................................................................................... 38 2.2.1 General Approach and Methodology ......................................................................................... 38 2.2.2 Market Characterization for DR Potential Assessment.............................................................. 39 2.2.3 Baseline Projections .................................................................................................................. 40 2.2.4 Descriptions of DR Options ........................................................................................................ 45 2.2.5 Key Assumptions for DR Potential and Cost Estimation ........................................................... 52 3. Energy Efficiency Achievable Potential Forecast ............................................................................. 57 3.1 Model Calibration .............................................................................................................................. 57 3.1.1 Achievable Potential Case Studies and Incentive Levels .......................................................... 57 3.1.2 Achievable Cases Analysis ........................................................................................................ 58 3.2 Energy Efficiency Achievable Potential Results ................................................................................ 58 3.2.1 Case-Level Results .................................................................................................................... 59 3.2.2 Achievable Potential Results by Sector ..................................................................................... 65 3.2.3 Results by Customer Segment .................................................................................................. 67 3.2.4 Results by End Use ................................................................................................................... 68 3.2.5 Achievable Potential Results by Measure ................................................................................. 70 3.2.6 Sensitivity Analysis .................................................................................................................... 72 Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page i DSM Potential Study 4. Demand Response Achievable Potential and Cost Results ............................................................. 74 4.1 Cost-Effectiveness Results ............................................................................................................... 74 4.1.1 Cost-Effectiveness Assessment Results ................................................................................... 74 4.1.2 Comparison of Cost-Effectiveness Results by Cases ............................................................... 75 4.2 Achievable Potential Results............................................................................................................. 76 4.2.1 Achievable Potential by DR Option ............................................................................................ 76 4.2.2 Case Analysis Results ............................................................................................................... 78 4.2.3 Achievable Potential by DR Sub-Option .................................................................................... 80 4.2.4 Achievable Potential by Customer Class ................................................................................... 81 4.2.5 Achievable Potential by Customer Segment ............................................................................. 82 4.3 Program Costs Results ..................................................................................................................... 83 4.3.1 Annual Program Costs ............................................................................................................... 83 5. Conclusions and Next Steps ............................................................................................................... 87 5.1 Benchmarking the Results ................................................................................................................ 87 5.2 IRP .................................................................................................................................................... 95 5.3 Program Planning ............................................................................................................................. 96 5.4 Further Research .............................................................................................................................. 96 Appendix A. Energy Efficiency Detailed Methodology ....................................................................... A-1 A.1 End-Use Definitions ........................................................................................................................ A-1 A.2 Residential Sector ........................................................................................................................... A-2 A.3 C&I Sector....................................................................................................................................... A-5 Appendix B. Energy Efficiency Input Assumptions ............................................................................ B-1 B.1 Measure List and Characterization Assumptions ........................................................................... B-1 B.2 Avoided Costs and Cost-Effectiveness .......................................................................................... B-1 B.3 Cost-Effectiveness Calculations ..................................................................................................... B-3 B.4 Retail Rates .................................................................................................................................... B-5 B.5 Other Key Input Assumptions ......................................................................................................... B-6 Appendix C. Hourly 8,760 Analysis and Measure/Program Mapping ................................................ C-1 C.1 End-Use Load Shape Development ............................................................................................... C-1 C.2 Hourly IRP Model Inputs Development .......................................................................................... C-4 Appendix D. Achievable Potential Modeling Methodology Details ................................................... D-1 D.1 Calculating Achievable Potential .................................................................................................... D-1 D.2 Calculation of Dynamic Equilibrium Market Share ......................................................................... D-1 D.3 Calculation of the Approach to Equilibrium Market Share .............................................................. D-2 Appendix E. Interactive Effects of Efficiency Stacking ....................................................................... E-1 E.1 Background on Efficiency Stacking ................................................................................................ E-1 E.2 Illustrative Calculation of Savings after Efficiency Stacking ........................................................... E-2 E.3 Impetus for Treating Measure Savings Independently ................................................................... E-3 Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page ii DSM Potential Study List of Figures and Tables Figure ES-1. EE Analysis Approach Overview ............................................................................................xii Figure ES-2. EE Potential Types ................................................................................................................xiv Figure ES-3. Cumulative Energy Achievable Savings EE Potential by Case (GWh/year) ......................... xv Figure ES-4. Cumulative Peak Demand Achievable Savings EE Potential by Case (MW) .......................xvi Figure ES-5. Base Case Cumulative Achievable Potential Savings Customer Segment Breakdown ...... xvii Figure ES-6. Top 40 Measures for Electric Energy Base Case Achievable Savings Potential: 2028 (GWh/year) ..................................................................................................................................................xix Figure ES-7. DR Potential Assessment Steps ............................................................................................xxi Figure ES-8. Customer Count Projections for DR Potential Assessment ................................................. xxii Figure ES-9. Peak Load Forecast by Customer Segment (MW) ............................................................... xxii Figure ES-10. Summer DR Achievable Potential by DR Option (MW) ......................................................xxv Figure ES-11. Summer DR Achievable Potential by DR Option (% of Peak Demand) ............................ xxvi Figure ES-12. Summer DR Achievable Potential by DR Sub-Option ...................................................... xxvii Figure ES-13. Summer DR Achievable Potential by Customer Segment .............................................. xxviii Figure ES-14. Benchmarking Pool Average EE Achievable Potential Savings (% of Sales) ................... xxix Figure ES-15. Benchmarking Pool State Level EE Achievable Potential (% of Savings) .........................xxx Figure ES-16. Benchmarking Pool DR Potential (% of Savings) .............................................................. xxxi Figure 2-1. Potential Study Inputs ................................................................................................................. 6 Figure 2-2. High Level Overview of Potential Study Methodology................................................................ 7 Figure 2-3. Base Year Electricity Profile – Residential Example .................................................................. 8 Figure 2-4. 2016 Base Year Electricity Sector Breakdown (%, GWh) ........................................................ 12 Figure 2-5. Base Year Residential Electricity End-Use Breakdown (%, GWh) .......................................... 14 Figure 2-6. Base Year C&I Electricity Segment Breakdown (%, GWh) ...................................................... 16 Figure 2-7. Schematic of Reference Case .................................................................................................. 16 Figure 2-8. Residential Reference Case Schematic ................................................................................... 17 Figure 2-9. C&I Reference Case Schematic ............................................................................................... 19 Figure 2-10. Measure Screening Process .................................................................................................. 24 Figure 2-11. Potential Calculation Methodology ......................................................................................... 31 Figure 2-12. Illustration of Achievable Potential Calculation....................................................................... 37 Figure 2-13. Customer Count Projections for DR Potential Assessment ................................................... 41 Figure 2-14. Peak Demand Forecast Comparisons ................................................................................... 43 Figure 2-15. Peak Load Forecast by Customer Segment (MW)................................................................. 44 Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page iii DSM Potential Study Figure 2-16. Peak Load Forecast by End Use for C&I customers (MW) .................................................... 44 Figure 3-1. Electric Energy Cumulative Achievable Savings Potential by Case (GWh/year) ..................... 61 Figure 3-2. Peak Demand Cumulative Achievable Savings Potential by Case (MW) ................................ 61 Figure 3-3. Electric Energy Cumulative Base Case Achievable Savings Potential by Sector (GWh/year) 66 Figure 3-4. Electric Demand Cumulative Base Case Achievable Savings by Sector (MW)....................... 66 Figure 3-5. Segment Electric Energy Base Case Achievable Potential Customer Segment Breakdown .. 68 Figure 3-6. Electric Energy Base Case Achievable Potential End Use Breakdown ................................... 69 Figure 3-7. Residential Electric Energy Achievable Potential End-Use Breakdown (%, GWh).................. 69 Figure 3-8. C&I Electric Energy Achievable Potential End-Use Breakdown (%, GWh) ............................. 69 Figure 3-9. Top 40 Measures for Electric Energy Base Case Achievable Savings Potential: 2028 (GWh/year) .................................................................................................................................................. 70 Figure 3-10. Top 40 Measures for Electric Demand Base Case Savings Potential: 2028 (MW) ............... 71 Figure 3-11. Supply Curve of Electric Energy Achievable Potential (GWh/year) vs. Levelized Cost ($/kWh): 2028 .............................................................................................................................................. 72 Figure 3-12. Cumulative Achievable GWh Savings in 2037 Sensitivity to Key Variables .......................... 73 Figure 4-1. Summer DR Achievable Potential by DR Option (MW)............................................................ 77 Figure 4-2. Summer DR Achievable Potential by DR Option (% of Peak Demand) ................................... 78 Figure 4-3. Summer DR Achievable Potential by Case (MW) .................................................................... 79 Figure 4-4. Summer DR Achievable Potential by Case (% of Peak Demand) ........................................... 80 Figure 4-5. Summer DR Achievable Potential by DR Sub-Option .............................................................. 81 Figure 4-6. Summer DR Achievable Potential by Customer Class (MW)................................................... 82 Figure 4-7. Summer DR Achievable Potential by Customer Segment ....................................................... 83 Figure 4-8. Annual Program Costs by DR Option ....................................................................................... 85 Figure 4-9. Annual Program Costs by DR Sub-Option ............................................................................... 86 Figure 5-1. Integrating Potential Study Outputs to IRP and DSM Planning ............................................... 87 Figure 5-2. Benchmarking Pool Average Achievable Potential Savings (% of Sales)................................ 91 Figure 5-3. Benchmarking Pool State Level Achievable Potential (% of Savings) ..................................... 92 Figure 5-4. Benchmarking Pool Actual Savings (% of Sales) vs. Spending ($/kWh) ................................. 93 Figure 5-5. Benchmarking Pool DR Potential (% of Savings)..................................................................... 95 Figure A-1. C&I Base Year Stock Formula ............................................................................................... A-7 Figure B-1. ENO BP18U Avoided Cost Projections .................................................................................. B-1 Figure B-2. ENO BP18U Avoided Capacity Projections ........................................................................... B-2 Figure B-3. ENO BP18U Carbon Pricing Projections ............................................................................... B-3 Figure B-4. Electricity Retail Rate Forecast: 2016-2037 ........................................................................... B-5 Figure D-1. Payback Acceptance Curves ................................................................................................. D-2 Figure D-2. Stock/Flow Diagram of Diffusion Model for New Products and Retrofits............................... D-4 Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page iv DSM Potential Study Figure D-3. Stock/Flow Diagram of Diffusion Model for ROB Measures .................................................. D-5 Figure E-1. Venn Diagrams for Various Efficiency Stacking Situations .................................................... E-2 Table ES-1. Study Objectives Overview ...................................................................................................... xi Table ES-2. Annual Incremental Achievable Energy Efficiency Savings by Case ....................................xvi Table ES-3. Incremental Energy Achievable Savings Potential as a Percentage of Sales by Case (%, GWh) ......................................................................................................................................................... xviii Table ES-4. Spending Breakdown for Achievable Potential ($ millions/year) ............................................ xx Table ES-5. Portfolio TRC Benefit-Cost Ratios for Achievable Potential (Ratio) ....................................... xx Table ES-6. Summary of DR Options ....................................................................................................... xxiii Table ES-7. Annual Incremental Achievable Summer DR Potential by Option ........................................ xxiv Table 1-1.Navigant’s Approach to Addressing ENO’s Objectives ................................................................ 1 Table 2-1. Customer Segments by Sector .................................................................................................... 9 Table 2-2. Residential Segment Descriptions ............................................................................................. 10 Table 2-3. C&I Segment Descriptions ......................................................................................................... 10 Table 2-4. End Uses by Sector ................................................................................................................... 11 Table 2-5. 2016 Base Year Electricity Sector Sales (GWh) ....................................................................... 12 Table 2-6. Base Year Residential Results .................................................................................................. 13 Table 2-7. Base Year C&I Results .............................................................................................................. 15 Table 2-8. Residential Reference Case Stock Forecast (Accounts)........................................................... 17 Table 2-9. Residential Reference Case EUI Forecast (kWh/Account) ....................................................... 18 Table 2-10. C&I Reference Case Stock Forecast (Thousands SF) ............................................................ 20 Table 2-11. C&I Reference Case EUI Forecast (kWh/Thousands SF) ...................................................... 20 Table 2-12. Fuel Share Splits for Domestic Hot Water and Heating .......................................................... 26 Table 2-13. Measure Characterization Input Data Sources........................................................................ 27 Table 2-14. Market Segmentation for DR Potential Assessment ............................................................... 39 Table 2-15. Peak Load Factors by Customer Segment Type ..................................................................... 42 Table 2-16. Summary of DR Options .......................................................................................................... 45 Table 2-17. Segmentation of DR Options into DR Sub-Options ................................................................. 46 Table 2-18. DLC Program Characteristics .................................................................................................. 47 Table 2-19. C&I Curtailment Program Characteristics ................................................................................ 49 Table 2-20. Dynamic Pricing Program Characteristics ............................................................................... 50 Table 2-21. BTMS Program Characteristics ............................................................................................... 51 Table 2-22. Key Variables for DR Potential and Cost Estimates ................................................................ 53 Table 2-23. Program Hierarchy to Account for Participation Overlaps ....................................................... 54 Table 3-1. Annual Incremental Achievable Energy Efficiency Savings by Case ........................................ 60 Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page v DSM Potential Study Table 3-2. Incremental Electric Energy Achievable Savings Potential as a Percentage of Sales, by Case (%, GWh)..................................................................................................................................................... 62 Table 3-3. Spending Breakdown for Achievable Potential ($ millions/year) ............................................... 63 Table 3-4. Portfolio TRC Benefit-Cost Ratios for Achievable Potential (Ratio) .......................................... 65 Table 3-5. Cumulative Electric Energy Base Case Achievable Savings Potential by Sector as a Percentage of Sales (%, GWh) ................................................................................................................... 67 Table 3-6. Percent Change to Cumulative Potential in 2037 with 25% Parameter Change ...................... 73 Table 4-1. Base Case Benefit-Cost Ratios by DR Options and Sub-Options ............................................ 74 Table 4-2. Benefit-Cost Ratio Comparisons by Cases by DR Options and Sub-Options .......................... 75 Table 4-3. Annual DR Portfolio Costs by Case ........................................................................................... 84 Table 5-1. EE Achievable Potential Benchmarking Pool and Sources ....................................................... 88 Table 5-2. EE Achievable Potential Savings by State Benchmarking Pool and Sources .......................... 89 Table 5-3. EE Actual Spending and Saving Benchmarking Pool and Sources .......................................... 90 Equation 2-1. Annual/Total RET/ROB Technical Savings Potential ........................................................... 33 Equation 2-2. Annual Incremental NEW Technical Potential (AITP) .......................................................... 33 Equation 2-3. Total NEW Technical Potential (TTP) .................................................................................. 34 Equation 2-4. Benefit-Cost Ratio for the TRC Test ..................................................................................... 35 Equation 2-5. Achievable Potential ............................................................................................................. 36 Equation 2-6. DR Technical Potential ......................................................................................................... 52 Equation 2-7. DR Achievable Potential ....................................................................................................... 52 Equation 3-1. Benefit-Cost Ratio for the TRC Test ..................................................................................... 63 Table A-1. Description of End Uses .......................................................................................................... A-1 Table A-2. Example Base Year Residential Stock and Sales Equations ................................................. A-3 Table A-3. ENO Residential Base Year Results ....................................................................................... A-3 Table A-4. Base Year Residential EUIs (kWh per Acct.) .......................................................................... A-4 Table A-5. Reference Case Residential Stock Forecast (Accounts) ........................................................ A-4 Table A-6. Reference Case EUI Forecast (Accounts) .............................................................................. A-5 Table A-7. C&I EIA EUI Segments to Study Segment Mappings ............................................................. A-5 Table A-8. ENO C&I Base Year Results (GWh) ....................................................................................... A-7 Table A-9. Reference Case C&I EUI, Sales, and Stock ........................................................................... A-8 Table B-1. Potential Study Assumptions ................................................................................................... B-6 Table C-1. Modeled Customer Segments by Sector ................................................................................ C-3 Table C-2. Modeled End Uses by Sector .................................................................................................. C-4 Table C-3. Program Categories ................................................................................................................ C-4 Table C-4. Measure and Program Mapping for IRP Modeling Inputs ....................................................... C-5 Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page vi DSM Potential Study Table E-1. Comparison of Savings Before and After Stacking ................................................................. E-3 Table E-2. Measures with Opportunity for Stacking in Commercial Lighting End Use ............................. E-4 Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page vii DSM Potential Study Disclaimer This report was prepared by Navigant Consulting Inc. (Navigant) for Entergy New Orleans. The work presented in this report represents Navigant’s professional judgment based on the information available at the time this report was prepared. Navigant is not responsible for the reader’s use of, or reliance upon, the report, nor any decisions based on the report. NAVIGANT MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESSED OR IMPLIED. Readers of the report are advised that they assume all liabilities incurred by them, or third parties, as a result of their reliance on the report, or the data, information, findings and opinions contained in the report. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page viii DSM Potential Study List of Acronyms BP18U Business plan 2018 Update kWh Kilowatt-hour BTMS Behind-the-meter storage LED Light emitting diode BYOT Bring your own thermostat LMP C&I Commercial and industrial MISO CAC MW CFL Central air conditioner Commercial Buildings Energy Consumption Survey Commercial Building Stock Assessment Compact fluorescent lamp Locational marginal price Midcontinent Independent System Operator Megawatt Northeast Energy Efficiency Partnership CFR Code of Federal Regulations NREL CNO Council of the City of New Orleans CPP CBECS CBSA NEEP NEW New construction NPV NTG Net present value National Renewable Energy Laboratory Net-to-gross Critical peak pricing Database for Energy Efficient Resources O&M Operations and maintenance PAC Program administrator cost DI Direct install PCT DLC Direct load control POU Programmable communicating thermostat Publicly-owned utility DOE Department of Energy (US) PV Present value DR Demand response PY Program year DR automation server RBSA Residential Building Stock Assessment Demand Response Simulator RET Retrofit Demand-side management Demand-Side Management Simulator RIM Ratepayer impact measure ROB Replace-on-burnout RTF Regional Technical Forum RUL Remaining useful life SEER SIC Seasonal energy efficiency ratio Standard industrial classification DEER DRAS DRSim TM DSM DSMSim TM EMS ENO Energy Efficiency Energy Information Administration (US) Energy Independence and Security Act Energy Management Systems Entergy New Orleans, LLC EUI End-use intensities T&D Transmission and distribution EUL Effective useful life Federal Energy Regulatory Commission Greenhouse gas TCO Total cost of ownership TMY Typical meteorological year TOU Time-of-use TRC Total resource cost TRM Technical resource manual IOU Gigawatt-hour Heating, ventilation, and air conditioning Investor-Owned Utility TSD Technical support documents IRP Integrated Resource Plan UCT Utility cost test EE EIA EISA FERC GHG GWh HVAC Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page ix DSM Potential Study Executive Summary Introduction In support of the process to develop the 2018 IRP, Entergy New Orleans, LLC (ENO) engaged Navigant Consulting, Inc. (Navigant or the team) to prepare a DSM potential study. 1 The study’s objective was to assess the long-term potential for reducing energy consumption in the residential and C&I sectors by analyzing energy efficiency and peak load reduction measures and improving end-user behaviors. The EE component of the potential study began with a rigorous analysis of input data necessary for Navigant to run the DSMSim™ model, which calculates various levels of EE savings potential across the ENO service area. Achievable potential was further delineated using a range of reasonable assumptions for alternative cases to estimate the effect on customer participation of funding for customer incentives, awareness, as well as other factors. The DR potential component of this study also began with a rigorous analysis of input data necessary for Navigant’s DRSim™ model. Using a range of reasonable assumptions, the DRSim™ model was used to estimate the DR potential for a low, base, and high case. While ENO explicitly plans to use the results from the potential study to inform the IRP, these results may also be used to further ENO’s DSM planning and long-term conservation goals, energy efficiency program design efforts, and long-term load forecasts. However, it should be noted that long-term potential studies do not replace the need for detailed near-term implementation planning and program design. As such, this study, as with any long-term potential study, should only be used to inform those planning and design efforts in combination with ENO’s EE and DR Energy Smart program experience and the market intelligence and insights of the Council of the City of New Orleans (Council), its Advisors, and stakeholders. Study Objectives ENO intends to use the results of the potential study as an input to its 2018 IRP. More specifically, ENO plans to use the results of this potential study to provide a long-range outlook on the cost-effective potential for delivering demand-side resources such as EE and DR and the associated levels of investment required to implement such programs. Given ENO’s objectives and Council’s rules, Navigant designed its project approach to ensure the study results adequately address those needs. Table ES-1 below provides a 1 The study period for the potential study is 2018-2037. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page x DSM Potential Study high-level overview of the study’s objectives and how Navigant met those objectives. Table ES- 1. Study Objectives Overview Objective 1 2 3 Use consistent methodology and planning assumptions Reflect current information Quantify achievable potential Navigant’s Approach  Navigant has developed a variety of analytical tools and approaches to inform DSM planning and the establishment of long-term conservation targets and goals (details provided in the following sections). The team also worked closely with ENO to vet methodology, assumptions, and inputs at each stage of this project.  Navigant leveraged learnings from its prior work with ENO to create a bottom up analysis that includes inputs, such as the New Orleans TRM, and other up-todate information (new codes and standards, saturation data from surveys and Energy Smart programs, avoided costs, etc.) are included in this study.  Navigant quantifies achievable potential for both EE and DR by first calculating the technical and economic potential. The achievable potential base case is then calibrated to the historical Energy Smart program data and the current programs approved by the Council for Energy Smart PYs 7-9. 4 Provide input to the IRP  Navigant’s approach provides the following for all modeled cases: • Supply curve of conservation potential for input to ENO’s IRP • Outputs available with 8,760 hourly impact load shapes 5 Present the scope and methodology of the study  Navigant’s approach to stakeholder engagement offers relevant information to key stakeholders Source: Navigant The team incorporated this high-level approach into both the EE and DR analyses. Energy Efficiency Detailed Approach For the EE analysis, Navigant analyzed potential in the ENO service area from 2018 through 2037. After gathering existing data sources, the team followed three steps: (1) characterize the market, (2) characterize measures, and (3) estimate potential, using the DSMSimTM tool, a bottom-up stock forecasting model. The third step involved three sub-steps, which included calculating technical, economic, and achievable potential. The figure below illustrates the EE analysis approach. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page xi DSM Potential Study Figure ES-1. EE Analysis Approach Overview Source: Navigant Market Characterization This part of the analysis involved understanding and defining key service area, or market, characteristics. Specifically, the market characterization required defining the sales and stock for 2016, the study’s base year, and then projecting the numbers from 2018 – 2037, the reference case, to provide a baseline for the study. To complete this effort, Navigant collected multiple datasets, which include, but are not limited to: • 2016 ENO billing and customer account data • ENO forecast sales and customer counts • US EIA CBECS • US Department of Labor SIC • Navigant research After defining the sales and stock, the team determined energy use at the customer segment and end-use levels. Navigant based the level of disaggregation for the segments and end-uses on existing program definitions, data availability, and requirements to sufficiently characterize the data at a granular level. The report contains further details on the selected customer segments as well as assumptions about the stock, electricity sales, end-use breakdown, and EUI for each segment and end-use. In addition to identifying sales, energy use, and stock data, the team aggregated Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page xii DSM Potential Study additional inputs from ENO for input into the model. These inputs include various economic and financial parameters, such as carbon pricing estimates, avoided costs, inflation assumptions, and historic program costs. Measure Characterization The measure characterization portion of the analysis sought to define key data points for the measures included in the study. These characteristics include assumptions about codes and standards, measure life, and measure costs. This analysis relied on data from ENO, other regional efficiency programs and utilities, and TRMs from New Orleans, 2 Arkansas, Pennsylvania, Illinois, Minnesota, Vermont, New York, and Massachusetts. The team used the measure list in this study to appropriately focus on those technologies likely to have the highest effect on savings potential over the study horizon. The study however, does not account for unknown emerging technologies that may arise that could increase savings opportunities over the forecast horizon. It also does not account for broader societal changes that may affect levels of energy use in ways not anticipated by this study. Estimation of Potential After defining the market and measure characteristics, Navigant employed its proprietary DSMSim potential model to estimate the technical, economic, and achievable savings potential for electric energy and demand across ENO’s service area. The list below defines each of these types of potential, as used in the study: • Technical potential is the total energy savings available assuming all installed measures can immediately be replaced with the efficient measure/technology— wherever technically feasible—regardless of cost, market acceptance, or whether a measure has failed and must be replaced. • Economic potential is a subset of technical potential, using the same assumptions regarding immediate replacement as in technical potential, but including only those measures that have passed the benefit-cost test chosen for measure screening; in this study, that is the TRC test. • Achievable potential is a subset of economic potential. The team determined achievable potential by incorporating measure adoption ramp rates and the diffusion of technology through the market. Figure ES-2 provides an overview of each of these potential types and the data inputs for each. 2 New Orleans Energy Smart Technical Reference Manual: Version 1.0, September 2017, prepared by ADM Associates, Inc. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page xiii DSM Potential Study Figure ES-2. EE Potential Types Source: Navigant Using these definitions and data inputs, the DSMSim uses a bottom-up technology diffusion and stock tracking model implemented using a System Dynamics framework to estimate the different potential types. 3 The model reports these potential savings for the service area, sector, customer segment, end-use category, and highest impact measures. Results Given that ENO’s objective for this study was to quantify the achievable potential for use in the 2018 IRP and gain a better understanding as to the best path for planning ENO’s Energy Smart programs, the project team modeled various future cases to further inform Energy Smart program preparation. These cases include: • Base case: Reflects current program spend targets with incentives on average at 50% of incremental measure cost • Low case: Uses the same inputs as the base case except incentives are at 25% of incremental measure cost 3 See Sterman, John D. Business Dynamics: Systems Thinking and Modeling for a Complex World. Irwin McGraw-Hill. 2000 for detail on System Dynamics modeling. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page xiv DSM Potential Study • High case: Uses the same inputs as the base case except incentives are at 75% of incremental measure cost • 2% case: Achieve a 2% reduction during the forecast period with a 0.2% ramp year over year starting in the first modeled year (2018). To achieve 2%, Navigant modified model parameters: o Increased marketing factor through 2021 o Increased incentive percent of incremental measure cost from 50% in 2018 then ramping up to 100% in 2024 (and maintaining 100% in remaining years) o Ramped down TRC Ratio threshold from 1 in 2018 to 0.87 in 2022 and remaining years. The study reports savings as gross rather than net, meaning they do not include the effects of natural change. Providing gross potential is advantageous because it permits a reviewer to more easily calculate net potential when new information about NTG ratios or changing EUIs become available. These results can then be used to define the portfolio energy savings goals, projected costs, and forecasts. Figure ES-3 and Figure ES-4 show the cumulative annual energy and demand savings for each case. Figure ES-3. Cumulative Energy Achievable Savings EE Potential by Case (GWh/year) Savings Potential (GWh/year) 1,600 1,400 1,200 1,000 800 600 400 200 0 Low Base High 2% Source: Navigant analysis Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page xv DSM Potential Study Figure ES-4. Cumulative Peak Demand Achievable Savings EE Potential by Case (MW) 400 Savings Potential (MW) 350 300 250 200 150 100 50 0 Low Base High 2% Source: Navigant analysis Table ES-2 lists the energy efficiency potential study results, showing the achievable annual incremental energy and peak demand savings in 5-year increments by case. The calculated total energy efficiency potential savings for the base case is 1,100 GWh and 220 MW in 2037. Table ES- 2. Annual Incremental Achievable Energy Efficiency Savings by Case Year Electric Energy (GWh/Year) Peak Demand (MW) Base Low High 2% Base Low High 2% 2018 46 41 52 46 11 10 12 11 2022 53 46 61 97 11 10 13 20 2027 61 54 70 116 12 10 14 26 2032 58 52 65 55 11 9 13 14 2037 43 39 43 25 9 8 9 7 Total 1,100 977 1,240 1,526 220 190 257 346 Source: Navigant analysis Figure ES-5 shows the cumulative electric energy achievable potential by customer segment. Residential single family is the largest segment. Small office and lodging contribute the most savings for the C&I sector. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page xvi DSM Potential Study Figure ES-5. Base Case Cumulative Achievable Potential Savings Customer Segment Breakdown 1,200 Res Single Family Res Multi-Family 1,000 C&I Schools Savings Potential (GWh/year) C&I Retail - Non-Food C&I Retail - Food 800 C&I Restaurants C&I Other Commercial 600 C&I Office - Small C&I Office - Large 400 C&I Lodging C&I Industrial/Warehouses C&I Healthcare 200 C&I Colleges/Universities 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 0 Source: Navigant analysis Table ES-3 shows the incremental electric energy achievable savings as a percentage of ENO's total sales for each case in 5-year increments. For the 2% case, 2% of sales savings is achieved in 2024 through 2026. In later years, the 2% case falls below the base case because most of the measures have been adopted, depleting the available potential in the future years. As mentioned above, this study only includes known, market-ready, quantifiable measures. However, over the lifetime of energy efficiency programs, new technologies and innovative program interventions could result in additional cost-effective energy savings. Therefore, ENO should periodically revisit and reanalyze the potential forecast to account for these technologies and programs. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page xvii DSM Potential Study Table ES- 3. Incremental Energy Achievable Savings Potential as a Percentage of Sales by Case (%, GWh) Year Base Low High 2% 2018 0.8% 0.7% 0.9% 0.8% 2022 0.9% 0.8% 1.0% 1.6% 2027 1.0% 0.9% 1.1% 1.9% 2032 0.9% 0.8% 1.0% 0.8% 2037 0.6% 0.6% 0.6% 0.3% Total 17.3% 15.3% 19.5% 24.0% Source: Navigant analysis Figure ES-6 shows the top 40 measures contributing to the electric energy achievable potential in 2028 (the middle of the study period and representative of the 20-year results). Interior 4 ft. LEDs in the C&I sector provide the most potential, followed by residential central air conditioning tune-up and commercial unitary and split system air conditioning/heat pump equipment. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page xviii DSM Potential Study Figure ES-6. Top 40 Measures for Electric Energy Base Case Achievable Savings Potential: 2028 (GWh/year) Source: Navigant analysis The total, administrative, and incentive costs for each case are provided in Table ES-4 in 5-year increments for the study period. It is important to note the differences in these cases as compared to the savings achieved. The administrative spending is relatively consistent between the cases, while the incentive spending varies significantly between the cases, with higher spending correlated to higher savings. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page xix DSM Potential Study Table ES- 4. Spending Breakdown for Achievable Potential ($ millions/year) 4 Total Incentives Admin Base Low High 2% Base Low High 2% Base Low High 2% 2018 $13 $8 $20 $13 $6 $2 $13 $6 $7 $6 $8 $7 2022 $15 $10 $25 $43 $7 $3 $16 $28 $8 $7 $10 $15 2027 $20 $12 $32 $79 $10 $4 $20 $59 $10 $9 $12 $20 2032 $24 $14 $37 $47 $13 $5 $25 $36 $11 $9 $12 $11 2037 $21 $13 $30 $25 $12 $5 $20 $20 $9 $8 $9 $5 Total $390 $238 $617 $960 $202 $75 $400 $698 $188 $162 $217 $262 Source: Navigant analysis Table ES-5. shows the portfolio TRC to be cost-effective for all cases. Table ES- 5. Portfolio TRC Benefit-Cost Ratios for Achievable Potential (Ratio) Year 2018-2037 Base Low High 2% 1.7 1.9 1.6 1.4 Source: Navigant analysis Demand Response Detailed Approach Navigant developed ENO’s DR potential and cost estimates using a bottom-up analysis. The analysis involved 5 steps: (1) characterize the market, (2) develop baseline projections, (3) define and characterize DR options, (4) develop key assumptions for potential and costs, and (5) estimate potential and costs. Navigant used both primary data from ENO and relevant secondary sources for this analysis as documented in this report. Figure ES-7 summarizes the DR potential estimation approach. 4 The values in this table are shown in nominal dollars and are rounded to the nearest million which may result in rounding errors. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page xx DSM Potential Study Figure ES-7. DR Potential Assessment Steps Source: Navigant Market Characterization The market characterization process for the DR assessment aimed to segment the market appropriately for the analysis. Specifically, Navigant aggregated data on key pieces of information, such as customer count and peak load, by customer segment and end-use to use as inputs into the model. The team based the segmentation on the examination of ENO’s rate schedules and the customer segments established in the energy efficiency potential study. Baseline Projections The baseline projections aimed to define and forecast customer data for the study period, similar to the market characterization in the EE assessment. The project team used these projections as a basis for modeling savings. More specifically, Navigant applied the year-over-year change in the stock forecast to the 2016 customer count data segmented by customer class and customer segment to produce a customer count forecast for the study. The team then trued up this forecast to the sector-level customer count forecast provided by ENO. Figure ES-8 shows the aggregate customer count forecast by segment only, summed across all customer classes. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page xxi DSM Potential Study Figure ES-8. Customer Count Projections for DR Potential Assessment Source: Navigant analysis Figure ES-9 shows the peak load forecast that Navigant developed based on the BP18U forecast data provided by ENO for ENO’s service area by customer segment. Figure ES-9. Peak Load Forecast by Customer Segment (MW) Source: Navigant analysis Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page xxii DSM Potential Study DR Options Once the baseline peak demand projections had been developed, the team characterized the different types of DR options that could be used to curtail peak demand. Table ES-6 summarizes the DR options included in the analysis. Most of these DR options are representative of DR programs commonly deployed in the industry. Table ES- 6. Summary of DR Options Targeted/ Controllable End Uses and/or Technologies DR Option Characteristics Eligible Customer Classes DLC  Load control switch  Thermostat Control of water heating/cooling load using either a load control switch or PCT Residential Small C&I Cooling, water heating C&I curtailment  Manual  Auto-DR enabled Firm capacity reduction commitment $/kW payment based on contracted capacity plus $/kWh payment based on energy reduction during an event Large C&I Various load types including HVAC, lighting, refrigeration, and industrial process loads Voluntary opt-in dynamic pricing offer, such as CPP All customer classes All 5 Dynamic pricing  Without enabling technology  With enabling technology Source: Navigant Estimation of Potential With the market, baseline projections, and options characterized, Navigant estimated technical and achievable potential by inputting the parameters into its model. To do this, Navigant used two key variables in addition to participation opt-out rates, technology market penetration, and enrollment attrition rates: 1. Customer participation rates; and 2. Amount of load reduction that could be realized from different types of control 5 Navigant did not include TOU rates in the DR options mix because this study only includes event-based dispatchable DR options. TOU rates lead to a permanent reduction in the baseline load and are not considered a DR option. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page xxiii DSM Potential Study mechanisms, referred to as unit impacts For purposes of the DR analysis, Navigant used the following definitions for calculating technical and achievable potential: • Technical potential refers to load reduction that results from 100% customer participation. This is a theoretical maximum. • Achievable potential accounts for customers opting out during DR events. The team calculated this by multiplying achievable participation assumptions (subject to program participation hierarchy) by the technical potential estimates. Results Achievable potential is estimated to grow from 0.7 MW in 2018 to 34.6 MW in 2037. Cost-effective achievable potential makes up approximately 3.3% of ENO’s peak demand in 2037. Navigant observed the following: • DLC has the largest achievable potential: 49% share of total potential in 2037. DLC potential grows from 0.5 MW in 2018 to 17.0 MW in 2037. • This is followed by dynamic pricing with a 47% share of the total potential in 2037. The dynamic pricing offer begins in 2020 because it is tied to ENO’s AMI implementation plan. The program ramps up over a 5-year period (2020-2024) until it reaches a value of 14 MW. From then on, potential slowly increases until it reaches a value of 16 MW in 2037. • C&I curtailment makes up the remainder of the cost-effective achievable potential with a 4% share of the total potential in 2037. C&I curtailment potential grows rapidly from 0.2 MW in 2018 to 1.9 MW in 2022. This growth follows the Sshaped ramp assumed for the program over a 5-year period. Beyond 2022, the program attains a steady participation level, and its potential slightly decreases over the remainder of the forecast period, ending at 1.2 MW in 2037. Table ES- 7 lists the DR results by option in 5-year increments. The calculated achievable potential for peak load reduction is 34.6 MW in 2037. This report provides the methodology, data inputs, and assumptions used to calculate these potentials. Table ES- 7. Annual Incremental Achievable Summer DR Potential by Option Year 2018 2022 2027 2032 2037 DLC 0.5 5.7 7.4 11.3 17.0 Dynamic C&I Total Pricing Curtailment 0.0 0.2 0.7 4.9 1.9 12.6 14.4 1.7 23.6 15.6 1.4 28.3 16.4 1.2 34.6 Source: Navigant analysis Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page xxiv DSM Potential Study Figure ES-10 summarizes the cost-effective achievable potential by DR option for the base case. Figure ES-11 shows the cost-effective achievable potential as a percentage of ENO’s peak demand. Figure ES-10. Summer DR Achievable Potential by DR Option (MW) 35 30 25 MW 20 C&I Curtailment Dynamic Pricing 15 DLC 10 5 2037 2036 2035 2034 2033 2032 2031 2030 2029 2028 2027 2026 2025 2024 2023 2022 2021 2020 2019 2018 0 Source: Navigant analysis Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page xxv DSM Potential Study Figure ES-11. Summer DR Achievable Potential by DR Option (% of Peak Demand) 3.5 3.0 2.5 2.0 % C&I Curtailment 1.5 Dynamic Pricing DLC 1.0 0.5 0.0 Source: Navigant analysis Figure ES-12 summarizes the cost-effective achievable potential by DR option for the base case. The team had the following key observations: • Only direct control of HVAC loads by small C&I customers (DLC-Switch-HVAC and DLC-Thermostat-HVAC in Figure ES-12) is cost-effective. This sub-option makes up nearly 50% of the total cost-effective achievable potential in 2037 at 17.0 MW. Of this 17.0 MW, 10.2 MW is from thermostat-based control, while the remaining 6.7 MW is from switch-based control. • Dynamic pricing makes up 47% of the total cost-effective achievable potential in 2037. Potential from customers with enabling technology in the form of thermostats/ EMS is slightly higher than that from customers without enabling technology—8.8 MW versus 7.6 MW in 2037. • Under the C&I curtailment program, reductions associated with manual HVAC control make up 4% of the total cost-effective potential in 2037. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page xxvi DSM Potential Study Figure ES-12. Summer DR Achievable Potential by DR Sub-Option 35 30 25 C&I Curtailment- Manual HVAC Control 20 MW DLC-Switch-HVAC 15 Dynamic Pricing with enabling tech. 10 Dynamic Pricing w/o enabling tech. 5 DLC-Thermostat-HVAC 2037 2036 2035 2034 2033 2032 2031 2030 2029 2028 2027 2026 2025 2024 2023 2022 2021 2020 2019 2018 0 Source: Navigant Figure ES-13 summarizes the cost-effective achievable potential by customer segment for the base case. The team observed the following: • Potential from C&I customers primarily comes from small offices, which make up 37% (12.9 MW) of the total cost-effective achievable potential in 2037. This is followed by retail buildings, restaurants, and the other C&I building category, which each make up between 7% and 9% of the total cost-effective achievable DR potential in 2037—3.1 MW, 2.7 MW, and 2.5 MW, respectively. • All other C&I segments make up less than 2.2% of the cost-effective achievable potential in 2037, which is less than 0.75 MW. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page xxvii DSM Potential Study Figure ES-13. Summer DR Achievable Potential by Customer Segment 40 35 Schools Colleges/Universities 30 Healthcare Lodging MW 25 Retail - Food 20 Industrial/Warehouses Office - Large 15 Other Commercial Restaurants 10 Retail (Non-Food) 5 Residential Office - Small 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 0 Source: Navigant analysis Conclusions and Next Steps After reviewing the study results, the team benchmarked them against similar utilities, identified how the results could be used in ENO’s 2018 IRP. Benchmarking Navigant benchmarked the energy efficiency achievable potential results against similar studies by other utilities. The goal of this exercise was to provide context for Navigant’s results and to understand how various factors such as region or program spend may affect the results. Based on the sources (provided in Section 5.1), Navigant aggregated the results into the figures below. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page xxviii DSM Potential Study Figure ES-14. Benchmarking Pool Average EE Achievable Potential Savings (% of Sales) 6 Snohomish PUD 1.07% ENO (Navigant 2018) 0.88% Seattle City Light 0.88% Austin Energy 0.73% Duke Energy IN (IRP) 0.54% ComEd 0.54% Colorado Springs Utilities CA Public Utilities (Aggregated) Louisville Gas & Electric / Kentucky Utilities 0.00% 0.49% 0.45% 0.31% 0.50% 1.00% 1.50% 2.00% Source: Navigant analysis When comparing potential estimates, it is important to note that although the utilities included in the benchmarking pool may have some similar characteristics, no two utilities are the same; therefore, the results may vary based on the inputs each utility provided to its respective potential study evaluator. Study methodologies may also differ based on the potential study evaluator, providing additional room for variances across studies. ENO’s achievable potential falls within the range of the benchmarking pool at an average of 0.88% savings per year over the study period (2018-2037). This is similar to Seattle City Light and slightly above Austin Energy (0.73%). Interestingly, the three all operate in large metropolitan areas and have similar governance structures in that they are regulated by a city council. 7 6 These savings are shown as an annual average, which Navigant derived by dividing the cumulative study averages by the number of years in the study. Navigant used this approach since study years tend to differ greatly. 7 It should be noted that, unlike ENO, which is an IOU, Austin Energy and Seattle City Light are both POUs that function as departments within their respective municipalities. However, all three must comply with the mandates of the local regulatory body. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page xxix DSM Potential Study In addition to benchmarking the results at the utility level, Navigant created a peer pool at the state level. The goal of this analysis was to understand ENO’s potential savings within the broader context of the state of Louisiana and its neighbors. Given that the states are mostly clustered within the Southeast region of the US, they have the same general climate (hot-humid) and, therefore, may experience similar levels of achievable potential savings. Figure ES-15 shows how ENO’s achievable potential fits into the broader state-level context. Figure ES-15. Benchmarking Pool State Level EE Achievable Potential (% of Savings) 1.40% Savings (% of Sales) 1.20% 1.00% 0.80% 0.90% 0.91% 0.94% 0.88% ENO (Navigant 2018) Arkansas Average Louisiana 1.04% 1.07% Tennessee (TVA) Mississippi 0.73% 0.60% 0.40% 0.20% 0.00% Texas As shown in the figure above, ENO’s achievable potential savings are within the range of the benchmarking pool (0.73%-1.07%), which makes sense given the similarities across the region. Its potential savings are only slightly less than the overall pool average and the state of Louisiana. The slight difference in savings between this ENO potential study and the overall state may be caused by several factors: • Updated inputs • Utilities outside New Orleans had not begun implementing energy efficiency programs at the time ACEEE conducted the Louisiana study in 2013 • Broader region covered (some areas may have potential savings based on stock type and other utilities’ energy efficiency spending) Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page xxx DSM Potential Study Navigant also benchmarked DR. The results are shown below in Figure ES-16. Figure ES-16. Benchmarking Pool DR Potential (% of Savings) 7.00% 6.10% 6.00% 5.00% 4.00% 4.00% 3.90% 3.90% 3.30% 3.00% 3.00% 2.00% 1.60% 1.00% 0.00% 0.00% ConEd ComEd (IL) PSE (WA) AmerenUE (NY) - 2026 (MO) ENO Electric HECO (HI) Southwest Reliability Power Pool Council of (SPP) TX (ERCOT As shown above, ENO falls in the middle of the benchmarking pool, only slightly higher than ERCOT and slightly below Ameren in Missouri. Given that DR, like EE, varies based on program administration and geographic location, amongst other factors, ENO’s DR potential aligns closely to its peers. IRP The potential study provides forecasted savings inputs for use in the IRP modeling. These inputs are provided by sector, segment, and end use because each combination of these items is mapped to a load shape (see Appendix C). Each measure is mapped to one or more DSM programs. Navigant then developed a load shape representative of each DSM program. The DSM program load shapes represent the aggregate hourly energy savings for the group of measures included in the program over the 20-year planning period. These load shapes are what define the hourly usage profiles for the DSM program portfolio. Program Planning This potential study provides ENO with a wealth of data to support and inform the DSM program planning efforts. However, it is important to note that programmatic design (such as delivery methods and marketing strategies) will have implications for the overall savings goals and projected cost. As mentioned above, near-term savings Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page xxxi DSM Potential Study potential, actual achievable goals, and program costs for a measure-level implementation will vary from the savings potential and costs estimated in this long-term study. This potential study is one element to be considered in program design, along with historical program participation and current market conditions with the program implementation team. Some observations on the potential study results that can provide input to program planning are: • There is strong potential with promoting advanced lighting, which includes networked lighting technology and controls in all sectors. • There is high potential in O&M and behavior-type programs such as retrocommissioning if they are cost-effective. • HVAC unitary equipment Confidential and Proprietary ©2018 Navigant Consulting, Inc. has high potential in both sectors. Page xxxii DSM Potential Study 1. Introduction 1.1 Context and Study Goals Entergy New Orleans, LLC (ENO) engaged Navigant Consulting, Inc. (Navigant or the team) to prepare a DSM potential study for electricity as an input to ENO’s 2018 IRP for the 2018-2037 period. The study’s objective was to assess the long-term potential for reducing energy consumption in the residential and C&I sectors by analyzing energy efficiency and peak load reduction measures and improving end-user behaviors. The energy efficiency potential analysis efforts provide input data to Navigant’s DSMSim™ model, which calculates achievable savings potential across the service area. This study also includes DR program potential analyzed within Navigant’s DRSim™. While ENO explicitly plans to use the results from the potential study to inform the IRP, these results may also be used as inputs to DSM planning and long-term conservation goals and energy efficiency program design. 1.1.1 Study Objectives Potential studies provide a long-range outlook on the cost-effective potential for delivering demand-side resources such as EE and DR. Having a comprehensive review of achievable potential across ENO’s service area helps forecast the effects customer actions can have over the forecast period. The level of detail and accuracy provided by the current study will allow ENO to incorporate DSM in its IRP modeling and analysis, inform the design of future customer efficiency programs, and have a clear understanding of the level of investment needed to pursue the demand-side resource options. Given ENO’s objectives and Council’s rules, Navigant designed its project approach to ensure the study results adequately address those needs. Table 1-1 details these objectives and offers Navigant’s approach to meeting each objective. Table 1-1.Navigant’s Approach to Addressing ENO’s Objectives Objective 1 Use consistent methodology and planning assumptions Confidential and Proprietary ©2018 Navigant Consulting, Inc. Navigant’s Approach  Navigant has developed a variety of analytical tools and approaches to inform DSM planning and the establishment of long-term conservation targets and goals (details provided in the following sections). Navigant’s model is transparent. The team also worked closely with ENO to vet methodology, assumptions, and inputs at each stage of this project. Page 1 DSM Potential Study Objective 2 Reflect current information 3 Quantify achievable potential Navigant’s Approach  Navigant leveraged learnings from its prior work with ENO to create a bottom up analysis that includes inputs, such as the New Orleans TRM, and other up-todate information (new codes and standards, saturation data from surveys and Energy Smart programs, avoided costs, etc.) are included in this study.  Navigant quantifies achievable potential by first calculating the technical and economic potential. The achievable potential base case is calibrated to the historical Energy Smart program data and the current programs approved by the Council for Energy Smart PYs 7-9. 4 Provide input to the IRP  Navigant’s approach will provide the following for all modeled cases: • Supply curve of conservation potential for input to ENO’s IRP • Output available with 8,760 hourly impact load shapes 5 Present the scope and methodology of the study  Navigant’s approach to stakeholder engagement will provide relevant information to key stakeholders. Source: Navigant 1.2 Organization of the Report Navigant organized this report into five sections that detail the study’s approach, results, and conclusions. The list below provides a description of each section. • Section 1 provides an overview of the study, including its background and purpose. • Section 2 describes the methodologies and approaches Navigant used to estimate energy efficiency and demand reduction potential, including discussions of base year calibration, reference case forecast, and measure characterization. • Section 3 details the energy efficiency achievable potential forecast, including the approach and results by case, segment, end use, and measure. • Section 4 details the process for estimating DR potential and offers the achievable potential savings forecast for ENO, including the modeling results by customer segment. • Section 5 summarizes the next steps that result from developing this potential study. Additionally, the section benchmarks the study’s results against similar studies and actual achieved savings from other utilities. The accompanying appendices provide detailed model results and additional context around modeling assumptions. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 2 DSM Potential Study 1.3 Caveats and Limitations There are several caveats and limitations associated with the results of this study, which are detailed below. Potential studies are typically a bottom-up effort and calibrated to system and sector base load and forecasted reference case. They are an exercise in data management and analysis and in balancing data abundance and data scarcity for different inputs. A study’s team must understand the data gaps and how to fill these to provide reasonable and realistic potential estimates. This report documents what approach the Navigant team took and the decisions made when appropriate data was not available. 1.3.1 Forecasting Limitations Navigant obtained historic and forecasted energy sales and customer counts from ENO by sector. Each rate class (residential and C&I) forecast contains its own set of assumptions based on ENO’s expertise and models. The team leveraged these assumptions as much as possible as inputs to develop the reference case stock and energy demand projections. Where sufficient and detailed information could not be extracted due to the granularity of the information available, Navigant developed independent projections based on best practices. These independent projections were based on secondary data resources and produced in collaboration with ENO. The secondary resources and any underlying assumptions used are referenced throughout this report. 1.3.2 Segmentation Navigant obtained several pieces of data from ENO to segment the two sectors (residential and C&I), including customer counts by premise type for residential and industry type for C&I. The team supplemented this data using its expertise and ENO’s input to ensure the allocation of sales and stock data aligned to the appropriate segments. Government customers are included as part of the C&I sector. Savings potential analysis from city-owned street lighting is not included in this study since the majority has been converted to LED. 1.3.3 Measure Characterization Efficiency potential studies may employ a variety of primary data collection techniques (e.g., customer surveys, onsite equipment saturation studies, and telephone interviews) that can enhance the accuracy of the results, though not without associated cost and time requirements. Energy efficiency measures: The scope of this study did not include primary data collection. Rather, the energy efficiency analysis relied on data from ENO, other Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 3 DSM Potential Study regional efficiency programs and utilities, and TRMs from New Orleans, 8 Arkansas Pennsylvania, Illinois, Minnesota, Vermont, New York, and Massachusetts to inform inputs to DSMSim. Navigant used the measure list in this study to appropriately focus on those technologies likely to have the highest impact on savings potential over the study horizon. However, there is always the possibility that emerging technologies may arise that could increase savings opportunities over the forecast horizon and broader societal changes may affect levels of energy use in ways not anticipated by this study. DR programs: The scope of this study leveraged available ENO data from the direct load control pilot over the last two PYs to characterize DR program participation and costs. Additional DR characterization is based on Navigant’s research on programs nationwide and other potential studies. This study leveraged ENO load and account data to size the market eligible for DR program participation. 1.3.4 Measure Interactive Effects This study models energy efficiency measures independently. Thus, the total aggregated energy efficiency potential estimates may be higher or lower than the actual potential available if a customer installs multiple measures in their home or business. Multiple measure installations at a single site generate two types of interactive effects: within end-use interactive effects and cross end-use interactive effects. An example of a within end-use interactive effect is when a customer implements temperature control strategies but also installs a more efficient cooling unit. To the extent that the controls reduce cooling requirements at the cooling unit, the savings from the efficient cooling unit would be reduced. An example of a cross end-use interactive effect is when a homeowner replaces heat-producing incandescent light bulbs with efficient LEDs. This influences the cooling and heating load of the space—however slightly—by increasing the amount of heat and decreasing the amount of cooling generated by the heating, ventilation, and air conditioning (HVAC) system. Navigant employed the following methods to account for measure interactive effects: • Where measures clearly compete for the same application (e.g., an air source heat pump being replaced by either a more efficient air source heat pump or a ground source heat pump), the team created competition groups to eliminate the potential for double counting savings. • For measures with significant interactive effects (e.g., HVAC control upgrades and building automation systems), the team adjusted applicability percentages to reflect varying degrees of interaction. • Wherever cross end-use interactive effects were appreciable (e.g., lighting and 8 New Orleans Energy Smart Technical Reference Manual: Version 1.0, September 2017, prepared by ADM Associates, Inc. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 4 DSM Potential Study HVAC), the team typically characterized those interactive effects for same fuel (e.g., lighting and electric heating) applications but not for cross fuel because no natural gas savings or consumptions were considered in this study. There may be instances where the stacking of savings was not considered. These included mostly measures from the TRM, the primary source for the measure characterization. For example, if an efficient cooling unit is installed at the same time as improved insulation, the overall effects will be lower than the sum of individual effects. Appendix E provides further discussion of the challenges involved with accurately determining interactive effects. 1.3.5 Measure-Level Results This report includes a high level account of potential results across the ENO service area and focuses largely on aggregated forms of potential. Navigant mapped the measure-level data to the customer segments and end-use categories so a reviewer can easily create custom aggregations. 1.3.6 Gross Savings Study Savings in this study are shown at the gross level, meaning natural change (either natural conservation or natural growth in consumption) or, in other words, free-ridership, is not included in the savings estimates. Providing gross potential is advantageous because it permits a reviewer to easily calculate net potential when new information about changing EUIs, considerations of program design, or NTG ratios becomes available. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 5 DSM Potential Study 2. Study Approach and Data 2.1 Energy Efficiency Navigant developed forecasts of technical, economic, and program achievable electric savings potential in the ENO service area from 2018 through 2037 using a bottom-up potential model. These efficiency forecasts relied on disaggregated estimates of building stock and electric energy sales before conservation and a set of detailed measure characteristics for a comprehensive list of energy efficiency measures relevant to ENO’s service region. This section details the team’s approach and methodology to develop the key inputs to the potential model, as illustrated in Figure 2-1. Figure 2-1. Potential Study Inputs Source: Navigant The methodology to calculate achievable potential includes several elements such as a base year calibration, a reference case forecast, and full measure characterization. Figure 2-2 shows how these elements interact to result in the achievable savings potential. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 6 DSM Potential Study Figure 2-2. High Level Overview of Potential Study Methodology Source: Navigant 2.1.1 Market Characterization Navigant’s model uses inputs from two workflows: Market Characterization and Measure Characterization. This section describes the steps involved in the first workflow, Market Characterization. The Market Characterization workflow aims to define both the base year profile and reference case used to calculate potential. 2.1.1.1 Base Year Profile This section describes the approach used to develop the base year (2016) profile of electricity use in ENO’s service area, a key input to the potential model. The objective of the base year is to define a detailed profile of electricity sales by customer sector, segment, and end use (Figure 2-3). The model uses the base year as the foundation to develop the reference case forecast of electricity demand from 2018 through 2037. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 7 DSM Potential Study Figure 2-3. Base Year Electricity Profile – Residential Example Source: Navigant Navigant developed the base year profile based on 2016 billing and customer account data provided by ENO because it was the most recent year with a fully complete and verified dataset. Where ENO-specific information was unavailable, Navigant used data from publicly available sources such as the US EIA CBECS and the US Department of Labor SIC System, in addition to internal Navigant data sources. The team used these resources to support the data sources provided by ENO and to ensure consistency with ENO data. 2.1.1.2 Defining Customer Sectors and Segments The first major task to develop the base year electricity calibration involved disaggregating the main sectors—residential and C&I—into specific customer segments. The team selected customer segments based on several factors, including data availability and level of detail. Table 2-1 shows the segmentation used for the residential and C&I sectors. The following subsections detail the segmentation used for these sectors. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 8 DSM Potential Study Table 2-1. Customer Segments by Sector Residential Commercial & Industrial Single Family Colleges/Universities Multifamily Healthcare Industrial/Warehouse Lodging Large Office Small Office Other Restaurants Retail – Food Retail – Non-Food Schools Source: Navigant analysis 2.1.1.3 Residential Segments After establishing the study sectors and segments, Navigant aligned ENO’s data to the definitions established above, working closely with ENO. For residential, the team divided the sector into two segments based on consumption: single family and multifamily. The data ENO provided did not align perfectly with these segments due to differences in disaggregation methods. Navigant took two steps to reconcile the data: 1. Sorted out unnecessary premises. Navigant analyzed the proportion of total consumption for the different premise types provided in ENO’s data. More specifically, the team calculated the total kilowatt-hour (kWh) consumption of each premise type (by multiplying the number of accounts by the average monthly kWh sales for each account) and compared those to the total monthly residential kWh consumption (by adding up all the total consumptions of each premise type). Based on this analysis, the team decided to exclude certain premise types depending on their proportion of the total consumption. For instance, if the premise type made up less than 1% of the residential sector’s kWh sales and did not align with the study’s residential segments (e.g., Boat Slip, Not Assigned), it was excluded. 2. Mapped the remaining premise types to the study segments. Navigant sorted the remaining premise types—house, apartment, duplex, condo, and mobile home—to the study segments. This process involved looking at each premise type’s average monthly kWh consumption. Based on this comparison, the team determined that houses, condos, and homes would be classified as Single Family and duplexes and apartments would be classified as Multifamily. Table 2-2 provides the finalized descriptions for each of these residential segments. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 9 DSM Potential Study Table 2-2. Residential Segment Descriptions Segment Description Single Family Detached, attached row and/or townhouses (condominium), and mobile homes residential dwellings Multifamily Apartment units located in low rise or high rise apartment buildings and duplexes Source: Navigant 2.1.1.4 C&I Segments Navigant combined the C&I sectors into one, noted as C&I, because ENO’s industrial sector made up roughly 13% of the total load based on ENO’s load forecast analysis. Working closely with ENO, the team divided the C&I sector into 11 customer segments. Table 2-3 provides descriptions for each segment. The team selected these C&I segments to be representative of the population of C&I customers in ENO’s service area by comparing similar building characteristics such as patterns of electricity use, operating and mechanical systems, and annual operating hours. Generally, the selection of these segments aligned with the New Orleans TRM v1 and the SIC code for the account and kWh sales data provided by ENO. This study differs from those sources in that it includes industrial/warehouses and other as standalone segments and aggregates fast food and full menu restaurant into a single segment. Appendix A.3 details on the allocation of the sales and stock data into the C&I sector. Table 2-3. C&I Segment Descriptions Segment Description Large Office Larger offices engaged in administration, clerical services, consulting, professional, or bureaucratic work; excludes retail sales. Small Office Smaller offices engaged in personal services (e.g., dry cleaning), insurance, real estate, auto repair, and miscellaneous work; excludes retail sales. Retail – Food Retail and distribution of food; excludes restaurants. Retail – Non-Food Retailing services and distribution of merchandise; excludes retailers involved in food and beverage products services. Healthcare Health services, including diagnostic and medical treatment facilities, such as hospitals and clinics. Lodging Short-term lodging and related services, such as restaurants and recreational facilities; includes residential care, nursing, or other types of long-term care. Restaurant Establishments engaged in preparation of meals, snacks, and beverages for immediate consumption including restaurants, taverns, and bars. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 10 DSM Potential Study Segment Description School Primary schools, secondary schools (K-12), and miscellaneous educational centers, like libraries and information centers. College/University Post-secondary education facilities such as colleges, universities, and related training centers. Industrial/Warehouse Establishments that engage in the production, manufacturing, or storing of goods, including warehouses, manufacturing facilities, and storage facilities for general merchandise, refrigerated goods, and other wholesale distribution. Other Establishments not categorized under any other sector including but not limited to recreational, entertainment, and other miscellaneous activities. Source: Navigant 2.1.1.5 Defining End Uses The next step in the base year analysis was to establish end uses for each customer sector. Navigant defined these uses based on best practices, past ENO potential studies, and internal expertise. The end uses selected in Table 2-4 are important for several reasons, including reporting and defining savings. For instance, the team uses the categories to report achievable savings with more granularity than at the sector and segment levels. Navigant derives these reported end-use savings by rolling up individual energy efficiency measures that map to the broader end-use categories. For example, savings from ENERGY STAR refrigerators and freezers are reported under the plug load end use. Table 2-4. End Uses by Sector Residential C&I Lighting Interior Lighting Interior Lighting Exterior Lighting Exterior Plug Loads Plug Loads Cooling Cooling Heating Heating Hot Water Fans/Ventilation Fans/Ventilation Refrigeration Hot Water Source: Navigant Navigant used two additional end uses in Table 2-4 to report measure savings: total facility and heating and cooling. The team used these end uses to report savings from measures that affect electricity consumption across an entire home or facility or from measures that affect both heating and cooling consumption. For example, because smart thermostats result in electricity savings associated with both heating and cooling, Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 11 DSM Potential Study savings from smart thermostats are assigned to the heating and cooling end use rather than individually to either heating or cooling. 2.1.1.6 Base Year Inputs This section summarizes the breakdown of stock (households), electricity sales, and EUIs at the sector level, segment level, and end-use level. The team used these base year sales as direct inputs to the potential model. Appendix A provides a detailed description of the methodology used to develop these estimates. The DR portion of this study reconciles and derives the breakdown of demand across the sectors, segments, and end uses. 9 Table 2-5 and Figure 2-4 show the high level breakdown of electricity sales by sector. Of total electricity sales, 61% comes from the C&I sector and 39% from the residential sector. Table 2-5. 2016 Base Year Electricity Sector Sales (GWh) Sector GWh Residential 2,230 C&I 3,503 Total 5,733 Figure 2-4. 2016 Base Year Electricity Sector Breakdown (%, GWh) Source: Navigant analysis All other base year inputs are shown and detailed below. Residential Sector To define the base year residential sector inputs, Navigant began by determining the base year stock and sales using ENO’s account and billing data as the starting point. Although the account and billing data provided an approximation of ENO’s stock by premise type (e.g., homes, condos, duplexes, etc.), the team further calibrated the 9 Navigant developed the peak demand base case using the average peak demand factors from the 2016 sales data for the top 50 hours in each season. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 12 DSM Potential Study numbers to ENO’s account and load forecasts to ensure all datasets aligned. See Appendix A.2 for more detail about the calibration. The next step in the base year definition process involved developing residential EUI values in kWh per household. Navigant used ENO’s 2016 base year and the residential sales and count forecast to develop these values at the sector and segment levels by dividing the sales by the stock. Once the team determined the base year sector- and segment-level EUIs, it then determined the end-use-level EUIs, a more granular view of the EUIs. In the absence of local, ENO-specific data sources, Navigant used the US DOE’s EnergyPLUS prototypical models in conjunction with its proprietary updates based on several different studies to determine the proportion of energy allocated to each of the study’s end uses. The team used these proportions to further disaggregate the segment-level EUIs to the end-use level. Table 2-6 shows the base year residential stock, electricity sales, and average electricity usage per home by segment. The base year residential stock is approximately 180,000 homes and accounts for just over 2,200 GWh of sales. Table 2-6. Base Year Residential Results Stock (Accounts) Electricity Use (GWh) kWh per Account Single Family 132,901 1,481 11,144 Multifamily 45,048 749 16,632 Total 177,949 2,230 Segment 12,533 10 Source: Navigant analysis of ENO data Figure 2-5 shows the breakdown of base year residential electricity sales by end use and segment, respectively. In terms of end uses, lighting, cooling, fans/ventilation, and plug loads represent the largest residential end uses and account for 90% of residential electricity sales. 10 Note that this number represents the average annual kWh consumption for all households (total electricity use/ total accounts) and not the sum of the kWh per account for the two segments. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 13 DSM Potential Study Figure 2-5. Base Year Residential Electricity EndUse Breakdown (%, GWh) Source: Navigant analysis C&I Sector Similar to the residential sector, Navigant needed to determine the base year stock (thousands square feet [SF]) by segment, sales (kWh) by segment, and EUIs (kWh/thousands SF) by end use. Navigant followed three steps to determine these values for the base year: 1. Identify EUI by sector and segment for ENO 2. Define sales usage based on ENO’s account and billing data 3. Determine the base year stock This section will outline the general processes for each of these steps. Appendix A.3 provides specific details on the calibrations, data, and calculations used to define the base year values. For step 1, Navigant used data from the EIA to determine 2016 EUIs at the sector and segment levels for ENO’s climate region, hot-humid. The team then further calibrated this data to align with ENO’s specific forecasts to finalize the EUIs. To disaggregate the EUIs by end use, Navigant created end-use allocations using the DOE’s EnergyPLUS model in conjunction with proprietary Navigant models. Once the EUIs were finalized, Navigant determined electricity usage, or sales, by segment by mapping ENO’s account and billing data, which was classified by SIC, to the study’s segments. The mapping process ultimately helped the team divide the total sales into segments, yielding the segment-level base year sales. This analysis included Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 14 DSM Potential Study government accounts within the C&I sector. Finally, Navigant determined the stock using the EUI and sales determined in the previous two steps. More specifically, the team divided the segment-level EUI, which was in kWh/thousands SF, by the segment-level sales, which was in kWh. This calculation yielded the stock by segment in thousands SF. Table 2-7 shows the base year C&I stock (SF of floor space), electricity sales, and average electricity usage per SF by segment. C&I floor space stock is estimated at 188 million SF and contributes approximately 3,503 GWh of sales. Table 2-7. Base Year C&I Results Stock (thousands SF) Electricity Use (GWh) kWh per SF College/University 15,388 196 12.7 Healthcare 8,318 237 28.5 Industrial/Warehouse 27,863 457 16.4 Lodging 34,693 523 15.1 Office – Large 15,875 270 17.0 Office – Small 36,365 619 17.0 Other Commercial 22,504 485 21.6 Restaurant 4,720 218 46.2 Retail – Food 2,574 125 48.7 Retail – Non-Food 16,548 327 19.8 School 3,494 45 12.7 188,340 3,503 -- Segment Total Source: Navigant analysis Figure 2-6 shows the breakdown of base year C&I electricity sales by segment, respectively. Offices and lodging consume the most electricity, accounting for almost half (40%) of C&I electricity sales. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 15 DSM Potential Study Figure 2-6. Base Year C&I Electricity Segment Breakdown (%, GWh) Source: Navigant analysis 2.1.2 Reference Case Forecast This section presents the reference case forecast from 2018 to 2037. The reference case represents the expected level of electricity sales over the study period, absent incremental DSM activities or load impacts from rates. Electricity sales in the reference case are consistent with ENO’s load forecast. The reference case is significant because it acts as the point of comparison (i.e., the reference) for the calculation of achievable potential cases. Figure 2-7 illustrates the process Navigant used to develop the reference case forecast. The reference case uses the base year profile as its foundation and applies changes in stock growth and EUI over time to develop the residential and C&I forecasts. Figure 2-7. Schematic of Reference Case Source: Navigant Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 16 DSM Potential Study Navigant constructed the reference case forecast by applying growth rates from ENO’s account and load forecasts directly to the base year stock, sales, and EUI values. The following sections describe the approach and assumptions employed and present the results of the residential and C&I reference case forecasts. 2.1.2.1 Residential Reference Case Figure 2-8 illustrates this process. Appendix A.2 provides a description of the process used to develop the residential stock forecast. Figure 2-8. Residential Reference Case Schematic Source: Navigant For the residential reference case, the first step involved developing stock growth rates for each residential segment over the 2018-2037 period. Navigant derived residential stock growth rates based on ENO’s residential account forecast and applied them to the base year residential stock. Table 2-8 shows the growth in residential stock forecast from 2018 to 2037. Residential stock increases at an average annual growth rate of 0.4% from approximately 178,000 accounts in 2016 to 194,000 accounts in 2037. Table 2-8. Residential Reference Case Stock Forecast (Accounts) Segment 2016 2037 Single Family 132,901 144,972 Multifamily 45,048 49,139 Total 177,949 194,111 Source: Navigant analysis of ENOs residential load forecast Navigant followed a similar methodology for sales, leveraging ENO’s forecasting. To forecast the sales, the team determined the growth rates for each year of ENO’s load forecast and then applied these rates directly to the load. Finally, Navigant needed to forecast the EUIs. Due to data availability, Navigant did not Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 17 DSM Potential Study apply individual EUI trends by end use. Instead, the team applied ENO’s residential account forecast growth rates at each level to determine the changes in EUI over time. Although it is unlikely that end-use EUI trends will follow the account-level trends exactly, Navigant did not have any other reliable estimates to leverage. 11 ENO currently does not estimate these values, and the team could not find any reliable secondary sources specifically for the New Orleans area. 12 Table 2-9 shows the resulting EUI trends by residential end use, which is an overall reduction per household. Table 2-9. Residential Reference Case EUI Forecast (kWh/Account) Segment Single Family Multifamily End Use 2016 2037 Cooling 3,229 3,138 Fans/Ventilation 1,790 1,740 Heating 304 296 Hot Water 493 479 Lighting Exterior 345 335 Lighting Interior 2,158 2,097 Plug Loads 2,824 2,744 Total 11,144 10,829 Cooling 4,819 4,683 Fans/Ventilation 2,672 2,596 Heating 454 441 Hot Water 736 715 Lighting Exterior 515 500 Lighting Interior 3,221 3,130 Plug Loads 4,215 4,095 Total 16,632 16,161 Source: Navigant analysis 2.1.2.2 C&I Reference Case Like the residential reference case, Navigant built the C&I reference case by applying growth rates from ENO’s load forecast to the base year values. Figure 2-9 provides an overview of the inputs and the EUI and stock analyses for the C&I sector. Appendix A.3 provides a detailed description of the process used to develop the C&I stock forecast. 11 In other studies, Navigant usually sees a decrease in lighting EUIs and an increase in plug load EUIs over time, which is consistent with the assumption made here. Other end-use EUI projection rates may also vary. 12 Navigant reviewed national-level data from the US EIA and methodologies from other Navigant potential studies; however, the trends did not align well with ENO-specific trends. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 18 DSM Potential Study Figure 2-9. C&I Reference Case Schematic Source: Navigant To forecast out the stock, Navigant applied the growth rate of 0.4% from ENO’s account forecast for each study year. 13 Similarly, the team used the growth rate of 0.4% from ENO’s load forecast to estimate sales by year. Because ENO only had sector-level forecasts, Navigant applied the growth rates evenly across all segments except for the industrial/warehouse segment. For that segment, the team applied the growth rate of 0.0% from the Industrial sector portion of ENO’s forecasts to ensure alignment. Appendix A.3 provides more details about the source data for the growth rates. Given data availability, Navigant leveraged these growth rates to determine the EUI trends as well. Although it is unlikely that end-use EUI trends will follow the account-level trends exactly, the team did not have any other reliable estimates to leverage. ENO currently does not estimate these values, and Navigant could not find any reliable secondary sources specifically for the New Orleans area. Table 2-10 and Table 2-11 show the results of the reference case analysis. 13 Note that the growth rates presented in the paragraph represent the compound annual growth rate (CAGR) over the entire study period. The annual rates vary based on specific inputs, such as job, stock, and industry growth rates, according to ENO’s load forecasting team. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 19 DSM Potential Study Table 2-10. C&I Reference Case Stock Forecast (Thousands SF) Segment 2016 2037 Colleges/Universities 15,388 16,580 Healthcare 8,318 8,962 Industrial/Warehouses 27,863 27,734 Lodging 34,693 37,381 Office – Large 15,875 17,105 Office – Small 36,365 39,183 Other Commercial 22,504 24,248 Restaurants 4,720 5,085 Retail – Food 2,574 2,773 Retail – Non-Food 16,548 17,830 Schools 3,494 3,765 188,340 200,648 Total Source: Navigant analysis Table 2-11. C&I Reference Case EUI Forecast (kWh/Thousands SF) Segment Colleges/Universities End Use 2016 2037 Cooling 2,662 2,820 Fans/Ventilation 2,468 2,615 Heating 1,885 1,998 Hot Water 196 207 Lighting Exterior 347 367 Lighting Interior 3,238 3,430 Plug Loads 1,804 1,911 148 156 Heating/Cooling 4,547 4,818 Total Facility 12,747 13,506 Cooling 7,803 8,268 Fans/Ventilation 2,806 2,974 Heating 4,217 4,468 Hot Water 356 377 Lighting Exterior 224 238 Lighting Interior 5,999 6,357 Plug Loads 6,978 7,394 141 149 Heating/Cooling 12,021 12,737 Total Facility 28,525 30,224 64 74 Refrigeration Healthcare Refrigeration Industrial/Warehouses Cooling Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 20 DSM Potential Study Segment End Use 2016 2037 Fans/Ventilation 4,006 4,595 Heating 3,171 3,637 Lighting Exterior 266 305 Lighting Interior 5,439 6,239 Plug Loads 883 1,012 Refrigeration 502 576 Hot Water 2,071 2,375 Heating/Cooling 3,235 3,711 Total Facility 16,402 18,813 Cooling 2,683 2,843 Fans/Ventilation 2,006 2,125 176 186 3,812 4,040 Lighting Exterior 176 187 Lighting Interior 2,402 2,546 Plug Loads 3,687 3,906 123 130 Heating/Cooling 2,859 3,029 Total Facility 15,065 15,962 Cooling 6,432 6,815 495 524 1,468 1,556 Hot Water 61 64 Lighting Exterior 34 36 Lighting Interior 5,291 5,606 Plug Loads 3,245 3,438 Heating/Cooling 7,900 8,371 Total Facility 17,026 18,040 Cooling 6,269 6,642 482 511 1,846 1,956 Hot Water 76 81 Lighting Exterior 33 35 Lighting Interior 5,157 5,464 Plug Loads 3,162 3,351 Heating/Cooling 8,115 8,598 Total Facility 17,026 18,040 687 727 Heating Hot Water Lodging Refrigeration Fans/Ventilation Heating Office – Large Fans/Ventilation Heating Office – Small Other Commercial Cooling Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 21 DSM Potential Study Segment End Use 2016 2037 Fans/Ventilation 4,096 4,340 Heating 1,805 1,912 Hot Water 1,457 1,543 Lighting Exterior 190 201 Lighting Interior 2,953 3,129 Plug Loads 9,608 10,181 777 823 Heating/Cooling 2,491 2,640 Total Facility 21,572 22,857 Cooling 8,553 9,062 Fans/Ventilation 6,578 6,970 Heating 1,970 2,088 Hot Water 2,389 2,531 Lighting Exterior 2,073 2,196 Lighting Interior 3,422 3,626 Plug Loads 19,710 20,884 Refrigeration 1,481 1,569 Heating/Cooling 10,523 11,150 Total Facility 46,175 48,925 Cooling 3,980 4,217 Fans/Ventilation 5,927 6,280 Heating 2,151 2,279 Hot Water 45 48 Lighting Exterior 595 631 Lighting Interior 10,889 11,538 Plug Loads 5,586 5,918 Refrigeration 19,498 20,659 Heating/Cooling 6,131 6,496 Total Facility 48,671 51,570 Cooling 1,915 2,030 Fans/Ventilation 2,916 3,089 Heating 1,795 1,902 Lighting Exterior 599 634 Lighting Interior 8,770 9,293 Plug Loads 980 1,039 Refrigeration 642 680 Hot Water 2,172 2,301 Heating/Cooling 3,711 3,932 Refrigeration Restaurants Retail – Food Retail – Non-Food Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 22 DSM Potential Study Segment Schools End Use 2016 2037 Total Facility 19,789 20,968 Cooling 2,504 2,653 Fans/Ventilation 2,322 2,460 Heating 2,459 2,605 Hot Water 255 271 Lighting Exterior 326 346 Lighting Interior 3,046 3,227 Plug Loads 1,697 1,798 139 147 Heating/Cooling 4,962 5,258 Total Facilities 12,747 13,506 Refrigeration Source: Navigant analysis 2.1.3 Energy Efficiency Measure Characterization Navigant fully characterized over 100 measures or measure groupings across ENO’s residential and C&I sectors. The team prioritized high-impact measures with good data availability that are most likely to be cost-effective for inclusion into DSMSim. 2.1.3.1 Measure List Navigant developed a comprehensive list of energy efficiency measures likely to contribute to achievable potential. The team reviewed current ENO Energy Smart program offerings, other regional programs, and potential model measure lists from other states to identify energy efficiency measures with the highest expected economic impact. The team supplemented the measure list using secondary data from publicly available sources such as TRMs from various US regions including Arkansas, Illinois, and the mid-Atlantic. Navigant prioritized measures in existing ENO Energy Smart programs based on data availability for appropriate characterization and measures most likely to be cost-effective. The team also ensured that high impact measures were captured in the list. The team worked with ENO and ENO contractors, including program implementers, to finalize the measure list and ensure it contained technologies viable for future ENO program planning activities. Figure 2-10 shows the process Navigant implemented to narrow down the measure list. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 23 DSM Potential Study Figure 2-10. Measure Screening Process Source: Navigant There were many measures included in the initial and assessment screens that did not make it into the study. The high potential measures that did not become candidate measures for the study are documented. Working sessions with ENO staff revealed topics of note regarding the following measures: • Residential lighting: Low efficiency residential lighting types such as incandescent and halogen lamps can be replaced with higher efficiency CFL and LED bulbs. As LED bulbs have become more common in the market and less expensive over time, they offer cost-effectiveness advantages over CFL bulbs. Navigant anticipates that future programs will no longer incentivize CFLs. Therefore, this study included LEDs but not CFLs. • Residential thermostats: Programmable thermostats control space temperatures according to a preset schedule, while smart thermostats are Wi-Fi controlled and implement a learning algorithm to control temperature to a desired level while managing HVAC energy use. ENO recently conducted a pilot study in low income housing in anticipation of developing a future program offering. Navigant included both programmable and smart thermostats in this study. • Industrial measures: ENO reported that its industrial energy use is relatively low compared to the commercial and residential sectors. Navigant used industrial measure expertise from previous potential studies and industrial subject matter experts to develop a limited list of industrial sector measures; the team then aggregated the industrial sector potential together with the commercial sector potential. 2.1.3.2 Measure Characterization Key Parameters The measure characterization effort consisted of defining nearly 50 individual parameters for each of the measures included in this study. This section defines the top nine key parameters and how each influences technical and economic, and therefore achievable, potential savings estimates. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 24 DSM Potential Study 1. Measure Definition: Navigant used the following variables to qualitatively define each characterized measure: o Replacement Type: Replacing the baseline technology with the efficient technology can occur in three variations: i. Retrofit (RET): In this variation, equipment is replaced before the end of its life. The model considers the baseline to be the existing equipment and uses the energy and demand savings between the existing equipment and the efficient technology during technical potential calculations. RET also applies the full installed cost of the efficient equipment during the economic screening. ii. Replace-on-Burnout (ROB): In this variation, equipment is replaced when it fails. The model considers the baseline to be the codecompliant technology option and uses the energy and demand savings between the current code option and the efficient technology during technical potential calculations. ROB also applies the incremental cost between the efficient and code-compliant equipment during the economic screening. iii. New Construction (NEW): In this variation, new equipment is installed in a new home or building. The model considers the baseline to be the least-cost, code-compliant option and uses the energy and demand savings between this specific current code option and the efficient technology during technical potential calculations. NEW also applies the incremental cost between the efficient and code-compliant equipment during the economic screening. o Baseline Definition: Describes the baseline technology. o Energy Efficiency Definition: Describes the efficient technology set to replace the baseline technology. o Unit Basis: The normalizing unit for energy, demand, cost, and density estimates. 2. Sector and End-Use Mapping: The team mapped each measure to the appropriate end uses, customer segments, and sectors across ENO’s service area. Section 2.1.1 describes the breakdown of customer segments within each sector. 3. Annual Energy Consumption: The annual energy consumption in kWh for each base and energy efficient technology. 4. Fuel Type Applicability Multipliers: Applies an adjustment to the total equipment stock to account for the proportion applicable to a given measure’s fuel type. For example, a measure that replaces a baseline efficiency resistance water heater with a more efficient unit is only applicable to existing electric resistance water heaters. The team used this multiplier to restrict the existing Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 25 DSM Potential Study water heater equipment stock to only those that use electricity. Table 2-12 provides the fuel share splits. Table 2-12. Fuel Share Splits for Domestic Hot Water and Heating DHW – Elec Only DHW – Gas Only Heating – Elec Only Heating – Gas Residential 50% 50% 50% 50% C&I 60% 40% 60% 40% Customer Segment Source: Navigant analysis 5. Measure Lifetime: The lifetime in years for the base and energy efficient technologies. The base and energy efficient lifetimes only differ in instances where the two cases represent inherently different technologies, such as LEDs compared to a baseline incandescent bulb. 6. Incremental Costs: The incremental cost between the assumed baseline and efficient technology using the following variables: o Base Costs: The cost of the base equipment, including both material and labor costs. o Energy Efficient Costs: The cost of the energy efficient equipment, including both material and labor costs. 7. Technology Densities: This study defines density as the penetration or saturation of the baseline and efficient technologies across the service area. For residential, these saturations are on a per-home basis and for C&I, they are per 1,000 SF of building space. 14 o Base Initial Saturation: The initial saturation of the baseline equipment for a given customer segment as defined by the fraction of the end-use stock that has the baseline equipment installed. o Energy Efficiency Initial Saturation: The initial saturation of the efficient equipment for a given customer segment as defined by the fraction of the end-use stock that has the efficient measure installed. o Total Maximum Density: The total number of both the baseline and efficient units for a given technology. 8. Technical Suitability: The percentage of the base technology that can be reasonably and practically replaced with the specified efficient technology. For instance, occupancy sensors are only practical for certain interior lighting fixtures 14 Navigant sourced density estimates from Energy Smart program data and other related secondary sources. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 26 DSM Potential Study (suitability less than 1.0), while all existing incandescent exit signs can be replaced with efficient LED signs (suitability of 1.0). 9. Competition Group: Navigant combined efficient measures competing for the same baseline technology density into a single competition group to avoid the double counting of savings. 2.1.3.3 Measure Characterization Approaches and Sources This section provides approaches and sources for the main measure characterization variables. Table 2-13. Measure Characterization Input Data Sources Measure Input Data Sources • • • • • • • Energy Smart program data 2017 New Orleans TRM 2017 ENO potential study data US DOE Appliance Standards and Rulemakings supporting documents Engineering analyses TRMs and RTF measure workbooks Navigant measure database and previous potential studies Fuel Type Applicability Splits, Density, Baseline Initial Saturation, Technical Suitability, End-Use Consumption Breakdown • • Energy Smart program data Navigant’s previous potential studies Codes and Standards • • US DOE CFR engineering analyses Local building code Measure Costs, Measure Life, Energy Savings Source: Navigant 2.1.3.4 Energy Savings Navigant used three general bottom-up approaches to analyze residential and C&I measure energy savings: 1. New Orleans Technical Reference Manual Calculations: Navigant used the New Orleans 2017 TRM as much as possible for unit energy savings calculations. The TRM provided deemed (default) savings values for most measures in this study. 2. Standard algorithms: Navigant used standard algorithms for unit energy savings calculations for most measures not contained in the New Orleans TRM. To supplement this, the team leveraged ENO data, DOE Appliance Standards and Rulemaking supporting documents, RTF measure workbooks, and other TRMs. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 27 DSM Potential Study 3. Engineering analysis: Navigant used appropriate engineering algorithms to calculate energy savings for any measures not included in the New Orleans TRM or available TRMs. The team leveraged its internal expertise and experience with potential studies to calculate the energy savings. 2.1.3.5 Peak Demand Savings Peak demand savings were either from the New Orleans TRM or generally calculated by dividing the annual energy use by the annual hours of use and then multiplying by a coincidence factor. The coincidence factor is an expression of how much of the equipment’s demand occurs during the system’s peak period. The defined peak period according to the TRM is the average peak demand savings, Monday-Friday, nonholidays from 4-6pm in the months of June, July, and August. 2.1.3.6 Incremental Costs Navigant relied on the cost information in the New Orleans TRM as much as possible. The team conducted secondary research and used other publicly available cost data sources such as regional TRMs, RTF measure workbooks, the California DEER, ENERGY STAR, US DOE Appliance Standards and Rulemaking, and other state databases for all other cost data. 2.1.3.7 Building Stock and Densities Navigant developed building stock estimates for the residential sector in terms of residential accounts and the C&I sector in terms of floor space. The approaches used to develop the base year and reference case building stock assumptions are described in Section 2.1.1. Measure densities—used to characterize the penetration or saturation of measures— were developed based on a variety of data sources including ENERGY STAR, the Northwest Energy Efficiency Alliance’s Residential Building Stock Assessment (RBSA) and Commercial Building Stock Assessment (CBSA), and previous potential studies from other jurisdictions. 2.1.3.8 8,760 Load Profile Appendix C provides detail on the development of the end-use profiles. These profiles are 8,760 (i.e., hourly annual) end-use load shapes. These profiles are by end use (e.g., heating, lighting, etc.), by sector (e.g., residential, commercial, etc.), and, where relevant and appropriate, by commercial and industrial segments (e.g., retail, office, etc.). 2.1.3.9 Codes and Standards Adjustments The US DOE publishes federal energy efficiency regulations for many types of Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 28 DSM Potential Study residential appliances and commercial equipment. The US DOE Technical Support Documents (TSD)15 contain information on energy and cost impacts of each appliance standard. In the TSD, engineering analysis is available in Chapter 5, energy use analysis in Chapter 7, and cost impact in Chapter 8. As these codes and standards take effect, the energy savings from existing measures impacted by these codes and standards decline and the reduction is transferred to the codes and standards savings potential. Navigant accounts for the effect of codes (including building code 16) and standards through baseline energy and cost multipliers (sourced from the DOE’s analysis), which reduce the baseline equipment consumption starting from the year a code or standard takes effect. The baseline cost of an efficient measure affected by codes and standards will often increase upon the code’s implementation. For example, Navigant incorporated the 2023 residential central air conditioners standard in this study, which results in the baseline for residential air conditioners changing from 14 Seasonal Energy Efficiency Ratio (SEER) to 14.3 SEER in 2023. Accordingly, the model accounts for a reduction in energy consumption and an increase in cost in 2023 for the baseline technology through the codes and standards multipliers. As such, computed measure-level potential is net of these adjustments from codes and standards implemented after the first year of the study. 17 2.1.3.10 Measure Quality Control Navigant fully vetted and characterized each measure in terms of its energy savings, costs, and applicability. The characterization includes the following: • Measure descriptions and baseline assumptions • Energy savings and cost associated with the measure • Cost of conserved energy, including O&M costs • Lifetime of the measure (Effective useful life and remaining useful life) • Applicability factors including initial energy efficient market penetration and technical suitability • Load shape of measure • Replacement type of measure 15 Appliance standards rulemaking notices and TSD can be found at: https://www.energy.gov/eere/buildings/appliance-and-equipment-standards-program 16 Section 26-15 of the New Orleans Code of Ordinances 17 It is important to note that the second tier of Energy Independence and Security Act of (EISA) 2007 regulations go into effect beginning January 2020 where the general service lamps must comply with a higher standard. Because the EUL of some lamps extend beyond this date, the baseline per guidance from the New Orleans TRM is adjusted to the second tier in years after 2022. For commercial lighting, these retrofits are considered as RET and baseline changes start in 2020. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 29 DSM Potential Study 2.1.4 Potential Estimation Approach Navigant employed its proprietary DSMSim potential model to estimate the technical, economic, and achievable savings potential for electric energy and demand across ENO’s service area. DSMSim is a bottom-up technology diffusion and stock tracking model implemented using a System Dynamics 18 framework. The DSMSim model explicitly accounts for different types of efficient measures such as RET, ROB, and NEW and the effects these measures have on savings potential. The model then reports the technical, economic, and achievable potential savings in aggregate for the service area, sector, customer segment, end-use category, and highest impact measures. This study defines technical potential as the total energy savings available assuming all installed measures can immediately be replaced with the efficient measure/technology—wherever technically feasible—regardless of the cost, market acceptance, or whether a measure has failed and must be replaced. Economic potential is a subset of technical potential, using the same assumptions regarding immediate replacement as in technical potential but including only those measures that have passed the benefit-cost test chosen for measure screening; in this case, that is the total resource cost (TRC) test. Finally, the achievable potential is analyzed based on the measure adoption ramp rates and the diffusion of technology through the market. Figure 2-11 provides an overview of the methodology. 18 See Sterman, John D. Business Dynamics: Systems Thinking and Modeling for a Complex World. Irwin McGraw-Hill. 2000 for detail on System Dynamics modeling. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 30 DSM Potential Study Figure 2-11. Potential Calculation Methodology Source: Navigant Savings reported in this study are gross rather than net, meaning they do not include the effects of natural change. Providing gross potential is advantageous because it permits a reviewer to more easily calculate net potential when new information about NTG ratios or changing EUIs become available. Once the potential results and cases are analyzed, the output can be used to define the portfolio energy savings goals, costs, and forecast for alignment into other utility planning landscapes like the IRP. 2.1.4.1 Technical Potential Approach to Estimating Technical Potential This study defines technical potential as the total energy savings available assuming all installed measures can immediately be replaced with the efficient measure/technology—wherever technically feasible—regardless of the cost, market acceptance, or whether a measure has failed and must be replaced. Navigant’s modeling approach considers an energy efficient measure to be any change made to a building, piece of equipment, process, or behavior that can save energy. 19 The savings can be defined in numerous ways depending on which method is most appropriate for a given measure. Measures that consist of a change to a single, discrete 19 This study does not examine the impact of end-user electricity rates on sales or energy efficiency’s impact on electricity rates. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 31 DSM Potential Study product or piece of equipment (e.g., lighting fixture replacements) are best characterized as some fixed amount of savings per fixture. Measures related to products or equipment that vary by size (e.g., air conditioning equipment) are best characterized on a basis that is normalized to a certain aspect of the equipment, such as per ton of air conditioning capacity. Other measures that could affect multiple pieces of equipment (e.g., behavior-based measures) are characterized as a percentage of customer segment sales saved. The calculation of technical potential in this study differs depending on the assumed measure replacement type. Technical potential is calculated on a per-measure basis and includes estimates of savings per unit, measure density (e.g., quantity of measures per home for residential or per 1,000 SF of floor space for C&I), and total building stock in each service area. The study accounts for three replacement types, where potential from RET and ROB measures are calculated differently from potential for NEW measures. The formulae used to calculate technical potential by replacement type are shown below. Retrofit and ROB Measures RET measures, commonly referred to as advancement or early retirement measures, are replacements of existing equipment before the equipment fails. RET measures can also be efficient processes that are not currently in place and that are not required for operational purposes. RET measures incur the full cost of implementation rather than incremental costs to some other baseline technology or process because the customer could choose not to replace the measure and would, therefore, incur no costs. In contrast, ROB measures—sometimes referred to as lost opportunity measures—are replacements of existing equipment that have failed and must be replaced or are existing processes that must be renewed. Because the failure of the existing measure requires a capital investment by the customer, the cost of implementing ROB measures is always incremental to the cost of a baseline (and less efficient) measure. RET and ROB measures have a different meaning for technical potential compared with NEW measures. In any given year, the model uses the existing building stock to calculate technical potential. 20 This method does not limit the calculated technical potential to any pre-assumed adoption rate of RET measures. Existing building stock is reduced each year by the quantity of demolished building stock in that year and does not include new building stock that is added throughout the simulation. For RET and ROB measures, annual potential is equal to total potential, thus offering an instantaneous view of technical potential. Navigant used Equation 2-1 to calculate technical potential for RET and ROB measures. 20 In some cases, customer segment-level and end-use-level sales are used as proxies for building stock. These sales figures are treated like building stock in that they are subject to demolition rates and stock tracking dynamics. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 32 DSM Potential Study Equation 2-1. Annual/Total RET/ROB Technical Savings Potential 𝑇𝑇𝑇𝑇𝑇 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 = 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸 𝑆𝑆𝑆𝑆𝑆 𝑥 𝑀𝑀𝑀𝑀𝑀𝑀𝑀 𝐷𝐷𝐷𝐷𝐷𝐷𝐷 𝑥 𝑆𝑆𝑆𝑆𝑆𝑆𝑆 𝑥 𝑇𝑇𝑇ℎ𝑛𝑛𝑛𝑛𝑛 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 𝑥 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵 𝐼𝐼𝐼𝐼𝐼𝐼𝐼 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 Where: • Total Potential: kWh • Existing Stock: 21 C&I floor space per year or residential households per year • Measure Density: Widgets per unit of stock • Savings: kWh per widget per year • Technical Suitability: Percentage of applicable stock • Baseline Initial Saturation: Percentage of energy efficient stock New Construction Measures The cost of implementing NEW measures is incremental to the cost of a baseline (and less efficient) measure. However, NEW technical potential is driven by equipment installations in new building stock rather than by equipment in existing building stock. 22 New building stock is added to keep up with forecast growth in total building stock and to replace existing stock that is demolished each year. Demolished (sometimes called replacement) stock is calculated as a percentage of existing stock in each year, and this study uses a demolition rate of 0.5% per year for residential and C&I stock. New building stock determines the incremental annual addition to technical potential, which is then added to totals from previous years to calculate the total potential in any given year. The equations used to calculate technical potential for new construction measures are provided in Equation 2-2 and Equation 2-3. Equation 2-2. Annual Incremental NEW Technical Potential (AITP) 𝐴𝐴𝐴𝐴 = 𝑁𝑁𝑁 𝑆𝑆𝑆𝑆𝑆 𝑥 𝑀𝑀𝑀𝑀𝑀𝑀𝑀 𝐷𝐷𝐷𝐷𝐷𝐷𝐷 𝑥 𝑆𝑆𝑆𝑆𝑆𝑆𝑆 𝑥 𝑇𝑇𝑇ℎ𝑛𝑛𝑛𝑛𝑛 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 Where: • Annual Incremental NEW Technical Potential: kWh 21 Units for building stock and measure densities may vary by measure and customer segment (e.g., 1,000 SF of building space, number of residential homes, customer segment sales, etc.). 22 In some cases, customer segment-level and end-use-level sales are used as proxies for building stock. These sales figures are treated like building stock in that they are subject to demolition rates and stock tracking dynamics. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 33 DSM Potential Study • New Stock: 23 C&I floor space per year or residential households per year • Measure Density: Widgets per unit of stock • Savings: kWh per widget per year • Technical Suitability: Percentage of the total baseline measures that could be replaced with the efficient measure. For example, CFLs cannot replace all incandescent bulbs because of their size, inability to be dimmed, and sensitivity to temperature. Equation 2-3. Total NEW Technical Potential (TTP) 𝑌𝑌𝑌𝑅=2037 TTP = ∑𝑌𝑌𝑌𝑌=2018 𝐴𝐴𝐴𝐴𝑌𝑌𝑌𝑌 Competition Groups Navigant’s modeling approach recognizes that some efficient technologies will compete against each other in the calculation of potential. The study defines competition as an efficient measure competing for the same installation as another efficient measure. For instance, a consumer has the choice to replace an air source heat pump with a more efficient air source heat pump or a ground source heat pump, but not both. These efficient technologies compete for the same installation. There are several general characteristics of competing technologies that Navigant used to define competition groups in this study: • Competing efficient technologies share the same baseline technology characteristics, including baseline technology densities, costs, and consumption. • The total (baseline plus efficient) measure densities of competing efficient technologies are the same. • Installation of competing technologies is mutually exclusive (i.e., installing one precludes installation of the others for that application). • Competing technologies share the same replacement type (RET, ROB, or NEW). To address the overlapping nature of measures within a competition group, Navigant’s analysis only selected one measure per competition group to include in the summation of technical potential across measures (e.g., at the end use, customer segment, sector, service area, or total level). The measure with the largest energy savings potential in each competition group was used to calculate total technical potential of that 23 Units for new building stock and measure densities may vary by measure and customer segment (e.g., 1,000 SF of building space, number of residential homes, customer segment consumption, etc.) Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 34 DSM Potential Study competition group. This approach ensures that the aggregated technical potential does not double count savings. The model does still, however, calculate the technical potential for each individual measure outside of the summations. 2.1.4.2 Economic Potential This section describes the economic savings potential—potential that meets a prescribed level of cost-effectiveness—available in ENO’s service area. The section explains Navigant’s approach to calculating economic potential. Approach to Estimating Economic Potential Economic potential is a subset of technical potential, using the same assumptions regarding immediate replacement as in technical potential but including only those measures that have passed the benefit-cost test chosen for measure screening (in this study the TRC test, as per the Council’s IRP rules). The TRC ratio for each measure is calculated each year and compared against the measure-level TRC ratio screening threshold of 1.0. A measure with a TRC ratio greater than or equal to 1.0 is a measure that provides monetary benefits greater than or equal to its costs. If a measure’s TRC meets or exceeds the threshold, it is included in the economic potential. The TRC test is a benefit-cost metric that measures the net benefits of energy efficiency measures from the combined stakeholder viewpoint of the utility (or program administrator) and the customers. The TRC benefit-cost ratio is calculated in the model using Equation 2-4. Equation 2-4. Benefit-Cost Ratio for the TRC Test Where: 𝑇𝑇𝑇 = 𝑃𝑃(𝐴𝐴𝐴𝐴𝐴𝐴𝐴 𝐶𝐶𝐶𝐶𝐶) 𝑃𝑃(𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 𝐶𝐶𝐶𝐶 + 𝐴𝐴𝐴𝐴𝐴 𝐶𝐶𝐶𝐶𝐶) • PV is the present value calculation that discounts cost streams over time. • Avoided Costs are the monetary benefits that result from electric energy and capacity savings—e.g., avoided or deferred costs of infrastructure investments and avoided long-run marginal cost (commodity costs) due to electric energy conserved by efficient measures. • Incremental Cost is the measure cost as defined (see definition in Section 2.1.3.6). • Admin Costs are the administrative costs incurred by the utility or program administrator (not including incentives). Navigant calculated TRC ratios for each measure based on the present value of benefits and costs (as defined in the numerator and denominator, respectively) over each measure’s life. Avoided costs, discount rates, and other key data inputs used in Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 35 DSM Potential Study the TRC calculation are presented in Appendix B. Effects of free ridership are not present in the results from this study, so the team did not apply a NTG factor. Providing gross savings results will allow ENO to easily apply updated NTG assumptions in the future and allows for variations in NTG assumptions by reviewers. Although the TRC equation includes administrative costs, the study did not consider these costs during the economic screening process because the study is concerned with an individual measure’s cost-effectiveness on the margin. Like technical potential, only one economic measure from each competition group was included in the summation of economic potential across measures (e.g., at the end-use category, customer segment, sector, service area, or total level). If a competition group was composed of more than one measure that passes the TRC test, then the economic measure that provides the greatest electric savings potential was included in the summation of economic potential. This approach ensures that double counting is not present in the reported economic potential, though economic potential for each individual measure is still calculated and reported outside of the summation. 2.1.4.3 Achievable Potential Achievable potential is defined as the subset of economic potential considered achievable given assumptions about the realistic market adoption of a given measure. It is the product of the economic potential with two measure-specific factors: 1) the assumed maximum long-run achievability of each measure, and 2) a time-dependent factor called ramp rate reflects barriers to market adoption. The adoption of measures can be broken down into calculation of the “equilibrium” market share and calculation of the dynamic approach to equilibrium market share. The effects or program intervention result in applying ramp rates to the maximum achievable potential to model the changes in time-dependent barriers to market adoption. These ramp rates spread each measure’s maximum achievable potential over the study horizon, accounting for assumptions about the timing of when this potential will be realized. Using the definitions of cumulative total technical potential provided in Section 2.1.4.1, Equation 2-5 provides the formula to calculate achievable potential. As shown, Navigant calculated achievable potential by multiplying each measure’s total economic potential by its maximum achievability factor and then applying a ramp rate for the adoption to the resulting maximum achievable potential. Equation 2-5. Achievable Potential 𝐴𝐴ℎ𝑖𝑖𝑖𝑖𝑖𝑖𝑖 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑌𝑌𝑌𝑌 = 𝑇𝑇𝑇𝑇𝑇 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 × 𝑀𝑀𝑀 𝐴𝐴ℎ𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 𝐹𝐹𝐹𝐹𝐹𝐹 × 𝑅𝑅𝑅𝑅 𝑅𝑅𝑅𝑅𝑌𝑌𝑌𝑌 Figure 2-12 illustrates the relationship between total economic potential, maximum achievable potential, and final computed achievable potential in each year of the study as a function of ramp rate choice. The timing of achievable potential across the study Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 36 DSM Potential Study horizon is driven by the choice of ramp rate. All values in the figure are for illustration purposes only. Figure 2-12. Illustration of Achievable Potential Calculation Source: Navigant For measures involved in competition groups, an additional computational step is required to compute achievable potential to ensure no double counting of savings. While the technical and economic potential for a competition group reflects only the measure in that group with the greatest savings potential, all measures in a competition group may be allocated achievable potential based on their attractiveness (relative to one another). Navigant allocated the economic potential proportionally across the various competing measures within the group based on their relative customer economics (payback). The team computed the relative customer economics ratio to reflect all costs and savings a customer would experience as a result of implementing the measure. The team multiplied the resulting market share splits by the maximum achievable potential for the group to get the achievable potential for each individual measure. This methodology ensured that final estimates of achievable potential reflected the relative economic attractiveness of measures in a competition group and that the sum of achievable potential from all measures in a competition group reflected the maximum achievable potential of the whole group. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 37 DSM Potential Study 2.2 Demand Response Navigant prepared a DR potential assessment for ENO’s electric service area from 2018 to 2037 as part of the DSM potential study. The objective of this assessment was to estimate the potential for using DR to reduce customer loads during peak summer periods. Navigant identified and analyzed a suite of DR options for potential implementation in ENO’s service area based on similar studies performed in other jurisdictions. These are: 1. Direct load control (DLC): This program controls water heating and cooling loads for residential and small business customers using either a DLC device (switch) or a PCT. 2. C&I curtailment: This program curtails a fixed amount of load reduction among C&I customers over a fixed contract period. 3. Dynamic pricing: This program encourages load reduction through CPP, with a 6:1 critical peak to off-peak price ratio. All customer types are eligible to participate. 4. Behind-the-meter storage (BTMS): This program triggers power dispatch from battery storage systems that are grid-connected during peak load conditions. Navigant developed achievable potential estimates for each of these DR options at various levels of disaggregation, along with the costs associated with rolling out and implementing a DR program portfolio. The assessment considered both conventional and advanced control methods to curtail load at customer premises. Navigant also assessed the cost-effectiveness of the DR program options and measure types. 2.2.1 General Approach and Methodology Navigant developed ENO’s DR potential and cost estimates using a bottom-up analysis. The analysis used primary data from ENO and relevant secondary sources. The team configured its DRSim model, which uses this data as inputs, for this study. The following subsections detail Navigant’s DR potential and cost estimation methodology: • Market Characterization: Segment ENO’s customer base into customer classes eligible to participate in DR programs. • Develop Baseline Projections: Develop baseline projections for customer count and peak demand over the 20-year forecast period. • Characterize DR Options: Define DR program options and map them to applicable customer classes. • Develop Model Inputs for Potential and Cost Estimates: Develop participation, load reduction, and cost assumptions that feed the DRSim model. • Case Analysis: Estimate DR potential and associated implementation costs for low and high cases relative to the base (medium) case. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 38 DSM Potential Study 2.2.2 Market Characterization for DR Potential Assessment Market characterization was the first step in the DR potential assessment process. Table 2-14 presents the different levels of market segmentation for the DR potential assessment. It is based on Navigant’s examination of ENO’s rate schedules and the customer segments established in the energy efficiency potential study. The team finalized the market segmentation for the DR potential assessment in consultation with ENO. The methodology Navigant used to segment the market at these levels is briefly described below. Government customers are included as part of the C&I sector. Savings potential analysis from street lighting is not included in this study. Table 2-14. Market Segmentation for DR Potential Assessment Level Level 1: Sector Level 2: Customer Class Description • • Residential C&I • • Residential C&I customers by size based on maximum demand values: o Small C&I: <= 100 kW maximum demand o Large C&I: >100 kW maximum demand • • Residential 24 C&I customer segments o Colleges/Universities o Healthcare o Industrial/Warehouse o Lodging o Office – Large o Office – Small o Other o Restaurants o Retail – Food o Retail – Non-Food o Schools Level 3: Customer Segment Source: Navigant Navigant first segmented customers into residential and C&I. The team combined single family and multifamily customers into a single residential category because DR program and pricing offers are typically not distinguished by dwelling type. Next, Navigant 24 Descriptions of these customer segments can be found in Table 2-3. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 39 DSM Potential Study segmented C&I customers into two size categories (small and large) and further segmented them into customer segments. To do this, the team requested 2016 account-level maximum billed demand data from ENO. As mentioned in Section 2.1.1, 2016 was chosen as the base year for this analysis because it was the most recent year with a fully complete and verified dataset. Navigant mapped the SIC codes associated with these accounts to customer segments in the analysis, similar to the approach used by the energy efficiency potential study team in its market characterization effort. Then, the team calculated the split of customers between the small and large size categories by customer segment using a cutoff value of 100 kW.25 This cutoff value was determined in consultation with ENO and is aligned to ENO’s energy efficiency programs when there is a specific offer to the small C&I market segment. These splits were then used to develop a customer count and sales forecast by customer class and segment for the DR study. This segmentation is necessary because DR program offerings typically vary by customer size. 2.2.3 Baseline Projections 2.2.3.1 Customer Count Projections Navigant applied year-over-year change in the stock forecast (described in Appendix A.2 and B.3) to the 2016 customer count data segmented by customer class and customer segment to produce a customer count forecast for the DR potential study. The team then trued up this forecast to the sector-level customer count forecast provided by ENO. Figure 2-13 shows the aggregate customer count forecast by segment only, summed across all customer classes. 25 Since specific NAICS codes map to small and large offices, Navigant did not use the 100 kW cutoff to segment office customers into the small and large categories. The small versus large distinction for offices is solely based on the NAICS code mapping. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 40 DSM Potential Study Figure 2-13. Customer Count Projections for DR Potential Assessment Source: Navigant 2.2.3.2 Peak Demand Projections Navigant worked with ENO to define the peak period for the DR potential assessment. The baseline peak demand forecast used the defined peak period; reduction estimates are applied to the peak period to estimate DR potential. ENO expressed a desire to align the peak period definition with times MISO is expected to see peak demand. This is so ENO can leverage the findings of the DR potential assessment should it seek to register any DR resources as load modifying resources with MISO. Per MISO’s business practice manual, “…the expected peak occurs during the summer (June through September) during the hours from 2:00 pm through 6:00 pm.” 26 Navigant added two additional constraints to this definition. First, the team only included weekdays in the peak period definition because it is not typical for utilities to call DR events on weekends. Second, Navigant only included the top 40 weekday hours within this window to better capture demand levels during a DR event. The team chose this threshold by studying ENO’s historic 8,760 system load data and found that the top 25 and 35 hours in this window were within 5% of their maximum peak demand in 2014 and 2015, respectively, which is a typical margin for when DR events typically occur. Navigant selected the top 40 hours to stay conservative and refined the peak definition to include just those hours. Once the peak period was defined, Navigant developed a disaggregated bottom-up peak demand forecast by customer class and segment. The team also estimated the 26 MISO. Business Practice Manual. Demand Response. Effective date: June 1, 2016. pg 15. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 41 DSM Potential Study end-use breakdown of the peak demand for C&I customers, as reduction estimates are typically expressed as a percentage of baseline load for these customers. The step-bystep methodology Navigant used to develop the baseline peak load projections is summarized as follows: 1. Disaggregate sales forecast by customer class and customer segment: Navigant first projected the base year (2016) sales data, segmented by customer class and customer segment, over the study horizon using the year-over-year change in building stock. The team then trued up the customer segment-level totals in this forecast to the sector-level totals in the forecast sent by ENO. 27 2. Develop 8,760 load shapes by customer segment: The team used ENERGYPlus to develop hourly load shapes for ENO’s service area to transform annual potential estimates into an 8,760 format (see Appendix C for description of load shape development). 3. Calculate peak load factors: Navigant calculated the average peak load factor over the hours that fell under the peak period definition for each customer class and customer segment combination. Per the industry-standard definition, peak load factor is defined as follows: 𝑃𝑃𝑃𝑃 𝐿𝐿𝐿𝐿 𝐹𝐹𝐹𝐹𝐹𝐹 = 𝐴𝐴𝐴𝐴𝐴𝐴 𝑆𝑆𝑆𝑆𝑆 𝐴𝐴𝐴𝐴𝐴𝐴 𝑃𝑃𝑃𝑃 𝐷𝐷𝐷𝐷𝐷𝐷 ∗ 8,760 Table 2-15 provides the calculated peak load factors by segment. Table 2-15. Peak Load Factors by Customer Segment Type Customer Segment Peak Load Factor Lodging 0.86 Healthcare 0.83 Schools 0.74 Colleges/Universities 0.70 Other 0.69 Retail – Food 0.66 Restaurants 0.62 Office – Small 0.59 27 Navigant did not directly use the account-level data provided by ENO to segment and roll up customer count and sales by customer class and customer segment. This is because the totals from this dataset did not match the totals from the SIC code-level data the energy efficiency potential study team received from ENO. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 42 DSM Potential Study Customer Segment Peak Load Factor Retail – Non-Food 0.59 Office – Large 0.58 Industrial/Warehouses 0.55 Residential 0.68 Source: Navigant 4. Disaggregate peak load forecast by customer class, customer segment, and end use (for C&I customers only): Navigant applied the peak load factors derived in the previous step to the sales forecast developed in the first step. The team also used the 8,760 normalized load shapes to estimate the breakdown of peak load by end use for C&I customers (load reduction estimates associated with DR programs for these customers are typically available as a percentage of end-use load). 5. Calibrate peak load forecast: Navigant trued up the annual totals in the disaggregated derived peak demand forecast to 95% of ENO’s BP18U peak forecast. 28 Figure 2-14. Peak Demand Forecast Comparisons 1,200 1,100 ENO Summer Peak Demand Forecast 1,000 MW 900 Derived Summer Peak Demand Forecast 800 Adjusted Summer Peak Demand Forecast 700 600 500 2015 2020 2025 2030 2035 2040 Source: Navigant 28 This calibration target was chosen because utilities typically aim to reduce load within 5% of their annual system peak through DR events. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 43 DSM Potential Study Figure 2-15. Peak Load Forecast by Customer Segment (MW) 1,200 1,000 MW 800 600 400 200 0 Residential Office - Small Industrial/Warehouses Other Commercial Lodging Retail (Non-Food) Office - Large Restaurants Healthcare Colleges/Universities Retail - Food Schools Source: Navigant Figure 2-16. Peak Load Forecast by End Use for C&I customers (MW) 1,200 1,000 MW 800 600 400 200 0 Residential Office - Small Industrial/Warehouses Other Commercial Lodging Retail (Non-Food) Office - Large Restaurants Healthcare Colleges/Universities Retail - Food Schools Source: Navigant Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 44 DSM Potential Study 2.2.4 Descriptions of DR Options Once the baseline peak demand projections have been developed, the next step was to characterize the different types of DR options that could be used to curtail peak demand. Table 2-16 summarizes the DR options included in the analysis. Most of these DR options are representative of programs commonly deployed in the industry. These programs also align with Council’s IRP rules, which state that DR programs should include those “…enabled by the deployment of Advanced Meter Infrastructure, including both direct load control and DR pricing programs for both Residential and Commercial customer class”. The different types of DR options are described in detail below. Table 2-16. Summary of DR Options Targeted/ Controllable End Uses and/or Technologies DR Option Characteristics Eligible Customer Classes DLC  Load control switch  Thermostat Control of water heating/cooling load using either a load control switch or PCT Residential Small C&I Cooling, water heating C&I Curtailment  Manual  Auto-DR enabled Firm capacity reduction commitment $/kW payment based on contracted capacity plus $/kWh payment based on energy reduction during an event Large C&I Various load types including HVAC, lighting, refrigeration, and industrial process loads Dynamic Pricing  Without enabling technology  With enabling technology Voluntary opt-in dynamic pricing offer, such as CPP All customer classes All BTMS  Standalone battery storage Power dispatch from battery storage systems installed by customers during peak load conditions Small C&I Large C&I Batteries 29 30 29 Navigant did not include time-of-use (TOU) rates in the DR options mix because this study only includes event-based dispatchable DR options. TOU rates lead to a permanent reduction in the baseline load and are not considered a DR option. 30 Residential customers are not expected to significantly adopt standalone battery storage systems because ENO does not have any residential demand charge. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 45 DSM Potential Study Source: Navigant Each DR option was segmented into several DR sub-options, each of which was tied to a specific end use and/or control strategy. Table 2-17 summarizes this segmentation. The different types of DR options are described in detail below. Table 2-17. Segmentation of DR Options into DR Sub-Options DR Option DLC DR Sub-Option Eligible Customer Classes Switch-Water Heating Residential Small C&I Thermostat-Heat Pump Residential Thermostat-Central Air Conditioning Residential Switch-Heat Pump Residential Switch-Central Air Conditioning Residential Thermostat-HVAC Small C&I Switch-HVAC Small C&I Curtailment-Manual HVAC Control Curtailment-Auto-DR HVAC Control Curtailment-Standard Lighting Control Curtailment-Advanced Lighting Control Curtailment-Water Heating Control C&I Curtailment Curtailment-Refrigeration Control Large C&I Curtailment-Compressed Air Curtailment-Fans/Ventilation Curtailment-Industrial Process Curtailment-Pumps Curtailment-Other Dynamic pricing with enabling tech Dynamic Pricing Dynamic pricing without enabling tech BTMS BTMS-Battery Storage Confidential and Proprietary ©2018 Navigant Consulting, Inc. Residential Small C&I Large C&I Small C&I Large C&I Page 46 DSM Potential Study 2.2.4.1 Direct Load Control DLC involves ENO directly controlling electric water heating and cooling load using a load control switch and cooling load using a PCT. There are two types of delivery models for DLC: DI and BYOT. In the DI approach, ENO would be responsible for installing the thermostat at the customer premises and bear part or all of the costs of thermostat purchase and installation and DR enablement. In the BYOT approach, the customer purchases and installs their own thermostat and is subsequently enrolled in the DR program. Therefore, the purchase and installation costs of the thermostat are borne by the customer, which would consequently lower ENO’s costs. This study considers only a DI approach for switch-based control and a BYOT approach for thermostat-based control. Table 2-18 summarizes the DLC program characteristics considered in this study. Table 2-18. DLC Program Characteristics Item Description Program Name Direct Load Control (DLC) Program Description This program controls electric water heating and cooling (including central air conditioning and heat pumps) loads for residential and small/medium business customers using either a DLC device (switch) or a PCT, where and when applicable. Water heating and cooling loads are cycled/turned off during the event period using a load control switch. For thermostat-based cooling load control, unit impact estimates are based on a 2°F-3°F temperature setback strategy using a smart thermostat. Purpose/Trigger DLC events will be called primarily to meet capacity shortfalls during summer, triggered primarily by a high day-ahead temperature forecast. Key Program Design Parameters Participation Eligibility • • • Events will be called during peak demand periods in summer. Participants will not have any advance notification for DR events. However, they can choose to opt out of an event at any time during the event. Average event duration is 4 hours. No more than one event is called in a day. Calling events for more than 2 consecutive days may lead to customer dissatisfaction and disenrollment. Residential and small C&I customers with HVAC and electric water heaters. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 47 DSM Potential Study Item Description Dependent Technology and Metering Technology: Switches control water heating, central air conditioning, or heat pumps. PCT temperature adjustment controls central air conditioning or heat pumps. Metering: Standard meter (no interval meter required). The program can use data loggers on a sample of participants to record interval usage for measurement and verification. Source: Navigant 2.2.4.2 C&I Curtailment The C&I curtailment program as represented in this study is the most commonly deployed program for large C&I customers in the industry. It involves a contract for a firm capacity reduction commitment from large C&I customers. Under this option, utilities typically enter into a turnkey implementation contract with a third-party DR service provider (commonly referred to as an aggregator) to deliver a certain fixed amount of megawatt (MW) load reduction. 31 Enrolled participants agree to curtail their demand to a pre-specified level. In return, they receive a fixed incentive payment in the form of capacity credits or reservation payments (expressed as $/kW-year). Customers are paid to be on-call even though actual load curtailments may not occur. The capacity payment level could vary with the load commitment level. In addition to the fixed capacity payment, participants typically receive a payment for energy reduction ($/kWh amount). Because it is a contractual arrangement for a specific level of load reduction, enrolled loads represent a firm resource. Once enrolled, participation during events is mandatory and there are penalty clauses. A specific site could curtail a variety of end-use loads depending on the types of business processes, either manually or automatically (Auto-DR-enabled). Auto-DR enablement can help provide greater reliability and higher predictability in load reductions. Table 2-19 describes the C&I curtailment program characteristics considered in this study. 31 With the aggregator model, the service provider can aggregate multiple small customers to deliver capacity reduction. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 48 DSM Potential Study Table 2-19. C&I Curtailment Program Characteristics Item Description Program Name C&I Curtailment Program Description Typically, this type of program is administered by a third-party DR service provider. This is usually a turnkey contract, in which the vendor is responsible for a fixed amount of load reduction over the contract period. The common approach is for the utility to pay a predetermined capacity payment ($/kW-yr.) based either on the nominated load reduction (if no event is called) or actual load reduction (if an event is called) to the third party administering the program. In addition, the utility would pay the vendor for actual energy reduced during an event based on a specified $/kWh level in the contract. Participating sites enrolled in the program curtail a variety of end uses (e.g., HVAC, water heating, lighting, refrigeration, process loads), depending on the business type. Load curtailment can be manual and/or 32 Auto-DR -enabled. Participants may also shift load to backup generators during the DR event period. Purpose/Trigger DR events are likely to be called to help meet summer capacity shortfalls. Key Program Design Parameters • • • • Participation Eligibility Events will be called during summer peak demand periods. Event notification is typically day-ahead and/or 1-2 hours ahead. Average event duration is 4 hours. No more than one event is called in a day. Calling events for more than 2 consecutive days may lead to customer dissatisfaction and disenrollment. Annual maximum event hours set at 80-100 hours. All Large C&I customers. 32 Under Auto-DR, customer loads will be curtailed automatically via a building EMS in response to a signal from ENO. Auto-DR is a platform to automatically activate a preprogrammed load reduction strategy in response to a signal from a DR automation server (DRAS). Load is curtailed by the customer’s building management after being triggered by a signal sent from ENO’s control room to the vendor’s operations center and on to the customer’s facility. The customer always retains the ability to override the curtailment sequence in the event a site cannot participate in a specific DR dispatch. Auto-DR ensures higher reliability of response than manual curtailment. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 49 DSM Potential Study Item Dependent Technology and Metering Description Dependent technology: Manual DR requires a communication channel between the vendor and the customers, which might include text, email, or telephone. Auto-DR requires a building automation system, a load control device, or breakers on specific circuits. All control mechanisms must be able to receive an electronic signal from the program administrator and initiate the curtailment procedure without manual intervention. Auto-DR dispatches are called using an open communication protocol known as Open-ADR. For Auto-DR customers, the vendor installs an Open-ADR-compliant gateway at the participating site, which is then able to notify the EMS or other control systems at the facility to run their preprogramed curtailment scripts. The vendor monitors energy reduction in real time and provides visual access to this demand data to the participant through a web-based software platform. This platform may be integrated for overall energy optimization, which may help realize energy efficiency benefits along with DR benefits. Metering: Interval meters. Source: Navigant 2.2.4.3 Dynamic Pricing Dynamic pricing refers to a CPP rate offer across all customer classes. This is the most commonly deployed dynamic rate in the industry. Customers who opt to participate in the program are placed on a CPP rate with a significantly higher rate during certain critical peak periods in the year and a lower off-peak rate than the standard offer rate. Customers enrolled in the CPP rate pay the higher critical peak rate for electricity consumption during the critical peak periods, which incentivizes them to reduce consumption during those periods. Customers enrolled in the CPP rate receive either day-of or day-ahead notification of the critical peak period. The unit impacts or per-customer load reductions depend on the critical peak to off-peak price ratio. This study assumes a 6:1 critical peak to off-peak price ratio. Industry experience suggests that enabling technology such as smart thermostats and Auto-DR can substantially enhance load reductions when customers on CPP rates are equipped with these technologies. CPP can be offered either as an opt-in rate or as a default rate with opt out. This study assumes an opt-in offer type for CPP. The CPP offer requires AMI meters for settlement purposes. Hence, the rate offer is tied to AMI deployment. Per discussions with ENO, the utility’s current plan is to fully deploy AMI by 2020. Table 2-20 describes the dynamic pricing program characteristics considered in this study. Table 2-20. Dynamic Pricing Program Characteristics Item Description Program Name Dynamic Pricing Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 50 DSM Potential Study Program Description Opt-in CPP offer to all customers with a 6:1 critical peak to off-peak price ratio. Purpose/Trigger Events are called to help meet summer capacity shortfalls. Key Program Design Parameters • • • • Events will be called during summer peak demand periods. Event notification is typically day-ahead and/or 1-2 hours ahead. Average event duration assumed to be 4 hours. No more than one event is called in a day. Calling events for more than 2 consecutive days may lead to customer dissatisfaction and disenrollment. Annual maximum event hours set at 80-100 hours. Participation Eligibility All customers. Dependent Technology and Metering All customers need smart meters for settlement purposes. Source: Navigant 2.2.4.4 Behind-the-Meter Storage BTMS refers to customers using their battery systems to discharge power to the grid during peak load conditions. Backup generators were not considered for this program in this study because ENO does not have data on the number or capacity of non-grid interconnected backup generators at customer sites in its service area. Navigant assumed the market adoption and size for battery storage systems using internal analysis. Therefore, BTMS only considers power dispatch from battery storage systems in this study. It is expected that customers would either charge their batteries during offpeak hours with grid power or by using solar PV. Table 2-21 describes the BTMS program characteristics considered in this study. Table 2-21. BTMS Program Characteristics Item Description Program Name Behind-the-Meter Storage (BTMS) Program Description Customers install battery storage systems that are interconnected with the grid. When there are peak load conditions, the utility sends signals to the battery system, which would trigger power dispatch to the grid. Purpose/Trigger Events are called to help meet summer capacity shortfalls. Key Program Design Parameters Participation Eligibility • • • • Events will be called during summer peak demand periods. Average event duration assumed to be 4 hours. Event notification is typically day-ahead and/or 1-2 hours ahead. Annual maximum event hours set at 80-100 hours. Large C&I customers such as manufacturing or big box retail with battery storage systems. Grid dispatch from batteries could also include new technologies targeted at smaller commercial customers or even residential. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 51 DSM Potential Study Dependent Technology and Metering All customers need PV-tied or standalone batteries with grid interconnection. Source: Navigant 2.2.5 Key Assumptions for DR Potential and Cost Estimation There are two key variables that feed the DR potential calculation in this study: • Customer participation rates • Amount of load reduction that could be realized from different types of control mechanisms, referred to as unit impacts Secondary variables that feed the DR potential calculation include participation opt-out rates, technology market penetration, and enrollment attrition rates. Navigant calculated both the technical and achievable potential associated with implementing DR programs for this study. Technical potential refers to load reduction that results from 100% customer participation. This is a theoretical maximum. The team calculated technical potential by multiplying the eligible load/customers by the unit impact for each DR sub-option. The technical potential calculation does not account for participation overlaps between the DR sub-options. Therefore, technical potential across the various sub-options is not additive and should not be added together to obtain a total technical potential. In other words, the technical potential estimates for each DR sub-option should be considered independently. The technical potential calculation is summarized through Equation 2-6. Equation 2-6. DR Technical Potential 𝑇𝑇𝑇ℎ𝑛𝑛𝑛𝑛𝑛 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝐷𝐷 𝑆𝑆𝑆 𝑂𝑂𝑂𝑂𝑂𝑂,𝐸𝐸𝐸 𝑈𝑈𝑈,𝑌𝑌𝑌𝑌 = 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸 𝐿𝐿𝐿𝐿𝐷𝐷 𝑆𝑆𝑆 𝑂𝑂𝑂𝑂𝑂𝑂,𝑆𝑆𝑆𝑆𝑆𝑆𝑆,𝐸𝐸𝐸 𝑈𝑈𝑈,𝑌𝑌𝑌𝑌 ∗ 𝑈𝑈𝑈𝑈 𝐼𝐼𝐼𝐼𝐼𝐼𝐷𝐷 𝑆𝑆𝑆 𝑂𝑂𝑂𝑂𝑂𝑂,𝑆𝑆𝑆𝑆𝑆𝑆𝑆,𝑌𝑌𝑌𝑌 Navigant then calculated the achievable potential by multiplying achievable participation assumptions (subject to the program participation hierarchy discussed below) by the technical potential estimates. Market potential also accounts for customers opting out during DR events. The achievable potential calculation is summarized through Equation 2-7. Equation 2-7. DR Achievable Potential 𝐴𝐴ℎ𝑖𝑖𝑖𝑖𝑖𝑖𝑖 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 = 𝑇𝑇𝑇ℎ𝑛𝑛𝑛𝑛𝑛 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝐷𝐷 𝑆𝑆𝑆 𝑂𝑂𝑂𝑂𝑂𝑂,𝑆𝑆𝑆𝑆𝑆𝑆𝑆,𝐸𝐸𝐸 𝑈𝑈𝑈,𝑌𝑌𝑌𝑌 ∗ 𝐴𝐴ℎ𝑖𝑖𝑖𝑖𝑖𝑖𝑖 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 𝑅𝑅𝑅𝑅𝐷𝐷 𝑆𝑆𝑆 𝑂𝑂𝑂𝑂𝑂𝑂,𝑆𝑆𝑆𝑆𝑆𝑆𝑆,𝑌𝑌𝑌𝑌 ∗ (1 − 𝐸𝐸𝐸𝐸𝐸 𝑂𝑂𝑂 𝑂𝑂𝑂 𝑅𝑅𝑅𝑅)𝐷𝐷 𝑆𝑆𝑆 𝑂𝑂𝑂𝑂𝑂𝑂,𝑌𝑌𝑌𝑌 Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 52 DSM Potential Study In addition to the potential estimates, the team developed annual and levelized costs by DR option and sub-option. Navigant subsequently assessed the cost-effectiveness of each sub-option and DR option in aggregate. Developing annual and levelized costs involves itemizing various cost components such as program development costs, equipment costs, participant marketing and recruitment costs, annual program administration costs, product lifetimes, and a discount rate. Table 2-22 summarizes the key variables Navigant used to calculate DR potential and its associated costs in this analysis. These key variables are discussed further in the following subsections. Table 2-22. Key Variables for DR Potential and Cost Estimates Key Variables Description Participation Rates Percentage of eligible customers by program type and customer class. Unit Impacts • • • • Costs • One-time fixed costs related to program development • One-time variable costs for customer recruitment, program marketing, and equipment installation and enablement • Recurring fixed and variable costs such as annual program admin. costs, customer incentives, O&M, etc. Global Parameters kW reduction per device for DLC Percentage of enrolled load by end use for C&I curtailment Percentage of total facility load for dynamic pricing Percentage of battery load for BTMS Program lifetime, discount rate, inflation rate, line losses, avoided costs Source: Navigant 2.2.5.1 Participation Assumptions and Hierarchy The participation assumptions differ by customer class and segment. Based on standard industry practice, Navigant assumed a 5-year S-shaped ramp for the DR options. For all DR options other than dynamic pricing, program participation is assumed to begin in 2018. As previously mentioned, dynamic pricing is tied to AMI deployment and starts in 2020. The participation assumptions are also tied to the market penetration of DR-enabling technologies such as EMSs. For example, only C&I customers with EMS are eligible for Auto-DR HVAC control. All other customers are eligible for manual HVAC control. Navigant also accounted for participation overlaps among the different DR programs in estimating potential. Table 2-23 presents the participation hierarchy considered in this study, whereby achievable participation estimates are applied to eligible customers only. The participation hierarchy presented here is a well-tested approach, initially Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 53 DSM Potential Study established in the National Assessment of DR Potential Study conducted by the Federal Energy Regulatory Commission (FERC) 33 and adopted in other DR potential studies. The participation hierarchy helps avoid double counting of potential through common load participation across multiple programs and is necessary to arrive at an aggregate potential estimate for the entire portfolio of DR programs. Table 2-23. Program Hierarchy to Account for Participation Overlaps Customer Class Residential DR Options Eligible Customers DLC - Thermostat Customers with central air conditioning or heat pumps controlled by thermostats DLC - Switch Customers with central air conditioning or heat pumps that are not enrolled in DLC – Thermostat; customers with water heating load Dynamic Pricing Customers not enrolled in DLC BTMS Customers with batteries DLC - Thermostat Customers with HVAC controlled by thermostats DLC - Switch Customers with HVAC that are not enrolled in DLC – Thermostat; customers with water heating load Dynamic Pricing Customers not enrolled in DLC BTMS Customers with batteries C&I Curtailment Customers with batteries not enrolled in BTMS; customers without batteries Dynamic Pricing Customers with batteries not enrolled in BTMS or C&I curtailment; customers without batteries not enrolled in C&I curtailment Small C&I Large C&I Source: Navigant 2.2.5.2 Unit Impact Assumptions The unit impacts specify the amount of load that could be reduced during a DR event by customers enrolled in a DR program. Unit impacts differ by sub-option because they are tied to specific end uses and control strategies. For example, the load reductions associated with manual HVAC control and Auto-DR HVAC control are different and are specified accordingly. Unit impacts can be specified either directly as kW reduction per 33 https://www.ferc.gov/legal/staff-reports/06-09-demand-response.pdfelow Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 54 DSM Potential Study participant or as percentage of enrolled load: 34 • DLC sub-options use kW reduction per participant for residential and percentage of the end-use load for small C&I • C&I curtailment sub-options use percentage of the end-use load • Dynamic pricing uses a percentage of the total facility load • BTMS uses a percentage of the battery load This study leveraged ENO’s DLC pilot program accomplishments and the latest available secondary sources of information for other programs for the unit impact assumptions. 2.2.5.3 Cost Assumptions Navigant developed itemized cost assumptions for each DR option to calculate annual program costs and levelized costs for each option. These assumptions also feed the cost-effectiveness calculations in this study. The cost assumptions fall into the following broad categories: • One-time fixed costs, specified in terms of $/DR option, including the program startup costs—for example, the software and IT infrastructure-related costs and associated labor time/costs (in terms of full time equivalents (FTEs)) incurred to set up the program. • One-time variable costs, which include marketing/recruitment costs for new participants, metering costs, and all other costs associated with control and communications technologies that enable load reduction at participating sites. The enabling technology cost is specified either in terms of $/new participant on a per-site basis or as $/kW of enabled load reduction on a participating load basis. • Annual fixed costs, specified in terms of $/yr, which primarily includes FTE costs for annual program administration. • Annual variable costs, which primarily includes customer incentives, specified either as a fixed monthly/annual incentive amount per participant ($/participant) or in terms of load and/or energy reduction ($/kW and $/kWh reduction) depending on the program type. It also includes additional O&M costs that may be associated with servicing technology installed at customer premises. • Program delivery costs, which is a fixed contracted payment for third-party delivery of DR programs and is specified as $/kW-yr. 34 The unit impact values assume a 4-hour event duration, and the values represent the average load reduction over the 4-hour event duration. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 55 DSM Potential Study In addition to these itemized program costs, the following variables feed the costeffectiveness calculations in this study. • Nominal discount rate of 7.72% used for net present value (NPV) calculations. • Inflation rate of 2% used to inflate the costs over the forecast period (20182037). • Transmission and distribution (T&D) line loss of about 2% for industrial/warehouse customers and 5% for all other customers; line loss is used to bring the potential at the customer meter up to the generator for the costeffectiveness assessment. • Program life, assumed to be 10 years for DLC, C&I curtailment, and BTMS and 20 years for dynamic pricing. • Derating factor, used to derate the benefits from DR to bring it to par with generation. The derating factor is used to derate the benefits from DR to account for program design constraints, such as limitations on how often events can be called, annual maximum hours for which events can be called, window of hours during the day during which events can be called, and sometimes even the number of days in a row that events may be called. The derating factor lowers the benefits from DR so that a megawatt from DR is not considered the same as a megawatt from a generator, which does not have similar availability constraints and could be available round the clock. 35 To assess the benefits associated with DR programs, Navigant used the avoided generation capacity projections provided by ENO. Navigant calculated benefit-cost ratios for the TRC, program administrator cost (PAC), ratepayer impact measure (RIM), and participant cost tests (PCT) for this study, consistent with the Council’s IRP rules. 35 “Valuing Demand Response: International Best Practices, Case Studies, and Applications.” Prepared by the Brattle Group. January 2015. Page 10 of this report explains why the derating factor is important, though its inclusion varies across utilities and jurisdictions: http://files.brattle.com/files/5766_valuing_demand_response__international_best_practices__case_studies__and_applications.pdf Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 56 DSM Potential Study 3. Energy Efficiency Achievable Potential Forecast This section provides the results of the energy efficiency achievable potential analysis. 3.1 Model Calibration Calibrating a predictive model imposes unique challenges, as future data is not available to compare against model predictions. While engineering models, for example, can often be calibrated to a high degree of accuracy because simulated performance can be compared directly with performance of actual hardware, predictive models do not have this luxury. Therefore, DSM models must rely on other techniques to provide both the developer and the recipient with a level of comfort that simulated results are reasonable. For this project, Navigant took several steps to ensure that the forecast model results are reasonable and consider historic adoption: • Comparing forecast values by sector and end use, typically against historic achieved savings (e.g., program savings from 2016) and planned savings for Energy Smart PY8. Although some studies indicate that DSM potential models are calibrated to ensure first-year simulated savings precisely equal prior-year reported savings, Navigant notes that forcing such precise agreement has the potential to introduce errors into the modeling process by effectively masking the explanation for differences—particularly when the measures included may vary significantly. Additionally, there may be sound reasons for first-year simulated savings to differ from prior-year reported savings (e.g., a program is rapidly ramping up or savings estimates have changed). Thus, while the team endeavored to achieve agreement to a degree believed to be reasonable between past results and forecast first-year results, the team’s approach did not force the model to do so, providing what the team believes is a degree of confidence that the model is internally consistent. • Identifying and ensuring an explanation existed for significant discrepancies between forecast savings and prior-year savings, recognizing that some ramp up is expected, especially for new measures or archetype programs. • Calculating $/first-year kWh costs and comparing them with past results. • Calculating the split (percentage) in spending between incentives and variable administrative costs predicted by the model to historic values. • Calculating total spending and comparing the resulting values to historical spending. 3.1.1 Achievable Potential Case Studies and Incentive Levels A key component of any potential study is determining the appropriate level at which to set measure incentives for each case. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 57 DSM Potential Study For ENO, the incentive-level strategy characterized is the percent of incremental cost approach. This approach calculates measure-level incentives based on a specified percentage of incremental measure costs. For example, if the specified incentive percentage was 50% and a measure’s incremental cost was $100, then the calculated incentive for that measure would be $50. 3.1.2 Achievable Cases Analysis Navigant ran multiple cases for achievable potential. These approaches are described briefly below. 3.1.2.1 2018 Savings Target Cases Navigant reviewed historic ENO data from PY4 through PY6 and found an average annual savings of approximately 20 GWh. However, ENO's target in Energy Smart Program Year 8 (2018), which coincides with the first year of the potential analysis, is 46 GWh. The 2018 target is significantly higher than the historic average given the CNO’s direction to implement programs that would seek to achieve the Council’s goal of 0.2% annual and 2% overall energy savings. Therefore, Navigant targeted a savings value of 46 GWh for the base case and a $/kWh value of 0.27, which represents both the planned and historic average of portfolio cost. The base case used an incentive level of 50% of incremental cost to align with ENO's assessed value as currently implemented. Navigant analyzed two additional cases that used the same inputs as the base case except for incentive values at 25% and 75% of incremental cost, respectively. 3.1.2.2 Council’s 2% DSM Goal Case In this case, Navigant started with incentives at 50% of the incremental cost in 2018 and then ramped up to 100% in 2024. When using the TRC test as the measure screen, incentive levels do not affect cost-effectiveness because incentives are treated as a pass through in the TRC test. Thus, setting incentives at 100% of incremental cost results in the highest forecast savings levels (effectively a zero-payback time) but also comes with a high level of investment forecasts. Navigant also changed the adoption parameters for the 2% case, including a ramp up of the marketing factor through 2021. Additionally, Navigant ramped down the TRC ratio threshold from a value of 1.0 in 2018 to 0.87 in 2022 and remaining years. This change in TRC ratio allowed more measures to pass through to achievable potential modeling. 3.2 Energy Efficiency Achievable Potential Results Values shown for achievable potential are termed annual incremental potential— they represent the incremental new potential available in each year. The total cumulative Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 58 DSM Potential Study potential over the time period is the sum of each year’s annual incremental achievable potential. Economic potential can be thought of as a reservoir of cost-effective potential36 from which programs can draw over time. Achievable potential represents the draining of that reservoir, the rate of which is governed by several factors including the lifetime of measures (for ROB technologies), market effectiveness, incentive levels, and customer willingness to adopt, among others. If the cumulative achievable potential ultimately reaches the economic potential, it would signify that all economic potential in the reservoir had been drawn down or harvested. However, achievable potential levels rarely reach the full economic potential level due to a variety of market and customer constraints that inhibit full economic adoption. 37 All tables and figures (except for Section 3.2.1) have the potential savings for the base case only. 3.2.1 Case-Level Results As explained in Section 2.1.4.3, the achievable potential analysis was modeled with four different case studies. The case studies are based on the incremental measure cost: • Base case: Reflects current program spend targets with incentives at 50% of incremental measure cost • Low case: Uses the same inputs as the base case except incentives are at 25% of incremental measure cost • High case: Uses the same inputs as the base case except incentives are at 75% of incremental measure cost • 2% case: Achieve 2% for at least 1 year during the forecast period with a 0.2% ramp year over year starting in the first modeled year (2018) Table 3-1 shows the incremental energy and demand savings per year for each case. Figure 3-1 and Figure 3-2 show the cumulative annual energy and demand savings for each case. 36 Cost-effectiveness threshold is a TRC = 1.0. Because the New Orleans TRM does not include gas or water savings in the benefit calculations, they were not calculated in this study. However, there were measures that were passed through with a TRC ratio <1.0 where it was reasonable to assume that the inclusion of gas or water savings would have enabled the measure to reach the 1.0 TRC threshold. These measures include: commercial clothes washer, commercial low flow showerheads, high efficiency windows, home energy report, and residential thermostatic shower valve. 37 Constraints on achievable potential that inhibit realization of the full economic potential include the rate at which homes and businesses will adopt efficient technologies, as well as the word of mouth and marketing effectiveness for the technology. If a technology already has high saturation at the beginning of the study, it may theoretically be possible to fully saturate the market and achieve 100% of the economic potential for that technology. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 59 DSM Potential Study Table 3-1. Annual Incremental Achievable Energy Efficiency Savings by Case Year Electric Energy (GWh/Year) Peak Demand (MW) Base Low High 2% Base Low High 2% 2018 46 41 52 46 11 10 12 11 2019 49 44 56 60 11 10 13 13 2020 51 45 58 74 11 10 13 16 2021 53 47 61 86 12 10 13 18 2022 53 46 61 97 11 10 13 20 2023 53 47 62 110 11 9 13 22 2024 57 50 66 123 11 10 13 26 2025 59 52 69 127 12 10 14 28 2026 60 53 70 122 12 10 14 27 2027 61 54 70 116 12 10 14 26 2028 65 58 75 104 12 10 15 24 2029 64 56 73 92 12 10 14 22 2030 64 57 73 79 12 10 15 19 2031 61 54 69 67 12 10 14 17 2032 58 52 65 55 11 9 13 14 2033 55 49 60 46 11 9 12 12 2034 52 46 56 38 10 9 11 10 2035 50 45 52 32 10 8 11 9 2036 47 42 48 29 9 8 10 8 2037 43 39 43 25 9 8 9 7 Total 1,100 977 1,240 1,526 220 190 257 346 Source: Navigant analysis Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 60 DSM Potential Study Figure 3-1. Electric Energy Cumulative Achievable Savings Potential by Case (GWh/year) Savings Potential (GWh/year) 1,600 1,400 1,200 1,000 800 600 400 200 0 Low Base High 2% Source: Navigant analysis Figure 3-2. Peak Demand Cumulative Achievable Savings Potential by Case (MW) 400 Savings Potential (MW) 350 300 250 200 150 100 50 0 Low Base High 2% Source: Navigant analysis Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 61 DSM Potential Study Table 3-2 shows the incremental electric energy achievable savings as a percentage of ENO's total sales for each case. For the 2% case, 2% of sales savings is achieved in 2024-2026. In later years, the 2% case falls below the base case since most of the measures have been adopted, depleting the available potential in the future years. This study only includes known, market-ready, quantifiable measures without introducing new measures in later years. However, over the lifetime of energy efficiency programs, new technologies and innovative program interventions could result in additional costeffective energy savings. Therefore, the need to periodically revisit and reanalyze the potential forecast is necessary. Table 3-2. Incremental Electric Energy Achievable Savings Potential as a Percentage of Sales, by Case (%, GWh) Year Base Low High 2% 2018 0.8% 0.7% 0.9% 0.8% 2019 0.8% 0.7% 0.9% 1.0% 2020 0.8% 0.7% 0.9% 1.2% 2021 0.9% 0.8% 1.0% 1.4% 2022 0.9% 0.8% 1.0% 1.6% 2023 0.9% 0.8% 1.0% 1.8% 2024 0.9% 0.8% 1.1% 2.0% 2025 1.0% 0.8% 1.1% 2.0% 2026 1.0% 0.8% 1.1% 2.0% 2027 1.0% 0.9% 1.1% 1.9% 2028 1.0% 0.9% 1.2% 1.6% 2029 1.0% 0.9% 1.1% 1.4% 2030 1.0% 0.9% 1.2% 1.2% 2031 1.0% 0.8% 1.1% 1.0% 2032 0.9% 0.8% 1.0% 0.8% 2033 0.8% 0.8% 0.9% 0.7% 2034 0.8% 0.7% 0.8% 0.5% 2035 0.7% 0.7% 0.8% 0.4% 2036 0.7% 0.6% 0.7% 0.4% 2037 0.6% 0.6% 0.6% 0.3% Total 17.3% 15.3% 19.5% 24.0% Source: Navigant analysis The total, administrative and incentive costs for each case are provided in Table 3-3 for each year of the study period. It is important to note the differences in these cases as compared to the savings achieved. Administrative spending is relatively consistent between the cases, while incentive spending varies significantly between the cases, with higher spending correlated to higher savings. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 62 DSM Potential Study Table 3-3. Spending Breakdown for Achievable Potential ($ millions/year) 38 Total Incentives Admin Base Low High 2% Base Low High 2% Base Low High 2% 2018 $13 $8 $20 $13 $6 $2 $13 $6 $7 $6 $8 $7 2019 $14 $9 $22 $17 $7 $3 $13 $8 $7 $6 $8 $9 2020 $14 $9 $23 $24 $7 $3 $14 $13 $8 $7 $9 $11 2021 $15 $9 $24 $31 $7 $3 $15 $18 $8 $7 $9 $13 2022 $15 $10 $25 $43 $7 $3 $16 $28 $8 $7 $10 $15 2023 $16 $10 $26 $52 $8 $3 $16 $34 $8 $7 $10 $17 2024 $17 $11 $28 $75 $8 $3 $18 $55 $9 $7 $11 $20 2025 $18 $11 $30 $81 $9 $3 $19 $60 $9 $8 $11 $21 2026 $19 $12 $31 $81 $9 $3 $19 $60 $10 $8 $12 $21 2027 $20 $12 $32 $79 $10 $4 $20 $59 $10 $9 $12 $20 2028 $22 $13 $37 $74 $11 $4 $24 $56 $11 $9 $13 $19 2029 $23 $14 $37 $69 $12 $4 $25 $52 $11 $9 $13 $17 2030 $24 $14 $39 $62 $12 $5 $26 $47 $11 $10 $13 $15 2031 $24 $14 $38 $54 $13 $5 $25 $42 $11 $10 $13 $13 2032 $24 $14 $37 $47 $13 $5 $25 $36 $11 $9 $12 $11 2033 $23 $14 $36 $40 $13 $5 $24 $31 $11 $9 $12 $9 2034 $23 $14 $35 $35 $13 $5 $23 $27 $10 $9 $11 $8 2035 $23 $14 $34 $30 $13 $5 $23 $24 $10 $9 $11 $7 2036 $22 $13 $32 $28 $13 $5 $22 $22 $10 $9 $10 $6 2037 $21 $13 $30 $25 $12 $5 $20 $20 $9 $8 $9 $5 Total $390 $238 $617 $960 $202 $75 $400 $698 $188 $162 $217 $262 Source: Navigant analysis The TRC test is a benefit-cost metric that measures the net benefits of energy efficiency measures from the combined stakeholder viewpoint of the program administrator (utility) and program participants. The TRC benefit-cost ratio is calculated in the model using Equation 3-1. Equation 3-1. Benefit-Cost Ratio for the TRC Test 𝑇𝑇𝑇 = 38 𝑃𝑃(𝐴𝐴𝐴𝐴𝐴𝐴𝐴 𝐶𝐶𝐶𝐶𝐶 + 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸) 𝑃𝑃(𝑇𝑇𝑇ℎ𝑛𝑛𝑛𝑛𝑛𝑛 𝐶𝐶𝐶𝐶 + 𝐴𝐴𝐴𝐴𝐴 𝐶𝐶𝐶𝐶𝐶) The values in this table are rounded to the nearest million and may result in rounding errors. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 63 DSM Potential Study Where: • PV( ) is the present value calculation that discounts cost streams over time. • Avoided Costs are the monetary benefits that result from electric energy and capacity savings—e.g., avoided costs of infrastructure investments and avoided fuel (commodity costs) due to electric energy conserved by efficient measures. • Externalities are the monetary or quantifiable benefits associated to greenhouse gas (GHG) gas reductions (i.e., the market cost of carbon). • Technology Cost is the incremental equipment cost to the customer to purchase and install a measure. • Admin are the costs incurred by the program administrator to deliver services (excluding incentive costs paid to participants). Navigant calculated TRC ratios for each measure based on the present value of benefits and costs (as defined by the numerator and denominator, respectively) over each measure’s life. Avoided costs, discount rates, and other key data inputs used in the TRC calculation are presented in Appendix B. Effects of free ridership are not present in the results from this study, so the team did not apply a NTG factor. Providing gross savings results will allow the utility to easily apply updated NTG assumptions in the future and allow for variations in NTG assumptions by reviewers. The TRC ratios for these cases are provided by year in Table 3-4. Even with the large increases in incentives for the high and 2% cases, all cases are cost-effective. Increasing incentives does not necessarily translate to a lower TRC because incentives are considered a transfer cost. However, higher incentives may make higher cost measures more attractive to end users and spur their adoption. Thus, where incentives increase as a percentage of measure cost, TRC scores can be lower even though incentives are not part of the TRC calculation. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 64 DSM Potential Study Table 3-4. Portfolio TRC Benefit-Cost Ratios for Achievable Potential (Ratio) Year Base Low High 2% 2018 1.5 1.6 1.4 1.5 2019 1.5 1.7 1.4 1.5 2020 1.6 1.7 1.5 1.4 2021 1.7 1.8 1.5 1.4 2022 1.8 1.9 1.6 1.4 2023 1.9 2.1 1.7 1.3 2024 1.8 2.0 1.7 1.3 2025 1.8 2.0 1.7 1.3 2026 1.9 2.1 1.7 1.3 2027 1.9 2.1 1.7 1.4 2028 1.7 1.9 1.6 1.4 2029 1.8 1.9 1.6 1.4 2030 1.7 1.9 1.6 1.4 2031 1.7 1.9 1.6 1.4 2032 1.7 1.9 1.6 1.5 2033 1.8 1.9 1.6 1.5 2034 1.8 1.9 1.6 1.5 2035 1.8 1.9 1.6 1.6 2036 1.8 1.9 1.7 1.6 2037 1.8 2.0 1.7 1.7 2018-2037 1.7 1.9 1.6 1.4 Source: Navigant analysis 3.2.2 Achievable Potential Results by Sector Figure 3-3 shows the cumulative electric achievable savings potential for all analysis years by sector for the base case. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 65 DSM Potential Study Figure 3-3. Electric Energy Cumulative Base Case Achievable Savings Potential by Sector (GWh/year) 700 Savings Potential (GWh/year) 600 500 400 300 200 100 0 Res C&I Source: Navigant analysis Figure 3-4 shows the cumulative demand achievable savings potential for all analysis years by sector for the base case. Figure 3-4. Electric Demand Cumulative Base Case Achievable Savings by Sector (MW) Savings Potential (MW) 120 100 80 60 40 20 0 Res C&I Source: Navigant analysis Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 66 DSM Potential Study Table 3-5 shows the cumulative electric energy achievable savings as a percentage of ENO's total sales for each sector. The residential sector accounts for a larger percentage than the C&I sector. Table 3-5. Cumulative Electric Energy Base Case Achievable Savings Potential by Sector as a Percentage of Sales (%, GWh) Year All C&I Residential 2018 0.8% 0.7% 0.9% 2019 1.6% 1.4% 1.9% 2020 2.4% 2.1% 3.0% 2021 3.3% 2.8% 4.0% 2022 4.1% 3.6% 5.1% 2023 5.0% 4.4% 6.1% 2024 5.9% 5.2% 7.1% 2025 6.8% 6.1% 8.2% 2026 7.8% 6.9% 9.3% 2027 8.8% 7.8% 10.4% 2028 9.8% 8.8% 11.5% 2029 10.8% 9.7% 12.6% 2030 11.8% 10.6% 13.9% 2031 12.7% 11.4% 15.0% 2032 13.6% 12.2% 16.2% 2033 14.5% 12.9% 17.3% 2034 15.3% 13.5% 18.3% 2035 16.0% 14.1% 19.2% 2036 16.7% 14.7% 20.1% 2037 17.3% 15.2% 20.9% Source: Navigant analysis 3.2.3 Results by Customer Segment Figure 3-5 shows the cumulative electric energy achievable potential by customer segment. Residential single family is the largest segment. Small office and lodging contribute the most savings for the C&I sector. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 67 DSM Potential Study Figure 3-5. Segment Electric Energy Base Case Achievable Potential Customer Segment Breakdown 1,200 Res Single Family Res Multi-Family 1,000 C&I Schools Savings Potential (GWh/year) C&I Retail - Non-Food C&I Retail - Food 800 C&I Restaurants C&I Other Commercial 600 C&I Office - Small C&I Office - Large 400 C&I Lodging C&I Industrial/Warehouses C&I Healthcare 200 C&I Colleges/Universities 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 0 Source: Navigant analysis 3.2.4 Results by End Use Figure 3-6 shows the electric energy cumulative achievable potential by end use. Figure 3-7 and Figure 3-8 show the percentage of each end use for each sector. The heating/cooling end use has the largest potential, with lighting interior also making a significant contribution. The heating and cooling end uses are high relative to cooling because this end use includes the sales associated with envelope and systems that affect both end uses. ENO has a relatively high penetration of electric heating, which contributes to this factor even though New Orleans experiences rather low heating degree days and high cooling degree days. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 68 DSM Potential Study Figure 3-6. Electric Energy Base Case Achievable Potential End Use Breakdown 1,200 1,000 Savings Potential (GWh/year) Hot Water Refrigeration 800 Fans/Ventilation Heating/Cooling Cooling 600 Plug Loads Lighting Exterior 400 Lighting Interior Total Facility 200 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 0 Source: Navigant analysis Figure 3-7. Residential Electric Energy Achievable Potential End-Use Breakdown (%, GWh) Fans/Ve ntilation 0% Hot Water 5% Total Facility 1% Lighting Interior 28% Heating/ Cooling 36% Lighting Exterior 2% Cooling 12% Plug Loads 16% Figure 3-8. C&I Electric Energy Achievable Potential End-Use Breakdown (%, GWh) Fans/Ve ntilation 1% Refrigera tion 3% Hot Water 1% Heating/ Cooling 42% Cooling 2% Total Facility 11% Lighting Interior 31% Plug Loads 9% Lighting Exterior 0% Source: Navigant analysis Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 69 DSM Potential Study 3.2.5 Achievable Potential Results by Measure Figure 3-9 shows the top 40 measures contributing to the electric energy achievable potential in 2028 (the middle of the study period and representative of the 20-year results). Interior 4 ft. LEDs in the C&I sector provide the most potential, followed by residential central air conditioning tune-up and commercial unitary and split system air conditioning/heat pump equipment. Measure Names Figure 3-9. Top 40 Measures for Electric Energy Base Case Achievable Savings Potential: 2028 (GWh/year) C&I Interior 4 ft LED Res Central AC Tune-Up C&I Unitary and Split System AC/HP… Res Duct Sealing Res Omni-Directional LEDs C&I Retrocommissioning C&I Commercial AC and HP Tune Up C&I LED Fixture - Interior Res Wall Insulation Res Ceiling Insulation C&I Smart Thermostats Res Refrigeration Res Ductless Heat Pump C&I Building Controls and Automation… C&I LED Screw In - Interior C&I Fan and pump optimization (VFD) C&I Controls Occ Sensor C&I Demand Control Ventilation Res ENERGY STAR Directional LEDs C&I Window Film Res High Efficiency Windows C&I General Process Improvements… Res Outdoor LED Light Bulb Res Advanced Power Strips Res Remove Second Refrigerator C&I Solid Door CRE Res Pipe Insulation Res Low-Flow Showerheads Res Air Infiltration C&I Interior LED High Bay Replacing HID Res Thermostatic shower valve C&I Advanced Lighting Controls C&I Refrigeration ECMs Res Pool Pumps Res Prepay Electricity Bills C&I Commercial Griddles C&I Vend Machine Ctrls C&I Computer Power Management Res Attic Knee Wall Insulation C&I Air and Water-Cooled Chillers Source: Navigant analysis Figure 3-10 shows the top 40 measures contributing to the demand achievable potential in 2028. The top measures are similar to those listed for electric energy. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 70 DSM Potential Study Figure 3-10. Top 40 Measures for Electric Demand Base Case Savings Potential: 2028 (MW) Measure Names 0 5 10 15 20 25 C&I Unitary and Split System AC/HP… Res Central AC Tune-Up C&I Commercial AC and HP Tune Up C&I Interior 4 ft LED Res Ceiling Insulation Res Duct Sealing Res Air Infiltration Res Omni-Directional LEDs Res Wall Insulation C&I Retrocommissioning C&I LED Fixture - Interior Res High Efficiency Windows C&I LED Screw In - Interior C&I Window Film C&I Demand Control Ventilation Res ENERGY STAR Directional LEDs C&I Controls Occ Sensor C&I Fan and pump optimization (VFD) C&I Solid Door CRE C&I Air and Water-Cooled Chillers Res Prepay Electricity Bills Res Pool Pumps C&I Commercial Clothes Dryer C&I Interior LED High Bay Replacing HID C&I PTAC/PTHP Equipment Res Remove Second Refrigerator C&I Commercial Griddles Res Advanced Power Strips Res Pipe Insulation C&I Advanced Lighting Controls C&I Building Benchmarking Res Attic Knee Wall Insulation Res Low-Flow Showerheads C&I Commercial Steam Cookers C&I Cool Roof C&I Refrigeration ECMs Res Central Air Conditioner Res Thermostatic shower valve C&I Faucet Aerator C&I General Process Improvements… Source: Navigant analysis Figure 3-11 provides a supply curve of savings potential versus the levelized cost of savings in $/kWh for all measures considered in the study. The achievable potential levels out at about $0.08/kWh; incremental savings above this level become costlier. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 71 DSM Potential Study Figure 3-11. Supply Curve of Electric Energy Achievable Potential (GWh/year) vs. Levelized Cost ($/kWh): 2028 Levelized Cost ($/kWh) $0.25 $0.20 $0.15 $0.10 $0.05 $0.00 0 50 100 150 200 250 300 350 400 450 Cumulative Potential (GWh/year) Source: Navigant analysis 3.2.6 Sensitivity Analysis Figure 3-12 shows a sensitivity analysis of the effect on energy savings potential that results from varying the most influential factors by +/- 25%. Table 3-6 shows the percent change to the cumulative energy savings potential for each sensitivity parameter in 2037. Unit energy savings have the largest impact, followed by incremental costs, avoided costs, and retail rates. Such understandings are critical to evaluating related policy decisions and informing effective program design. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 72 DSM Potential Study Figure 3-12. Cumulative Achievable GWh Savings in 2037 Sensitivity to Key Variables Source: Navigant analysis Table 3-6. Percent Change to Cumulative Potential in 2037 with 25% Parameter Change Parameter Low (-25%) High (25%) Marketing Effect -6% 4% Word-of-Mouth Effect -8% 6% Initial Awareness -1% 0% Incremental Cost 20% -18% Discount Rate 4% -5% Avoided Costs -16% 10% -8% 5% -42% 45% Retail Rates Unit Energy Savings 39 Source: Navigant analysis 39 Unit energy savings are the same as deemed savings and sourced from the New Orleans TRM to the extent possible. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 73 DSM Potential Study 4. Demand Response Achievable Potential and Cost Results This chapter presents the DR achievable potential and cost results based on the approach described in Section 2.2. 4.1 Cost-Effectiveness Results This section presents cost-effectiveness results by DR option and sub-option based on the TRC test. Navigant also calculated the cost-effectiveness results based on three additional tests: the utility cost test (UCT), RIM test, and the Participant Cost Test (PCT). 4.1.1 Cost-Effectiveness Assessment Results Table 4-1 shows benefit-cost ratios calculated for each DR sub-option based on the TRC test over the forecast period. Only the following programs are cost-effective: • Residential: Dynamic pricing sub-options. • Small C&I customers: HVAC DLC and dynamic pricing with enabling technology sub-options • Large C&I customers: Manual curtailment of HVAC loads and dynamic pricing with enabling technology Based on data made available by ENO, the only benefit stream captured by the TRC test is the avoided cost of generation capacity. ENO does not currently have a way to value avoided T&D capacity. These cost-effectiveness results would improve if avoided T&D capacity benefits were also included in the cost-effectiveness assessment. Only cost-effective sub-options are shown in the achievable potential results in subsequent sections. Table 4-1. Base Case Benefit-Cost Ratios by DR Options and Sub-Options Customer Class DR Option DLC Residential Dynamic Pricing Small C&I BTMS Confidential and Proprietary ©2018 Navigant Consulting, Inc. DR Sub-Option TRC Ratio DLC-Switch-Water Heating 0.21 DLC-Thermostat-Heat Pump 0.95 DLC-Thermostat-Central Air Conditioning 0.95 DLC-Switch-Heat Pump 0.56 DLC-Switch-Central Air Conditioning 0.56 Dynamic pricing without enabling tech 1.38 Dynamic pricing with enabling tech 1.89 BTMS-Battery Storage 0.18 Page 74 DSM Potential Study Customer Class DR Option DR Sub-Option DLC Dynamic Pricing BTMS Large C&I C&I Curtailment Dynamic Pricing TRC Ratio DLC-Switch-Water Heating 0.17 DLC-Thermostat-HVAC 6.53 DLC-Switch-HVAC 2.96 Dynamic pricing without enabling tech 0.24 Dynamic pricing with enabling tech 2.90 BTMS-Battery Storage 0.16 C&I Curtailment-Advanced Lighting Control 0.53 C&I Curtailment-Auto-DR HVAC Control 0.57 C&I Curtailment-Industrial 0.66 C&I Curtailment-Manual HVAC Control 1.02 C&I Curtailment-Other 0.61 C&I Curtailment-Refrigeration Control 0.68 C&I Curtailment-Standard Lighting Control 0.36 C&I Curtailment-Water Heating Control 0.72 Dynamic pricing without enabling tech 3.18 Dynamic pricing with enabling tech 0.90 Source: Navigant 4.1.2 Comparison of Cost-Effectiveness Results by Cases As described in Section 2.2.5, in addition to the base case, Navigant modeled potential results for low and high cases. For these cases, the team adjusted assumed participation levels and incentive amounts to determine the impacts on the DR achievable potential. Table 4-2 shows cost-effective results across the three cases for the DR sub-options that pass the cost-effectiveness screen for the base case. The C&I curtailment-manual HVAC control sub-option for large C&I participants under the low case is not cost-effective. All other base case cost-effective measures remain costeffective under the low and high cases. Table 4-2. Benefit-Cost Ratio Comparisons by Cases by DR Options and SubOptions Customer Class DR Option DR Sub-Option Residential Dynamic Dynamic pricing without enabling tech Confidential and Proprietary ©2018 Navigant Consulting, Inc. Base TRC Ratio Low TRC Ratio High TRC Ratio 1.38 1.39 1.38 Page 75 DSM Potential Study Customer Class Base TRC Ratio Low TRC Ratio High TRC Ratio Dynamic pricing with enabling tech 1.89 1.90 1.89 DLC-Thermostat-HVAC 6.53 6.09 6.76 DLC-Switch-HVAC 2.96 2.84 3.02 Dynamic Pricing Dynamic pricing with enabling tech 2.90 2.91 2.89 C&I Curtailment C&I Curtailment-Manual HVAC Control 1.02 0.96 1.05 Dynamic Pricing Dynamic pricing without enabling tech 3.18 3.21 3.17 DR Option Pricing 40 DLC Small C&I Large C&I DR Sub-Option Source: Navigant 4.2 Achievable Potential Results This section presents cost-effective achievable potential results by DR option, suboption, customer class and segment. 4.2.1 Achievable Potential by DR Option Figure 4-1 summarizes the cost-effective achievable potential by DR option for the base case. Figure 4-2 shows the cost-effective achievable potential as a percentage of ENO’s peak demand. Achievable potential is estimated to grow from 0.7 MW in 2018 to 34.6 MW in 2037. Cost-effective achievable potential makes up approximately 3.3% of ENO’s peak demand in 2037. The team made several key observations: • DLC has the largest achievable potential: a 49% share of total potential in 2037. DLC potential grows from 0.5 MW in 2018 to 17.0 MW in 2037. • This is followed by dynamic pricing with a 47% share of the total potential in 2037. As previously mentioned, the dynamic pricing offer begins in 2020 because it is tied to ENO’s smart meter rollout plan. The program ramps up over a 5-year period (2020-2024) until it reaches a value of 14 MW. From then on, potential slowly increases until it reaches a value of 16 MW in 2037. • C&I curtailment makes up the remainder of the cost-effective achievable potential with a 4% share of the total potential in 2037. C&I curtailment potential grows 40 There are no incentives provided to customers for participating in dynamic pricing. Hence, participation, corresponding potential, costs and cost-effectiveness stay the same across scenarios. The low case ratio is slightly higher than the base and high case ratios due to lower interactive/competing effects with other programs. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 76 DSM Potential Study rapidly from 0.2 MW in 2018 to 1.9 MW in 2022. This growth follows the Sshaped ramp assumed for the program over a 5-year period. Beyond 2022, the program attains a steady participation level and its potential slightly decreases over the remainder of the forecast period, ending at 1.2 MW in 2037. • BTMS, as described in this report, is not cost-effective; thus, it contributes 0 MW to the DR achievable potential. Figure 4-1. Summer DR Achievable Potential by DR Option (MW) Source: Navigant analysis Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 77 DSM Potential Study Figure 4-2. Summer DR Achievable Potential by DR Option (% of Peak Demand) Source: Navigant analysis 4.2.2 Case Analysis Results Navigant developed DR potential estimates for three different cases. These cases are based on the DR program incentive levels: • Base case: Reflects DR program participation based on incentives at levels that match current programs (e.g., ENO’s Smart Easy Cool program) and industry best practice. • Low case: Assumes incentives are 50% lower than in the base case. This drives program participation down and results in lower implementation costs. • High case: Assumes incentives are 50% higher than in the base case. This drives program participation up and results in higher implementation costs. The low and high cases do not apply to the dynamic pricing program, as participation is strictly based on customer response to real-time price signals. The change in participation levels due to changes in incentives is based on price response curves developed by the Lawrence Berkeley National Laboratory (Berkeley Lab) for the 2025 Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 78 DSM Potential Study California Demand Response Potential Study. 41, 42 Figure 4-3 and Figure 4-4 show the achievable potential results in terms of MW and percentage of peak demand, respectively. Under the base case, the achievable potential makes up approximately 3.3% of ENO’s peak load in 2037. Under the low and high cases, the achievable potential represents approximately 3.1% and 3.4% of ENO’s peak demand in 2037, respectively. Figure 4-3. Summer DR Achievable Potential by Case (MW) 40 35 30 MW 25 Low 20 Medium High 15 10 5 0 2018 2023 2028 2033 2037 Source: Navigant 41 Lawrence Berkeley National Laboratory. 2025 California Demand Response Potential Study: Charting California’s Demand Response Future. Appendix F. March 1, 2017. 42 Navigant assumed medium marketing spending levels for DR programs across cases. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 79 DSM Potential Study Figure 4-4. Summer DR Achievable Potential by Case (% of Peak Demand) 4.0 3.5 3.0 2.5 % Low 2.0 Medium High 1.5 1.0 0.5 0.0 2018 2023 2028 2033 2037 Source: Navigant analysis 4.2.3 Achievable Potential by DR Sub-Option This section presents the breakdown of cost-effective potential by DR sub-option. Each sub-option is tied to a specific control technology and/or end use. Any sub-option that is tied to a control technology is tied to the penetration of that technology in the market. This penetration trajectory is informed by saturation values from the energy efficiency potential study. Figure 4-5 summarizes the cost-effective achievable potential by DR option for the base case. Navigant had the following key observations: • Only direct control of HVAC loads by small C&I customers is cost-effective (DLCSwitch-HVAC and DLC-Thermostat HVAC in Figure 4-5). This sub-option makes up 50% of the total cost-effective achievable potential in 2037 at 17.0 MW. Of this 17.0 MW, 10.2 MW is from thermostat-based control, while the remaining 6.7 MW is from switch-based control. • Dynamic pricing makes up 47% of the total cost-effective achievable potential in 2037. Potential from customers with enabling technology in the form of thermostats/EMS (8.8 MW in 2037) is slightly higher than from customers without enabling technology—8.8 MW versus 7.6 MW in 2037. • Under the C&I curtailment program, reductions associated with manual HVAC Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 80 DSM Potential Study control make up 4% of the total cost-effective potential in 2037. Figure 4-5. Summer DR Achievable Potential by DR Sub-Option 40 35 30 C&I Curtailment- Manual HVAC Control MW 25 DLC-Switch-HVAC 20 15 Dynamic Pricing with enabling tech. 10 Dynamic Pricing w/o enabling tech. DLC-Thermostat-HVAC 5 2037 2036 2035 2034 2033 2032 2031 2030 2029 2028 2027 2026 2025 2024 2023 2022 2021 2020 2019 2018 0 Source: Navigant analysis 4.2.4 Achievable Potential by Customer Class This section presents the breakdown of cost-effective potential by customer class. The three customer classes included in the study are residential, small C&I, and large C&I. Figure 4-6 summarizes the cost-effective achievable potential by customer class for the base case. The team had the following key observations: • Potential from residential customers makes up 30% (10.3 MW) of the total costeffective achievable potential in 2037. C&I customers make up the remaining 70%. • Potential from small C&I customers makes up 61% (21.5 MW) of the total costeffective achievable potential in 2037. DLC of HVAC loads makes up 79% (48% from thermostat-based control and 31% from switch-based control) of this 21.5 MW, while dynamic pricing with enabling technology in the form of thermostats makes up the remaining 21%. • Potential from large C&I customers makes up 8% (2.8 MW) of the total costeffective achievable potential in 2037. Dynamic pricing with enabling technology in the form of an EMS makes up 57% of this 2.8 MW, while manual curtailment of HVAC loads makes up the remaining 43%. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 81 DSM Potential Study Figure 4-6. Summer DR Achievable Potential by Customer Class (MW) 35 30 25 MW 20 Large C&I 15 Residential Small C&I 10 5 0 Source: Navigant analysis 4.2.5 Achievable Potential by Customer Segment This section presents the breakdown of cost-effective potential by customer segment. As previously discussed in the DR methodology section, these segments align with those included in the energy efficiency potential study. Navigant combined single family and multifamily customers into a single residential category because DR program and pricing offers are typically not distinguished by dwelling type. Government customers are included as part of the C&I sector. Savings potential analysis from street lighting is not included in this study. Figure 4-6 summarizes the cost-effective achievable potential by customer segment for the base case. Navigant had the following key observations: • Potential from C&I customers primarily comes from small offices, which make up 37% (12.9 MW) of the total cost-effective achievable potential in 2037. This is followed by retail buildings, restaurants and the other commercial building category, which each make up between 7% and 9% of the total cost-effective achievable DR potential in 2037—3.1 MW, 2.7 MW, and 2.5 MW, respectively. • All other C&I segments make up less than 2.2% of the cost-effective achievable potential in 2037, which is less than 0.75 MW. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 82 DSM Potential Study Figure 4-7. Summer DR Achievable Potential by Customer Segment 40 35 C&I_Schools 30 C&I_Colleges/Universities C&I_Healthcare MW 25 C&I_Lodging C&I_Retail - Food 20 C&I_Industrial/Warehouses C&I_Office - Large 15 C&I_Other Commercial C&I_Restaurants 10 C&I_Retail (Non-Food) Residential 5 C&I_Office - Small 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 0 Source: Navigant analysis 4.3 Program Costs Results This section presents annual program costs by case and DR option and sub-option. It also presents levelized cost estimates by DR sub-option. Annual costs and levelized costs are only shown only for cost-effective DR sub-options. 4.3.1 Annual Program Costs 4.3.1.1 Annual Costs by Case Table 4-3 shows annual implementation costs for the entire cost-effective DR portfolio by case. These costs represent the estimated total annual costs that ENO is likely to incur to realize the potential values discussed in Section 4.2. Relative to the base case, costs are lower and higher in the low and high cases, respectively, due to varied incentive levels paid to customers. This affects the level of participation from customers, which varies technology enablement costs, marketing costs, and O&M costs. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 83 DSM Potential Study Table 4-3. Annual DR Portfolio Costs by Case Year Low Base High 2018 $148,508 $243,263 $250,759 2019 $112,142 $207,712 $232,074 2020 $579,445 $730,905 $778,924 2021 $655,853 $860,253 $930,087 2022 $806,604 $1,022,063 $1,095,810 2023 $813,775 $1,027,173 $1,100,283 2024 $459,268 $675,505 $751,149 2025 $295,230 $513,121 $590,823 2026 $313,916 $531,256 $609,786 2027 $331,867 $548,701 $628,218 2028 $510,256 $765,016 $847,832 2029 $431,276 $654,068 $741,000 2030 $486,591 $712,843 $803,392 2031 $511,942 $738,094 $830,734 2032 $500,861 $725,569 $820,344 2033 $455,034 $675,430 $769,963 2034 $473,555 $694,172 $791,230 2035 $497,330 $720,181 $820,580 2036 $599,336 $834,295 $945,949 2037 $557,774 $790,710 $903,719 Source: Navigant analysis 4.3.1.2 Annual Costs by DR Option and Sub-Option Figure 4-8 summarizes the annual program costs by DR option. Figure 4-10 summarizes the annual program costs by DR sub-option. The team observed the following: • The program costs for DLC increase steadily from 2018 to 2021 and then drop in 2022, once the program is fully ramped up. By 2021, 90% of the program is ramped up, so the incremental cost to recruit new customers is lower in 2022. The costs remain steady and then spike back up in 2028 because the DLC program has a program life of 10 years, so technology enablement and program development costs are re-incurred at this time. From then on, costs fluctuate in accordance with program participation, which is tied in part to thermostat market penetration, until it reaches its final value of $389,000 in 2037. • The program costs for C&I curtailment increase steadily from 2018 to 2022 until the program is fully ramped up. Because manual HVAC control is the only C&I Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 84 DSM Potential Study curtailment sub-option that is cost-effective, these costs do not include any technology enablement costs. There is a spike in costs in 2028 because, like DLC, the C&I curtailment program has a program life of 10 years, so program development costs are re-incurred at this time. From then on, costs fluctuate with program participation until it reaches its final value of $166,000 in 2037. • Dynamic pricing program costs are relatively high during its initial ramp up between 2020 and 2023, and then drop in 2024 when the program is fully ramped up. By 2023, 90% of the program is ramped up, so the incremental cost to recruit new customers is lower in 2024. Beyond 2024, costs remain low and relatively steady. • Annual BTMS program costs are zero as the program is not cost-effective. Figure 4-8. Annual Program Costs by DR Option $1,200,000 $1,000,000 $800,000 C&I Curtailment $600,000 Dynamic Pricing DLC $400,000 $200,000 2037 2036 2035 2034 2033 2032 2031 2030 2029 2028 2027 2026 2025 2024 2023 2022 2021 2020 2019 2018 $0 Source: Navigant analysis Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 85 DSM Potential Study Figure 4-9. Annual Program Costs by DR Sub-Option $1,200,000 $1,000,000 DLC-Switch-HVAC $800,000 Dynamic Pricing with enabling tech. $600,000 Dynamic Pricing w/o enabling tech. $400,000 DLC-Thermostat-HVAC $200,000 C&I Curtailment- Manual HVAC Control 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 $0 Source: Navigant analysis Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 86 DSM Potential Study 5. Conclusions and Next Steps Figure 5-1 provides an illustrative view of the data inputs and outputs of the potential study, most notably for IRP and program planning. Figure 5-1. Integrating Potential Study Outputs to IRP and DSM Planning Source: Navigant 5.1 Benchmarking the Results Energy Efficiency After completing the potential study analysis, Navigant benchmarked the energy efficiency achievable potential results against similar studies by other utilities. The goal of this exercise was to provide context for Navigant’s results and to understand how various factors such as region or program spend may affect the results. For this exercise, Navigant conducted a literature review on recent potential studies and aggregated the results. In conducting this review, the team aimed to include a mixture of utilities that had comparable electric customer counts, climate regions, regulatory requirements (e.g., publicly owned utilities), and/or locales (e.g., metropolitan centers). Based on this literature review, Navigant conducted three comparisons: • Average annual achievable potential savings at the utility level • Average annual potential savings at the state level • Energy savings per dollar of program spend Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 87 DSM Potential Study Note that the sources and points of comparison differ due to data availability. The tables below list the final benchmarking pool for these comparisons and their respective data sources. Table 5-1. EE Achievable Potential Benchmarking Pool and Sources Utility Data Source Austin Energy Austin Energy DSM Market Potential Assessment, 2012 Louisville Gas & Electric / Kentucky Utilities Louisville Gas & Electric Company and Kentucky Utilities 43 Company, Demand-Side Management Potential Study, 2017 Commonwealth Edison (ComEd) ComEd Energy Efficiency Potential Study, 2016 Duke Energy (Indiana) The Duke Energy Indiana 2015 Integrated Resource Plan, 45 2015 California Public Utilities 46 44 California Public Utilities Commission, 2018 Potentials & Goals 47 Study Results Viewer Colorado Springs Utilities Colorado Springs Utilities 2015 Demand Side Management 48 Potential Study, 2016 Seattle City Light Seattle City Light Conservation Potential Assessment, 2016 43 49 CADMUS, Louisville Gas & Electric Company and Kentucky Utilities Company, Demand-Side Management Potential Study 2019-2038, 2017, https://lge-ku.com/sites/default/files/2017-10/LGE-KUDSM-Potential-Study.pdf 44 ICF, ComEd Energy Efficiency Potential Study, 2017-2030, May 2016, http://ilsagfiles.org/SAG_files/Potential_Studies/ComEd/ComEd_20172030_EE_Potential_Final_Report_5-2016.pdf 45 Duke Energy Indiana, The Duke Energy Indiana 2015 Integrated Resource Plan, 2015, https://www.in.gov/iurc/files/2015_Duke_IRP_Report_Volumn_1_Public_Version.pdf 46 CA Public Utilities are grouped together due to data availability and the study results referenced. 47 Navigant, California Public Utilities Commission 2018 Potentials & Goals (PG) Study Results Viewer, 2018, http://www.cpuc.ca.gov/General.aspx?id=6442452619 48 CADMUS, Colorado Springs Utilities 2015 Demand Side Management Potential Study, 2016, https://www.csu.org/CSUDocuments/dsmpotentialstudyvolume1.pdf 49 Seattle City Light 2016 IRP “Appendix 6, Conservation Potential Assessment,” https://www.seattle.gov/light/IRP/docs/2016App-6-Conservation%20Potential%20Assessment.pdf Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 88 DSM Potential Study Table 5-2. EE Achievable Potential Savings by State Benchmarking Pool and Sources State Data Source Arkansas Arkansas Energy Efficiency Potential Study Mississippi A Guide to Growing an Energy-Efficient Economy in Mississippi Louisiana Louisiana’s 2030 Energy Efficiency Roadmap Tennessee Tennessee Valley Authority Potential Study Texas Potential for Energy Efficiency, Demand Response, and Onsite Renewable Energy 54 to Meet Texas’s Growing Electricity Needs 50 51 52 53 50 Navigant, Arkansas Energy Efficiency Potential Study, 2015, www.apscservices.info/pdf/13/13-002U_212_2.pdf 51 ACEEE, A Guide to Growing an Energy-Efficient Economy in Mississippi, 2013, http://aceee.org/research-report/e13m 52 ACEEE, Louisiana’s 2030 Energy Efficiency Roadmap, 2013, http://aceee.org/research-report/e13b 53 Global Energy Partners, Tennessee Valley Authority Potential Study, 2011, http://152.87.4.98/news/releases/energy_efficiency/GEP_Potential.pdf 54 ACEEE, Potential for Energy Efficiency, Demand Response, and Onsite Renewable Energy to Meet Texas’s Growing Electricity Needs, 2007, https://aceee.org/research-report/e073 Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 89 DSM Potential Study Table 5-3. EE Actual Spending and Saving Benchmarking Pool and Sources Utility Data Source Anaheim Public Utilities Pasadena Water & Power Los Angeles Department of Water & Power Energy Efficiency in California’s Public Power Sector th 55 11 Edition Sacramento Municipal Utility District SWEPCO Entergy Texas, Inc. Texas Efficiency, Energy Efficiency Accomplishments 56 of Texas Investor-Owned Utilities 2016 El Paso Electric CPS Energy (City of San Antonio) Evaluation, Measurement & Verification of CPS 57 Energy’s DSM Programs FY 2016 Louisville Gas & Electric/Kentucky Utilities LG&E/KU DSM Advisory Group Meeting, 2017 58 Based on the sources above, Navigant aggregated the results into the figures below. 55 California Municipal Utilities Association, Northern California Power Agency, Southern California th Agency, Energy Efficiency in California’s Public Power Sector, 11 Edition, 2017, http://docketpublic.energy.ca.gov/PublicDocuments/17-IEPR06/TN217680_20170522T124015_Energy_Efficiency_in_California's_Public_Power_Sector_11th_Edit.pd f 56 Frontier Associates, Energy Efficiency Accomplishments of Texas Investor-Owned Utilities 2016, 2017, http://www.texasefficiency.com/images/documents/Publications/Reports/EnergyEfficiencyAccomplishmen ts/EEPR2016.pdf 57 Frontier Associates, Evaluation Measurement & Verification of CPS Energy’s FY 2016 DSM Programs, https://www.sanantonio.gov/portals/0/files/sustainability/Environment/CPSFY2016.pdf 58 LG&E and KU, “DSM Advisory Group Meeting,” 2017, https://lge-ku.com/sites/default/files/2017-10/926-2017-EE-Advisory-Group-Presentation.pdf Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 90 DSM Potential Study Figure 5-2. Benchmarking Pool Average Achievable Potential Savings (% of Sales) 59 Snohomish PUD 1.07% ENO (Navigant 2018) 0.88% Seattle City Light 0.88% Austin Energy 0.73% Duke Energy IN (IRP) 0.54% ComEd 0.54% Colorado Springs Utilities CA Public Utilities (Aggregated) Louisville Gas & Electric / Kentucky Utilities 0.00% 0.49% 0.45% 0.31% 0.50% 1.00% 1.50% 2.00% Source: Navigant analysis When comparing potential estimates, it is important to note that although the utilities included in the benchmarking pool may have some similar characteristics, no two utilities are the same; therefore, the results may vary based on the inputs each utility provided to its respective potential study evaluator. Study methodologies may also differ based on the potential study evaluator, providing additional room for variances across studies. With that in mind, achievable potential savings range from 0.31% to 1.07% of sales. Snohomish Public Utility District in Washington has the highest potential and Louisville Gas & Electric/Kentucky Utilities, the lowest. As mentioned above, these differences may be driven by many factors, including measures studied, cost inputs, study years, and study methodology. ENO’s achievable potential falls within the range of the benchmarking pool at an average of 0.88% savings per year over the study period (2017-2038). This is similar to Seattle City Light and slightly above Austin Energy (0.73%). Interestingly, the three all operate in large metropolitan areas and have similar 59 These savings are shown as an annual average, which Navigant derived by dividing the cumulative study averages by the number of years in the study. The team used this approach because study years tend to differ greatly. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 91 DSM Potential Study governance structures in that they are regulated by a city council. 60 In addition to benchmarking the results at the utility level, Navigant created a peer pool at the state level. The goal of this analysis was to understand ENO’s potential savings within the broader context of the state of Louisiana and its neighbors. Given that the states are mostly clustered within the Southeast region of the US, they have the same climate (hot-humid) and, therefore, may experience similar levels of achievable potential savings. Figure 5-3 shows how ENO’s achievable potential fits into the broader statelevel context. Figure 5-3. Benchmarking Pool State Level Achievable Potential (% of Savings) 1.40% Savings (% of Sales) 1.20% 1.00% 0.80% 0.90% 0.91% 0.94% 0.88% ENO (Navigant 2018) Arkansas Average Louisiana 1.04% 1.07% Tennessee (TVA) Mississippi 0.73% 0.60% 0.40% 0.20% 0.00% Texas As shown in the figure above, ENO’s achievable potential savings are within the range of the benchmarking pool (0.73%-1.07%), which makes sense given the similarities across the region. Its potential savings are only slightly less than the overall pool average and the state of Louisiana. The slight difference in savings of this potential study and the state may be caused by several factors, including: • Updated inputs 60 It should be noted that, unlike ENO, which is an IOU, Austin Energy and Seattle City Light are both POUs that function as departments within their respective municipalities. However, all three must comply with the mandates of the local regulatory body. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 92 DSM Potential Study • Utilities outside New Orleans had not begun implementing energy efficiency programs at the time ACEEE conducted the Louisiana study in 2013 • Broader region covered (some areas may have more or less potential savings based on stock type and other utilities’ energy efficiency spending) Figure 5-4. Benchmarking Pool Actual Savings (% of Sales) vs. Spending ($/kWh) Source: Navigant analysis Like achievable potential estimations, actual savings and spending may vary greatly among utilities based on inputs. In this case, inputs may include how the study is administered, what measures are offered, how the program is designed, and the number of years the program has been in place. The figure above shows that CPS Energy in San Antonio spends the most ($0.46/kWh) for less savings (0.54%), while the larger California public utilities (Sacramento Municipal Utilities District, Los Angeles Department of Water & Power, and Pasadena Water & Power) spend the least ($0.16/kWh-$0.18/kWh) but achieve the most (1.25%+). ENO falls in between these two, spending $0.24/kWh and saving 0.55% in 2016. Looking at its Southern peers, ENO’s most recent spending and savings align closely, suggesting regional program administration and design variances. Additionally, California programs have been around for significantly longer, which may account for additional cost/savings differentials. Demand Response In addition to EE potential, the team also benchmarked DR potential, following a similar Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 93 DSM Potential Study process. The process included creating a peer pool based on ENO’s characteristics and data availability. This particular effort included both individual utilities and two nearby Independent System Operators (ISOs) or Regional Transmission Authorities (RTOs). The table below includes the sources used for this analysis. Utility or ISO/RTO Data Source Ameren Union Electric (AmerenUE) AmerenUE DSM Market Potential Study Con Edison (Con Ed) DER Potential Study Commonwealth Edison (ComEd) Comprehensive Assessment of Demand-Side Resource 63 Potentials Electric Reliability Council of TX (ERCOT) Assessment of Demand Response and Advanced 64 Metering Hawaii Electric Company (HECO) Fast DR Pilot Program Evaluation Puget Sound Energy (PSE) 2017 IRP Demand-Side Resource Conservation 66 Potential Assessment Report Southwest Power Pool (SPP) Assessment of Demand Response and Advanced 67 Metering 61 62 65 The results of this analysis are shown in the graphic below. 61 Global Energy Partners, AmerenUE Demand Side Management (DSM) Market Potential Study Volume https://www.ameren.com/-/media/missouri1: Executive Summary, January 2010, site/Files/Environment/Renewables/AmerenUEVolume1ExecutiveSummary.pdf. 62 Navigant, DER Potential Study, 2016. 63 Cadmus Group, Comprehensive Assessment of Demand-Side Resource Potentials, February 2009, https://www.illinois.gov/sites/ipa/Documents/Appendix%20C-1%20%20ComEd%20Potential%20Study.pdf 64 Federal Energy Regulatory Commission (FERC) Assessment of Demand Response and Advanced Metering, 2016, https://www.ferc.gov/legal/staff-reports/2016/DR-AM-Report2016.pdf 65 Navigant, Fast DR Pilot Program Evaluation, May 2015, http://media.navigantconsulting.com/emarketing/Documents/Energy/HawaiianElectricFastDREvaluationR eport_Sept302014NavigantRevisedMay192015v2.pdf 66 Navigant, 2017 IRP Demand-Side Resource Conservation Potential Assessment Report, June 2017, https://pse.com/aboutpse/EnergySupply/Documents/DSR-Conservation-Potential-Assessment.pdf 67 FERC, Assessment of Demand Response and Metering. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 94 DSM Potential Study Figure 5-5. Benchmarking Pool DR Potential (% of Savings) 7.00% 6.10% 6.00% 5.00% 4.00% 4.00% 3.90% 3.90% 3.30% 3.00% 3.00% 2.00% 1.60% 1.00% 0.00% 0.00% As shown above, ENO falls in the middle of the benchmarking pool, only slightly higher than ERCOT and slightly below Ameren in Missouri. Given that DR, like EE, varies based on program administration and geographic location, amongst other factors, ENO’s DR potential aligns closely to its peers. 5.2 IRP The IRP is typically an iterative process to optimize the mix of supply- and demand-side resources to meet the utility’s demand. The mix of supply-side resources dictates the costs to be used as avoided costs, but if energy efficiency programs can vary the supply-side mix (i.e., reduce the need of costlier resources), the avoided costs will vary. The IRP outputs feed into the projected cost and goals used to formulate the near-term DSM program implementation portfolio. The potential study provides forecasted savings inputs for use in the IRP modeling. These inputs are provided by sector, segment, and end use because each combination of these items is mapped to a load shape (see Appendix C). Each measure is mapped to one or more DSM programs. Navigant then developed a load shape representative of each DSM program. The DSM program load shape represents the aggregate hourly energy savings for the group of measures included in the program over the 20-year planning period. These load shapes are what define the hourly usage profiles for the DSM program portfolio. The data provided is aligned with the Council’s IRP rulemaking, R-17-429 which requests that the data supplied should include: a description of each demand-side resource considered, including a description of resource expected Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 95 DSM Potential Study penetration levels by year; hourly load reduction profiles for each DSM program; and results of all 4 standard cost-effectiveness tests. 5.3 Program Planning It is important to recognize that DSM potential studies like this one are inherently different from DSM program portfolio designs. The long-term achievable potential identified for a 20-year period through this study is different from the short-term savings potential that would be identified though a DSM program portfolio design effort targeting a 3-year period. However, it is important to note that programmatic design (such as delivery methods and marketing strategies) will have implications for the overall savings goals and projected cost. As mentioned above, near-term savings potential, actual achievable goals, and program costs for a measure-level implementation will vary from the savings potential and costs estimated in this long-term study. This potential study is one element to be considered in program design, along with historical program participation and current market conditions (with the team members on the ground). Some observations on the potential study results that can provide input to program planning are: • There is strong potential with promoting advanced lighting, which includes networked lighting technology and controls in all sectors. • There is high potential in O&M and behavior-type programs such as retrocommissioning if they are cost-effective. • HVAC unitary equipment has high potential in both sectors. 5.4 Further Research Finally, the potential study identified data gaps in characterizing ENO’s market and measures. This is common for most utilities; however, for ENO to have more accurate potential estimates and information to support DSM planning, there is ENO-specific data that could support this end goal: • Baseline and saturation studies for each sector • Updated residential end-use survey • C&I end-use survey • Customer payback acceptance analysis specific to the ENO service area (in particular due to the high penetration of renters) Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page 96 DSM Potential Study Appendix A. Energy Efficiency Detailed Methodology A.1 End-Use Definitions Table A-1. Description of End Uses Segment End Use Definition Total Facility Consumption of all electric end uses in aggregate Lighting Interior Overhead lights, lamps, etc. Lighting Exterior Spotlighting, security lights, holiday/seasonal lighting, etc. Plug Loads Large/small appliances including ovens, refrigerators, freezers, clothes washers, etc. Televisions, computers and related peripherals, and other electronic systems Cooling All cooling, including both central air conditioning and room or portable air conditioning Heating All heating, including both primary heating and supplementary heating Fans/Ventilation Motor drives associated with heating and cooling Water Heating Heating of water for domestic hot water use Other Miscellaneous loads Total Facility Consumption of all electric end uses in aggregate Lighting Interior Overhead lights, lamps, etc. (main building and secondary buildings) Lighting Exterior Spotlighting, security lights, holiday/seasonal lighting, etc. (main building and secondary buildings) Plug Loads Computers, monitors, servers, printers, copiers, and related peripherals Cooling All cooling equipment, including chillers and direct expansion cooling Heating All heating equipment, including boilers, furnaces, unit heaters, and baseboard units Fans/Ventilation Motor drives associated with heating and cooling Refrigeration Refrigeration equipment including fridges, coolers, and display cases Water Heating Hot water boilers, tank heaters, and others Other Miscellaneous loads including elevators, gym equipment, and other plug loads Residential C&I Source: Navigant Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page A-1 DSM Potential Study A.2 Residential Sector The following sections describe the detailed approach used to determine electricity consumption by segment, the approach used to estimate end-use intensities (EUIs), and the resulting residential household stock. To do this, Navigant needed to determine four pieces of information: 1. Base year stock 2. Base year consumption 3. Base year EUIs 4. Reference case forecast for all values 1. Base Year Residential Stock and 2. Base Year Electricity To estimate the residential stock, Navigant proposed an approach that leveraged ENO’s billing data. The challenge with this approach was that ENO’s billing data identifies residential accounts using a customer name rather than a billing address. This can overstate the residential stock, as multiple tenants may occupy a single billing address over time. For example, a home with two different tenants (e.g., tenant A from January to June, and tenant B from July to December) are reported as two separate accounts and thus imply two separate residential households. This approach can also underestimate the average electricity usage by account. In fact, the team compared the billing and consumption data against historical sales and found that the data did not align. Navigant overcame these challenges by: • Determining residential electricity sales (GWh) with a full year of data (e.g., an account with 12 consecutive months of sales) by segment and calibrating these values to ENO’s sales forecast to ensure alignment with ENO’s sales planning assumptions moving forward. • Determining stock (#) from accounts with a full year of data (e.g., an account with 12 consecutive months of sales) by segment and calibrating these to ENO’s account forecast to ensure alignment with ENO’s account planning assumptions moving forward. The team applied this approach to the two residential segments to ensure that all datasets provided by ENO aligned to their internal planning assumptions. Table A-2 provides an example of the base year residential stock and sales calculations. 68 68 Note these do not represent actual values provided by ENO. All values are meant to illustrate the methodology. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page A-2 DSM Potential Study Table A-2. Example Base Year Residential Stock and Sales Equations Step Value Calculation (1) Aggregate sales from residential sector billing data to get a sector-level sales value (1,000 GWh for single family and 900 GWh for multifamily) 1,900 GWh (2) Determine sales for residential sector from billing data 1,700 GWh Provided by ENO 0.89 (2) / (1) 895 GWh for single family; 805 GWh for multifamily Segment-level sales from (1)*(3) (3) Compare (1) to (2) to get a calibration factor (4) Calibrate segment-level sales by calibration factor from (3) Note: Navigant used this process for both the residential stock (accounts) and sales (load). As mentioned above, the team used ENO’s billing data as a starting point and the account forecast as the basis for calibration. Source: Navigant analysis Table A-3 shows the segment-level stock and base year sales derived from the calibration analysis outlined above. Table A-3. ENO Residential Base Year Results Sales (GWh) Stock (Households) kWh/ Household 749 132,901 11,144 Multifamily 1,481 45,048 16,632 Total 2,230 177,949 Segment Single Family 12,533 69 Source: Navigant analysis 3. Base Year EUIs To determine residential EUIs at the segment level, Navigant leveraged the calibrated sales and stock derived above. The team then divided the load per segment by the stock per segment to get the EUIs. After calculating the segment-level EUIs, Navigant further disaggregated the values to get EUIs, a key model input. This process consisted of multiplying the segment-level EUIs by end-use allocations, or the proportion of energy used by a certain end use (e.g., this proportion of the EUI is X% of the total EUI). Navigant derived these proportions using the DOE’s EnergyPLUS model in 69 This figure represents the total consumption divided by the total number of households and not the addition of the single family and multifamily kWh/household EUI values. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page A-3 DSM Potential Study conjunction with an internal model. Table A-4 provides the derived end-use allocations by residential segment. Cooling Fans/ Ventilation Heating Hot Water Lighting Exterior Lighting Interior Plug Loads Heating/ Cooling Total Facility Table A-4. Base Year Residential EUIs (kWh per Acct.) Single Family 3,229 1,790 304 493 345 2,158 2,824 3,533 11,144 Multifamily 4,819 2,672 454 736 515 3,221 4,215 5,273 16,632 Building Segment Source: Navigant analysis 4. Reference Case Stock and EUIs To develop the residential stock forecast through 2037, ENO provided Navigant with its residential account and sales forecasts. Based on these forecasts, the team derived the annual growth rates by dividing the difference of the new and old stock by the old stock (e.g., (2017 stock – 2016 stock) / 2016 stock). Navigant used the same approach to determine the annual sales forecast growth. After deriving the growth rates, the team applied them directly from the account forecast to determine the growth in stock across all segments over the forecast period. Likewise, the team applied the annual growth rates directly from the sales forecast to determine the growth in sales across all segments over time. Table A-5 shows the growth in stock from 2016 to 2037 used in the reference case by segment. Table A-5. Reference Case Residential Stock Forecast (Accounts) Segment 2016 2037 Single Family 132,901 144,972 Multifamily 45,048 49,139 Total 177,949 194,111 Source: Navigant analysis Because the EUI formula leverages the stock and load directly, the EUI growth trends follow both the stock and load trends. More specifically, the team divided the load by the stock to get the base year’s EUIs. Therefore, the overall growth rate is 0.4% from 2016 to 2037 for both segments and all end uses. Table A-6 shows the change in EUI from 2016 to 2037. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page A-4 DSM Potential Study Table A-6. Reference Case EUI Forecast (Accounts) Segment 2016 2037 Single Family 11,144 10,829 Multifamily 16,632 16,161 A.3 C&I Sector To determine the total C&I floor space stock in ENO’s service area, Navigant needed to determine four key pieces of information: 1. Base EUI for ENO’s climate region in kWh/thousands SF 2. ENO’s base year sales by segment in kWh 3. Base year C&I stock in thousands SF 4. Reference case forecast based on the base year numbers The approach used to determine each of these pieces of information and the methodology for deriving the floor space stock is described below. 1. Base EUIs for ENO’s Climate Region As a starting point for the analysis, Navigant needed to determine a base EUI value by segment that the team could calibrate to ENO’s stock and climate. Navigant first began with the US Energy Information Administration’s (EIA’s) electricity energy (use) intensity in kWh/SF by EIA principal building activity for ENO’s climate category, the hot-humid region. 70 The team then mapped the principal building activities to the study’s segments as a basis for the EUI. Table A-7 shows the mappings. Table A-7. C&I EIA EUI Segments to Study Segment Mappings EIA Principal Building Activity Study Segment Education Colleges/Universities Health care Healthcare All Buildings Industrial/Warehouses Lodging Lodging 70 Source: CBECS, Table C20. Electricity consumption and conditional energy intensity by climate region, 2012, May 2016, https://www.eia.gov/consumption/commercial/data/2012/c&e/cfm/c20.php Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page A-5 DSM Potential Study EIA Principal Building Activity Study Segment Office Office – Large Office Office – Small Public Assembly Other Commercial Food Service Restaurants Food Sales Retail – Food Mercantile Retail – Non-Food Education Schools Source: Navigant analysis After deriving the calibrated segment-level EUIs, Navigant further disaggregated by end use to obtain EUIs. The team disaggregated the values by first determining the end-use allocations for each segment, leveraging the US Department of Energy’s (DOE’s) EnergyPLUS model in conjunction with proprietary internal models. Like residential, these values represented a proportion of each segment and were applied by multiplying the proportion by the segment-level EUIs. As noted above, Navigant used a top-down approach rather than bottom-up for this particular analysis due to data availability. The team wanted to leverage as many ENOspecific sources as possible to ensure consistency with ENO’s planning. In this case, ENO had not conducted any recent commercial end-use saturation studies, and Navigant could not find any reliable secondary studies specifically for the New Orleans area. For this reason, the team used the best data available at the time of modeling, which was ENO’s internal forecasts and Navigant’s end-use allocation estimates. 2. Base Year Electricity Sales To determine the base year electricity sales of each C&I segment, ENO provided SIC account data, which the team used to create a breakdown of electricity sales by SIC. Navigant and ENO then worked together to develop a mapping of SIC data to C&I segments. It is generally recognized that SIC assignment to account data may have errors. The team developed this mapping through various reviews of the data to minimize electricity sales allocated to the other commercial segment. The mapping yielded a breakdown of accounts by segments (e.g., 5.6% of accounts are colleges/universities). Navigant used this breakdown to disaggregate the 2016 sales into segments (e.g., 5.6% of accounts are colleges/universities; therefore, 5.6% of the load belongs to that segment). One exception to the account and sales mapping process was the industrial/warehouses segment. For this specific segment, Navigant noticed that the proportion of accounts mapped to this segment was greater than ENO’s industrial load forecast by roughly 3%. To ensure complete alignment with ENO’s internal planning assumptions, the team moved the excess 3% sales into the other commercial segment after discussions with the utility. Navigant then added in the industrial proportion, which Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page A-6 DSM Potential Study was negligible (0%). This resulted in industrial/warehouses having 13.0% of the sales and other commercial having 13.9% of the sales. Table A-8 shows the breakdown of C&I sales resulting from this analysis. Table A-8. ENO C&I Base Year Results (GWh) Stock (thousands SF) Total Sales (GWh) Percentage of Total Colleges/Universities 15,388 196 5.6% Healthcare 8,318 237 6.8% Industrial/Warehouses 27,863 457 13.0% Lodging 34,693 523 14.9% Office – Large 15,875 270 7.7% Office – Small 36,365 619 17.7% Other Commercial 22,504 485 13.9% Restaurants 4,720 218 6.2% Retail – Food 2,574 125 3.6% Retail – Non-Food 16,548 327 9.3% Schools 3,494 45 1.3% 188,340 3,503 100% Segment Total Source: Navigant analysis 3. Base Year Stock Calibration Approach After determining the base EUIs from EIA data and disaggregating ENO’s sales data, Navigant calculated the base year C&I stock using the formula in Figure A-1. Figure A-1. C&I Base Year Stock Formula Source: Navigant analysis The calculation yielded the base year stock by segment, which the team then used to determine the reference case stock and EUI. 4. Reference Case Stock and EUI Approach Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page A-7 DSM Potential Study The team used the base year values to create the reference case stock and EUI forecasts. To do this, Navigant used the growth rates directly from ENO’s sales and account forecast, applying the C&I sector forecasts to all segments except for industrial/warehouses. 71 For that specific segment, Navigant applied the industrial sector forecast to ensure consistency with ENO’s data. The team then applied these growth rates to each of the base year values to obtain the reference case. Table A-9 shows the results of these analyses. Table A-9. Reference Case C&I EUI, Sales, and Stock Data Point 2016 2037 Sales (GWh) 3,503 3,999 EUI (kWh/thousands SF) 255,744 272,412 Stock (thousands SF) 188,340 200,648 Source: Navigant analysis 71 Note that the growth rates for the forecasts aligned at 0.4% for commercial and 0.0% for industrial/warehouses over the study period. These rates represent the compound annual growth rate (CAGR) across the entire study period. Actual growth rates fluctuate from year to year following the load forecast provided by ENO. The load forecasts are largely driven by industry indices. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page A-8 DSM Potential Study Appendix B. Energy Efficiency Input Assumptions B.1 Measure List and Characterization Assumptions Navigant developed the measure list and characterizations based on internal expertise, ENO-specific data, the New Orleans TRM, and secondary sources where necessary. B.2 Avoided Costs and Cost-Effectiveness In addition to the reference case and measure characterization assumptions, Navigant input several cost-related inputs to determine the cost-effectiveness of measures over the study period. This section details those inputs. Avoided Energy Costs ENO provided the BP18U72 avoided costs over the study period plus the longest measure life (2037 + 25 years) to Navigant to input into the model. Figure B-1 shows the avoided energy cost projections, or forecasted locational marginal prices (LMPs). Figure B-1. ENO BP18U Avoided Cost Projections $0.30 Avoided Cost ($/kWh) $0.25 $0.20 $0.15 $0.10 $0.05 $0.00 Year 72 BP18U refers to the vintage of a set of planning and modeling assumptions. At the time of this study, BP18U was the latest assumption set available. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page B-1 DSM Potential Study Avoided Capacity Cost ENO also gave Navigant avoided capacity costs to input into the model for costs over the study period plus the longest measure life (2037 + 25 years). Like the avoided energy costs, the capacity costs align with ENO’s BP18U and its internal planning. Figure B-2 shows these costs over the study period. Figure B-2. ENO BP18U Avoided Capacity Projections Avoided Capacity Costs ($/kW-yr) $200.00 $180.00 $160.00 $140.00 $120.00 $100.00 $80.00 $60.00 $40.00 $20.00 $0.00 Year Carbon Pricing In addition to avoided costs, ENO provided carbon pricing estimates through 2050 for the potential model. However, the carbon pricing inputs needed to extend further out than the study period to accurately model measure costs over their lifetime. More specifically, Navigant needed to model carbon prices up until the end of the study period plus the longest measure life (25 years). The team extrapolated these last years by taking the average growth (8%) for the last 5 years of the forecast (2045-2050) and applying it to the remaining 11 years. 73 Figure B-3 shows the carbon pricing estimates provided and extrapolated. 73 Note that the growth rate was flat for the remaining 5 years provided. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page B-2 DSM Potential Study Figure B-3. ENO Carbon Pricing Projections 74 $120.00 $100.00 $80.00 $60.00 $40.00 $20.00 2060 2058 2056 2054 2052 2050 2048 2046 2044 2042 2040 2038 2036 2034 2032 2030 2028 2026 2024 2022 2020 2018 $0.00 B.3 Cost-Effectiveness Calculations The potential analysis uses two forms of cost-effectiveness calculations. The total resource cost (TRC) test is for utility cost-effectiveness. There is also the participant cost test (PCT), which is mostly addressed by calculating the participant payback period instead of the benefit-cost ratio for the PCT. This section describes these tests, the inputs, and how they are used for the potential study. TRC Test The TRC test is a benefit-cost metric that measures the net benefits of energy efficiency measures from the combined stakeholder viewpoint of the utility (or program administrator) and the customers. The TRC benefit-cost ratio is calculated in the model using Equation B-1. Equation B-1. Benefit-Cost Ratio for TRC Test 𝑇𝑇𝑇 = 𝑃𝑃(𝐴𝐴𝐴𝐴𝐴𝐴𝐴 𝐶𝐶𝐶𝐶𝐶) 𝑃𝑃(𝑇𝑇𝑇ℎ𝑛𝑛𝑛𝑛𝑛𝑛 𝐶𝐶𝐶𝐶 + 𝐴𝐴𝐴𝐴𝐴 𝐶𝐶𝐶𝐶𝐶) 74 Note that the forecast extends until 2061, although the label for year 2061 is not visible. This is because the chart shows years in increments of two for aesthetic purposes. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page B-3 DSM Potential Study Where: • PV( ) is the present value calculation that discounts cost streams over time. • Avoided Costs are the monetary benefits resulting from electric energy and capacity savings—e.g., avoided costs of infrastructure investments and avoided fuel (commodity costs) due to electric energy conserved by efficient measures. • Technology Cost is the incremental equipment cost to the customer. • Admin Costs are the administrative costs incurred by the utility or program administrator. Navigant calculated TRC ratios for each measure based on the present value of benefits and costs (as defined above) over each measure’s life. Effects of free ridership are not present in the results from this study, so the team did not apply a NTG factor. Providing gross savings results will allow ENO to easily apply updated NTG assumptions in the future and allow for variations in NTG assumptions. The administrative costs are included when reporting sector-specific or portfolio-wide cost-effectiveness. However, they are not included at the measure level for economic potential screening. For this screening, it is important to identify measures that are costeffective on the margin prior to assessing effects for the achievable potential where administrative costs are considered depending on the amount and level of programmatic spend. Participant Payback Period Navigant calculates the customer payback period to assess customer potential to implement the energy-saving action. The payback period is used to assess customer acceptance and adoption of the measure. Additional details are described in the achievable potential methodology section 2.1.4.3. The payback period is calculated after the incentive is applied to the measure cost. Equation B-2 demonstrates the calculation. Equation B-2. Participant Payback Period 𝑃𝑃𝑃𝑃𝑃𝑃𝑃 = 𝐴𝐴𝐴𝐴𝐴𝐴 𝑘𝑘ℎ 𝑆𝑆𝑆𝑆𝑆 × 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴e𝑑 𝑅𝑅𝑅𝑅𝑅𝑅 𝑅𝑅𝑅𝑅 �$�𝑘𝑘ℎ� Confidential and Proprietary ©2018 Navigant Consulting, Inc. 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 𝑀𝑀𝑀𝑀𝑀𝑀𝑀 𝐶𝐶𝐶𝐶 − 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 Page B-4 DSM Potential Study Where: • Annual kWh Saved is calculated for each measure and segment (as appropriate). • Annualized Retail Rate is the overall cost a customer pays per kWh consumed (see Appendix B.4). • Incremental Measure Costs are the costs the participant would pay (without an incentive) to implement the measure. In replace-on-burnout (ROB) and new construction (NEW), depending on the measure, the difference in the cost of the efficiency and standard equipment is used instead of the full cost of installation (material and labor costs). • Incentives are the incentive costs paid for a customer’s out of pocket costs to be reduced. B.4 Retail Rates Because customer economics is a primary driver of energy efficiency measure adoption, Navigant used a forecast of electric retail rates for each sector to estimate achievable energy and demand potential. Because ENO did not have a forecast of retail rates readily available, the team calculated the retail rates by dividing the historic revenue ($) by the historic sales (kWh) to yield an approximation of retail rates ($/kWh) by sector for the base year (2016). Navigant then assumed that the rates would increase with inflation, or 2% per year. Figure B-4. Electricity Retail Rate Forecast: 2016-2037 Retail Rate ($/kWh) $0.35 $0.30 $0.25 $0.20 $0.15 $0.10 $0.05 $0.00 Year Res Confidential and Proprietary ©2018 Navigant Consulting, Inc. C&I Page B-5 DSM Potential Study Source: Navigant analysis B.5 Other Key Input Assumptions As shown in Table B-1 below, Navigant used ENO’s financial WACC as the discount rate 75 and an inflation rate consistent with the utility’s planning. Table B-1. Potential Study Assumptions Variable Name Percentage Discount Rate 7.72% Inflation Rate 2.00% Source: ENO 75 See, Docket UD-08-02, Technical Advisors’ Evaluation of Energy Smart Program Years 7-9 Proposed Program Budget, dated July 6, 2017, for discussion of appropriate use of utility WACC as discount rate in evaluating cost effectiveness of DSM programs. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page B-6 DSM Potential Study Appendix C. Hourly 8,760 Analysis and Measure/Program Mapping Navigant developed an 8,760 hourly normalized end-use load shape library to support case-specific assessments of specific energy efficiency, demand response (DR), and other technologies assessed as part of this study. For this task, the team created representative end-use load shapes for each customer segment identified by ENO. Navigant also used these load shapes to calculate the peak savings for energy efficiency measures. In the absence of end-use metered consumption, the US Department of Energy (DOE) prototype reference building models, simulated with local weather files, provide reasonable end-use load shapes to use in the potential model. The end-use load profiles are sensitive to several of the building model inputs (temperature setpoints, operation schedules, etc.); however, Navigant put considerable thought into adjusting these inputs to model typical consumption profiles for each building segment. End-use metering provides load shapes with a higher degree of certainty, but the costs far exceed those of using prototypical building models. The resulting end-use load shape estimates may have high uncertainty. Additional rigor of the end-use load shape estimate becomes critical when the valuation of energy efficiency and understanding of each electric using equipment load profile must match each kW as tracked by supplyside resource planning. In these instances, end-use metering may be warranted. C.1 End-Use Load Shape Development Navigant’s load profile development followed these steps: 1. Assess measures and identify load profiles. Following ENO approval of the final list of measures to be characterized and included in the analysis, Navigant staff identified a set of end use/sector/segment combinations of load profiles such that each conservation measure and base technology has an assigned load profile. 2. Present load profile mapping for ENO feedback and approval. Once Navigant staff mapped a load profile type to each measure, ENO reviewed the list of load profiles and the measures to which they map. 3. Identify appropriate base load shapes. To maximize value for ENO, Navigant leveraged its existing database of end-use sectoral load profiles for this analysis. 4. Adapt load shapes to New Orleans. Navigant include New Orleans-specific weather and residential sector consumption data to adapt load shapes to be ENO-specific. The next section describes the approach used for this step. 5. Apply load profiles to DSMSim outputs. Navigant applied the final load shapes to the aggregated DSMSim outputs to deliver the 8,760 profile of conservation impacts required by ENO. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page C-1 DSM Potential Study Load Shape Development Approach Navigant used the EnergyPlus building simulation software to run prototypical building energy models for residential and C&I customer segments. The team used updated versions of the US DOE commercial and residential reference building models to complete the simulations; these are representative of typical building constructions and represent typical energy and demand for buildings within the building stock. Navigant maintains this model set for extracting end-use load shapes for potential studies. The team leveraged EnergyPLUS prototype models that include several updates made during a previous study to more accurately reflect typical hourly energy consumption of buildings. These updates include smoothing HVAC operation schedules and ramping HVAC setpoint changes over many hours instead of a step-change in setpoint between two adjacent hours. Navigant also leveraged various end-use load shape metering studies to make informed model updates to more accurately reflect real-world operation of these equipment types: • Navigant updated the lighting profiles contained in the DOE commercial reference building models with Northeast Energy Efficiency Partnerships (NEEP) lighting profiles. 76 The NEEP lighting profiles are weather-normalized lighting profiles that were developed for the Northeast and Mid-Atlantic regions of the US using data from integral lighting meters. The metered data was collected for energy efficiency project evaluations ranging from 2000 to 2011. It is important to note that non-weather dependent end uses can be transferable from one region to another, such as lighting and appliances. 77 • Navigant updated the lighting profiles for the residential reference building with the residential lighting load shapes from a metering study in the Northeast. The metered data was collected in 2015. Navigant used typical meteorological year (TMY) weather data for New Orleans in the EnergyPLUS modeling environment. Residential Load Shapes ENO provided Navigant with 2015 distribution-level data containing hourly energy consumption for residential buildings across the ENO service area. The team used the consumption data for the residential sector to visually calibrate the load shape outputs 76 Lighting hourly load profiles were taken from the July 19, 2011 C&I Lighting Load Shape Project for NEEP (associated spreadsheet - Profiles v2.6_4_18-KIC.xls). 77 End-Use Load Data Update Project Final Report, www.neep.org/file/2693/download?token=aOWk8oud. Tables 3 and 4 in the report identify the load shapes that are highly transferrable across regions. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page C-2 DSM Potential Study from the residential building models for the 2015 model year. To do this, Navigant processed the consumption data and the hourly building energy model output data to visually compare average daily profiles (weekday and weekend) for each month of the year. The team adjusted building model inputs to calibrate the total building load to the ENO distribution data. For the residential building model, Navigant used the average daily load shapes from the ENO residential distribution data to adjust various inputs in the building model. The team adjusted building model input parameters to match the on-peak and off-peak energy consumption shapes and to ensure that the total facility energy peaks developed with the building model lined up temporally with the system peaks represented within the distribution data. Navigant made slight adjustments to lighting, equipment, and heating and cooling schedules to calibrate the residential model to the ENO distribution data. Load profiles were then developed using the calibrated building models and a TMY3 New Orleans weather file. Table D-1 and Table D-2 list the residential customer segment building types and end uses modeled, respectively. C&I Load Shapes The Navigant team used the commercial building models from its model library and simulated typical load shapes using the TMY3 New Orleans weather files. Navigant inputted these load shapes into the ENO potential model. Table C-1 and Table C-2 list the C&I customer segment building types and end uses modeled, respectively. Table C-1. Modeled Customer Segments by Sector Residential Commercial and Industrial Multifamily Colleges/Universities Single Family Healthcare Industrial/Warehouses Lodging Office‐Large Office‐Small Schools Restaurants Retail - Food Retail (Non-Food) Other Commercial Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page C-3 DSM Potential Study Table C-2. Modeled End Uses by Sector Residential Commercial & Industrial Total Facility (Electric) Total Facility (Electric) Lighting Interior (Electric) Lighting Interior (Electric) Lighting Exterior (Electric) Lighting Exterior (Electric) Plug Loads (Electric) Plug Loads (Electric) Cooling (Electric) Cooling (Electric) Heating (Electric) Heating (Electric) Heating/Cooling (Electric) Heating/Cooling (Electric) Hot Water (Electric) Fans/Ventilation (Electric) Other Refrigeration (Electric) Hot Water (Electric) Other C.2 Hourly IRP Model Inputs Development The Navigant team used the 8,760 loadshapes developed using the approach described in the previous section to convert the annual potential estimates into hourly potential estimates. In doing so, Navigant created program categories (Table C-3) to aggregate these hourly potential estimates to the program level and develop the input files necessary to support the IRP modeling. Navigant performed this aggregation using the mapping in Table C-4, below. The table shows a many-to-one mapping between measures and programs because some measures belong to more than one program. Navigant used the verified savings breakdown by program in ENO’s PY6 Energy Smart EM&V report to weight the savings allocation of these measures to programs. Table C-3. Program Categories Sector C&I Res Confidential and Proprietary ©2018 Navigant Consulting, Inc. Program Name Program Abbreviation Commercial Behavior Com Behavior Large Commercial & Industrial Large C&I Small Commercial & Industrial Small C&I Consumer Products Consumer Products Home Performance with Energy Star HPwES Heating, Ventilation, Air Conditioning HVAC Low Income_ Multi-Family LI_MF Residential Behavior Res Behavior Page C-4 DSM Potential Study School Kits School Kits Table C-4. Measure and Program Mapping for IRP Modeling Inputs Sector Program C&I Com Behavior C&I Building Benchmarking C&I Com Behavior C&I Retro commissioning C&I Large C&I C&I Advanced Lighting Controls C&I Large C&I C&I Advanced Roof Top Unit (RTU) Controls C&I Large C&I C&I Air and Water-Cooled Chillers C&I Large C&I C&I Air Compressor Improvements C&I Large C&I C&I Building Controls and Automation Systems (applicable to central/RTU systems) C&I Large C&I C&I Combination Ovens C&I Large C&I C&I Commercial Clothes Dryer C&I Large C&I C&I Commercial Clothes Washer C&I Large C&I C&I Commercial Fryers C&I Large C&I C&I Commercial Griddles C&I Large C&I C&I Commercial Steam Cookers C&I Large C&I C&I Computer Power Management C&I Large C&I C&I Controls Continuous Dimming C&I Large C&I C&I Controls Occupancy Sensor C&I Large C&I C&I Convection Ovens C&I Large C&I C&I Cool Roof C&I Large C&I C&I Demand Control Ventilation C&I Large C&I C&I Demand Controlled Ventilation (DCV) Exhaust Hood C&I Large C&I C&I Door LEDs C&I Large C&I C&I Ductless Mini-Split Heat Pump C&I Large C&I C&I Electric Storage Water Heater C&I Large C&I C&I Electric tankless water heater C&I Large C&I C&I ENERGY STAR Clothes Washers C&I Large C&I C&I ENERGY STAR Residential-size Refrigerator in Commercial Buildings C&I Large C&I C&I Evap Fan Controls C&I Large C&I C&I Fan and pump optimization (variable frequency drive) C&I Large C&I C&I Faucet Aerator C&I Large C&I C&I General Process Improvements (Strategic Energy management) C&I Large C&I C&I Heat Pump Water Heater C&I Large C&I C&I High Efficiency Fans and energy management Confidential and Proprietary ©2018 Navigant Consulting, Inc. Measure Page C-5 DSM Potential Study Sector Program C&I Large C&I C&I Interior 4 ft LED C&I Large C&I C&I Interior LED High Bay Replacing HID C&I Large C&I C&I Interior LED High Bay Replacing T8HO HB C&I Large C&I C&I LED Fixture - Interior C&I Large C&I C&I LED Screw In - Interior C&I Large C&I C&I LED Traffic Signals C&I Large C&I C&I Low-Flow Showerheads C&I Large C&I C&I Plug Load Occupancy Sensors C&I Large C&I C&I Pre-rinse spray valve C&I Large C&I C&I PTAC/PTHP Equipment C&I Large C&I C&I Smart Thermostats C&I Large C&I C&I Unitary and Split System AC/HP Equipment C&I Large C&I C&I Variable Air Volume HVAC C&I Large C&I C&I Water Heater Pipe Insulation C&I Large C&I C&I Window Film C&I Large C&I C&I Zero Energy Doors C&I Small C&I C&I Advanced Lighting Controls C&I Small C&I C&I Advanced Power Strips C&I Small C&I C&I Advanced Roof Top Unit (RTU) Controls C&I Small C&I C&I Building Controls and Automation Systems (applicable to central/RTU systems) C&I Small C&I C&I Combination Ovens C&I Small C&I C&I Commercial AC and HP Tune Up C&I Small C&I C&I Commercial Clothes Dryer C&I Small C&I C&I Commercial Clothes Washer C&I Small C&I C&I Commercial Fryers C&I Small C&I C&I Commercial Griddles C&I Small C&I C&I Commercial Steam Cookers C&I Small C&I C&I Computer Power Management C&I Small C&I C&I Controls Continuous Dimming C&I Small C&I C&I Controls Occupancy Sensor C&I Small C&I C&I Convection Ovens C&I Small C&I C&I Cool Roof C&I Small C&I C&I Demand Control Ventilation C&I Small C&I C&I Demand Controlled Ventilation (DCV) Exhaust Hood C&I Small C&I C&I Door Heater Controls C&I Small C&I C&I Door LEDs C&I Small C&I C&I Ductless Mini-Split HP C&I Small C&I C&I Electric Storage Water Heater Confidential and Proprietary ©2018 Navigant Consulting, Inc. Measure Page C-6 DSM Potential Study Sector Program Measure C&I Small C&I C&I Electric tankless water heater C&I Small C&I C&I Electronically Commutated Motors (ECMs) for Refrigeration and HVAC Applications C&I Small C&I C&I ENERGY STAR Clothes Washers C&I Small C&I C&I ENERGY STAR Residential-size Refrigerator in Commercial Buildings C&I Small C&I C&I Evap Fan Controls C&I Small C&I C&I Fan and pump optimization (variable frequency drive) C&I Small C&I C&I Faucet Aerator C&I Small C&I C&I Heat Pump Water Heater C&I Small C&I C&I Interior 4 ft LED C&I Small C&I C&I Interior LED High Bay Replacing HID C&I Small C&I C&I Interior LED High Bay Replacing T8HO HB C&I Small C&I C&I LED Fixture - Interior C&I Small C&I C&I LED Screw In - Interior C&I Small C&I C&I Low-Flow Showerheads C&I Small C&I C&I Plug Load Occupancy Sensors C&I Small C&I C&I Package terminal air conditioner/Package terminal heat pump Equipment C&I Small C&I C&I Refrigeration electronically commutated motor C&I Small C&I C&I Smart Thermostats C&I Small C&I C&I Solid Door commercial refrigerator C&I Small C&I C&I Strip Curtain C&I Small C&I C&I Variable Air Volume HVAC C&I Small C&I C&I Vend Machine Controls C&I Small C&I C&I Water Heater Pipe Insulation C&I Small C&I C&I Window Film C&I Small C&I Res Consumer Products Res Dehumidifiers C&I Zero Energy Doors Res Consumer Products Res Dryers Res Consumer Products Res ENERGY STAR Directional LEDs Res Consumer Products Res Omni-Directional LEDs Res Consumer Products Res Outdoor LED Light Bulb Res Consumer Products Res Pool Pumps Res Consumer Products Res Refrigeration Res Consumer Products Res Remove Second Refrigerator Res Consumer Products Res Window AC Res HPwES Confidential and Proprietary ©2018 Navigant Consulting, Inc. Res Advanced Directional LEDs Networked Lighting Controls Page C-7 with DSM Potential Study Sector Program Measure Res HPwES Res Advanced Networked Lighting Controls with OmniDirectional LEDs Res HPwES Res Advanced Power Strips Res HPwES Res Air Infiltration Res HPwES Res Attic Knee Wall Insulation Res HPwES Res Ceiling Insulation Res HPwES Res Central AC Tune-Up Res HPwES Res Duct Sealing Res HPwES Res ENERGY STAR Directional LEDs Res HPwES Res Faucet Aerators Res HPwES Res Furnace Filter Whistle Res HPwES Res High Efficiency Windows Res HPwES Res Low-Flow Showerheads Res HPwES Res Omni-Directional LEDs Res HPwES Res Outdoor Dusk-Til-Dawn LED Light Bulb Res HPwES Res Outdoor LED Light Bulb Res HPwES Res Pipe Insulation Res HPwES Res Smart Thermostats Res HPwES Res Thermostatic shower valve Res HPwES Res Wall Insulation Res HPwES Res Window Film Res HVAC Res Air Source Heat Pump Res HVAC Res Central AC Tune-Up Res HVAC Res Central Air Conditioner Res HVAC Res Duct Sealing Res HVAC Res Ductless Heat Pump Res LI_MF Res Air Infiltration Res LI_MF Res Attic Knee Wall Insulation Res LI_MF Res Ceiling Insulation Res LI_MF Res Central AC Tune-Up Res LI_MF Res Duct Sealing Res LI_MF Res ENERGY STAR Directional LEDs Res LI_MF Res Faucet Aerators Res LI_MF Res Furnace Filter Whistle Res LI_MF Res High Efficiency Windows Res LI_MF Res Low-Flow Showerheads Res LI_MF Res Omni-Directional LEDs Res LI_MF Res Outdoor Dusk-Til-Dawn LED Light Bulb Res LI_MF Res Outdoor LED Light Bulb Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page C-8 DSM Potential Study Sector Program Measure Res LI_MF Res Pipe Insulation Res LI_MF Res Smart Thermostats Res LI_MF Res Thermostatic shower valve Res LI_MF Res Wall Insulation Res LI_MF Res Window Film Res Res Behavior Res Home Energy Report Res Res Behavior Res Large Residential Competitions Res Res Behavior Res Prepay Electricity Bills Res Res Behavior Res Web-based Real-time Feedback Res School Kits Res ENERGY STAR Directional LEDs Res School Kits Res Faucet Aerators Res School Kits Res Low-Flow Showerheads Res School Kits Res Outdoor LED Light Bulb Note that the following programs that appear in the PY6 Energy Smart EM&V report have been rolled up to broader program categories in Table C-3, as follows: • Low Income/Multi-Family—includes the Low Income and Multi-Family programs from the EM&V report • Consumer Products—includes the GreenLight, Residential Lighting, and Other programs from the EM&V report Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page C-9 DSM Potential Study Appendix D. Achievable Potential Modeling Methodology Details D.1 Calculating Achievable Potential This section demonstrates Navigant’s approach to calculating achievable potential, which is fundamentally more complex than calculating technical or economic potential. The critical first step in the process to accurately estimate achievable potential is to simulate market adoption of energy efficient measures. The team’s approach to simulating the adoption of energy efficient technologies for purposes of calculating achievable potential can be broken down into the following two strata: 1. Calculation of the dynamic approach to equilibrium market share 2. Calculation of the equilibrium market share D.2 Calculation of Dynamic Equilibrium Market Share The equilibrium market share can be thought of as the percentage of individuals choosing to purchase a technology, provided those individuals are fully aware of the technology and its relative merits (e.g., the energy- and cost-saving features of the technology). For energy efficient technologies, a key differentiating factor between the base technology and the efficient technology is the energy and cost savings associated with the efficient technology. That additional efficiency often comes at a premium in initial cost. Thus, in efficiency potential studies, equilibrium market share is often calculated as a function of the payback time of the efficient technology relative to the inefficient technology. While such approaches have limitations, they are nonetheless directionally reasonable and simple enough to permit estimation of market share for the dozens or even hundreds of technologies that are often considered in potential studies. Navigant uses equilibrium payback acceptance curves that were developed using primary research conducted by Navigant in the Midwest US in 2012. 78 To develop these curves, Navigant conducted surveys of 400 residential, 400 commercial, and 150 industrial customers. These surveys presented decision makers with numerous choices between technologies with low upfront costs but high annual energy costs and measures with higher upfront costs but lower annual energy costs. Navigant conducted statistical analysis to develop the set of curves shown in Figure D-1, which were leveraged in this study. Though ENO-specific data is not currently available to estimate these curves, Navigant considers that the nature of the decision-making process is such 78 A detailed discussion of the methodology and findings of this research is contained in the Demand Side Resource Potential Study, prepared for Kansas City Power and Light, August 2013. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page D-1 DSM Potential Study that the data developed using these surveyed customers represents the best data available for this study at this time. Figure D-1. Payback Acceptance Curves 100% Market Share 80% 60% 40% 20% 0% 0 5 10 15 Payback Time (Years) Industrial Res--Homeowner & Landlord, Low Cost Res -- Landlord, High Cost 20 25 Commercial Res--Homeowner, High Cost Source: Navigant, 2015 Because the payback time of a technology can change over time, as technology costs and/or energy costs change over time, the equilibrium market share can also change over time. The equilibrium market share is, thus, recalculated for every time-step within the market simulation to ensure the dynamics of technology adoption considers this effect. As such, the term equilibrium market share is a bit of an oversimplification and a misnomer, as it can itself change over time and is, therefore, never truly in equilibrium. It is used nonetheless to facilitate understanding of the approach. D.3 Calculation of the Approach to Equilibrium Market Share The team used two approaches to calculate the approach to equilibrium market share (i.e., how quickly a technology reaches final market saturation): one for new technologies or those being modeled as a retrofit (a.k.a. discretionary) measures, and one for technologies simulated as replace-on-burnout (ROB, a.k.a. lost opportunity) measures. 79 A high level overview of each approach is provided in the following sections. 79 Each of these approaches can be better understood by visiting Navigant’s technology diffusion simulator, available at: http://forio.com/simulate/navigantsimulations/technology-diffusion-simulation. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page D-2 DSM Potential Study Retrofit/New Technology Adoption Approach Retrofit and new technologies employ an enhanced version of the classic Bass diffusion model 80,81 to simulate the S-shaped approach to equilibrium that is commonly observed for technology adoption. Figure E-2 provides a stock/flow diagram illustrating the causal influences underlying the Bass model. In this model, achievable potential flows to adopters through two primary mechanisms: adoption from external influences such as program marketing/advertising, and adoption from internal influences including word of mouth. The fraction of the population willing to adopt is estimated using the payback acceptance curves illustrated in Figure D-1. The marketing effectiveness and external influence parameters for this diffusion model are typically estimated upon the results of case studies where these parameters were estimated for dozens of technologies. 82 Additionally, the calibration process permits adjusting these parameters as warranted (e.g., to better align with historic adoption patterns within the ENO market). Recognition of the positive or self-reinforcing feedback generated by the word of mouth mechanism is evidenced by increasing discussion of concepts like social marketing and the term viral, which has been popularized and strengthened by social networking sites such as Facebook and YouTube. However, the underlying positive feedback associated with this mechanism has always been part of the Bass diffusion model of product adoption since its inception in 1969. 80 Bass, Frank (1969). "A new product growth model for consumer durables." Management Science 15 (5): p215–227. 81 See Sterman, John D. Business Dynamics: Systems Thinking and Modeling for a Complex World. Irwin McGraw-Hill. 2000. p. 332. 82 See Mahajan, V., Muller, E., and Wind, Y. (2000). New Product Diffusion Models. Springer. Chapter 12 for estimation of the Bass diffusion parameters for dozens of technologies. This model uses the median value of 0.365 for the word of mouth strength in the base case. The Marketing Effectiveness parameter was assumed to be 0.04, representing a somewhat aggressive value that exceeds the most likely value of th 0.021 (75 percentile value is 0.055) per Mahajan 2000. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page D-3 DSM Potential Study Figure D-2. Stock/Flow Diagram of Diffusion Model for New Products and Retrofits Source: Navigant, 2015 ROB Technology Adoption Approach The dynamics of adoption for ROB technologies are somewhat more complicated than for new/retrofit technologies because it requires simulating the turnover of long-lived technology stocks. To account for this, the DSMSim model tracks the stock of all technologies, both base and efficient, and explicitly calculates technology retirements and additions consistent with the lifetime of the technologies. Such an approach ensures that technology churn is considered in the estimation of achievable potential, as only a fraction of the total stock of technologies are replaced each year, which affects how quickly technologies can be replaced. A model that endogenously generates growth in the familiarity of a technology, analogous to the Bass approach described above, is overlaid on the stock tracking model to capture the dynamics associated with the diffusion of technology familiarity. A simplified version of the model employed in DSMSim is illustrated graphically in Figure D-3. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page D-4 DSM Potential Study Figure D-3. Stock/Flow Diagram of Diffusion Model for ROB Measures Source: Navigant, 2015 Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page D-5 DSM Potential Study Appendix E. Interactive Effects of Efficiency Stacking The report’s results assume that all measures are implemented in isolation from one another and that the measures do not include adjustments for interactive effects from efficiency stacking. Interactive effects from efficiency stacking are different from cross end-use interactive effects (e.g., efficient lighting affects heating/cooling loads), which are present regardless of stacking assumptions and are included in the reported savings estimates. This appendix describes the challenges related to accurately determining the effects of efficiency stacking, and why Navigant has modeled savings as though measures are implemented independently from one another. E.1 Background on Efficiency Stacking When a home or business installs two or more measures that affect the same enduse energy consumption in the same building, the total achievable savings is less than the sum of the savings from those measures independently. For example, in isolation, the installation of light-emitting diode (LED) lighting might save 40% of electric consumption relative to baseline linear fluorescent fixtures, while occupancy sensors might save 25% of electric consumption relative to fixtures without occupancy sensors. However, if both LED fixtures and occupancy sensors are installed in the same facility, the savings from the LED lighting decrease due to the reduced lighting operating hours caused by the occupancy sensors. Navigant generalizes this concept by referring to measures that convert energy as engines (boilers, light bulbs, motors, etc.) and measures that affect the amount of energy an engine must convert as drivers (insulation, thermostats, lighting controls, etc.). Any time an engine and driver are implemented in the same building, the expectation is savings from the engine measure will decrease. 83 Figure E-1 provides an illustration of three different efficiency stacking approaches. The modeled approach assumes no overlap in measure implementation and no efficiency stacking, which leads to an upper bound on savings potential. The opposite of the modeled approach is to assume all measures are stacked wherever possible, which provides a lower bound on savings. Lastly, there is the real-world approach where some measures are implemented in isolation and others are stacked. However, the data is simply not available to accurately estimate the savings from the real-world approach. 83 In practice, it does not matter whether one assumes the engine’s savings decrease or the driver’s savings decrease, as the final savings result is the same. In this discussion, Navigant chose to always reduce the savings from the engine measures, while holding the savings from the driver measures fixed. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page E-1 DSM Potential Study Figure E-1. Venn Diagrams for Various Efficiency Stacking Situations Upper Bound (Modeled) Real World Lower Bound Savings are independent Uncertain mix of independent and stacked savings Savings are stacked wherever possible Source: Navigant The area of the colored circle represents the number of buildings with a given savings opportunity. Overlapping circles indicate a building has implemented both measures. E.2 Illustrative Calculation of Savings after Efficiency Stacking For a simplistic scenario looking at only two measures it is possible to determine the stacked savings from the lower bound approach, which assumes efficiencies are stacked wherever possible. To find the LED lighting savings relative to the baseline after stacking: 1. Find the complement of the occupancy sensor savings percentage. Occupancy Sensor Savings Complement = 100% - Occupancy Sensor Savings Occupancy Sensor Savings Complement = 100% - 25% = 75% 2. Reduce the LED lighting unstacked savings by the complement of the occupancy sensor savings. Stacked LED Lighting Savings = Unstacked LED Lighting Savings x Occupancy Sensor Savings Complement Stacked LED Lighting Savings = 40% x 75% = 30% 3. Find the greatest percentage of buildings where LED lighting and occupancy sensor stacking is possible. % of Buildings with Stacking = Buildings with Occupancy Sensors / Buildings with LED lighting x 100% % of Buildings with Stacking = 145,300 / 720,200 x 100% = 20.2% Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page E-2 DSM Potential Study 4. Calculate the LED lighting weighted average savings across all buildings with occupancy sensors. Weighted LED Lighting Savings = Stacked LED Lighting Savings x % of Buildings with Stacking + Unstacked LED Lighting Savings x (100% - % of Buildings with Stacking) Weighted LED Lighting Savings = 30% x 20.2% + 40% x (100% - 20.2%) = 38% Table E-1 summarizes the example for the LED lighting and occupancy sensors before and after stacking. As expected, when treated independently the combined savings from the measures exceeds the combined savings after stacking. Table E-1. Comparison of Savings Before and After Stacking Applicable Buildings LED Lighting Occupancy Sensors 720,200 145,300 40% 25% 38% 25% Savings Treated Independently (No Stacking) Savings Relative to Baseline (%) Savings Treated Interactively (Stacking) Savings Relative to Baseline (%) Source: Navigant analysis E.3 Impetus for Treating Measure Savings Independently Although it is possible to find the lower bound on savings with just one driver and one engine measure, the process becomes intractable when multiple drivers and engines can be installed in the same facility. Table E-2 lists all the engine and driver measures included in this study that could have interactive effects within the commercial lighting end use, which is just one of many end uses across multiple sectors where stacking could occur. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page E-3 DSM Potential Study Table E-2. Measures with Opportunity for Stacking in Commercial Lighting End Use Engine Measures Driver Measures Exterior LED Photocell Interior LED Tube Interior Daylighting Controls Interior LED MR/PAR Lamps Fixture or Wall-Mounted Occupancy Sensors Interior Recessed LED Downlighting (Troffer LEDs) - Interior High Bay LED - LED Luminaire - Source: Navigant Determining the appropriate stacking and correctly weighting the savings percentages from each of the engine measures requires the following: • Case-by-case expert judgment about the combinations of driver and engine measures that might realistically be found in the same building given historic and future construction practices • The conditional probability that a building has an inefficient driver A and an inefficient engine B for all drivers and engines relevant to a given end use • In-depth knowledge of program design and how managers are considering pursuing participants and bundling measure offerings Lastly, at low levels of customer participation, assuming savings are independent is the best representation of what the actual measure stacking would be. When customer participation is high, the real-world scenario is the best representation of actual measure stacking. Thus, under the plausible ranges of customer participation, the modeled (upper bound) scenario is likely to be a better representation of actual measure stacking than the lower bound scenario. Although this report does not rigorously attempt to quantify the impact from efficiency stacking within the ENO service area, Navigant’s experience indicates stacking can lead to a 5%-10% reduction in savings potential at high levels of technology adoption. This estimate is applicable to the residential and C&I sectors but is less applicable for the industrial sector because of reduced opportunity for stacking among the industrial measures considered in this study. Additionally, the 5%-10% reduction is highly uncertain and dependent upon the characteristics of any given building and grouping of measures. Confidential and Proprietary ©2018 Navigant Consulting, Inc. Page E-4 BEFORE THE COUNCIL OF THE CITY OF NEW ORLEANS EX PARTE: IN RE: 2018 TRIENNIAL INTEGRATED RESOURCE PLAN OF ENTERGY NEW ORLEANS, INC. ) ) ) ) DOCKET NO. UD-17-03 APPENDIX E Study of Potential for Energy Savings in New Orleans prepared by Optimal Energy JULY 2019 Study of Potential for Energy Savings in New Orleans Final: August 31, 2018 Prepared for by with Optimal Energy, Inc. 802-482-5600 www.optenergy.com 10600 Route 116, Suite 3 Hinesburg, VT 05461 Study of Potential for Electric Energy Savings New Orleans, Louisiana Contents EXECUTIVE SUMMARY ........................................................................................................... 1 Study Context and objectives ......................................................................................................................1 Summary of Results.....................................................................................................................................1 INTRODUCTION...................................................................................................................... 12 Study Overview and scope ........................................................................................................................12 Summary of study process and timeline ....................................................................................................12 Definition of Scenarios ..............................................................................................................................12 ENERGY EFFICIENCY ............................................................................................................. 14 Summary of Approach & Major Assumptions ..........................................................................................14 Methodology Overview .............................................................................................................................14 Results .......................................................................................................................................................19 Benchmarking the Results .........................................................................................................................34 Other Benefits of Efficiency ......................................................................................................................36 DEMAND RESPONSE ............................................................................................................. 41 Summary of Approach & Major Assumptions ..........................................................................................41 Methodology..............................................................................................................................................41 Measure Charaterizations ..........................................................................................................................43 Results .......................................................................................................................................................49 COMBINED RESULTS ............................................................................................................. 56 RATE DESIGN ........................................................................................................................... 57 Summary of Approach & Major Assumptions ..........................................................................................57 Methodology..............................................................................................................................................59 Results .......................................................................................................................................................60 METHODOLOGY DETAILS .................................................................................................. 63 Overview ...................................................................................................................................................63 Energy Forecasts........................................................................................................................................65 Measure Characterization ..........................................................................................................................65 Top-Down Methodology ...........................................................................................................................67 Cost-Effectiveness Analysis ......................................................................................................................69 Avoided Energy Supply Costs ...................................................................................................................70 Energy Retail Rates ...................................................................................................................................71 Electric Load Shapes .................................................................................................................................72 Economic Potential Analysis .....................................................................................................................72 Program Potential Scenario .......................................................................................................................73 WORKS CITED .......................................................................................................................... 75 APPENDIX A: DELPHI PROCESS ......................................................................................... 78 APPENDIX B: SALES DISSAGGREGTION ........................................................................ 85 APPENDIX C: LOADSHAPES ................................................................................................ 87 APPENDIX D: MEASURE CHARACTERIZATIONS ........................................................ 88 Optimal Energy, Inc. Study of Potential for Electric Energy Savings New Orleans, Louisiana Tables Table 1 Cumulative Energy Efficiency Potential as Percent of Sales Forecast .............................................3 Table 2 Incremental Annual Savings by Year as Percent of Sales Forecast ..................................................3 Table 3 Cumulative Demand Savings Potential by Sector and Scenario (MW) ............................................5 Table 4 Scenario TRC Cost-Effectiveness by Sector – Full 20 Years ...........................................................6 Table 5 Scenario TRC Cost-Effectiveness by Sector – First 10 Years ..........................................................6 Table 6 Nominal Program Potential Budgets by Year (Millions$) ................................................................7 Table 7 Demand Response Peak Load Reductions (MW) – Scenario One ...................................................8 Table 8 Demand Response Peak Load Reductions (MW) – Scenario Two ...................................................8 Table 9 Cumulative Rate Design Potential Relative to Sales Forecast ........................................................10 Table 10 Cumulative Peak Demand Reduction from EE, DR, and Rate Design .........................................11 Table 11 Cumulative Energy Savings As Percent of Sales by Sector and Scenario (MWh) .......................20 Table 12 Average Incremental Annual Savings ...........................................................................................21 Table 13 Incremental Annual Savings by Year as Percent of Sales ............................................................21 Table 14 Cumulative Demand Savings Potential by Sector and Scenario (MW) ........................................22 Table 15 Scenario TRC Cost-Effectiveness by Sector – Full 20 Years .......................................................24 Table 16 Scenario TRC Cost-Effectiveness by Sector – First 10 years .......................................................25 Table 17 Program Potential Nominal Budgets ($MM) ................................................................................25 Table 18 Maximum Achievable Potential Nominal Budgets ($MM) ..........................................................26 Table 19 Residential Electric Energy Top Saving Measures (2037) ...........................................................27 Table 20 Residential Electric Demand Top Saving Measures ( 2037) ........................................................28 Table 21 LI Residential Electric Energy Top Saving Measures (2037) .......................................................29 Table 22 LI Residential Electric Demand Top Saving Measures (2037) .....................................................30 Table 23 C&I Electric Energy Top Saving Measures (2037) ......................................................................31 Table 24 C&I Electric Demand Top Saving Measures (2037) ....................................................................32 Table 25 Measures not Cost-Effective with Higher WACC Discount Rate ................................................34 Table 26 Potential Study Benchmarking .....................................................................................................35 Table 27 Efficiency 2016 Top Savers ..........................................................................................................36 Table 28 Effect of Efficiency Spending on Jobs ..........................................................................................38 Table 29 Residential DLC/ADR Model Inputs ............................................................................................44 Table 30 Residential PTR Model Inputs ......................................................................................................47 Table 31 Residential CPP Model Inputs ......................................................................................................48 Table 32 Large Customer Program Assumptions ........................................................................................49 Table 33 Demand Response Peak Load Reductions Summary – Scenario One ..........................................51 Table 34 Demand Response Peak Load Reductions Summary– Scenario Two ..........................................51 Table 35 Demand Response Peak Load Reductions By Year – Scenario One ............................................52 Table 36 Demand Response Peak Load Reductions By Year – Scenario Two ...........................................53 Optimal Energy, Inc. Study of Potential for Electric Energy Savings New Orleans, Louisiana Table 37 Net Costs and Benefits of DR Potential in Scenarios One and Two .............................................55 Table 38 Cumulative Peak Demand Reduction from EE, DR, and Rate Design .........................................56 Table 39 ENO Existing Residential Rates ...................................................................................................57 Table 40 Summary of Rate Design Scenarios..............................................................................................58 Table 41 Price Elasticity Assumptions ........................................................................................................60 Table 42 Summary of Results ......................................................................................................................61 Table 43 Overview of Cost-Effectiveness Tests ..........................................................................................70 Table 44 Delphi Panel Residential Program Potential Multipliers ..............................................................79 Table 45 Delphi Panel C&I Program Potential Multipliers .........................................................................80 Table 46 Delphi Panel Measure Mapping ....................................................................................................80 Table 47 Residential Sales Disaggregation ..................................................................................................85 Table 48 Commercial and Industrial Sales Disaggregation .........................................................................86 Table 49 Residential Loadshapes .................................................................................................................87 Table 50 C&I Loadshapes ...........................................................................................................................87 Table 51 Residential Measure Level Information ........................................................................................88 Table 52 Commercial Measure Level Information ......................................................................................91 Figures Figure 1 Electric Energy Savings Relative to Sales Forecast ........................................................................4 Figure 2 Electric Peak Demand Savings From Efficiency Relative to Sales Forecast ...................................5 Figure 3 Electric Demand Savings—Scenario One .......................................................................................8 Figure 4 Electric Demand Savings Relative to Sales Forecast--Scenario Two ..............................................9 Figure 5 Average Hourly Forecast Energy Price – Summer Months...........................................................17 Figure 6 Delphi Panel Adoption Curves – Residential Sector .....................................................................18 Figure 7 Electric Energy Savings Relative to Sales Forecast ......................................................................20 Figure 8 Electric Peak Demand Savings From Efficiency Relative to Sales Forecast .................................23 Figure 9 Cumulative Avoided CO2 Emissions (Thousands of Metric Tons) ...............................................23 Figure 10 Residential Electric Energy Savings by End Use (2037) .............................................................27 Figure 11 Residential Electric Demand Savings by End Use (2037)...........................................................28 Figure 12 LI Residential Electric Energy Savings by End Use (2037) ........................................................29 Figure 13 LI Residential Electric Demand Savings by End Use (2037) ......................................................30 Figure 14 C&I Electric Energy Savings by End Use (2037) .......................................................................31 Figure 15 C&I Electric Demand Savings by End Use (2037) .....................................................................32 Figure 16 Electric Demand Savings - Scenario One ....................................................................................50 Figure 17 Electric Demand Savings - Scenario Two ...................................................................................51 Figure 18 Annual Program Costs—Scenario One .......................................................................................54 Figure 19 Annual Program Costs—Scenario Two.......................................................................................54 Figure 20 Average Hourly Forecast Energy Price – Summer Months .........................................................71 Optimal Energy, Inc. Study of Potential for Electric Energy Savings New Orleans, Louisiana Figure 21 Residential Adoption Curves .......................................................................................................79 Figure 22 Delphi Panel C&I Responses ......................................................................................................80 Optimal Energy, Inc. Study of Potential for Electric Energy Savings New Orleans, Louisiana EXECUTIVE SUMMARY STUDY CONTEXT AND OBJECTIVES This study provides an estimate of the potential for energy savings and peak demand reduction through utility run energy efficiency, peak demand, and rate design programs in Entergy New Orleans’ (ENO or “Entergy”) service territory. Energy efficiency is typically a less expensive way to meet customer load requirements than traditional supply side investments. Furthermore, energy efficiency produces significant additional benefits, such as lower electric bills for ratepayers, lower carbon emissions, and healthier buildings. For these reasons, efficiency has increasingly been used by utilized as an alternative to supply side investments on the electric grid. This study will be used to inform ENO’s future Energy Smart programs; it will also produce inputs for ENO’s upcoming Integrated Resource Plan (IRP). An IRP is an analysis that seeks to optimize a utility’s supply portfolio to meet its load requirements at lowest cost, subject to fulfilling criteria related to reliability, risk, and other metrics. To this end, Optimal Energy, Inc. (Optimal) will provide ENO with a 20-year forecast of potential energy and demand savings as a demand-side resource in the IRP modeling, which will “compete” with other resources for inclusion in an overall strategy for least-cost planning. SUMMARY OF RESULTS High-level results for the three components of this study (i.e., energy efficiency, demand response, and rate design) are presented separately in the sections below. Further detail is provided in the full report that follows. Energy Efficiency As discussed in detail in the methodology section, the energy efficiency potential analysis included three levels of potential. • • • Economic – All measures that are cost-effective and technically feasible, assuming no market barriers to adoption. Maximum Achievable – All cost-effective measures are promoted by aggressive programs, including incentives covering 100% of the total incremental costs of the measure, with the intent of securing the maximum amount of efficiency savings possible given real-world constraints of customer behavior. Program Achievable – The amount of available potential assuming “best practice” program design, with incentives covering, on average, 50% of the incremental costs of the measures. An exception is made for income-eligible customers, who still receive 100% incentives, as with ENO’s current Energy Smart programs. Optimal Energy, Inc. 1 Study of Potential for Electric Energy Savings New Orleans, Louisiana Our energy efficiency analysis begins by characterizing hundreds of possible energy efficiency measures as to their costs and energy savings. Savings are expressed as percentage reduction in energy use for the relevant “end-use” (e.g., lighting, cooling, refrigeration). An overall estimate of efficiency potential is generated by first dividing all energy use by ENO’s customers into end-uses and then applying relevant measures and their respective savings percentages to these “buckets” of energy use. This “top-down” approach ensures that energy savings are appropriately scaled to the actual energy consumption of ENO’s customers, and will be described in greater detail later in the report. Overall, we examined 173 different measures over 3 different market types (new construction/renovation, market opportunity, and retrofit/early retirement) and 14 different building types, for 1,491 permutations of unique measures. Comparisons across potential types are useful for understanding the bounds of achievement. Following the portfolio level results we present more detailed results for the program potential, including disaggregated results for each sector (Residential, Low-Income, and Commercial/Industrial). Table 1 provides a summary of the economic, maximum achievable, and program potential for electric energy savings relative to the sales forecast after 10 and 20 years. Savings as a percentage of forecast sales is a common metric for comparing efficiency programs and potential estimates, as it provides an understandable scale for those not familiar with energy measurements such as megawatt-hours (MWh). Overall, program potential for electricity is 21% of the forecasted load in 2038. This means that the cumulative result after 20 years of energy efficiency programs with incentives covering 50% of the incremental cost is that New Orleans electric load is 21% lower than it would be with no efficiency programs. The maximum achievable potential for electricity is 30% by 2038, roughly 40% greater than the program potential. Potential after 20 years is not much greater than after 10 years, particularly for economic and max achievable scenarios, because the majority of equipment has been upgraded after the initial 10 year period. Over the course of the next 10 years, equipment that reaches the end of its useful life provides further opportunities for efficient measures, but savings from measures installed in the earlier years are expiring. Table 1 shows the cumulative savings in year 10 and year 20. In other words, it represents the total reduction in the given year from all the efficiency measures installed in prior years that have not reached the end of their useful life. However, due to variations in measure lives and baseline adjustments for retrofit, the sum of incremental annual savings (the “new” savings achieved in each year of an efficiency program, independent of what has been achieved in other years) is typically higher than the cumulative savings totals. It is therefore instructive to see the incremental annual savings for each year of the study horizon. This is shown in Table 2. Optimal Energy, Inc. 2 Study of Potential for Electric Energy Savings New Orleans, Louisiana Table 1 Cumulative Energy Efficiency Potential as Percent of Sales Forecast Year 2027 2037 Scenario Residential Savings Low Income Savings C&I Savings Total Economic 49% 49% 43% 45% Max Achievable 27% 27% 25% 25% Program 9% 27% 18% 18% Economic 49% 49% 45% 46% Max Achievable 33% 33% 29% 30% Program 9% 33% 21% 21% Table 2 Incremental Annual Savings by Year as Percent of Sales Forecast Year 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 Economic Potential Total Res C&I 5.7% 7.5% 4.6% 5.5% 7.3% 4.4% 5.2% 6.7% 4.4% 4.4% 4.6% 4.3% 4.2% 4.3% 4.2% 4.4% 4.5% 4.3% 4.5% 4.7% 4.4% 4.5% 4.6% 4.4% 4.6% 4.7% 4.5% 4.7% 5.0% 4.5% 2.7% 2.5% 2.8% 3.0% 2.8% 3.1% 3.0% 2.9% 3.1% 3.3% 3.0% 3.6% 3.4% 3.0% 3.6% 3.6% 3.1% 3.9% 3.6% 3.1% 3.9% 3.6% 3.1% 3.9% 3.7% 3.4% 3.9% 3.7% 3.4% 3.9% Optimal Energy, Inc. Max Achievable Potential Total Res C&I 0.7% 0.7% 0.6% 1.3% 1.4% 1.2% 2.0% 2.0% 1.9% 2.6% 2.5% 2.7% 2.8% 2.7% 2.8% 3.0% 3.1% 3.0% 3.1% 3.3% 3.0% 3.2% 3.5% 3.1% 3.3% 3.7% 3.1% 3.4% 3.8% 3.1% 1.3% 1.7% 1.1% 1.6% 1.9% 1.5% 1.9% 2.0% 1.8% 2.3% 2.2% 2.4% 2.3% 2.2% 2.4% 2.5% 2.2% 2.6% 2.5% 2.2% 2.6% 2.5% 2.2% 2.6% 2.6% 2.4% 2.6% 2.6% 2.4% 2.6% Program Potential Total Res C&I 0.5% 0.5% 0.5% 1.0% 1.0% 0.9% 1.4% 1.4% 1.4% 1.9% 1.7% 2.0% 2.0% 1.8% 2.1% 2.2% 2.1% 2.2% 2.3% 2.3% 2.3% 2.3% 2.4% 2.3% 2.4% 2.5% 2.3% 2.4% 2.6% 2.3% 0.9% 1.2% 0.8% 1.2% 1.3% 1.1% 1.4% 1.4% 1.4% 1.7% 1.5% 1.8% 1.7% 1.5% 1.8% 1.8% 1.6% 2.0% 1.8% 1.6% 2.0% 1.8% 1.6% 2.0% 1.9% 1.7% 2.0% 1.9% 1.7% 2.0% 3 Study of Potential for Electric Energy Savings New Orleans, Louisiana Figure 1 shows the historic and forecasted sales of electric energy. As seen, the forecast is expected to be relatively flat over the next 20 years.1 Total sales could be reduced significantly, however, through energy efficiency. Electric Sales Forecast Figure 1 Electric Energy Savings Relative to Sales Forecast 0 2018 2021 2024 Baseline Forecast Program Potential 2027 2030 2033 2036 Max Achievable Potential Economic Potential Table 3 shows the peak demand reduction in 2027 and 2037 for the different potential scenarios. These represent the savings associated with efficiency programs only. Savings from demand response programs are discussed separately below. In contrast to the energy savings estimates, these are given in megawatts (MW) instead of percent of total load. This allows for an easier comparison to traditional generation assets, such as the recently approved 150 MW gas turbine plant. Figure 2 shows the historic and forecasted sales of electric peak demand. The other lines show the reduction in peak demand from each scenario. This graph only shows peak demand reduction from efficiency. Demand response impacts are discussed separately in the next section. 1 The scale for the y-axis of this figure is omitted to avoid disclosing data considered by ENO as Highly Sensitive Protected Material (HSPM). Optimal Energy, Inc. 4 Study of Potential for Electric Energy Savings New Orleans, Louisiana Table 3 Cumulative Demand Savings Potential by Sector and Scenario (MW) Year 2027 2037 Scenario Residential Savings LI Savings C&I Savings Total Economic Potential 137 133 260 530 Max Achievable Potential 73 71 150 294 Program Potential 17 71 106 194 Economic Potential 137 134 286 557 Max Achievable Potential 89 86 186 361 Program Potential 24 86 133 243 Electric Sales Forecast Figure 2 Electric Peak Demand Savings From Efficiency Relative to Sales Forecast 0 2018 2021 2024 Baseline Forecast Program Potential 2027 2030 2033 2036 Max Achievable Potential Economic Potential The next table shows the Total Resource Cost Effectiveness Test results for each scenario. The costs and benefits below represent the net present value of running 20 years of programs. As seen, Optimal Energy, Inc. 5 Study of Potential for Electric Energy Savings New Orleans, Louisiana while the scenarios incur significant costs (i.e., utility administrative costs, incentive costs, and customer contributions), the total benefits are two to four times larger than the costs. Table 4 Scenario TRC Cost-Effectiveness by Sector – Full 20 Years Sector Residential C&I Total Scenario Costs ($MM) Benefits ($MM) Net Benefits ($MM) BCR Economic 461 1,216 754 2.6 Max Achievable 310 716 406 2.3 Program 207 467 260 2.3 Economic 516 2,486 1,970 4.8 Max Achievable 304 1,129 825 3.7 Program 202 823 621 4.1 Economic 978 3,702 2,724 3.8 Max Achievable 614 1,845 1,231 3.0 Program 409 1,290 880 3.2 Table 5 shows the same information, but for the 2018-2027 time frame instead of the full 20years. As seen, BCRs are very similar, but slightly lower. This is because of a higher share of retrofit measures which, on average, have lower BCRs than market driven measures. Table 5 Scenario TRC Cost-Effectiveness by Sector – First 10 Years Sector Residential C&I Total Optimal Energy, Inc. Scenario Costs ($MM) Benefits ($MM) Net Benefits ($MM) BCR Economic $335 792 $457 2.37 Max Achievable $203 $409 $207 2.02 Program $134 $267 $133 1.99 Economic $333 1,445 $1,112 4.33 Max Achievable $185 597 $412 3.23 Program $118 $427 $309 3.62 Economic $668 $ 2,237 $1,569 3.35 Max Achievable $388 $ 1,006 $619 2.60 Program $252 $694 $442 2.75 6 Study of Potential for Electric Energy Savings New Orleans, Louisiana The costs presented in the tables above represent the net present value of the total costs of energy efficiency programs and the resulting investment in efficient measures. This includes the administrative costs of running the programs and the full incremental costs of installing the measures, regardless of the amount paid for by the utility vs. paid for by the customer. In the program potential scenario, the utility only covers a portion of the measure costs; the table below shows the utility program budget needed to achieve the savings in the program potential scenario. Table 6 Nominal Program Potential Budgets by Year (Millions$) Year 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 NonIncentive $1.6 $3.2 $4.7 $6.4 $6.7 $7.2 $7.6 $7.8 $8.0 $8.1 Incentive Total Year $4.8 $9.7 $14.4 $19.4 $20.6 $22.4 $23.6 $24.4 $25.2 $25.8 $6.5 $12.9 $19.2 $25.8 $27.3 $29.7 $31.2 $32.2 $33.2 $34.0 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 NonIncentive $2.6 $3.1 $3.5 $4.2 $4.2 $4.6 $4.6 $4.7 $4.8 $4.8 Incentive Total $9.9 $11.2 $12.4 $14.3 $14.3 $15.4 $15.5 $15.6 $16.0 $16.0 $12.5 $14.3 $15.9 $18.5 $18.5 $20.1 $20.2 $20.2 $20.7 $20.8 Demand Response While energy efficiency investments result in “permanent” load reductions (i.e., throughout the useful life of the measure), demand response (DR) strategies aim to reduce usage during peak load conditions. This may mean shifting consumption to off-peak periods or simply reducing consumption without replacing it at another time. Because energy prices are typically highest during peak load conditions, this can substantially reduce total system costs. Furthermore, in areas with constrained generation, transmission, or distribution capacity, it can avoid the need to invest in additional capacity, again typically at lower cost. The DR analysis in this study is based on the demonstrated performance of DR programs in other utility-implemented programs and extrapolating to the ENO service territory. The DR analysis considered two scenarios, which roughly align with the max achievable and program potential scenarios from the energy efficiency analysis. Scenario One assumes participation on the lower end of the range of what is being achieved in other jurisdictions for residential and large customer direct load control (DLC), residential automated demand response (ADR), and large customer standard offer program (SOP). Scenario Two assumes participation on the upper end for these programs. In addition, Scenario One assumes a residential peak time rebate program (in which customers can receive an incentive payment for reducing usage during times of highest load, e.g., “peak time event”), while Scenario Two assumes residential critical peak pricing (in which customers must pay a much higher rate for usage during peak time events). Studies have been shown that because consumers tend to be more averse to losing money Optimal Energy, Inc. 7 Study of Potential for Electric Energy Savings New Orleans, Louisiana than to missing out on a similar windfall, critical peak pricing can have a somewhat bigger impact on behavior than peak time rebates. Results for each of the scenarios are presented in the Figures and Tables below. Table 7 Demand Response Peak Load Reductions (MW) – Scenario One Program 2018 2027 2037 Residential DLC and ADR Residential PTR pricing Large Customer SOP Total 2.0 4.9 1.1 8.0 16.0 12.6 10.9 39.5 20.2 15.5 16.9 52.5 Table 8 Demand Response Peak Load Reductions (MW) – Scenario Two Program 2018 2027 2037 Residential DLC and ADR Residential PTR pricing Large Customer SOP Total 3.9 5.6 1.9 11.5 31.9 14.2 13.4 59.6 40.3 17.5 23.2 81.1 Figure 3 Electric Demand Savings—Scenario One Optimal Energy, Inc. 8 Study of Potential for Electric Energy Savings New Orleans, Louisiana Figure 4 Electric Demand Savings Relative to Sales Forecast--Scenario Two Rate Design The design of rate tariffs can have a significant impact on patterns of electric consumption. For example, inclining block rates (in which the price per unit of energy increases as consumption increases) tend to discourage energy use as the marginal cost of consumption exceeds the average. Declining block rates and large monthly fixed costs may encourage additional electric use for the opposite reason; lower marginal energy costs increase consumption. Now that advanced metering infrastructure (AMI) enables more sophisticated tariffs such as time-of-use (TOU) rates, utilities can better correlate prices with the costs of energy at different times. This can shift usage to off-peak periods, resulting in benefits similar to demand response efforts. Importantly, all of these rate options can be implemented in a way that does not change the total revenue collected from customers, which means neither the customers as a whole or the utility are disadvantaged. For this study, we use recently published estimates of the price elasticity of electricity to calculate the impact of various revenue neutral rate designs for the residential sector. We considered the following rate structures. • • • Higher monthly customer charges – this will decrease the marginal price of electricity, and thus increase the total usage Time-of-use rates – we examined both “opt-in” and “opt-out” scenarios Seasonal inclining block rate – the cost per unit of electricity increases as consumption increases, thus increasing the incentive to use less electricity at the margin Optimal Energy, Inc. 9 Study of Potential for Electric Energy Savings New Orleans, Louisiana The table below shows the results from the analysis. Increased customer charges would increase the total electric load, while the inclining block rate would decrease total load. The timeof-use rate would produce a small decrease in total load, but create a fairly significant shift from on-peak to off-peak periods. Note that these impacts are one-time events – they do not accumulate from year-to-year as efficiency savings do. Table 9 Cumulative Rate Design Potential Relative to Sales Forecast Rate Scenario Optional time of use Default time of use Inclining block rate Seasonal ($25/mo. customer charge) Seasonal ($50/mo. customer charge) Change in energy consumption -0.5% -0.9% -2.1% 3.6% 8.9% Change in peak demand -4.4% -7.9% N/A N/A N/A Total Peak Demand Savings, all DSM Although this analysis mainly treats the demand response, energy efficiency, and rate design portions as independent and separate, we do provide a high level analysis of the likely total peak demand reduction from all three DSM types (efficiency, demand response, and rate design). The table below shows project total demand reduction by year. We derived these values by assuming a simple “loading order” of the categories: first rate design first, then energy efficiency, and then demand response. In other words, if in a given year the three categories would each produce a 10% reduction in peak separately, we assume that the rate design reduces the forecast by 10%, then the efficiency reduces the new forecast by 10%, and then demand response reduces the remaining peak by another 10%. This way, total demand is reduced by around 27%, instead of the 30% that would result if you simply added the reductions together. Table 10 presents the results of this analysis, assuming an optional time of use rate design, the program potential energy efficiency savings, and scenario two for demand response. Optimal Energy, Inc. 10 Study of Potential for Electric Energy Savings New Orleans, Louisiana Table 10 Cumulative Peak Demand Reduction from EE, DR, and Rate Design Year 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 Optimal Energy, Inc. Peak Reduction (MW) 67 83 104 129 154 181 209 236 262 288 Year 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 Peak Reduction (MW) 297 305 313 321 329 335 340 343 347 350 11 Study of Potential for Electric Energy Savings New Orleans, Louisiana INTRODUCTION STUDY OVERVIEW AND SCOPE This section provides a brief overview of the study scope and approaches, with more detail provided in the sections below. This study was conducted to provide a set of inputs for use in Entergy New Orleans’ (ENO or “Entergy”) upcoming Integrated Resource Plan (IRP), as well as to inform spending and savings targets for future Energy Smart program years. The study looked at savings opportunities from energy efficiency, demand response, and rate designs independently over a 20-year horizon. Each can serve as a resource for meeting some of ENO’s forecast load requirements in the IRP modeling. The study scope was limited in several important respects: • • • Except for input from the Delphi panel, no primary data were collected; the study thus relies primarily on existing available data, in some cases from outside of ENO’s service territory or Louisiana Does not include combined heat and power (CHP) opportunities Did not attempt to project future changes in code that are not currently planned, nor changes in costs and savings from current technologies over time The Methodology section later in this report describes the methods and assumptions used in the analysis in detail. The efficiency, demand response, and rate design analyses are each present in their own section that includes the methodology, data sources, and results. SUMMARY OF STUDY PROCESS AND TIMELINE The study was initiated in late March, followed by a kick-off meeting in New Orleans on April 4, attended by Entergy New Orleans, several stakeholders, and representatives of the City Council, the client for the project. At this meeting, the Optimal Energy team (Optimal) presented a preliminary measure list and described the study methodology. Analytical work began in earnest after the stakeholder meeting, including the creation of two Delphi panels to provide key input to the efficiency potential study. Draft results for a “maximum achievable” scenario were distributed to stakeholders on July 9, followed by another stakeholder meeting on July 13. Stakeholders submitted comments and questions on the draft results on July 23, and responses were provided on August 6. A draft final report was circulated on August 16, followed by the receipt of comments from stakeholders and the release of this report on August 31. DEFINITION OF SCENARIOS This study evaluated energy efficiency potential for three separate scenarios: • Economic – Everything that is cost-effective and technically feasible, assuming no market barriers. A measure is considered to be cost-effective if the net present value of the avoided energy and capacity costs over its effective useful life is equal to or greater than the net present value of the measure cost. Optimal Energy, Inc. 12 Study of Potential for Electric Energy Savings New Orleans, Louisiana • • Maximum Achievable – The maximum level of program activity and savings that is possible given the market barriers to adoption of energy efficient technologies, with no limits on incentive payments, but including administrative costs necessary to implement programs. Program Achievable – A feasible and practical level of achievable savings given a specific set of programs targeting specific markets, with realistic estimates of incentive payments. Administrative costs are again included. The analyses of demand response and rate design opportunities proceeded using slightly different methodologies, so the scenario definitions for efficiency are not directly applicable to these resources. The demand response analysis includes two scenarios of greater or lesser “aggressiveness” in assumed customer response and a key programmatic difference. The rate design analysis included five different scenarios with a range of impacts. Optimal Energy, Inc. 13 Study of Potential for Electric Energy Savings New Orleans, Louisiana ENERGY EFFICIENCY This section presents the methodology for and detailed results from our analysis of the energy efficiency potential. SUMMARY OF APPROACH & MAJOR ASSUMPTIONS The major steps in conducting the energy efficiency potential study were as follows: • • • • • • Develop energy use forecasts Disaggregate energy forecasts by sector (e.g., residential vs. commercial), and end uses (e.g., lighting, cooling, refrigeration) Characterize efficiency measures Screen measures and programs for cost-effectiveness Develop measure penetrations for “achievable” scenarios Determine scenario potential and develop outputs A key characteristic of our approach to efficiency potential studies is that it proceeds using a “top-down” methodology. This involves beginning with the entirety of ENO’s electric sales, then “disaggregating” those sales into many smaller quantities of electricity that represent consumption by various customer types and several building types. From there, energy efficiency measures—in the form of percentage reductions in consumption—are applied to the portion of each quantity of electricity to which they are applicable. This is in contrast to a “bottom-up” methodology that seeks to build up the efficiency potential by estimating the quantity of measures that could be installed and the per-unit energy savings of that measure. The top-down method insures that the energy savings are calibrated to actual energy sales. METHODOLOGY OVERVIEW This section gives a short summary of the overall methodology used to perform the efficiency analysis. For much more detail see the later section on methodology. Energy Use Forecast and Disaggregation For consistency with the IRP process, we started from Entergy’s sales forecast, and adjusted to add back savings from current levels of savings from existing Energy Smart Programs. Energy use was disaggregated using multiple sources. In the commercial sector, data provided by Entergy was used to segment sales by building type based on customer SIC code. In both the Optimal Energy, Inc. 14 Study of Potential for Electric Energy Savings New Orleans, Louisiana residential and commercial sectors, disaggregation by end-use relied on data from the Energy Information Administration23. Measure Selection and Characterizations The measure list for the study was initially developed from several sources in combination, including the NOLA TRM and previous potential studies conducted by Optimal Energy. Each measure included in the study must be characterized, which is a process of specifying the costs, savings, effective useful life, and other impacts of the measure. This is at the core of any potential study. To characterize the measures for this study we used data from the NOLA TRM where applicable and practical. This information was supplemented with other regional TRMs and Optimal’s existing measure characterization database. In addition, we drew on data from a Residential Appliance Saturation Survey conducted by Entergy, as well as other similar studies conducted more in nearby states. All told, we examined 173 different measures over 3 different market types (New Construction/Renovation, market opportunity, and retrofit) and 14 different building types for 1,491 permutations of unique measure types. See the section on methodology details for more information. Assessing Economic Potential & Cost-Effectiveness Once the measure list is complete and fully characterized, we can develop an initial estimate of potential that assumes all cost-effective measures are fully implemented where technically feasible. Although this “economic” potential does not represent an outcome that could reasonably be expected under any conditions, it helps to calibrate the remaining scenarios that take into account customer behavior and the many barriers to efficiency investment. This study uses a “Total Resource Cost” (TRC) test to evaluate cost-effectiveness, by comparing the economic benefits resulting from the program activity to the costs of efficiency investments. The TRC test is the most commonly used cost-effectiveness test for evaluating energy efficiency programs and measures, and attempts to consider a total, economy-wide vision of the costs and benefits of the program. On the cost side, program administration costs and the full incremental costs of the efficiency measures are included. The precise incentive amount does not impact the TRC, as the total incremental cost is incurred by the economy, regardless of whether it is paid for by the participant or the program administrator. Efficiency measures and programs are considered to be cost-effective if the net present value of benefits exceeds the net present value of costs. Assessing the cost-effectiveness of efficiency measures means comparing the costs of investing in the measure with the economic benefits realized from that investment. With most efficiency measures, the vast majority of economic benefits are derived from the value of avoiding 2 US Energy Information Administration. Commercial Building Energy Consumption Survey. https://www.eia.gov/consumption/commercial/reports.php. Used data from 2012 survey in West South Central Census Division. 3 US Energy Information Administration. Residential Energy Consumption Survey. https://www.eia.gov/consumption/residential/. Used data from 2009 survey in West South Central Census Division Optimal Energy, Inc. 15 Study of Potential for Electric Energy Savings New Orleans, Louisiana the energy consumption that would otherwise other in the absence of the efficiency measure. These “avoided costs” are therefore a key input to the potential model. The benefits listed below are included. For more detailed descriptions, please refer to the Methodology Section • • • • Avoided Energy Costs: These represent the variable costs associated with producing the marginal unit of electricity. For this study, we used forecasts of location marginal prices (LMPs) for the relevant zone within the Midcontinent Independent System Operator (MISO) footprint. Avoided Capacity Costs: This is the value of avoiding new generation equipment. For this study, we use ENO’s forecast cost of a new combustion turbine plant. Avoided Fuel Costs: Some measures, such as insulation, result in fossil fuel savings in addition to electric savings. These savings are included in a TRC test. Avoided Non-Energy Costs: Some measures produce quantifiable non-energy benefits, such as operation and maintenance savings and water savings. These have been included in the measure TRCs to the extent feasible given current estimates of their magnitude and value. For this study, we developed avoided energy costs from ENO’s forecast of annual energy prices4 and historical hourly Locational Marginal Price (LMP) data.5 We simplified these thousands of data points into average costs during four energy periods. The year was first divided in “summer” months (April through October) and “winter” months (November through March) based on observed patterns of energy consumption revealed in. Figure 5, below, shows the average hourly price for each summer month. In each season, on-peak and off-peak periods were determined, again using hourly LMP data. For summer, on-peak hours are weekdays between 11 AM and 9 PM; winter on-peak hours are weekdays between 7 and 10 AM and between 6 and 10 PM. At the beginning of the study period, 2018, avoided energy costs ranged from 2.7 cents/kWh winter off-peak hours to 4.6 cents/kWh for summer on-peak hours. We also developed loadshapes for each sector and end use. These loadshapes determine what portion of the total annual energy savings coincides with each peak period. This means that cooling measures, for example, will have larger benefits than outdoor lighting measures, where the savings general fall on off-peak hours. As indicated earlier, if the net present value of the future stream of benefits (energy and demand, but also other societal benefits such as gas, water, or maintenance savings) exceeds the costs, then the measure is considered cost-effective. Avoided costs for peak demand reduction were based on ENO’s forecast cost of a new combustion turbine plant. No value was placed on avoided transmission and distribution (T&D) costs; ENO did not provide an estimate of these values and we wished to use assumptions consistent with the other aspects of the IRP modeling. Our cost-effectiveness results are therefore likely to be conservative. Line losses were calculated by using ENO’s estimates of average losses 4 Forecast annual LMPs for Zone ENOI were provided by Entergy, and are Highly Sensitive Protected Material (HSPM) 5 Historical hourly real-time LMP for 2015 for zone EES.NOPLD from MISO Optimal Energy, Inc. 16 Study of Potential for Electric Energy Savings New Orleans, Louisiana adjusted for the fact that efficiency reduces consumption on the margin and therefore should result in marginal line loss savings, which are higher than average losses. Finally, we use a discount rate of three percent to better reflect the public policy nature of energy efficiency programs. We also include a sensitivity analysis using ENO’s weighted average cost of capital (WACC), again for consistency with the IRP. Figure 5 Average Hourly Forecast Energy Price – Summer Months The avoided costs and loadshapes allow us to calculate the net present value of each measure’s energy and capacity savings. A measure is considered cost-effective if this value exceeds the measure’s cost. For the economic potential estimate, we generally assumed that all cost-effective measures would be immediately installed for market-driven measures such as for new construction, major renovation, and natural replacement (“replace on failure”). For retrofit measures we generally assumed that resource constraints (primarily contractor availability) would limit the rate at which retrofit measures could be installed, depending on the measure, but that all or nearly all efficiency retrofit opportunities would be realized over the 10-year study period. Spreading out the retrofit opportunities results in a more realistic distribution of efficiency investment over time, providing a better basis for the later achievable scenarios. Estimating Achievable Potential using a Delphi Panel As noted earlier, one of the key objectives of this study is to provide inputs to ENO’s Integrated Resource Plan (IRP). To properly define the efficiency resource that is available as part of the IRP analysis, there must be a high level of confidence that the resource can be “built” in the required timeframe using tested programmatic and policy approaches. In short, the level of efficiency must be “achievable.” From an analytical perspective, this means that we developed a set of assumptions about the rate at which efficiency measures will be adopted by customers if promoted by an energy efficiency program. Typically, this means that the utility provides a monetary incentive to offset the increased capital cost of high efficiency equipment or retrofit Optimal Energy, Inc. 17 Study of Potential for Electric Energy Savings New Orleans, Louisiana activity (e.g., adding insulation). An achievable efficiency scenario therefore assumes some level of incentive and attempts to model customer response. This can be done either quantitatively or qualitatively. The former models customers’ willingness to participate as a function of the financial impacts of each measure, usually in terms of a measure called simple payback. Simple payback is the ratio of the required investment to the annual cost savings from the investment. It is a measure of the length of time required for the savings to repay the initial investment. A qualitative approach relies on data from existing programs to estimate program participation as function of incentive levels and various program approaches. That is, if a particular program type succeeded in convincing 10% of customers to invest in an efficiency measure, this value could be used as an estimate for the participation of a similar measure under similar conditions. We developed two estimates of achievable potential using a combination of these methods. First, we developed an estimate of “maximum achievable” potential. Maximum achievable potential assumes that efficiency programs cover 100% of the incremental cost of efficiency measures. As a result, the simple payback is undefined, because there is no investment to repay from bill savings. Therefore, we used a set of qualitative estimates developed by a panel of experts. Please refer to Appendix A for more information on the Delphi panel process. These estimates indicate, for several prototypical efficiency measures, the likely maximum adoption rate by customers and the time required to reach that maximum. See the figure below for the residential adoption curves that resulted from the Delphi Panel. Figure 6 Delphi Panel Adoption Curves – Residential Sector Although the maximum achievable potential is theoretical possible, it is usually considered an extreme upper-bound. As with any product, at any given price there are those who will purchase the product and those who will not. But within those who would purchase the product, some would have purchased it at an even higher cost. With energy efficiency programs, it is difficult to provide different incentive levels for the same product to different customers. Optimal Energy, Inc. 18 Study of Potential for Electric Energy Savings New Orleans, Louisiana Therefore, if incentives are raised to increase participation, all participants must receive the higher payment, even those who would have participated at the lower incentive lever. As a result, increasing incentive levels results in diminishing returns, and programs rarely provide full or nearly full coverage of measure costs (with the exception of low-income programs). Therefore, we developed a “program achievable” potential that is based on incentive levels that are more in keeping with actual program practice. For this study, we assumed an average incentive of 50% of measure costs for the program achievable potential. The Delphi panel provided estimates of how measure adoption would change based on changes in customer simple payback. Therefore, this step in the process used a quantitative approach that adjusted the maximum achievable potential based on the calculated simple payback for measure. The Delphi Panel developed consensus on the amount by which the maximum adoption curve would be reduced given certain simple paybacks in the residential and C&I sectors. See Appendix A for more details. Hourly Efficiency Savings (“8760” Outputs) Because the results of this report will be used as inputs for ENO’s IRP, we will provide an efficiency savings potential estimate for each hour of the year for the next 20-years. In order to produce this “8760” output (so-called because there are 8,760 hours in a non-leap year), we use the same efficiency loadshapes provided by ENO in their 2015 potential study. Note that since these loadshapes are not identical to those we use to model cost-effectiveness, the resulting peak demand impacts implied by the 8760 output may be slightly different than those reported in our study. RESULTS Overall Results This section presents the overall results of the three scenarios examined. The results are given at the sector level – residential and C&I. Low-income results, where measures with a TRC above 0.8 were counted as economic, are separated from residential further below. Further, note that the cumulative potential does not significantly change between year 10 and year 20. This is because all the adoption curves, as defined by the Delphi Panels, reach full or nearly-full adoption by year 10. Thus most of the market is addressed in the first 10 years, and the additional potential in the last 10 years is largely due to equipment turnover. We also want to emphasize that, due to inherent uncertainties in predicting the future, the results get less and less certain the further out in time. We therefore would recommend placing a focus on the first 10 years when evaluating the results of this study. The figure below shows the baseline forecasted electric usage (purple line) over the 20-year study horizon, and compares to what sales would be under the three scenarios examined for the study. As expected, sales decline significantly under the efficiency scenarios. This represents electricity that will not be sold if the given scenario is followed. Optimal Energy, Inc. 19 Study of Potential for Electric Energy Savings New Orleans, Louisiana Electric Sales Forecast Figure 7 Electric Energy Savings Relative to Sales Forecast 0 2018 2021 2024 2027 Baseline Forecast Program Potential 2030 2033 2036 Max Achievable Potential Economic Potential The table below gives the specific figures. There is economic potential of 49% in the residential sector, and 45% in the C&I sector. This drops of to 22% and 21% in the program potential scenario. Table 11 Cumulative Energy Savings As Percent of Sales by Sector and Scenario (MWh) Year Scenario Economic Potential Residential Savings Low Income Savings C&I Savings Total 49% 49% 43% 45% 27% 27% 25% 25% Program Potential 9% 27% 18% 18% Economic Potential 49% 49% 45% 46% 33% 33% 29% 30% 9% 33% 21% 21% 2027 Max Achievable Potential 2037 Max Achievable Potential Program Potential Note that the above values represent cumulative savings. Due to many measures having a useful life of less than 20 years, a cumulative savings value of, for example, 20% in year 20 does not mean that incremental annual savings will be 1% in each year (i.e., 20% / 20 years). Our modeling tool provides the incremental savings in each year, from which we calculate the average incremental annual savings for the first and second 10 years show in the table below. Optimal Energy, Inc. 20 Study of Potential for Electric Energy Savings New Orleans, Louisiana Table 12 Average Incremental Annual Savings Scenario 2018-2027 2028-2037 Economic 4.8% 3.4% Max Achievable 2.5% 2.2% Program 1.8% 1.6% The average savings above still masks some granularity, for example in the early years while the program is ramping up. The table below shows the incremental annual savings for every year of the analysis period. Table 33 Incremental Annual Savings by Year as Percent of Sales Year 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 Economic Potential Total Res C&I 5.7% 7.5% 4.6% 5.5% 7.3% 4.4% 5.2% 6.7% 4.4% 4.4% 4.6% 4.3% 4.2% 4.3% 4.2% 4.4% 4.5% 4.3% 4.5% 4.7% 4.4% 4.5% 4.6% 4.4% 4.6% 4.7% 4.5% 4.7% 5.0% 4.5% 2.7% 2.5% 2.8% 3.0% 2.8% 3.1% 3.0% 2.9% 3.1% 3.3% 3.0% 3.6% 3.4% 3.0% 3.6% Max Achievable Potential Total Res C&I 0.7% 0.7% 0.6% 1.3% 1.4% 1.2% 2.0% 2.0% 1.9% 2.6% 2.5% 2.7% 2.8% 2.7% 2.8% 3.0% 3.1% 3.0% 3.1% 3.3% 3.0% 3.2% 3.5% 3.1% 3.3% 3.7% 3.1% 3.4% 3.8% 3.1% 1.3% 1.7% 1.1% 1.6% 1.9% 1.5% 1.9% 2.0% 1.8% 2.3% 2.2% 2.4% 2.3% 2.2% 2.4% Program Potential Total Res C&I 0.5% 0.5% 0.5% 1.0% 1.0% 0.9% 1.4% 1.4% 1.4% 1.9% 1.7% 2.0% 2.0% 1.8% 2.1% 2.2% 2.1% 2.2% 2.3% 2.3% 2.3% 2.3% 2.4% 2.3% 2.4% 2.5% 2.3% 2.4% 2.6% 2.3% 0.9% 1.2% 0.8% 1.2% 1.3% 1.1% 1.4% 1.4% 1.4% 1.7% 1.5% 1.8% 1.7% 1.5% 1.8% Peak demand reduction for each scenario in 2027 and 2037 are reported in megawatts, rather than percent of forecast peak load, in order to make it more easily comparable to power generation that may be avoided through efficiency. See Table 14. Optimal Energy, Inc. 21 Study of Potential for Electric Energy Savings New Orleans, Louisiana Table 14 Cumulative Demand Savings Potential by Sector and Scenario (MW) Year 2027 2037 Scenario Residential Savings LI Savings C&I Savings Total Economic Potential 137 133 260 530 Max Achievable Potential 73 71 150 294 Program Potential 17 71 106 194 Economic Potential 137 134 286 557 Max Achievable Potential 89 86 186 361 Program Potential 24 86 133 243 Figure 8 shows the demand under each scenario compared to the baseline peak demand forecast. As seen, similar to the chart for energy, peak demand quickly starts to diverge from the forecast. Note that this figure includes peak demand reduction from efficiency only. Demand response programs will provide additional savings and are discussed fully below. Figure 9 shows the cumulative avoided CO2 emissions achieved through the efficiency programs. By the end of the study horizon, the program potential scenario would avoid almost 800 thousand metric tons of CO2, the equivalent to the emissions from over 160,000 cars. This represents a reduction of emissions of over 26% compared to the baseline forecast. Optimal Energy, Inc. 22 Study of Potential for Electric Energy Savings New Orleans, Louisiana Electric Sales Forecast Figure 8 Electric Peak Demand Savings From Efficiency Relative to Sales Forecast 0 2018 2021 2024 Baseline Forecast Program Potential 2027 2030 2033 2036 Max Achievable Potential Economic Potential Figure 9 Cumulative Avoided CO2 Emissions (Thousands of Metric Tons) The next table shows the Total Resource Cost Effectiveness Test results in each scenario. The costs and benefits below represent the net present value of running 20 years of programs. As seen, while the scenarios incur significant costs (i.e., utility administrative costs, incentive costs, and customer contributions), there societal benefits are 2-4 times larger. Optimal Energy, Inc. 23 Study of Potential for Electric Energy Savings New Orleans, Louisiana Table 15 Scenario TRC Cost-Effectiveness by Sector – Full 20 Years Sector Residential C&I Total Scenario Costs ($MM) Benefits ($MM) Net Benefits ($MM) BCR Economic 461 1,216 754 2.6 Max Achievable 310 716 406 2.3 Program 207 467 260 2.3 Economic 516 2,486 1,970 4.8 Max Achievable 304 1,129 825 3.7 Program 202 823 621 4.1 Economic 978 3,702 2,724 3.8 Max Achievable 614 1,845 1,231 3.0 Program 409 1,290 880 3.2 Table 16 shows the same information, but for the 2018-2027 time frame instead of the full 20 years. As seen, BCRs are very similar, but slightly lower. This is because of a higher share of retrofit measures during this period which, on average, have lower BCRs than market driven measures. Finally, tables 17 and 18 show the utility budgets, by year, for the max achievable and program potential scenarios. As seen, the year 1 budget would be $6.5 million, a slight increase from the current Energy Smart Program budgets of $6.2 million.6 From there, budgets would continue to increase until reaching $58.5 million in 2027. After 2027, budgets begin to decline as retrofit opportunities decline. Achieving the program potential would represent a significant investment. However, it would also avoid significant electricity generation need and produce benefits of three to four times greater than the cost (as seen in the TRC ratios above).. 6 Entergy New Orleans. Program Year Six Annual Report. Optimal Energy, Inc. 24 Study of Potential for Electric Energy Savings New Orleans, Louisiana Table 16 Scenario TRC Cost-Effectiveness by Sector – First 10 years Sector Residential C&I Total Scenario Costs ($MM) Benefits ($MM) Net Benefits ($MM) BCR Economic $335 792 $457 2.37 Max Achievable $203 $409 $207 2.02 Program $134 $267 $133 1.99 Economic $333 1,445 $1,112 4.33 Max Achievable $185 597 $412 3.23 Program $118 $427 $309 3.62 Economic $668 $ 2,237 $1,569 3.35 Max Achievable $388 $ 1,006 $619 2.60 Program $252 $694 $442 2.75 Table 17 Program Potential Nominal Budgets ($MM) Year 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 NonIncentive $1.6 $3.2 $4.7 $6.4 $6.7 $7.2 $7.6 $7.8 $8.0 $8.1 Optimal Energy, Inc. Incentive Total Year $4.8 $9.7 $14.4 $19.4 $20.6 $22.4 $23.6 $24.4 $25.2 $25.8 $6.5 $12.9 $19.2 $25.8 $27.3 $29.7 $31.2 $32.2 $33.2 $34.0 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 NonIncentive $2.6 $3.1 $3.5 $4.2 $4.2 $4.6 $4.6 $4.7 $4.8 $4.8 Incentive Total $9.9 $11.2 $12.4 $14.3 $14.3 $15.4 $15.5 $15.6 $16.0 $16.0 $12.5 $14.3 $15.9 $18.5 $18.5 $20.1 $20.2 $20.2 $20.7 $20.8 25 Study of Potential for Electric Energy Savings New Orleans, Louisiana Table 18 Maximum Achievable Potential Nominal Budgets ($MM) NonIncentive $1.9 $3.7 $5.6 $7.5 $7.8 $8.3 $8.7 $8.9 $9.1 $9.3 Year 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 Incentive Total Year $10.8 $21.7 $33.0 $44.9 $47.5 $51.4 $53.7 $55.4 $57.1 $58.5 $12.8 $25.5 $38.5 $52.4 $55.3 $59.8 $62.4 $64.3 $66.2 $67.9 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 NonIncentive $2.7 $3.2 $3.6 $4.5 $4.5 $5.0 $5.0 $5.0 $5.1 $5.1 Incentive Total $21.2 $24.3 $26.9 $32.2 $32.3 $35.3 $35.4 $35.5 $36.2 $36.3 $23.9 $27.5 $30.5 $36.7 $36.8 $40.2 $40.4 $40.5 $41.3 $41.4 Detailed Results Overview This section drills down into the results in more detail. We focus on the Program Potential Scenario, since that is the scenario most likely to be implemented in New Orleans. For each sector (Residential, Low-Income, and Commercial and Industrial), we show the savings by end use, as well as the top 10 saving measures. Note that the percentages in the tables showing the top savings measures represent the portion of total 2037 savings by that measure. A few items to note: • • • • There are very little residential lighting savings remaining in 2037. This is due to federal regulations that essentially eliminate the opportunity in that sector. The “ElecTotal” end-use represents measures that reduce full building electric use. This includes measures such as Conservation Voltage Reduction, Commissioning, and integrated New Construction. There are significant space heating savings in the residential sector. This is due to a high saturation of electric resistance heat – a prime candidate for significant savings from replacing with an air source heat pump. Residential demand savings are dominated by cooling, while C&I demand reduction is due to a broader combination of measures. Optimal Energy, Inc. 26 Study of Potential for Electric Energy Savings New Orleans, Louisiana Residential Non Low-Income Figure 10 Residential Electric Energy Savings by End Use (2037) Indoor Lighting Plug Load Cooling ElecTotal Appliance Water Heating Space Heating Table 19 Residential Electric Energy Top Saving Measures (2037) Measure Name Optimal Energy, Inc. Percent of Total Savings Low Flow Showerhead 14.2% Conservation Voltage Reduction 8.0% Duct Sealing 7.0% Ductless Mini-split Heat Pump 5.5% Faucet Aerator 4.9% Quality Install Air Source Heat Pump 4.1% Air Source Heat Pump 3.5% Learning Thermostat 3.5% Desktop Computer 2.9% Fridge and Freezer Removal 2.9% SubTotal 56.5% 27 Study of Potential for Electric Energy Savings New Orleans, Louisiana Figure 11 Residential Electric Demand Savings by End Use (2037) ElecTotal Indoor Lighting Water Heating Appliance Cooling Plug Load Table 20 Residential Electric Demand Top Saving Measures ( 2037) Measure Name Optimal Energy, Inc. Cumulative MW Duct Sealing, Electric Heat 1.56 Duct Sealing, Gas Heat Learning Thermostat, Gas Heat Learning Thermostat, Electric Heat Central AC Energy Star Ceiling Fan Energy Star Room AC Ductless Mini-Split HP Quality Install ASHP Tier 2 Power Strip SubTotal Total 1.56 1.48 1.48 1.44 1.37 1.33 1.30 1.20 0.97 13.68 24 28 Study of Potential for Electric Energy Savings New Orleans, Louisiana Residential Low-Income Figure 12 LI Residential Electric Energy Savings by End Use (2037) Indoor Lighting Exterior Lighting Appliance ElecTotal Cooling Plug Load Water Heating Space Heating Table 21 LI Residential Electric Energy Top Saving Measures (2037) Measure Name Optimal Energy, Inc. Percent of Total Savings Air Source Heat Pump 9.6% Ductless Mini-split Heat Pump 7.3% Low Flow Showerhead Quality Install Air Source Heat Pump Learning Thermostat 6.6% Attic Insulation 4.1% Central AC 3.8% Duct Sealing 3.8% ES Ceiling Fan 3.8% Conservation Voltage Reduction 3.8% SubTotal 54.5% 5.9% 5.8% 29 Study of Potential for Electric Energy Savings New Orleans, Louisiana Figure 13 LI Residential Electric Demand Savings by End Use (2037) ElecTotal Indoor Lighting Water Heating Appliance Cooling Plug Load Table 22 LI Residential Electric Demand Top Saving Measures (2037) Measure Name Air Source Heat Pump Learning Thermostat, Elec Heat Learning Thermostat, Gas Heat Central AC ES Ceiling Fan Quality Install Air Source Heat Pump Ductless Mini Split Heat Pump Quality Install Central AC Efficient Windows Window Attachments SubTotal Total Optimal Energy, Inc. Cumulative MW 7.43 7.16 7.16 6.97 6.65 5.20 5.09 3.33 3.32 2.78 55.08 86.36 30 Study of Potential for Electric Energy Savings New Orleans, Louisiana Commercial and Industrial Figure 14 C&I Electric Energy Savings by End Use (2037) Plug Load Water Heating Space Heating Other Ventilation Cooking Exterior Lighting Cooling Refrigeration Indoor Lighting Industrial Process ElecTotal Table 234 C&I Electric Energy Top Saving Measures (2037) Measure Name Percent of Total Savings Retrocommissioning/Calibrate Sensors - Elec Heat 10.5% Com LED Tube Replacement Lamps 8.9% Interior Lighting Controls 7.9% Compressed Air 6.9% Industrial Process 5.8% VSD, HVAC Fan 5.0% Conservation Voltage Reduction 4.6% Heat Pump Tune Up 4.5% Refrigeration Retrofit 3.9% High Efficiency Heat Pump 3.6% SubTotal 61.5% Optimal Energy, Inc. 31 Study of Potential for Electric Energy Savings New Orleans, Louisiana Figure 15 C&I Electric Demand Savings by End Use (2037) Cooking Ventilation Other Refrigeration Plug Load Water Heating ElecTotal Cooling Industrial Process Indoor Lighting Table 24 C&I Electric Demand Top Saving Measures (2037) Measure Name Optimal Energy, Inc. Cumulative MW Com LED Tube Replacement Lamps 13 HP Tune Up Int Lighting Controls Mini Split Ductless HP-Cool High Efficiency AC Compressed Air High Efficiency HP Industrial Process Retrocommissioning/Calibrate Sensors - Electric Heat Cool Roof SubTotal Total 13 12 10 9 9 8 8 6 4 92 133 32 Study of Potential for Electric Energy Savings New Orleans, Louisiana Rate and Bill Impacts Although cost-effective energy efficiency lowers overall utility bills and the utility’s revenue requirement, it also affects customer rates. A utility that promotes efficiency will see a reduction in revenue from the reduced sales volume. Because a portion of the variable rate that customers pay compensates the utility for their fixed costs, the utility will under-recover their fixed costs as a result. In the absence of a rate case that resets rates to meet the revenue requirement with the new, lower volume of sales, the utility will suffer lost fixed cost revenues (sometime referred to as lost base revenues). When rates are reset, they will be higher than they were in absence of energy efficiency, but total customer bills will still be reduced, because the variable costs of efficiency are lower than the variable costs of traditional supply. Some jurisdictions rely on the Ratepayer Impact Measure (RIM) test to assess whether or not rates will increase as a result of efficiency. The RIM test is a poor measure of this, though, as it provides no information about the magnitude of the rate increase, nor how changes in total utility bills will be distributed among the customer population. Furthermore, nearly every efficiency program will fail the RIM test, precisely because it reduces consumption. It is not a sign that an efficiency measure or program is a bad investment for the utility or their customers. Not surprisingly, the program achievable scenario fails the RIM test, with a benefit-cost ratio of 0.6. More relevant information can be gleaned from assessing the size of the rate increase and the change in overall utility revenue requirement from efficiency programs. For the program potential scenario, the rate impacts in the short term are minor. Through the first five years of the program, the cumulative reduction of roughly 7% of sales results in a 4% increase in rates. The utility’s revenue requirement decreases by nearly $16 million, which indicates that overall customer bills are also reduced by this amount. As efficiency savings accumulate, the rate impacts and the customer bill savings both grow. In 2027, the cumulative sales reduction is approximately 18%, while rates will have increased by approximately 13%. The results in total annual bill savings of over $41 million. Sensitivity Analysis As discussed earlier, we used a discount rate of three percent to evaluate the measures for cost-effectiveness. However, Entergy has normally used a higher discount rate reflecting their weighted average cost of capital (WACC) of seven to eight percent in screening measures for costeffectiveness. A higher discount rate has the effect of placing a lower value on future costs and benefits. The costs of efficiency measures are generally incurred at the time of installation while the benefits of energy savings occur over the life of the measure. A higher discount rate thus lowers the value of the future energy savings relative to the costs, which lowers the costeffectiveness of measures and programs. In this case, it is possible that some measures that pass the TRC with a three percent discount rate would not be cost-effective using a higher rate such as WACC. In order to estimate how large an impact this may have, we performed a sensitivity analysis looking at the TRCs of each measure using both the societal discount rate and the WACC. The table below shows the measures that passed the TRC using the societal discount rate but not with the WACC. Only 12 measures of the over 190 examined in the study meet this criterion. Optimal Energy, Inc. 33 Study of Potential for Electric Energy Savings New Orleans, Louisiana The table also shows the Year 2037 savings from each of these measures in the Max Achievable Scenario. The cumulative savings from these 12 measures represents 8% of the total potential. Looking at program potential, these measures also consist of about 8% of the total. However, this does not necessarily mean that the potential would be 8% lower using the WACC; some measures could be replaced with similar measures with the same or higher savings. For example, even though air source heat pumps do not pass using the WACC, air source heat pumps with quality install still do pass, as do ductless mini-split heat pumps. In a scenario where air source heat pumps do not pass the TRC, these other measures could be promoted in their place. Table 255 Measures not Cost-Effective with Higher WACC Discount Rate Measure Name Sector TRC (3%) Central AC RET Quality Install Central AC RET Air Source Heat Pump Water Heater Jacket RET Window Attachments RET LED DI (2018) RET* Occupancy Sensors RET Energy Efficient New Home - Multi Family MD Retrofit duct sealing Integrated bldg design -Elec MD Advanced RTU Control - Gas Heat MD Integrated bldg design -Gas MD Res Res Res Res Res Res Res Res C&I C&I C&I C&I 1.09 1.30 1.38 1.06 1.14 1.01 1.00 1.59 1.16 1.76 1.15 1.59 TRC (WACC) 0.72 0.84 0.99 0.80 0.92 0.92 0.82 0.87 0.85 0.98 0.84 0.88 Savings in Max Achievable Scenario (MWh, 2037) 8,441 6,357 60,992 1,121 22,040 10,006 16,661 16,356 10,107 7,458 2,074 BENCHMARKING THE RESULTS In addition to conducting this New Orleans-specific potential study, we examined how our results compare to the results from other recent potential studies in the region. Table 26 shows the results of both economic and achievable potential scenarios from these studies. Our New Orleans study generated results that are higher than these other studies, sometimes by a substantial amount. While it is hard to know the specific reasons other studies generated lower results, some of the reasons may include the following. • • • • • Lower penetration rate assumptions, based solely on customer “willingnessto-accept” studies Fewer measures included Failure to include early retirement measures Lower avoided costs and/or higher discount rates Different assumptions regarding free-ridership and spillover Optimal Energy, Inc. 34 Study of Potential for Electric Energy Savings New Orleans, Louisiana • Limited inclusion of potential savings from “custom” projects (i.e., projects involving efforts beyond narrowly-defined “prescriptive” measures) Table 26 Potential Study Benchmarking State Study Year Study Period Analysis Period Economic Potential Achievable Potential Scenario Description Funding set at levels 8% comparable to levels in the past Arkansas 2015 2016-2025 10 17% Georgia (Georgia Power) 2015 2015-2026 12 19% 14% Max achievable Mississippi Missouri (Ameren) Austin, Texas 2013 2014-2025 12 N/A Reflects other programs in the 13% region, does not attempt to examine maximum 2013 2016-2030 15 23% 16% Max achievable 2012 2011-2020 10 26% Economic only Tennessee 2011 2012-2030 20 25% Incentives cover "a 20% substantial portion of the incremental cost" Oklahoma 2015 2015-2024 10 15% Economic only Penn. 2015 2016-2024 10 New Orleans 2018 2018-2037 20 13% Max achievable 46% 21%/30% Program/Max achievable It is also instructive to consider actual program experience in leading efficiency states. The program potential scenario in this study indicates average incremental annual savings of 1.7%. Table 27 shows the 10 states with the highest actual achieved savings in 2016. The average savings is 1.82% of sales, slightly higher than our result. We also note that these top states represent a wide variety of climates and demographics; they are not limited to a particular set of circumstances that are not applicable to Louisiana or New Orleans. In fact, there are many good reasons to believe that New Orleans can at least match the performance of these other jurisdictions with high levels of savings. For example: • • • New Orleans does not have a long a history of aggressive efficiency programs, and the existing stock of equipment and buildings is likely of lower efficiency than in areas where efficiency savings have been pursued for many years New Orleans has a high cooling load, due to its hot, humid climate New Orleans has a high heating load, due to a preponderance of electric resistance heating in the residential sector and relatively low levels of insulation and air sealing. Optimal Energy, Inc. 35 Study of Potential for Electric Energy Savings New Orleans, Louisiana Table 27 Efficiency 2016 Top Savers State Massachusetts Rhode Island Vermont Washington California Connecticut Arizona Maine Hawaii Minnesota Average % of 2016 retail sales 3.0% 2.8% 2.5% 1.5% 1.5% 1.5% 1.4% 1.4% 1.3% 1.1% 1.82% OTHER BENEFITS OF EFFICIENCY Our assessment of the efficiency potential assessed cost-effectiveness using a set of benefits limited largely to directly avoided supply costs and readily quantifiable resource impacts. Yet efficiency produces many other benefits that are difficult to quantify and often excluded from benefit-cost analysis. This can result in an underestimate of efficiency potential, the net benefits of efficiency, or both. This section briefly describes several benefit categories not quantified in our analysis. Risk Reduction Because the largest portion of the marginal costs of producing electricity are related to fuel expenses, electric prices are highly correlated to the underyling commodity. Commodity prices can be highly volatile and cyclical, and thus leave ratepayers exposed to the risk of price shocks. The costs related to energy efficiency, by contrast, are largely related to local labor and expenses, can be ramped up and down much more easily, and are thus much less exposed to the ups and downs of the global commodity markets. Another type of risk relates to the construction of new generation facilities. These facilities may take 10 years or longer to begin producing power, while demand side investments start saving energy right away. Generation facilities are therefore far more exposed to unexpected capital cost overruns (such as from rising labor and/or material costs), as well as lower than projected load requirements. Some states have begun to quantify the value of reduced risk from efficiency and include it as a benefit in the TRC test. Vermont, for example, adds 10% to the benefits of avoided energy and capacity as a proxy for this risk reduction. However, this practice is still fairly rare. Transmission and Distribution Avoidance In addition to peak demand savings from avoided generation, there is often additional savings from lowering the load on the Transmission and Distribution System. These savings can be significant, but they are highly variable from jurisdiction to jurisdiction and difficult to Optimal Energy, Inc. 36 Study of Potential for Electric Energy Savings New Orleans, Louisiana estimate without a dedicated study. We do include these benefits in the analysis of this study due to lack of ENO specific data, but they likely do exist and are possibly significant. Demand Reduction Induced Price Effects Many states, especially in New England, are beginning to recognize Demand Reduction Induced Price Effects (DRIPE) as a quantifiable benefit of energy efficiency and demand response. DRIPE is a measurement of the value of efficiency provides by reducing the wholesale energy prices borne by all retail customers. The reduced energy demand due to efficiency programs removes the most expensive marginal generating resources and lowers the overall costs of energy. This reduces the wholesale prices of energy and demand, and this reduction, in a relatively deregulated market, is in theory passed on to retail customers. The effects on energy prices are small in terms of percentages, but the absolute dollar impacts are significant because the price reduction applies to all energy usage on the system. Originally, it was thought that DRIPE would only be significant in the short-term. In the long run, market actors would react to lower energy consumption and peak demand by retiring inefficient generators. With lower available supply, wholesale prices would begin to increase again, assuming no other changes in demand. However, the most recent study on avoided costs in New England concluded that DRIPE impacts persist far longer than had been assumed. DRIPE effects in New England are now estimated to last 11 years for peak capacity reductions and 13 years for energy reductions. The value of DRIPE varies based on energy period and region, but for New England range from $0.001/kWh to $0.032/kWh and from $2.23/kW to $59.07/kW for peak demand. Economic Benefits There is a large and growing body of evidence that money spent on energy efficiency creates more jobs and provides a greater stimulus to local economies than equivalent money spent on supply-side resources. Efficiency investments are far more labor intensive than supply-side resources and require significant effort from contractors, design professionals, and suppliers/distributors. Academic research and interviews with business owners from process evaluations both confirm that utility-run efficiency programs can be an enormous boon for small businesses. According to 2009 study done by the University of Massachusetts, Amherst, a $1 million investment in supply-side resources will create 5.3 jobs, while an equivalent investment in efficiency can be expected to create 16.7 jobs.7 The table below shows estimates of the jobs effect of efficiency spending.8 The multipliers are based on modeling by ACEEE, with multipliers adapted from a regional economic modeling tool. Typically, studies have found that around 1020 net jobs are created per million dollars spent on efficiency. 7 Throughout the report, one “job” represents one full time job for one year. 8 ACEEE. Potential for Energy Efficiency, Demand Response, And Onsite Solar Energy in Pennsylvania. April, 2009. Optimal Energy, Inc. 37 Study of Potential for Electric Energy Savings New Orleans, Louisiana Table 28 Effect of Efficiency Spending on Jobs9 Spending Category Impact Amount Job (Millions) Multiplier Upfront payment for efficiency $100 measures Because of efficiency spending, Consumer Spending -$100 consumers spend less in the short term Because of energy savings, consumers Consumer Savings $200 spend more in the long term Utility revenues decrease because of Lost Utility Revenues -$200 energy savings Net effect of a $100 million investment in efficiency measures Installation Job Impact (job-years) 13 1,300 12 -1,200 12 2,400 5 -1,000 1,500 In addition to direct job benefits, one dollar of efficiency spending creates more than one dollar of economic activity. In economics, this is known as the multiplier effect. While every economic activity has some multiplier, the multiplier for efficiency spending is larger than that of many other activities, particularly compared with supply-side spending. The efficiency multiplier occurs as 1) people who are employed due to the efficiency program re-spend their new income into the economy; 2) increased demand for efficient products causes increased demand for upstream suppliers; and, 3) money saved by ratepayers from lower energy bills is spent on other goods and services. These estimates have been validated by economic studies of specific investment decisions. For example, a 2009 study in East Kentucky found that efficiency investment of $634.2 million would create $1.2 billion of local economic activity and over 5,400 jobs, not including the effect of energy savings being reinvested into the local economy. A coal plant to produce the equivalent amount of energy would not only be more expensive, but would create only 700 jobs during the 3-year construction phase and 60 positions once operational.10 Health Benefits Air pollution such as sulfur dioxide, nitrogen oxides, and particulate matter emitted during electricity generation causes health effects that damage both public well-being and the economy. Additionally, there is mounting evidence that weatherization programs can have significant health benefits in low-income households. Adverse effects include increased incidences of asthma, respiratory, and cardiac diseases; higher mortality rates, and increased medical and hospitalization spending. In fact, there is reason to believe that increased health costs due to air emissions effectively double the price of coal-fired electricity. For example, a recent study from Harvard University finds that adverse health impacts from coal generation cost the public an 9 This study uses the same job multiplier as was found in the PA ACEEE study, or 15 jobs per million dollars spent. This number is actually on the low side of multipliers found in the economic literature. When this paper references jobs created, it is referring to a job as one full time job for one year. 10 http://www.ochscenter.org/documents/EKPC_report.pdf Optimal Energy, Inc. 38 Study of Potential for Electric Energy Savings New Orleans, Louisiana average of 9.3 cents per kWh of power generated.11,12 A study for the European Union estimates direct externalities at between 4 and 15 euro cents per kWh for coal generation, between 3 and 11 euro cents per kWh for oil, and between 1 and 3 cents per kWh for gas, consistent with the Harvard study.13 Another study found that Ontario’s electric generation produces 668 premature deaths, 928 extra hospital admissions, 1,100 extra emergency room visits, and 333,600 minor illnesses. The financial impact of these health effects is estimated to be over $3 billion per year. The study estimates total Ontario consumption at 26.6 TWh/year, implying health costs for Ontario of over $0.11 per kWh. Environmental Benefits In addition to the health effects discussed above, emissions from electricity generation carry significant environmental costs. Although environmental damage can be very difficult to quantify, they can be avoided by investing in efficiency rather than traditional supply-side resources. • • • • • • Surface water and soil acidification Damage to vegetation and forests Contributions to coastal eutrophication, causing algal blooms, depletion of dissolved oxygen, changes in biodiversity, and losses in the tourism/fishing industry Faster weathering of buildings Reduced visibility from smog and haze Mercury accumulation in fish Other Benefits Efficient buildings tend to have smaller temperature swings, better lighting levels, less glare, lower temperature gradients, and better indoor air quality than standard buildings. These additional benefits partly improve participant comfort and quality of life, but may also manifest as decreased illnesses and increased worker productivity which can translate into additional economic benefits. The links between buildings and occupant health and productivity are very complex and difficult to generalize. However, the Center for Building Performance Diagnostics at Carnegie Mellon University has created a database of studies that have attempted to quantify this link. Overall, it finds that building environments that are associated with efficiency, such as increased outside air circulation, individual control of lights, moisture control, and pollutant source controls reduce symptoms of illnesses such as flu, asthma, sick building syndrome, and headaches by an average of 43%. Other measures, such as window views, natural ventilation, and increased day-lighting reduce symptoms by an average of 36%. Further, the studies find that lighting measures in offices increase worker productivity by a median of 3.2%. These estimates 11 This is an average. The actual value varies widely from plant to plant based on its age, type of pollution controls, and downwind population. 12 Epstein et al. Page 86. http://solar.gwu.edu/index_files/Resources_files/epstein_full%20cost%20of%20coal.pdf 13 Page 13. http://www.externe.info/externpr.pdf Optimal Energy, Inc. 39 Study of Potential for Electric Energy Savings New Orleans, Louisiana are highly uncertain, and the past efforts to quantify the benefits have found a range of from less than $10 to $50 per square foot over 20 years. Since the energy savings over 20 years for a typical LEED-certified building are about $10 per square foot, even the low range of this estimate would mean that health and productivity benefits equal the energy saving benefits of green buildings.14 14 Kats, Greg. Greening Our Built World. Optimal Energy, Inc. 40 Study of Potential for Electric Energy Savings New Orleans, Louisiana DEMAND RESPONSE SUMMARY OF APPROACH & MAJOR ASSUMPTIONS Demand response (DR) is defined by the Federal Energy Regulatory Commission (FERC) as changes in electric usage by end-use customers from their normal consumption patterns in response to either short-term changes in the price of electricity or to incentive payments designed to induce lower electricity use at times of high wholesale market prices or when system reliability is jeopardized.15 We estimate the potential and costs for demand reductions from DR in New Orleans based on a review of DR programs by other utilities, with an emphasis on Southern states. We collected data on participation rates, average savings per participating customer, and cost of reduced demand ($/kW). We apply these representative values, adjusted to an ENO context, to estimate the savings and costs for various DR program strategies in the appropriate customer groups in the ENO service territory, and estimate benefits based on the avoided cost of capacity in New Orleans. ENO customers have had limited experience with DR offerings, in particular those strategies that rely upon advanced metering infrastructure (AMI), which ENO is just beginning to implement in its service territory. Program marketing will be important to build customer awareness and encourage participation. As AMI becomes available to more customers, the range of program offerings can diversify to take advantage of these new technology opportunities. METHODOLOGY This section provides an overview of our approach to the DR portion of the potential study analysis. The subsequent sections provide more detailed descriptions of the analysis methodology and assumptions for each program area. The DR potential analysis involved several steps. We began by conducting a literature review of previous DR potential studies, including at the national, state, and utility-territory levels. We reviewed DR program evaluations from utilities and their evaluators, as well as available metastudies of demand response. We reviewed relevant literature throughout the study and used previous studies and program results to compare and check the general scale and validity of our own data. Following the initial literature review, we compiled a database of utility demand response program evaluations and results. These evaluations are typically publicly available on utility websites and public utility commission docketing systems. We spoke with evaluators and program administrators to collect further documentation or to clarify methodology and results where necessary. We collected program evaluations from programs run in the same or similar climate regions to Entergy New Orleans in order to have data that would be comparative. To supplement the somewhat limited in-region data, we also collected data for programs run in 15 https://www.ferc.gov/industries/electric/indus-act/demand-response/dr-potential.asp Optimal Energy, Inc. 41 Study of Potential for Electric Energy Savings New Orleans, Louisiana different climate zones in order to build out a more robust data set. For each program, we collected data points including: • • • • • • • • • • • • • • Program title and utility administrator Year of program Location or state of program Program description Target sector including customer demand level cutoffs when applicable Key demand response technique(s) used Measures, appliances, or technologies targeted, including use of AMI Season and time period of demand response events called Participation rates Number of events called Incentive amount Program spending Demand savings Energy savings In order to build the savings models, we used data from Entergy New Orleans as well as other publicly available data sources. The latter included assessments of peak demand, penetration of central air conditioning, and load growth projections. Entergy New Orleans provided data including number of customers in each class and avoided capacity costs. To calculate costeffectiveness, we assume a discount rate of 3%. Subsequent to our analysis we were able to obtain monthly peak demand for 2017 disaggregated by residential, small commercial, and large commercial customers. Annual peaks occurred during the three summer months (June, July, and August) with residential and nonresidential each contributing about half of the total peak. This distribution of the annual peak confirms our assumptions based on power sales to these customers used in our analysis. The review of the monthly peaks also revealed a winter peak primarly driven by residential load, assumed to result from resistance heating load. While our residential demand response measures will also ameliorate these peaks, an increased use of heat pumps in residential sector would also provide an important contribution to managing this peak in the future, as is mentioned in the residential energy efficiency discussion. From the data points we collected, we created a taxonomy of major demand response program types.16 We determined major programs to be based on sector (residential, small, medium, and large commercial and industrial), program type (e.g., direct control, automated response, or use of rates), and the targeted energy end-use (e.g., lighting, heating, air conditioning). For each program, we developed two achievable scenarios, as described below. Data on demand by disaggregated customer types was not available from ENO. We therefore use various strategies to estimate the share of demand attributable to these customers. 16 We relied upon Peters and Cappers 2017 to inform the general taxonomy of DR programs. Optimal Energy, Inc. 42 Study of Potential for Electric Energy Savings New Orleans, Louisiana Availability of Data With limited current presence of DR programs from Louisiana utilities, we have had to rely upon data from a larger geographic area. For some program models the available data are limited or incomplete, creating uncertainty in our estimates of performance, costs and participation, particularly for large customers. The available program data limits our ability to project the likely participation by specific target audiences, limiting our ability to reflect unique demographic characteristics for ENO with a greater share of some customer classes such as hospitality and healthcare. Additionally, residential rate demand response programs vary widely in design, including number and magnitude of peak prices or rebates. This creates variability in program costs and achieved savings. We used estimates for costs and savings consistent with levels we observed in the programs we reviewed, although the utility could choose to spend more to achieve deeper savings, such as through increased recruitment and marketing efforts. MEASURE CHARATERIZATIONS Residential Direct Load Control (DLC) and Automated Demand Response (ADR) The objective of both residential direct load control (DLC) and automated demand response (ADR) programs is to reduce residential peak demand (as measured in kW) during load control events, which typically occur during the summer months. In the case of DLC programs, for example program participants have a load control receiver installed typically on their central air conditioners (CAC) that allows the program administrator to remotely shut down or reduce the amount of time the unit is running. Water heaters and pool pumps are other common technology applications. Participants typically have an option of 50% or 75% cycling of their CAC during the events and receive an incentive based on the level of cycling. Participants may also receive a onetime bill credit for installation and successful testing of the load control device. An example of an ADR program is the Bring Your Own Thermostat (BYOT) program, which has recently emerged as a residential demand response program opportunity. Through the program, consumers purchase Wi-Fi-enabled smart thermostats and participate in this cloudbased demand response programs. The DR implementer provides a software solution to coordinate and communicate with the thermostat to cycle air conditioning use during calledupon event days. To estimate demand reductions and costs for residential DLC and ADR programs, we first estimated local penetration of residential central air conditioning (CAC) from the American Housing Survey (Census 2015). We assume that the presence of CAC would determine the households that would be the target of such a program. The Census survey identified that an estimated 89% of housing units in the NOLA metro area have CAC. We use this estimate because it is more recent than the Residential Appliance Saturation Survey (RASS) data from Entergy, which found that 83% of customers had CAC in 2006 (Entergy 2006)17. Next, we estimated participation levels based on data from other utility demand response programs in the region. 17 Full citations for this and other in-text references are forthcoming. Optimal Energy, Inc. 43 Study of Potential for Electric Energy Savings New Orleans, Louisiana For example, Entergy Arkansas’ DLC program reached a participation rate of about 6.5% of eligible customers after about six years.18 Participants in that program received an installation incentive of $25 or $40 as well as an annual incentive of up to $25 or $40 (depending on the cycling level with higher incentives for the higher level of cycling). Another example is Duke Energy, which had achieved 13% customer participation over five years. Yet another example is PNM in New Mexico, which has reached an estimated 22% of eligible customers.19 Similarly, we estimated energy and demand savings and costs assumptions based on DLC and ADR program data from other programs in the region. Savings are based on portfolio results from programs in our review, which may include multiple measures such as HVAC, hotwater, and pool pumps. In addition to Entergy Arkansas, Duke Energy Carolinas and PNM New Mexico, we also used DR program data from utilities in Texas, including Centerpoint Energy Houston, AEP Texas Central, and Oncor. Table 29 Residential DLC/ADR Model Inputs Program Measure Peak Reduction per Participant (kW) Residential DLC/ADR (Scenario 1) Residential DLC/ADR (Scenario 2) 1.25 (DLC) and 1.2 (ADR) 1.25 (DLC) and 1.2 (ADR) Participation Rate (% residential customers)1 Cost per kW Saved2 1%-9% $48-$160 2%-18% $48-$160 Assumes total starting participation of 1% for both DLC and ADR programs combined ramping up to total participation of 9% by 2037 in scenario one.In scenario two, participation starts at 2% combined for DLC and ADR programs ramping up to total participation of 18% by 2037. 1 Assumes costs range from $48/kW for ADR to $160/kW for new DLC customers. 2 Residential Time-Varying Rates Demand Response Programs The objective of residential time-varying rates in demand response portfolios is to use price signals to reduce residential peak loads during load control events. Residential demand response rate programs vary in design. Some offer customers a rebate for reducing load during peak times, while others increase prices during peak load events. Programs vary in the number of pricing blocks used throughout the day and in the magnitude of the rebate or price increase. These price blocks can range from “real-time pricing” where prices may vary by the hour or even smaller intervals, to programs with a few or even just two different price blocks (off-peak and peak/critical peak). Peak times typically cover a span of a few hours in the afternoon/evening and are also influenced by the weather. The magnitude of the price signal influences the savings achieved. Time-varying rates are “carrot and stick” approaches. Rewards can include very low 18 This assumes 55% saturation of CAC in Arkansas per FERC 2009 and a total number of residential customers in the service area in 2016 per EIA 2017. 19 This estimate again uses statewide penetration of CAC per FERC 2009 and total number of residential customers in the service area per EIA 2017. Optimal Energy, Inc. 44 Study of Potential for Electric Energy Savings New Orleans, Louisiana prices for energy usage in off-peak periods or rebates for demand reductions in peak periods. Penalties can include very high prices for usage in on-peak times. For this analysis, we consider two common residential rate options: Residential Peak Time Rebates (PTR) and Residential Critical Peak Pricing (CPP). We consider these two programs because they aim to specifically reduce demand during peak times (load control events), rather than during multiple time periods throughout the day. Additionally, these programs are commonly included in utility demand response portfolios, meaning that there are adequate data available for conducting analysis. We explain each program in further detail below. The use of advanced metering technology (such as programmable communicating thermostats or Wi-Fi thermostats) in conjunction with these programs influences the level of savings achieved. For that reason, we model savings potential with and without these technologies for both program types (“without tech.” or “with tech.”). Rate programs can be designed as “opt-in” or “opt-out” programs. For opt-out programs, the time-varying rate is the default, and customers can decide not to participate, and for opt-in programs, customers must actively sign up for the time-varying rate. We consider only opt-in programs in this analysis, as these are typically pursued prior to implementing opt-out or default time-varying rates. For opt-in programs, spending on marketing and outreach to recruit customers influences participation and savings rates. Some utilities administer these programs as they would any other rate option, meaning that their only costs are program evaluations. Other utilities invest in marketing and outreach to increase rate subscriptions. Programs that use high on-peak prices to penalize energy use during certain times attract customers by focusing on low off-peak prices that they can take advantage of. There are limited data available to determine a direct ratio between spending and savings for time-varying rate programs, and utility spending on time-varying rate programs varies widely. For this reason, we use a median cost estimate of $50/kW-saved based on utility evaluations we reviewed and keep this estimate consistent over time. We use the same cost estimate for both rate programs in this analysis because utilities often market their time-varying rate options together and evaluation or other costs are similar for both types of programs. For example, in 2015 Arizona Public Service reports spending $2.24/kW-saved on marketing and outreach for their timevarying rates with low participation, while BGE reports spending $154.58/kW-saved in total for their opt-out program, which achieved high participation (APS 2016; BG&E 2016). In coordination with the mid-range spending value used, we also used conservative estimates for participation rates. For the PTR program, we assume participation rates begin at 5% based on utility evaluations and recruitment rates, and end with just over 15% participation in 2037. We use similar estimates for the CPP program, starting at 4% participation based on utility evaluations and recruitment rates, and end with just over 12.5% participation in 2037. These are reasonable estimates using a mid-range spending value over time, as other utilities have achieved similar or higher participation for opt-in time-varying rates. For example the Salt River Project achieved over 30% participation in the opt-in time-of-use rate program in 2015, and OG&E achieved about 15% participation in their time-varying rate program in 2016 (Relf, Baatz, and Nowak 2017; OG&E 2017). We split participation rates between those with and without technology, based on technology adoption rates of past utility programs. For example, OG&E has Optimal Energy, Inc. 45 Study of Potential for Electric Energy Savings New Orleans, Louisiana achieved between 45% and 65% technology adoption rates in past program years. This is consistent with other utility technology adoption rates. Residential Peak Time Rebates (PTR) Peak time rebates (PTR) are pay-for-performance incentive programs that pay participants to reduce energy use during certain hours of selected days when a peak event is called. The number of events called varies by year based on weather and system needs. Our methodology does not attempt to assume a certain number of events, but rather uses the percent of peak energy saved based on the median data point from a meta-analysis of PTR programs with and without AMI technologies. The incentive payment is calculated based on the difference between actual metered electricity use and estimated participant use in the absence of a called event (i.e. baseline electricity use). PTR programs provide only “carrots,” or rewards, for reducing energy during peak times, rather than using a “stick,” or penalties, in the rate structure. Examples of PTR programs include Baltimore Gas & Electric's (BG&E) PTR program and Oklahoma Gas & Electric's (OG&E) PeakTime Rewards program. PTR has also been offered as a default rate with the option to opt-out in Southern California, Maryland, and Washington, D.C. (Brattle 2014). The price ratio for a peak rebate to off peak price typically falls in a range of about 4 to 9, meaning that the peak rebate is 4 to 9 times the off-peak price. Examples include (Fenrick et. al 2014): • • • SDG&E’s PTR program that provides incentives of $0.75/kWh for manual reduction and $1.25/kWh for automated demand response AEP Central Power and Light’s PTR program that provided incentives ranging from $0.65-$1.60/kWh Pepco’s PTR that provided an incentive of $0.75/kWh Residential PTR Model Inputs For the PTR potential savings model, we used residential customer and peak demand load forecast data from ENO. We used ENO 2017 residential peak demand data and estimated savings using the median data point of percent of peak energy saved from a meta-analysis of PTR programs with and without AMI technologies. These estimates are consistent with peak savings percentage data from additional utility program evaluations we reviewed. Participation rates and costs per kW saved data are based on utility program evaluations. Table 30 shows the major assumptions and inputs to the PTR models. Optimal Energy, Inc. 46 Study of Potential for Electric Energy Savings New Orleans, Louisiana Table 30 Residential PTR Model Inputs Residential PTR w/o tech. Baseline Demand (average peak kW per customer)1 3.35 Peak Reduction per Participant 12% Participation Rate (% residential customers)2 2.3% Residential PTR with tech. 3.35 20% 2.7% Program Measure Cost per kW Saved $50 $50 Asummes total starting participation of 5% for both programs combined (without tech at 2.3% and with tech at 2.7%). Analysis assumes an annual participation growth rate of 15% that declines by 1% annually. We assume no growth in participation after 2032 to be conservative. . Total participation reaches a maximum of 15.7% in 2033. 2We use a median cost estimate of $50/kW-saved for PTR and CPP programs, based on utility evaluations we reviewed; this estimate remains constant over time. 2 Residential Critical Peak Pricing (CPP) Residential Critical Peak Pricing (CPP) programs charge customers a higher peak price during certain hours of selected days when events are called. The number of events called varies by year based on weather and system needs. Our methodology does not attempt to assume a certain number of events, but rather uses the percent of peak energy saved based on the median data point from a meta-analysis of CPP programs with and without AMI technologies. CPP programs provide “carrots”, or incentives of very low energy prices, for using energy during peak times. They also use a “stick”, or penalty of very high prices for energy use during peak times in the rate structure. Opt-in CPP programs attract customers by focusing on the ability of participants to manage their consumption and to take advantage of very low off-peak prices. The ratio of the peak price to off-peak prices typically falls around 8 or 9, meaning that the critical peak price is 8 or 9 times the off-peak price. Examples include (Fenrick et. al 2014): • • • • OG&E’s critical peak price of $0.42/kWh PSE&G’s critical peak price added to the off-peak price in a range from $0.23/kWh (non-summer) to $1.37/kWh (summer) Pacific Gas & Electric’s critical peak price adder of $0.60/kWh DTE’s critical peak price of $1.00/kWh (DTE 2014) Examples of CPP programs include OG&E’s SmartHours program and Arizona Public Service’s residential Super Peak CPP program. Residential CPP Model Inputs For the CPP savings potential model, we used residential customer and peak demand load forecast data from ENO. We used ENO 2017 residential peak demand data and estimated savings using the median data point of percent of peak energy saved from a meta-analysis of CPP programs with and without AMI technologies. This estimate is consistent with peak savings data from additional utility program evaluations we reviewed. Participation rates and estimated costs per kilowatt saved are based on averages of utility evaluation program data. Table 31 shows the major assumptions and inputs to the CPP models. Optimal Energy, Inc. 47 Study of Potential for Electric Energy Savings New Orleans, Louisiana Table 31 Residential CPP Model Inputs Residential CPP w/o tech. Baseline Demand (average peak kW per customer)1 3.35 Peak Reduction per Participant 20% Participation Rate (% residential customers)2 1.6% Residential CPP with tech. 3.35 25% 2.4% Program Measure Cost per kW Saved $50 $50 Asummes total starting participation of 4% for both programs combined (without tech at 1.6% and with tech at 2.4%). Analysis assumes an annual participation growth rate of 15% that declines by 1% annually. We assume no growth in participation after 2032 to be conservative. Total participation reaches a maximum of 12.5% in 2033. 2We use a median cost estimate of $50/kW-saved for PTR and CPP programs, based on utility evaluations we reviewed; this estimate remains constant over time. 2 Large Customer Programs The only current large customer demand response offering from ENO is a curtailment tariff that is used by Air Products for their air separation plant. Expanded participation will likely come with the implementation of advanced metering infrastructure (AMI) that would enable bidirectional communications between the customer and the utility, which should be available for large customers in the early to mid-2020s. Reviewing the literature, we chose to research three program models for the large customers: • • • Standard offer program (SOP), where the customer is paid to allow the utility to curtail load for a maximum number of times during a set period, usually with 24 hours advance notice. Direct load control (DLC), where the utility installs equipment on large energy using equipment, predominately HVAC, that allows the utility to remotely control the equipment during certain prescribed periods of time. Automated demand response (ADR), which makes use of AMI system bidirectional communications to provide information to the customer that allows their intelligent building management system to take steps, such as precooling of the facility, to anticipate future grid needs that would allow the facility to reduce energy consumption during peak periods. In exchange, the customer is compensated for their reductions. In some cases, the customer is also incented to install necessary equipment to participate in the program. In general, these programs are made available to all larger customers. Looking at the examples of these programs from across the country for which data was available, with a particular focus on programs in the south, we found multiple examples of SOP that showed a consistent pattern of cost and performance. Data on large customer DLC and ADR programs are more limited, with significant variation in cost of avoided capacity despite similarities in the programs. In particular, the data for ADR showed a wide variation in cost and in many cases lacked other performance indicators. Optimal Energy, Inc. 48 Study of Potential for Electric Energy Savings New Orleans, Louisiana Because of this limited data for the large customer ADR, and its dependence on availability of AMI, we opted to collapse these two categories (i.e., DLC and ADR) into a single load control measure. We anticipate that initially the load control would make use of DLC technologies, but as the technologies continue evolve and AMI becomes available that the program would likely transition to next generation ADR in those applications where it is more cost effective than traditional DLC. We would anticipate that the cost per kW would likely remain the same, but that the reductions per customer would increase. Because there is significant uncertainty in projecting this results into future years, we elected to make a conservative assumption of holding percustomer savings and costs constant for the study period. We propose two large customer DR program bundles: 1) a standard offer that would be available initially to about half of the commercial, industrial, and government load, with modest participation increases during the study period, and 2) the standard offer combined with a direct load control offering that would initially be available to about 20% of the load, increasing to 40% of load by the end of the study period as the program transitions from DLC to next generation ADR system that can control a larger range of loads. In the second scenario we assume that the SOP and DLC/ADR programs are complementary and additive. As noted above, ENO does not have a history of DR programs for the majority of their C&I customers, which means that it will take several years of marketing and customer experience to build participation in the program. As a result, we project a relatively modest trajectory of increasing program participation. In addition, the ENO commercial base has a higher share of hospitality customers than we see in most customer bases. The large national chains are likely to participate in DR programs, but we might anticipate that locally-owned customers would be less likely to participate in DR programs because they have limited familiarity with DR programs and concerned about customer comfort in a hot and humid climate, and therefore less willing to participate in any program that might interrupt cooling and negatively affect customer comfort. For both of these reasons we feel that a lower ultimate participation of the large customer DR program is reasonable. We estimate that the non-residential customers account for about half of the peak for the study period, as reflected in data from 2017. Table 32 Large Customer Program Assumptions Program Measure Standard Offer Program (SOP)1 Large Customer Direct Load Control (DLC)2 Savings per Customer (kW) 5.1 1.7 Spending per kW Saved $37.26 $33.50 Notes: 1) Assumes an average 10% reduction for participating customers; 2) assumes an average 3% reduction for participating custom RESULTS This section presents results including total costs, peak demand savings, and costeffectiveness for the demand response programs evaluated. We present findings from two Optimal Energy, Inc. 49 Study of Potential for Electric Energy Savings New Orleans, Louisiana scenarios for years 2018-2037. Both scenarios are achievable and are based on participation rates that have been achieved in other jurisdictions. In Scenario One, we assume participation rates at the lower end the range that we see from other jurisdictions. In Scenario Two, we assume participation rates at the upper end of the range that we see from other jurisdictions. Scenario Two therefore assumes more aggressive program participation and marketing and as a result higher levels of demand reduction. Another important distinction between the two scenarios is for the residential pricing programs. In Scenario One we model a residential PTR program and in Scenario Two we model a residential CPP program that would achieve higher levels of demand reduction. Scenario One includes the following measures: • • • Residential DLC and ADR Residential PTR pricing with and without AMI technology Large customer standard offer program (SOP) Scenario Two includes the following measures: • • • Residential DLC and ADR Residential CPP pricing with and without AMI technology Large customer SOP plus a DLC/ADR offering Peak Demand Savings Results for each of the scenarios are presented in the Figures and Tables below. Figure 16 Electric Demand Savings - Scenario One Optimal Energy, Inc. 50 Study of Potential for Electric Energy Savings New Orleans, Louisiana Figure 17 Electric Demand Savings - Scenario Two Table 33 Demand Response Peak Load Reductions Summary – Scenario One Program Residential DLC and ADR Residential PTR pricing Large Customer SOP Total 2018 2.0 4.9 1.1 8.0 2027 16.0 12.6 10.9 39.5 2037 20.2 15.5 16.9 52.5 Table 34 Demand Response Peak Load Reductions Summary– Scenario Two Program Residential DLC and ADR Residential CPP pricing Large Customer SOP Total 2018 3.9 5.6 1.9 11.5 2027 31.9 14.2 13.4 59.6 2037 40.3 17.5 23.2 81.1 Scenario One reached peak reductions from these programs equivalent to 2.7% of total system forecast peak in 2027 and 3.6% in 2037. In Scenario Two, peak reductions from these programs are equivalent to 4.5% of forecasted system peak in 2027 and 5.9% in 2037. The tables below gives a more detailed breakout of savings by year for every year of the study horizon for each scenario. Optimal Energy, Inc. 51 Study of Potential for Electric Energy Savings New Orleans, Louisiana Table 35 Demand Response Peak Load Reductions By Year – Scenario One 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 Optimal Energy, Inc. Res Res Pricing DLC/ADR PTR 2 5 4 6 6 6 8 7 10 8 11 9 13 10 14 11 15 12 16 13 17 13 18 14 18 15 19 15 19 15 19 16 20 16 20 16 20 16 20 16 Large Cust SOP 1 2 3 4 5 6 8 9 10 11 11 12 13 13 14 14 15 16 16 17 Total 8 12 16 19 23 27 30 34 37 39 42 44 45 47 48 49 50 51 52 53 52 Study of Potential for Electric Energy Savings New Orleans, Louisiana Table 36 Demand Response Peak Load Reductions By Year – Scenario Two 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 Res Res Pricing DLC/ADR PTR 4 6 8 6 12 7 16 8 19 9 23 10 25 11 28 12 30 13 32 14 34 15 35 16 36 16 37 17 38 17 39 17 39 17 40 17 40 17 40 17 Large Cust SOP 2 3 4 6 7 8 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Total 11 18 24 30 35 41 46 51 55 60 63 66 69 72 74 76 77 79 80 81 Budgets and Cost-Effectiveness Program budgets are presented in the figures below and overall cost-effectiveness results for each program, scenario, and the overall DR portfolio are presented in the table below. Optimal Energy, Inc. 53 Study of Potential for Electric Energy Savings New Orleans, Louisiana Figure 18 Annual Program Costs—Scenario One Figure 19 Annual Program Costs—Scenario Two Optimal Energy, Inc. 54 Study of Potential for Electric Energy Savings New Orleans, Louisiana Table 376 Net Costs and Benefits of DR Potential in Scenarios One and Two Scenario One Program Residential DLC and ADR Residential pricing Large Customer SOP Total Optimal Energy, Inc. Costs (Million$) $14.0 $8.4 $1.87 $27.5 Benefits (Million$) $19.8 $16.4 $4.99 $50.1 Scenario Two BCR 1.4 2.0 2.7 1.8 Costs (Million$) $18.3 $9.4 $6.6 $34.3 Benefits (Million$) $25.2 $18.5 $18.1 $61.9 BCR 1.4 2.0 2.8 1.8 55 Study of Potential for Electric Energy Savings New Orleans, Louisiana COMBINED RESULTS Total Peak Demand Savings, all DSM Although this analysis mainly treats the demand response, energy efficiency, and rate design portions as independent and separate, we do provide a high level analysis of the likely total peak demand reduction from all three DSM types (efficiency, demand response, and rate design). The table below shows project total demand reduction by year. We derived these values by assuming a simple “loading order” of the categories: first rate design first, then energy efficiency, and then demand response. In other words, if in a given year the three categories would each produce a 10% reduction in peak separately, we assume that the rate design reduces the forecast by 10%, then the efficiency reduces the new forecast by 10%, and then demand response reduces the remaining peak by another 10%. This way, total demand is reduced by around 27%, instead of the 30% that would result if you simply added the reductions together. Table 10 presents the results of this analysis, assuming an optional time of use rate design, the program potential energy efficiency savings, and scenario two for demand response. Table 38 Cumulative Peak Demand Reduction from EE, DR, and Rate Design Year 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 Optimal Energy, Inc. Peak Reduction (MW) 67 83 104 129 154 181 209 236 262 288 Year 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 Peak Reduction (MW) 297 305 313 321 329 335 340 343 347 350 56 Study of Potential for Electric Energy Savings New Orleans, Louisiana RATE DESIGN SUMMARY OF APPROACH & MAJOR ASSUMPTIONS Electric rate design holds promise as a tool to incent specific behavior or consumption pattern changes from customers. In the assessment of demand response potential, we considered rate design approaches that focus on short-duration price signals for specific events (i.e., critical peak pricing and peak time rebates). This section of the analysis describes other rate design options (such as a time of use rates) that apply to all hours of the year and therefore can result in larger shifts in customer energy consumption patterns. Decades of study has demonstrated positive customer response to changes in marginal prices or electric rates.20 In this section we present results of our analysis of how Entergy New Orleans residential customers may respond to different rate design alternatives. Rate design refers to the process of translating utility revenue requirements into the prices paid by customers.21 Rates for residential customers are typically composed of two parts, a fixed customer charge and a volumetric energy rate. The fixed customer charge is a flat fee paid by customers regardless of how much energy they use in a given month. This is often intended to recover specific costs of utility service, including billing, metering, and customer service. The volumetric energy component bills customers for each unit of energy consumed. While the majority of residential customers in the United States are subject to a flat energy charge, meaning they pay the same price for each unit of energy regardless of what time of day it is used or the total level of consumption, many utilities also offer time varying volumetric energy rates, charging customers different prices for energy consumed based on the time of day or year. Finally, some utilities also offer tiered rates, charging customers a higher or lower rate for each unit of consumption based on the total usage for the month. Entergy New Orleans currently relies on a rate structure with a flat energy charge in the summer and a declining block rate in the winter. Table 39 shows the current residential rate design. Table 39 ENO Existing Residential Rates Component Customer charge (monthly) Summer $8.07 Winter $8.07 Energy Charge per kWh Tier 1 (0-800 kWh) Tier 2 (over 800 kWh) $0.06002 $0.06002 $0.06002 $0.04766 To estimate potential changes in consumption for the Entergy New Orleans service territory we relied on existing evidence from prior pricing studies regarding customer price response and participation. We developed five revenue neutral rate design scenarios to understand consumer 20 Electric Power Research Institute. 2008. Price Elasticity of Demand for Electricity: A Primer and Synthesis. epri.com/#/pages/product/1016264/?lang=en. 21 National Association of Regulatory Utility Commissioners. 2016. Distributed Energy Resources Rate Design and Compensation. pubs.naruc.org/pub/19FDF48B-AA57-5160-DBA1-BE2E9C2F7EA0. Optimal Energy, Inc. 57 Study of Potential for Electric Energy Savings New Orleans, Louisiana price response using the current rate structure as a baseline. Revenue neutral rate approaches are designed to recover the same level of revenue in the analysis period, which is one year for this analysis. The various rate design scenarios are based on commonly used and industry-accepted approaches to residential rate design. Table 40 shows the five rate scenarios, with customer charge and volumetric rate values for each scenario. The table also shows the participation assumption for the analysis, which is described in greater detail below. Table 40 Summary of Rate Design Scenarios Description Customer charge ($/month) Seasonal with higher customer charge $25 Seasonal with higher customer charge $50 Time of use (opt in) Time of use (opt-out) Seasonal inclining block rate $8.07 $8.07 $8.07 Season Summer Winter Summer Winter summer summer winter winter summer summer winter winter summer summer winter winter Period or block all all all all on peak off peak on peak off peak on peak off peak on peak off peak tier 1 tier 2 tier 1 tier 2 Volumetric rate ($/kWh) $0.0508 $0.0351 $0.0278 $0.0168 Participation Assumption 100% (mandatory) 100% (mandatory) $0.1231 $0.0424 $0.0925 25% (opt in) $0.0463 $0.1231 $0.0424 $0.0925 90% (opt-out) $0.0463 $0.0550 $0.0850 $0.0343 100% (mandatory) $0.0548 The first two scenarios are both seasonal rates with higher customer charges. The volumetric price varies from summer to winter to reflect the higher cost of energy production in the summer. The customer charge for the first scenario is $25 per month and $50 per month for the second scenario. The time of use rate relies on the same customer charge as the current residential offering, but uses on- and off-peak periods in both summer and winter for the volumetric charge. This structure more accurately reflects the cost to serve residential customers throughout the day. Finally, the seasonal inclining block rate relies on the existing customer charge and an inclining block structure for volumetric prices. As with the other scenarios, the seasonal price varies to reflect the higher cost to serve customers in the summer months. The inclining tier structure assesses a higher cost per unit of energy consumed based on higher levels of consumption. In this analysis, the first tier includes consumption from 0-500 kWh per month. The second tier captures all consumption in excess of 500 kWh per month. The current ENO Optimal Energy, Inc. 58 Study of Potential for Electric Energy Savings New Orleans, Louisiana residential rate uses a declining block rate in the winter months, meaning customers are billed a lower cost per unit of energy in the second tier (800 kWh or greater). For all seasonal rates we assumed a summer period of May through October and a winter period of November through April. For the time of use rate, we assume a peak period between 3 and 8 pm in summer and 6 to 9 am in the winter. These periods are based on consumption patterns presented in the load research sample data from Entergy. These scenarios represent a range of potential rate designs for Entergy New Orleans. The time-of-use scenario relies on an on-peak to off-peak ratio of 3:1 in the summer. Prior research demonstrates that this ratio is a critical factor in how customers respond and modify their energy consumption.22 METHODOLOGY We took several steps to estimate changes in consumption and peak demand for various rate designs. First, we created revenue neutral rate designs using a load research sample provided by Entergy New Orleans. We determined revenue targets using revenues per customer provided in the most recent Federal Energy Regulatory Commission (FERC) Form 1, an industry data reporting form required for all investor-owned utilities. We then applied applicable price elasticities from relevant, recent pricing studies to usage in specific periods to measure changes in consumption. According to EPRI, the price elasticity of demand is a measure of how price changes influence electricity use.23 Price elasticities for electricity, as with nearly all consumer products and services, are generally negative, meaning that as prices increase, consumption declines. The EPRI study surveyed prior literature on price elasticity and concluded that residential short-run price elasticity ranges between -0.2 and -0.6, with a mean value of -0.3. The long-run elasticities were estimated between -0.7 and -1.4, with a mean value of -0.9. Short run is considered 1-5 years, while long run is anything beyond five years. The value represents the ratio of a percentage change in quantity demanded and the percentage change in price. For example, a 10% increase in residential electricity prices would result in a 3% decline in short term consumption, relying on the mean estimate from the EPRI study. These values allow us to estimate how residential customers may adjust their electric consumption in responses to changes in prices. Entergy New Orleans has not conducted any recent pricing studies which would offer primary data for this purpose. Instead, we reviewed several recent pricing studies to source applicable elasticities for the Entergy New Orleans service territory. Table 29 shows the elasticities used for this analysis. For the seasonal two-part rate, we relied on the first tier elasticity for the summer period consumption. All elasticities are short run, meaning they only capture changes in consumption in the near term. . We did not estimate customer response in the long 22 Faruqui, A. et al. 2017. Arcturus 2.0: A Meta-Analysis Of Time Varying Rates For Electricity. The Electricity Journal. Vol 30, Issue 10, December 2017, pages 64-72. 23 Electric Power Research Institute. 2008. Price Elasticity of Demand for Electricity: A Primer and Synthesis. epri.com/#/pages/product/1016264/?lang=en. Optimal Energy, Inc. 59 Study of Potential for Electric Energy Savings New Orleans, Louisiana run because the results are less certain than the short run customer response. However, we would expect greater price response in the long run as customers have more options to reduce or shift consumption over time.. Table 41 Price Elasticity Assumptions24 Rate IBR first tier IBR second tier TOU on peak TOU off peak Elasticity -0.130 -0.260 -0.083 -0.0265 Our analysis applies these elasticities to consumption data for a sample population of 319 residential ENO customers. The sample is intended to represent the larger population of residential customers. However, Entergy New Orleans has approximately 178,000 residential customers. The sample, if properly drawn, should represent the larger population of residential customers.25 To estimate changes for the entire customer class, we extrapolate the results from the price response analysis of the sample population to the entire residential customer class. This allows us to understand the potential impacts of implementation of a given rate design to all residential customers. For this exercise, we must also make assumptions on uptake or participation of specific rates by the customer class. This is primarily important because customers have demonstrated greater changes in consumption when opting-in or subscribing to a specific rate on a voluntary basis.26 Customers as a whole show a lower response when placed on a rate on a nonvoluntary basis. For the optional time-of-use (TOU) rate, we assumed 25% of customers would enroll, with the remaining customers staying on the existing rate. Under the default TOU rate, we assumed 90% of customers stayed on the rate, while the other 10% opted back to the existing two part seasonal rate. The inclining block and seasonal two part iterations were assumed to be mandatory, with 100% of customers subject to the rate. RESULTS High-level results include: • Under an optional time-of-use rate with on and off-peak pricing for both summer and winter periods, overall consumption declined by 0.5% for the entire class, with a summer peak period reduction of 4.4%. 24 The tier rate elasticities are sourced from Faruqui, A. 2008. Inclining Towards Efficiency. Public Utilities Fortnightly. August. The time-of-use rate elasticities are sourced from Faruqui et al. 2016. Analysis of Ontario’s Full Scale Rollout of TOU Rates. 25 We did not conduct a review of the accuracy of the sample of residential customers and assume it accurately matches the rest of the customer class. 26 George, S. et al. 2014. SMUD SmartPricing Options Pilot Evaluation. August 6. smartgrid.gov/files/SMUDCBS_Final_Evaluation_Submitted_DOE_9_9_2014.pdf. Optimal Energy, Inc. 60 Study of Potential for Electric Energy Savings New Orleans, Louisiana • • • If the time-of-use rate were default instead of optional, we estimate a decrease in overall consumption of 0.9%, with a summer peak period reduction of 7.9%. If all customers were moved to an inclining block rate, we estimate a decrease in overall consumption of 2.1%. If the customer charge was increased to $25 a month (from the current $8.07 per month) and the second tier in the winter rate were eliminated, we estimate overall consumption would increase by 3.6%. If it were increased to $50, we estimate overall consumption could increase by 8.9%. Table 42 presents a summary of these results. Table 42 Summary of Results Rate Scenario Optional time of use Default time of use Inclining block rate Seasonal ($25/mo. customer charge) Seasonal ($50/mo. customer charge) Change in Energy Consumption Change in Peak Demand -0.5% -0.9% -2.1% 3.6% 8.9% -4.4% -7.9% N/A N/A N/A Our analysis shows that time-of-use and inclining block rates would marginally reduce consumption, while also providing a price signal to customers to engage in energy efficiency programs and behavior. The reductions of peak demand are driven by higher rates in those time periods. These results also suggest not all consumption in the peak period is reduced, but some is shifted to off peak periods. The seasonal rate options with higher customer charges would lead to higher consumption overall and provide a poor price signal to conserve electricity and engage in energy efficiency programs. Peak Demand Savings The analysis showed a summer peak period demand savings of 7.9% under the default timeof-use rate, but only a 4.4% reduction under an optional time-of-use rate. The inclining and seasonal rate options with higher customer charge are not intended to drive changes in the timing of consumption or reductions in peak demand. Effect on System Costs There are several categories of utility system costs that may be affected through the changes in overall consumption and peak demand presented in table 4. Reducing peak demand allows a utility to reduce production during peak periods, which lowers overall energy costs and the need for increased peaking production capacity. Energy and other variable costs are also avoided through consumption reductions during off peak periods. Conversely, increasing consumption and peak demand would likely increase system costs. At a minimum, variable energy and maintenance costs would increase. However, generation and distribution system Optimal Energy, Inc. 61 Study of Potential for Electric Energy Savings New Orleans, Louisiana capacity cost increases will depend on current system conditions and needs. Future rate increases because of investment in new assets may be avoided through the reduction in peak demand and localized demand reductions. The cost associated with rolling out new rate design approaches varies significantly based on the level of marketing and customer outreach employed by the utility. There are also many other considerations for a utility or regulator in any new rate design approach. Not all customers will respond and some will face higher bills as a result of the new rates. Before implementing any new rate design, the effect on vulnerable customers should be assessed and attempts made to mitigate any negative outcomes they may face. Discussion of methods for doing so are beyond the scope of this study. Optimal Energy, Inc. 62 Study of Potential for Electric Energy Savings New Orleans, Louisiana METHODOLOGY DETAILS OVERVIEW This section provides a brief overview of our approach to the study analysis. The subsequent sections provide more detailed descriptions of the analysis methodology and assumptions. The energy efficiency potential analysis involves several steps. The first several are required regardless of the scenario being analyzed, and were first performed in order to build the base model used to run each scenario. These steps include: • • • Assess and adjust energy forecast. In this case, we used the forecast from Entergy New Orleans, and added back the projected savings from current Energy Smart Programs. Disaggregate adjusted energy forecasts by sector (residential, low-income, commercial and industrial), by market segment (e.g., building types), and end uses (e.g., lighting, cooling, etc.) Characterize efficiency measures, including estimating costs, savings, lifetimes, and share of end use level forecasted usage for each market segment To develop each scenario (economic, maximum achievable, and program potential) required additional steps specific to the assumptions in each scenario. These steps are listed below. • • • • • Build up savings by measure/segment based on measure characterizations calibrated to total energy usage Account for interactions between measures, including savings adjustments based on other measures as well as ranking and allocating measures when more than one measure can apply to a particular situation Run the stock adjustment model to track existing stock and new equipment purchases to capture the eligible market for each measure in each year Run the efficiency potential model to estimate the total potential for each measure/segment/market combination to produce potential results Screen each measure/segment/market combination for cost-effectiveness. Remove failing measures from the analysis and rerun the model to re-adjust for measure interactions Annual energy sales forecasts were for each sector (residential, low income, commercial/industrial), for the 20-year study period. The electric forecasts was provided by Entergy, and adjusted to add back in the Energy Smart savings. The sales forecasts was then disaggregated by end use and building type in order to apply each efficiency measure to the appropriate segment of energy use. This study applied a top-down analysis of efficiency potential relative to the energy sales disaggregation for each sector, merged with a bottom-up measure level analysis of costs and savings for each applicable technology. The study applied a Total Resource Cost (TRC) Test to determine measure cost-effectiveness. The TRC test considers the costs and benefits of efficiency measures from the perspective of society as a whole. Efficiency measure costs for market-driven measures represent the Optimal Energy, Inc. 63 Study of Potential for Electric Energy Savings New Orleans, Louisiana incremental cost from a standard baseline (non-efficient) piece of equipment or practice to the high efficiency measure. For retrofit markets the full cost of equipment and labor was used because the base case assumes no action on the part of the building owner. Measure benefits are driven primarily by energy savings over the measure lifetime, but also may include other easily quantifiable benefits associated with the measures, including water savings, and operation and maintenance savings. The energy impacts may include multiple fuels and end uses. For example, efficient lighting reduces waste heat, which in turn reduces the cooling load, but increases the heating load. All of these impacts are accounted for in the estimation of the measure’s costs and benefits over its lifetime. There are two aspects of electric efficiency savings: annual energy and coincident peak demand. The former refers to the reductions in actual energy usage, which typically drive the greatest share of electric economic benefits as well as emissions reductions. However, because it is difficult to store electricity the total reduction in the system peak load is also an important impact. Power producers need to ensure adequate capacity to meet system peak demand, even if that peak is only reached a few hours each year. As a result, substantial economic benefits can accrue from reducing the system peak demand, even if little energy and emissions are saved during other hours. The electric benefits reported in this study reflect both electric energy savings (MWh) and peak demand reductions (MW) from efficiency measures. The primary scenario for the study was the program potential, which best reflects what could actually be accomplished by efficiency programs given real-world constraints, and assumes incentive amounts of 50% of the incremental cost for residential and C&I sectors, and 100% for the low-income sector. We have also estimated the economic and maximum achievable potentials. The general approach for these three scenarios differed as follows: • • Economic potential scenario: We generally assumed that all cost-effective measures would be immediately installed for market-driven measures such as for new construction, major renovation, and natural replacement (“replace on failure”). For retrofit measures we generally assumed that resource constraints (primarily contractor availability) would limit the rate at which retrofit measures could be installed, depending on the measure, but that all or nearly all efficiency retrofit opportunities would be realized over the 10-year study period. Spreading out the retrofit opportunities results in a more realistic ramp up, providing a better basis of comparison for the achievable scenarios. In years 11-20 the retrofit activity significantly declines as the entire market has been reached, and any new retrofits are just replacing another technology that has failed (such as re-commissioning a building that was commissioned 10 years earlier). Maximum achievable scenario: This scenario is based on the economic potential but accounts for real-world market barriers. We assumed that efficiency programs would provide incentives to cover 100% of the incremental costs of efficiency measures, so that program participants would have no out-of-pocket costs relative to standard baseline equipment. Measure Optimal Energy, Inc. 64 Study of Potential for Electric Energy Savings New Orleans, Louisiana • participation was estimated using the Delphi Process, described earlier in the report. Program potential scenario: For this scenario, we assume that most incentives are set to 50% of the incremental cost. Penetration rates are based on the simple payback of the measure, as defined by the Delphi Panels. The one exception is that, for low income, we assume that programs will still provide 100% incentives. These programs therefore achieve the same participation as in the Max Achievable scenario. ENERGY FORECASTS Electric Forecast The electric usage forecast was developed primarily from the information provided by Entergy New Orleans. Reported sales categories aligned with traditional utility categories, which closely mirror the three customer sectors that were analyzed. In some cases, energy loads were aggregated to the sector level using standard conventions (e.g., street lighting energy use is included in the commercial sector). Assumed savings from the Energy Smart Programs running at constant savings into the future were added back into the provided forecast. Current programs save about 0.4% of total sales, at a cost of $6.2 million. By adding these savings back to the forecast, the results of the study reflect a base case where no utility run efficiency programs exist. Forecast Disaggregation by Segment and End Use The commercial and residential sales disaggregations draw upon many sources. The commercial and industrial disaggregation relies on a number of sources. First, total forecasted energy sales are divided across building types using data from Entergy showing usage by SIC code, supplemented with data from EIA. Low-income buildings were separated from non-LI residential based on the statistical atlas27. Next, energy use was disaggregated into end use using the data from the EIA, and especially the Commercial Building Energy Consumption Survey (CBECS) and the Residential Energy Consumption Survey (RECS) . Sales were further disaggregated into sales for new construction and renovated spaces and those for existing facilities. New construction activity was based on Entergy’s projection of customer count growth, compared with EIA data on the consumption of new versus existing facilities. MEASURE CHARACTERIZATION The first step for developing measure characterizations is to define a list of measures to be considered. This list was developed and qualitatively screened for appropriateness in consultation with stakeholders to the study process. The final list of measures considered in the 27 https://statisticalatlas.com/place/Louisiana/New-Orleans/Household-Income Optimal Energy, Inc. 65 Study of Potential for Electric Energy Savings New Orleans, Louisiana analysis is shown with their characterizations in Appendix I, which also shows the markets for which each measure was considered. A total of 173 measures were included and characterized for up to three applicable markets (new construction/renovation, natural replacement, and retrofit). This is important because the costs and savings of a given measure can vary depending on the market to which it is applied. For example, a retrofit or early retirement of operating but inefficient equipment entails covering the costs of entirely new equipment and the labor to install it and dispose of the old equipment. For new construction or other market-driven opportunities, installing new high efficiency equipment may entail only the incremental cost difference between a standard efficiency piece of equipment and the high efficiency one, as other labor and capital costs would be incurred in either case. Similarly, on the savings side, retrofit measures can initially save more when compared to older existing equipment, while market-driven measure savings reflect only the incremental savings over current standard efficiency purchases. For retrofit measures, often we model a baseline efficiency shift at the time when the retrofit measure being replaced is assumed to have needed to be replaced anyway. For each measure, in addition to separately characterizing them by market, we also separately analyze each measure/market combination for each building segment (e.g., small office, large office, industrial, restaurant, etc.). The result is that we modeled 1,591 distinct measure/market/segment permutations for each year of the analysis. The overall potential model relies on a top-down approach that begins with the forecast and disaggregates it into loads attributable to each possible measure, as described in the following section. In general, measure characterizations include defining the following characteristics for each combination of measure, market, and segment: • • • • • Measure lifetime (both baseline and high efficiency options if different) Measure savings (relative to baseline equipment) Measure cost (incremental or full installed depending on market) O&M impacts (relative to baseline equipment) Water impacts (relative to baseline equipment). Energy Savings For each technology, we estimate the energy usage of baseline and high efficiency measures based primarily on engineering analysis. We rely heavily on the New Orleans Technical Resource Manual (TRM), as well as other TRMs from other jurisdictions, and Optimal’s existing database of measure characteristics. For more complex measures not addressed by the TRMs engineering calculations are used based on the best available data about current baselines in New Orleans and the performance of high efficiency equipment or practices. The New Orleans Appliance Saturation Survey, done in 2006, was used to determine they type of equipment and fuel used, but was too old to use to determine the efficiencies. Due to budget and time constraints we did not include any building simulation modeling or other sophisticated engineering approaches to establishing detailed, weather normalized savings. Optimal Energy, Inc. 66 Study of Potential for Electric Energy Savings New Orleans, Louisiana Costs Measure costs were drawn from Optimal Energy’s measure characterization database when no specific Louisiana costs were available. These costs have been developed over time, and are continually updated with the latest information, including a recent update for an ongoing potential study in Minnesota. Major sources include the New Orleans TRM and Mid-Atlantic TRMs, baseline studies, incremental cost studies, direct research into incremental costs, and other analyses and databases that are publicly available. Lifetimes As with measure costs, lifetimes are drawn from Optimal’s measure characterization database. These have been developed over time, and were revised for this study based on the New Orleans TRM. Operations and Maintenance Impacts Operation and maintenance (O&M) impacts are those other than the energy costs of operations. They represent, for example, things like replacement lamp purchases for new high efficiency fixtures, or changes in labor for servicing high-efficiency vs. standard-efficiency measures. High efficiency equipment can often reduce O&M costs because of higher quality components that require less-frequent servicing. On the other hand, some high efficiency technologies require enhanced servicing, or have expensive components that need to be replaced prior to the end of the measure’s lifetimes. For most measures, O&M impacts are very minimal, as many efficient and baseline technologies have the same O&M costs over time. Where they are significant, we estimate them based on our engineering and cost analyses, the New Orleans TRM, and other available data. Additional aspects of measure characterization are more fully described below in the potential analysis section, along with other factors that merge the measure level engineering data with the top-down forecast of applicable loads to each measure. TOP-DOWN METHODOLOGY The general approach for this study, for all sectors, is “top-down” in that the starting point is the actual forecasted loads for each sector. As described above, we then break these down into loads attributable to individual building equipment. In general terms, the top-down approach starts with the energy sales forecast and disaggregation and determines the percentage of the applicable end use energy that may be offset by the installation of a given efficiency measure in each year. This contrasts with a “bottom-up” approach in which a specific number of measures are assumed installed each year. Optimal Energy, Inc. 67 Study of Potential for Electric Energy Savings New Orleans, Louisiana Various measure-specific factors are applied to the forecasted building-type and end use sales by year to derive the potential for each measure for each year in the analysis period. This is shown below in the following central equation: Measure Savings = Segment/ End use /year kWh Sales x Applicability Factor x Feasibility Factor x Turnover Factor (replacement only) x Not Complete Factor (retrofit only) x Savings Fraction x Net Penetration Rate Where: • • • • • Applicability is the fraction of the end use energy sales (from the sales disaggregation) for each building type and year that is attributable to equipment that could be replaced by the high-efficiency measure. For example, for replacing office interior linear fluorescent lighting with a higher efficiency LED technology, we would use the portion of total office building interior lighting electrical load consumed by linear fluorescent lighting. Feasibility is the fraction of end use sales for which it is technically feasible to install the efficiency measure. Numbers less than 100% reflect engineering or other technical barriers that would preclude adoption of the measure. Feasibility is not reduced for economic or behavioral barriers that would reduce penetration estimates. Rather, it reflects technical or physical constraints that would make measure adoption impossible or ill advised. An example might be an efficient lighting technology that cannot be used in certain low temperature applications. Turnover is the percentage of existing equipment that will be naturally replaced each year due to failure, remodeling, or renovation. This applies to the natural replacement (“replace on failure”) and renovation markets only. In general, turnover factors are assumed to be 1 divided by the baseline equipment measure life (e.g., assuming that 5% or 1/20th of existing stock of equipment is replaced each year for a measure with a 20-year estimated life). Not Complete is the percentage of existing equipment that already represents the high-efficiency option. This only applies to retrofit markets. For example, if 30% of current single family homes already have learning thermostats, then the not complete factor for residential thermostats would be 70% (1.0-0.3), reflecting that only 70% of the total potential from thermostats remains. Savings Fraction represents the percent savings (as compared to either existing stock or new baseline equipment for retrofit and non-retrofit markets, respectively) of the high efficiency technology. Savings fractions are calculated based on individual measure data and assumptions about existing stock efficiency, standard practice for new purchases, and high efficiency options. - Optimal Energy, Inc. Baseline Adjustments adjust the savings fractions downward in future years for early-retirement retrofit measures to account for the fact that newer, standard equipment efficiencies are higher than older, existing 68 Study of Potential for Electric Energy Savings New Orleans, Louisiana • stock efficiencies. We assume average existing equipment being replaced for retrofit measures is at 60% of its estimated useful life. The baseline adjustment also comes with a cost credit to reflect the standard equipment that the participant would have had to install to replace the failed unit. Annual Net Penetrations are the difference between the base case measure penetrations and the measure penetrations that are assumed for an economic potential. For the economic potential, it is assumed that 100% penetration is captured for all markets, with retirement measures generally being phased in and spread out over time to reflect resource constraints such as contractor availability. The product of all these factors results in the total potential for each measure permutation. Costs are then developed by using the “cost per energy saved” for each measure applied to the total savings produced by the measure. The same approach is used for other measure impacts, e.g., operation and maintenance savings. COST-EFFECTIVENESS ANALYSIS Cost-Effectiveness Tests This study applies the Total Resource Cost (TRC) Test as the basis for excluding non-costeffective measures from the potential. The TRC test considers the costs and benefits of efficiency measures from the perspective of society as a whole. In addition, for the program potential scenario we report the cost-effectiveness of the efficiency programs using the Program Administrator Cost Test and the Participant Cost Test. The principles of these cost tests are described in the California Standard Practice Manual.28 Table 43 provides the costs and benefits from the perspective of each of the cost-effectiveness tests. Discounting the Future Value of Money Future costs and benefits are discounted to the present using a real discount rate of 3%. The U.S. Department of Energy recommends a real discount rate of 3% for projects related to energy conservation, renewable energy, and water conservation, which is consistent with the Federal Energy Management Program (FEMP).29 For discounting purposes we assume that initial measure costs are incurred at the beginning of the year, whereas annual energy savings are incurred half way through the year. As described further above, we also performed a sensitivity analysis on the cost-effectiveness of each measure using a higher discount rate representing Entergy’s Weighted Average Cost of Capital (WACC). 28 California Standard Practice Manual: Economic Analysis Of Demand-Side Programs And Projects, July 2002; Governor’s Office of Planning and Research, State of California; http://www.calmac.org/events/SPM_9_20_02.pdf 29 See page 1 in http://www1.eere.energy.gov/femp/pdfs/ashb10.pdf. Optimal Energy, Inc. 69 Study of Potential for Electric Energy Savings New Orleans, Louisiana Table 43 Overview of Cost-Effectiveness Tests Monetized Benefits / Costs Total Resource Cost (TRC) Measure cost (incremental over baseline) Cost Program Administrator incentive costs Program Administrator non-incentive program costs Energy & electric demand savings* Fossil fuel increased usage Program Administrator Cost Test Participant Cost Test Cost Cost Benefit Cost Cost Benefit Benefit Benefit Cost Cost Cost Operations & Maintenance savings Benefit Benefit Water savings Benefit Benefit Deferred replacement credit** Benefit Benefit *For the TRC and PACT, energy and electric demand savings are valued using avoided cost values that represent wholesale marginal costs, varying by time of day and season. For the Participant Cost Test, energy savings are valued at average retail costs for each customer sector. **For early-retirement retrofit measures, the Deferred Replacement Credit is a credit for when the existing equipment would have needed replacement. The equipment’s replacement cycle has been deferred due to the early replacement. AVOIDED ENERGY SUPPLY COSTS Avoided energy supply costs are used to assess the economic value of energy savings (or the costs of increased consumption). Developing a set of avoided costs specific to energy efficiency in New Orleans was outside the scope of the project; we relied on the best available data to prepare a set of values that represent reasonable estimates without a substantial investment of time and resources. We developed electric energy avoided costs using a set of forecast hourly marginal energy prices in the relevant load zone operated by the Midcontinent Independent System Operator (MISO). We reduced this detailed information into forecast energy prices in four energy costing periods for use in our modeling software. We had previously determined that using four distinct energy periods would produce a more accurate estimate of avoided energy benefits than would a single annual average value, particularly for cooling measures that save energy during expensive summer on-peak hours. To develop the energy costing periods we reviewed and plotted the daily average hourly marginal energy prices for each month. This is shown in the figure below, with summer months in orange and winter months in blue. Optimal Energy, Inc. 70 Study of Potential for Electric Energy Savings New Orleans, Louisiana Figure 20 Average Hourly Forecast Energy Price – Summer Months 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 As seen, there is a clear difference in price between peak and off peak periods, as well as between summer and winter periods. Based on this review, we defined four energy periods: Summer On-Peak, Summer Off-Peak, Winter On-Peak, and Winter Off-Peak. • • • • Summer is April through October; peak hours are 11 AM – 9 PM weekdays (1,683 hours) Off-peak Summer is the rest of the summer months (3,453 hours) Winter November through March; peak hours are 7 AM – 10 AM and 6 PM – 10PM weekdays (972 hours) Off-peak Winter is the rest of the winter months (2,652 hours) In addition to avoided electric energy costs, we develop avoided capacity costs to value reductions in peak demand. For this study, these costs are based on Entergy’s projected cost to build a new gas turbine plant. Gas avoided costs are based on the long-term Henry Hub price forecast. Entergy did not provide any information on the value of avoided capacity on the transmission and distribution network, the result of which is that our analysis is likely to understate the cost-effectiveness of efficiency savings. ENERGY RETAIL RATES Retail rates are not used in the TRC, and so do not impact the net benefits of efficiency from those perspectives. However, they were used in this study to determine the simple payback of each efficiency measure, which in turn determined the penetration rates for the program potential Optimal Energy, Inc. 71 Study of Potential for Electric Energy Savings New Orleans, Louisiana based on the outcome of the Delphi Panel. Retail rates were developed from Entergy New Orleans’ published rate tariffs. For purposes of the simple payback analysis, only the variable portion of rates was included. For residential customers, we estimated a price of 8.5 cents/kWh. For commercial customers whose rates also depend on billing demand, we converted projected demand savings into a per kWh rate to simplify the analysis. Taking an average of both small and large commercial rates, we estimate an avoidable retail price of 9 cents/kWh. ELECTRIC LOAD SHAPES Electric energy load shapes are used to distribute annual efficiency measure energy savings into the energy costing periods of the avoided costs. Although previous potential studies conducted by Entergy included detailed hourly loadshapes, these were specific to particular efficiency programs (e.g., commercial new construction, residential consumer products, etc.). Our analysis applies load-shapes by energy end-use (e.g., residential lighting, commercial refrigeration, etc) and therefore could not make use of these loadshapes, because the efficiency programs each include measures of several end-uses. Instead, we relied on end-use load shapes information developed by the Electric Power Research Institute (EPRI)30. These end-use loadshapes are region-specific; we relied on the Southeast Reliability Council region (excluding Florida). At the level of precision in this study, any differences in the distribution of energy reductions across the four energy costing periods between this regional average and New Orleans are not expected to be significant. For each end-use, the EPRI data include hourly loadshapes for average weekdays, peak weekdays, and average weekend days, for both summer and winter seasons. From these data, we developed a loadshape for each end-use that defines the percentage of annual energy consumption occurring in each period. ECONOMIC POTENTIAL ANALYSIS The top-down analysis, along with all the data inputs, produces the measure-level potential, with the economic potential being limited to installation of cost-effective measures. However, the total economic potential is less than the sum of each separate measure potential. This is because of interactions between measures and competition between measures. Interactions result from installation of multiple measures in the same facility. For example, if one insulates a building, the heating load is reduced. As a result, if one then installs a high efficiency furnace, savings from the furnace will be lower because the overall heating needs of the building have been lowered. As a result, interactions between measures should be taken into account to avoid over-estimating savings potential. Because the economic potential assumes all possible measures are adopted, interactions assume every building does all applicable measures. Interactions are accounted for by ranking each set of interacting measures by total savings, and assuming the greatest savings measure is installed first, and then the next highest savings measure. 30 Electric Power Research Institute (EPRI). Loadshape Library. http://loadshape.epri.com/ Optimal Energy, Inc. 72 Study of Potential for Electric Energy Savings New Orleans, Louisiana Measures that compete also need to be adjusted for. These are two or more efficiency measures that can both be applied to the same application, but only one can be chosen. An example is choosing between installing an air source heat pump or an efficient central air conditioner, but not both. In this case, the total penetration for all competing measures is 100%, with priority given to the measures based on ranking them from highest savings to lowest savings. If the first measure is applicable in all situations, it would have 100% penetration and all other competing measures would show no potential. If on the other hand, the first measure could only be installed in 50% of opportunities, then the second measure would capture the remaining opportunities. To estimate the economic potential we generally assumed 100% installation of market-driven measures (natural replacement, new construction/renovation) constrained by measure costeffectiveness and other limitations as appropriate, such as to account for mutually exclusive measures. Implementation of retrofit measures was considered to be resource-constrained, i.e., it would not be possible to install all cost-effective retrofit measures all at once. The retrofit penetrations rates are assumed to be 10% of the market for the first 10 years. After this, the entire retrofit market has been adjusted, and any additional retrofits only occur after the life of the original retrofit expires, and there is no market driven measure that addresses the same energy use. For example, since retro-commissioning has a measure life shorter than the analysis period, the same building my become eligible for a second retro-commissioning once the first one has expired. PROGRAM POTENTIAL SCENARIO For the achievable potential scenarios (both max achievable and program achievable), we did not attempt to develop detailed program designs to group each measure into. Instead, we make the simplifying assumption that the programs will be well designed and able to capture the amount of market adoption as determined by the local experts on the Delphi Panel. Thus, this study can help determine the amount of efficiency available, and which measures may offer the most opportunity, but is not a detailed roadmap on how to group these measures into programs or how to best promote and market the programs to customers. Measure Incentives and Penetration Rates Measure penetration rates, or adoption rates, are affected by a broad variety of factors depending on the measure: the market barriers that apply and to what degree, the program delivery strategy, incentive levels, marketing and outreach, technical assistance to installers, etc. While penetration rates will generally increase with increased spending, how the spending is applied can have a huge impact on actual participation rates. There is large uncertainty inherent in developing penetration rates, and self-reported surveys are often not a reliable indicator of eventual adoption. Further, these rates have an outsized impact on the final efficiency available in the max achievable and program potential scenarios. For this study, we avoided these issues by convening a group of local experts to determine the penetrations rate. We asked these panels Optimal Energy, Inc. 73 Study of Potential for Electric Energy Savings New Orleans, Louisiana for penetrations both at 100% incentives, and as a function of simple payback. See the Appendix on the Delphi Panel for more information. Non-Incentive Program Budgets The costs of implementing efficiency programs include both the cost of the efficiency measures themselves and the associated administrative costs for marketing, customer interactions, incentive and rebate processing, evaluation activities, etc. To estimate these costs for inclusion in both program budgets and cost-effectiveness testing, we relied on actual program data from a number of efficiency portfolios. We previously developed these estimates for another potential study and believe them to be reasonable for use in this study. The estimates are specific to our major program categories (e.g., residential new construction, commercial equipment replacement), because different program types and delivery models can have different administrative needs. Data were sourced from recent program performance in New England, the Mid-Atlantic states, and Minnesota, totaling 8 individual utility or state-wide portfolios. All of these portfolios are generating savings substantially greater than Entergy New Orleans’ current programs, and are likely to be a better predicted of the administrative costs needed to achieve the level of savings found by our maximum achievable and program potential analyses. The average administrative costs for the various program types range from 25 percent to 37 percent of total program costs. Optimal Energy, Inc. 74 Study of Potential for Electric Energy Savings New Orleans, Louisiana WORKS CITED ACEEE. 2009. “South Carolina’s Energy Future: Minding its Efficiency Resources.” http://aceee.org/sites/default/files/publications/researchreports/E099.pdf ACEEE. 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A National Assessment of Demand https://www.ferc.gov/legal/staff-reports/06-09-demand-response.pdf Response Potential. George, S. et al. 2014. SMUD SmartPricing Options Pilot Evaluation. August 6. smartgrid.gov/files/SMUD-CBS_Final_Evaluation_Submitted_DOE_9_9_2014.pdf. Global Energy Partners. 2011. “Tennessee Valley Authority Study“ http://152.87.4.98/news/releases/energy_efficiency/Executive.pdf Potential National Association of Regulatory Utility Commissioners. 2016. Distributed Energy Resources Rate Design and Compensation. pubs.naruc.org/pub/19FDF48B-AA57-5160-DBA1-BE2E9C2F7EA0. Navigant. 2015. Arkansas Energy http://www.apscservices.info/pdf/13/13-002-U_212_2.pdf Efficiency Potential Study. Nexant. 2015. Achievable Energy Efficiency Potentials Assessment. 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Rushing, Amy S. et al., The National Institute of Standards and Technology, Energy Price Indices and Discount Factors for Life-Cycle Cost Analysis – 2010: Annual Supplement to NIST Handbook 135 and NBS Special Publication 709. http://www1.eere.energy.gov/femp/pdfs/ashb10.pdf (accessed August 18, 2010). Optimal Energy, Inc. 77 Study of Potential for Electric Energy Savings New Orleans, Louisiana APPENDIX A: DELPHI PROCESS As described in the report, this report used Delphi Panels in order to estimate the penetration rates for the max achievable and program potential scenarios. There were two separate panels convened – one panel for residential measures with 9 participants, and a panel for commercial and industrial measures, with 8 participants. Each participant is a local expert with appropriate knowledge to allow them to be a good judge on potential measure adoption. Each panel contained representation from each of the following categories: • • • • • • • • Trade Allies/Contractors Academics Program Implementers Program Planner/Managers Distributor/Manufacturing Representatives Government Officials Real Estate Developers Building/Facility Managers The Delphi Process is used to develop a consensus estimate for uncertain or contentious values. It involves sending the same survey to each participant on the panel. The participant then fills out their best estimates for each survey question and gives some indication of their reasoning. We then compile all answers together and send the survey back for a second round. In this round, each participant will have the opportunity to adjust their responses based on the responses and reasonings of the other participants. The survey is done anonymously, so that the loudest voices do not have disproportionate influence on the other members of the panel. The idea is that, after two or three rounds, the answers from each participant will converge on a consensus estimate. In this case, consensus was already largely achieved after two rounds. For this study, two Delphi Panels were formed, one focusing on the residential sector and one on the C&I sector. For each sector, the survey asked for measure adoption rates assuming 100% incentives (instantaneous payback) for five different types of measures with different levels of first costs, complexities, and other market barriers. The survey asks for three different datapoints to develop this curve – the percent adoption at program maturity, the number of years to reach 10% of full adoption, and the number of years to reach 90% of full adoption. We then assume a typical “S” curve using these three datapoints, where there is fairly slow adoption until 10% adoption is reached, a steeper ramp up until 90% adoption is reached, and then slower growth until the full adoption is reached. For retrofit measures, we converted these curves to cumulative numbers, so that, for example, instead of achieving 80% penetration per year by year 12, the retrofits would reach a total of 80% market share by year 12 (in other words the sum of adoption in years 1-12 would be 80%). In addition to the above questions, which apply to the max achievable scenario, the survey also developed estimates of adoption for the program potential scenario, which only provides incentives at 50% of the full incremental cost. In order to derive these numbers, we asked the Delphi participants by what percent the penetrations in the max achievable scenario would be Optimal Energy, Inc. 78 Study of Potential for Electric Energy Savings New Orleans, Louisiana reduced under numerous simple payback scenarios. This number will be applied to every year of the max achievable curve to derive the curve used for the program potential scenarios The table below shows the curve for each scenario for the Residential sector. As seen, simple measures that are easy to install quickly achieve a fairly high adoption. Other measures types with higher market barriers tend to take longer to ramp up and achieve a lower maximum adoption. Figure 21 Residential Adoption Curves 90% Scenario 1 - Simple Replace 80% 70% Scenario 2 - High Cost Replace (non-discretionary) Scenario 2 - High Cost Replace (discretionary) Scenario 3 - Active Engagement Adoption 60% 50% 40% 30% Scenario 4 - Low Cost Complex 20% 10% Scenario 5 - High Cost Complex 0% 1 2 3 4 5 6 7 8 Year 9 10 11 12 13 14 Scenario 6 - Emerging technologies The next table shows the percent that the above curve would be reduced by, if instead of paying the full incremental cost, the incentive just buys the measure down to a specified payback. For example, if an LED screw-in bulb (scenario 1) achieved a simple payback of 2-years after the incentive is applied, every datapoint in the “Scenario 1” curve from the above table would be multiplied by 0.4 to derive the new adoption curve. Table 44 Delphi Panel Residential Program Potential Multipliers 1-year payback 2-year payback 4-year payback 8-year payback Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5 70% 70% 60% 60% 60% 40% 30% 30% 30% 30% 20% 20% 10% 10% 20% 5% 5% 10% 5% 10% The next two charts give the same information for the Commercial and Industrial Sector. Optimal Energy, Inc. 79 Study of Potential for Electric Energy Savings New Orleans, Louisiana Figure 22 Delphi Panel C&I Responses 90% Scenario 1 - Simple Replace Adoption 80% 70% Scenario 2 - High Cost Replace (non-discretionary) 60% Scenario 2 - High Cost Replace (discretionary) 50% Scenario 3 - Active Engagement 40% 30% Scenario 4 - Core Business (nondiscretionary) 20% Scenario 4 - Core Business (discretionary) 10% Scenario 5 - High Cost Complex 0% 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Scenario 6 - Emerging technologies Year Table 45 Delphi Panel C&I Program Potential Multipliers Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5 83% 78% 78% 78% 85% 43% 48% 45% 65% 60% 23% 25% 28% 48% 38% 11% 10% 13% 15% 20% 1-year payback 2-year payback 4-year payback 8-year payback Finally, we mapped each of the curves from the Delphi Panel to specific measures. The next table shows this mapping for each measure, for both market driven transactions (e.g., new construction, replace-on-failure) and retrofit transactions. If no curve number is given, that market is not applicable for that measure. Table 46 Delphi Panel Measure Mapping Measure Name ESTAR Room AC Exterior Canopy/Soffit LED Exterior Wall Pack LED Improved Ext Lgt Design Heat Pump Water Heater High Volume Low Speed Fans Mini Split Ductless HP-Cool Mini Split Ductless HP-Heat Optimized unitary HVAC distribution/control system Optimized chiller distribution/control system Int Ltg Controls Optimal Energy, Inc. Sector Curve (market driven) 1 1 1 1 2 2 2 2 Curve (Retrofit) C&I C&I C&I C&I C&I C&I C&I C&I C&I 5 5 C&I C&I 5 5 1 1 1 1 2 2 2 80 Study of Potential for Electric Energy Savings New Orleans, Louisiana Exit Sign Retrofit High Bay LED Incand. Over 100W Ret, Fixt. Incand. Over 100W Ret, Lamp Incand. Up to 100W Ret, Fixt. Incand. Up to 100W Ret, Lamp LED Troffers Com LED Tube Replacement Lamps Refrigerated Case LED Stairwell Occupancy Sensors LED Street Lighting Pre-Rinse Sprayers Chiller Tune-Up VSD, Chilled Water Pump VSD, Heating Hot Water Pump VSD, Condenser Water Pump VSD, HVAC Fan VSD, Cooling Tower Fan Demand Control Ventilation-Cool Demand Control Ventilation-Heat Demand Control Ventilation-Vent Screw-Based LED Retrofit duct sealing fan energy Retrofit duct sealing cool Retrofit duct sealing HS fan Retrofit duct sealing HS cool Ground Source HP (Heating) Ground Source HP (Cooling) HE Clothes Washer, elec DHW Ozone Laundry System Office Equipment Controls Window Film Cool Roof LED Ped Light (Sign Lighting) HE Kitchen Equipment HP Window Glaze (Cooling) HP Window Glaze (Heating) Compressed Air Industrial Process High Efficiency HP (Heating) High Efficiency HP (Cooling) High Efficiency AC HP Tune Up (Heating) HP Tune Up (Cooling) Optimal Energy, Inc. C&I C&I C&I C&I C&I C&I C&I C&I C&I C&I C&I C&I C&I C&I C&I C&I C&I C&I C&I C&I C&I C&I C&I C&I C&I C&I C&I C&I C&I C&I C&I C&I C&I C&I C&I C&I C&I C&I C&I C&I C&I C&I C&I C&I 6 6 1 2 5 2 2 4 2 2 2 1 1 1 1 1 1 1 1 2 2 1 1 3 2 2 2 2 2 3 3 3 1 5 5 5 5 6 6 1 6 3 3 5 4 4 2 2 2 1 1 81 Study of Potential for Electric Energy Savings New Orleans, Louisiana AC Tune Up Cooler Night Cover Commercial Faucet Aerator (Elec WH) High Efficiency Chiller ECM Blower Motors Conservation Voltage Reduction Building Management System - Elec Heat Control System for Hopitality Retrocommissioning/Calibrate Sensors Electric Heat Integrated bldg design -Elec Replace Cooler and Freezer Door Gaskets Reach-in Storage Refrigerator HE Small Walk-In Refrigeration Retrofit Strip Curtains Advanced RTU Control - Elec Heat Advanced RTU Control - Gas Heat Network Connected LEDs High Efficiency Chiller vs DX System Replace Pneumatic contols with DDC - Elec Heat Replace Pneumatic contols with DDC - Gas Heat Central AC QI Central AC ASHP (Cooling) ASHP (Heating) QI ASHP (Cooling) QI ASHP (Heating) CAC Tune-Up ASHP Tune-Up (Cooling) ASHP Tune-Up (Heating) ES Room AC GSHP (Cooling) GSHP (Heating) DMSAC DMSHP (Cooling) DMSHP (Heating) Duct Sealing, E (Cooling) Duct Sealing, E (Heating) Duct Sealing, G Smart Tstat, E (Cooling) Smart Tstat, E (Heating) Smart Tstat, G Learning Tstat, E (Cooling) Optimal Energy, Inc. C&I C&I C&I C&I C&I C&I C&I C&I 4 5 C&I C&I C&I C&I C&I C&I C&I C&I C&I C&I C&I 1 1 1 5 1 1 1 5 2 5 4 2 2 3 3 4 4 3 3 6 5 C&I 2 C&I 2 Res Res Res Res Res Res Res Res Res Res Res Res Res Res Res Res Res Res Res Res Res Res 2 2 2 2 4 4 1 5 5 3 3 3 2 2 2 2 2 2 2 2 2 2 3 3 3 1 2 2 3 3 3 1 1 1 1 82 Study of Potential for Electric Energy Savings New Orleans, Louisiana Learning Tstat, E (Heating) Learning Tstat, G ES Ceiling Fan ES Bathroom Ventilation Fan ECM Blower Motor ECM Circulators, DHW ECM Circulators, CW ECM Circulators, HW HEMS ES Solar Water Heater Heat Pump Water Heater Faucet Aerator Low Flow Showerhead Water Heater Pipe Insulation Water Heater Jacket WH Drainpipe Heat Exchange Water Heater Setback Therm Restriction Valve ES SF Clothes Washer (App) ES SF Clothes Washer (WH) ES MF Clothes Washer (App) ES MF Clothes Washer (WH) ES SF Clothes Dryer ES MF Clothes Dryer ES Dehumidifier ES Dishwasher (App) ES Dishwasher, WH ES Refrigerator ES Freezer Fridge and Freezer Removal ES Air Purifier ENERGY STAR Pool Pump Tier 2 Power Strip ES Desktop Computer Efficient Windows (Cooling) Efficient Windows (Heating) Window Attachments (Cooling) Window Attachments (Heating) Attic Insulation, E (Cooling) Attic Insulation, E (Heating) Attic Insulation, G Air Sealing, E (Cooling) Air Sealing, E (Heating) Air Sealing, G Optimal Energy, Inc. Res Res Res Res Res Res Res Res Res Res Res Res Res Res Res Res Res Res Res Res Res Res Res Res Res Res Res Res Res Res Res Res Res Res Res Res Res Res Res Res Res Res Res Res 2 2 1 1 4 3 5 2 1 1 1 4 5 1 1 1 1 1 1 1 1 1 1 1 1 1 4 4 4 3 5 2 1 1 3 1 4 3 5 2 2 2 2 2 2 1 1 2 2 1 1 2 2 5 5 3 3 3 3 3 3 83 Study of Potential for Electric Energy Savings New Orleans, Louisiana LED Screw-in Lamp (18) LED Screw-in Lamp (19) LED Screw-in Lamp (20) LED Screw-in Lamp (21) ES LED Downlight Fixture (18) ES LED Downlight Fixture (19) ES LED Downlight Fixture (20) ES LED Downlight Fixture (21) LED DI (18) LED DI (19) LED DI (20) LED DI (21) Occupancy Sensors Smart LED Screw-in Lamp Ext Motion Sensor Net Zero Energy Home Energy Efficient New Home - Single Family ENERGY STAR Manufactured Home Energy Efficient New Home - Multi Family Home Energy Reports Q3, Electric Conservation Voltage Reduction Integrated bldg design -Gas Retrocommissioning/Calibrate Sensors - Gas Heat Building Management System - Gas Heat HP Window Glaze Gas ES LED PAR/Flood Lamp, Ext (18) ES LED PAR/Flood Lamp, Ext (19) ES LED PAR/Flood Lamp, Ext (20) ES LED PAR/Flood Lamp, Ext (21) ENERGY STAR Pool Pump Data Center Retrofit Optimal Energy, Inc. Res Res Res Res Res Res Res Res Res Res Res Res Res Res Res Res Res Res Res Res Res C&I 1 1 1 1 1 1 1 1 1 1 1 1 2 3 2 5 3 3 3 1 1 3 C&I C&I C&I Res Res Res Res C&I C&I 4 4 2 4 1 1 1 1 4 5 84 Study of Potential for Electric Energy Savings New Orleans, Louisiana APPENDIX B: SALES DISSAGGREGTION Table 47 Residential Sales Disaggregation End Use Space Heating Cooling Water Heating Indoor Lighting Exterior Lighting Plug Load Appliance Total Optimal Energy, Inc. Non Low-Income 9% 16% 7% 4% 1% 6% 7% 51% Low-Income 9% 16% 7% 4% 1% 6% 6% 49% 85 Study of Potential for Electric Energy Savings New Orleans, Louisiana Table 48 Commercial and Industrial Sales Disaggregation End Use Small Office Large Office Small Retail Large Retail Wareh ouse Education Food Sales Health Lodging Restaurant Space Heating Cooling Ventilation Water Heating Indoor Lighting Exterior Lighting Cooking Refrigeration Plug Load Other Industrial Process ElecTotal 0.1% 1.7% 1.8% 0.1% 1.3% 0.1% 0.0% 0.2% 1.6% 1.2% 0.0% 8.0% 0.1% 1.1% 1.2% 0.0% 0.9% 0.1% 0.0% 0.2% 1.0% 0.8% 0.0% 5.4% 0.1% 1.8% 1.4% 0.1% 1.7% 0.1% 0.1% 2.1% 0.4% 1.2% 0.0% 9.0% 0.1% 1.2% 0.9% 0.1% 1.1% 0.1% 0.1% 1.4% 0.3% 0.8% 0.0% 6.0% 0.0% 0.4% 0.1% 0.0% 0.7% 0.1% 0.0% 0.3% 0.2% 0.6% 0.0% 2.4% 0.1% 3.1% 1.4% 0.1% 1.7% 0.1% 0.1% 0.8% 1.8% 1.4% 0.0% 10.5% 0.1% 0.2% 0.2% 0.1% 0.3% 0.0% 0.2% 2.4% 0.1% 0.2% 0.0% 3.9% 0.0% 2.1% 1.4% 0.1% 1.1% 0.1% 0.1% 0.3% 0.8% 1.3% 0.0% 7.4% 0.2% 3.0% 2.2% 0.2% 2.0% 0.2% 0.6% 1.6% 2.3% 3.8% 0.0% 16.3% 0.1% 1.1% 0.7% 0.1% 0.4% 0.0% 1.2% 2.4% 0.2% 0.5% 0.0% 6.8% Optimal Energy, Inc. Industrial 0.0% 0.5% 0.5% 0.0% 0.4% 0.0% 0.0% 0.1% 0.5% 0.4% 10.7% 13.1% Other 0.1% 3.5% 1.0% 0.0% 1.9% 0.2% 0.0% 0.6% 1.1% 3.0% 0.0% 11.3% 86 Study of Potential for Electric Energy Savings New Orleans, Louisiana APPENDIX C: LOADSHAPES See below for the loadshapes used to distribute the savings to the four avoided costs periods. As described above, these periods are: • • • • Summer on-peak is April – October, 9 AM – 9 PM Weekdays Summer off-peak is the rest of the time in April-October Winter on-peak is Nov- Mar, 7 AM – 10 AM and 6 PM – 10PM Weekdays Winter off-peak is the rest of the time in Nov-Mar Table 49 Residential Loadshapes End Use Space Heating Cooling Ventilation Water Heating Indoor Lighting Outdoor Lighting Refrigeration Plug Load Other Appliance Total Building Summer OnPeak Summer Off-Peak Winter On-Peak Winter Off-Peak 0.4% 44.5% 22.4% 18.2% 28.7% 11.2% 20.2% 24.2% 23.0% 22.7% 23.0% 0.8% 49.1% 25.0% 33.7% 30.6% 47.4% 41.5% 34.9% 32.0% 34.8% 32.0% 27.6% 1.9% 14.8% 15.3% 13.9% 8.8% 10.0% 12.8% 13.4% 12.2% 13.4% 71.2% 4.5% 37.9% 32.8% 26.8% 32.6% 28.2% 28.1% 31.6% 30.3% 31.6% Table 50 C&I Loadshapes End Use Summer OnPeak Summer OffPeak Winter On-Peak Winter Off-Peak Space Heating Cooling Ventilation Water Heating Indoor Lighting Outdoor Lighting Cooking Refrigeration Plug Load Other Industrial Process Total Building 0.6% 41.8% 19.1% 22.7% 26.6% 11.2% 20.8% 21.6% 23.0% 20.8% 23.0% 20.8% 3.5% 47.2% 38.9% 28.4% 32.2% 47.4% 34.4% 42.0% 35.7% 34.4% 35.9% 34.4% 28.5% 3.0% 10.6% 13.8% 11.9% 8.8% 12.1% 9.5% 11.1% 12.1% 12.4% 12.1% 67.4% 8.0% 31.4% 35.1% 29.2% 32.6% 32.6% 27.0% 30.2% 32.6% 28.7% 32.6% Optimal Energy, Inc. 87 Study of Potential for Electric Energy Savings New Orleans, Louisiana APPENDIX D: MEASURE CHARACTERIZATIONS This appendix shows the measure characterizations used for the study. Each measure characterization may have two different characterizations, one for market driven (MD) transactions and one for retrofit (RET) situations. Measures that show “N/A” for the TRC are one part of a linked measure. Linked measures are measures that produce savings for more than end use. For example, a heat pump produces different savings percentages for cooling and heating savings. Our analysis allocates all of the costs to just one of the end uses, but savings are kept separate because they have different loadshapes. In order to calculate TRC, costs and benefits are summed across all parts of the linked measures. Table 51 Residential Measure Level Information Measure Name Central AC Central AC QI Central AC QI Central AC ASHP (Cooling) ASHP (Heating) ASHP (Cooling) ASHP (Heating) QI ASHP (Cooling) QI ASHP (Heating) QI ASHP (Cooling) QI ASHP (Heating) CAC Tune-Up ASHP Tune-Up (Cooling) ASHP Tune-Up (Heating) ES Room AC ES Room AC GSHP (Cooling) GSHP (Heating) DMSAC DMSHP (Cooling) DMSHP (Heating) DMSHP (Cooling) DMSHP (Heating) Duct Sealing, E (Cooling) Duct Sealing, E (Heating) Duct Sealing, G Smart Tstat, E (Cooling) Smart Tstat, E (Heating) Smart Tstat, G Optimal Energy, Inc. Market TRC % Savings MD RET MD RET MD MD RET RET MD MD RET RET RET RET RET MD RET MD MD MD MD MD RET RET RET RET RET RET RET RET 2.65 1.09 2.12 1.30 1.38 1.57 N/A N/A 4.51 1.71 N/A N/A 0.30 0.63 N/A 2.96 1.58 N/A 0.24 0.43 6.79 2.00 N/A N/A 9.60 N/A 6.84 0.65 0.79 N/A 26% 38% 28% 45% 28% 72% 45% 72% 36% 75% 51% 75% 5% 8% 8% 9% 9% 36% 76% 43% 50% 81% 50% 81% 21% 21% 21% 5% 5% 5% $/kWh (annual) $0.44 $1.39 $0.55 $1.25 $1.18 $$1.26 $$$0.28 $1.17 $$0.54 $0.31 $$0.20 $1.23 $$2.01 $2.61 $0.28 $$0.95 $$0.23 $$0.23 $1.26 $$1.26 Measure Life 19 19 19 19 16 16 16 16 16 16 16 16 2 2 2 9 9 18 18 18 18 18 18 18 18 18 18 10 10 10 88 Study of Potential for Electric Energy Savings New Orleans, Louisiana Smart Tstat, E (Cooling) Smart Tstat, E (Heating) Smart Tstat, G Learning Tstat, E (Cooling) Learning Tstat, E (Heating) Learning Tstat, G Learning Tstat, E (Cooling) Learning Tstat, E (Heating) Learning Tstat, G ES Ceiling Fan ES Bathroom Ventilation Fan ECM Blower Motor ECM Circulators, DHW ECM Circulators, CW ECM Circulators, HW HEMS HEMS ES Solar Water Heater ES Solar Water Heater Heat Pump Water Heater Heat Pump Water Heater Faucet Aerator Faucet Aerator Low Flow Showerhead Low Flow Showerhead Water Heater Pipe Insulation Water Heater Jacket WH Drainpipe Heat Exchange WH Drainpipe Heat Exchange Water Heater Setback Therm Restriction Valve Therm Restriction Valve ES SF Clothes Washer (App) ES SF Clothes Washer (WH) ES SF Clothes Washer (App) ES SF Clothes Washer (WH) ES MF Clothes Washer (App) ES MF Clothes Washer (WH) ES SF Clothes Dryer ES MF Clothes Dryer ES Dehumidifier ES Dishwasher (App) ES Dishwasher, WH ES Refrigerator Optimal Energy, Inc. MD MD MD RET RET RET MD MD MD MD MD MD RET RET RET MD RET RET MD RET MD RET MD RET MD RET RET RET MD RET RET MD MD MD RET RET MD MD MD MD MD MD MD MD N/A 0.68 0.68 1.71 1.66 N/A N/A 1.32 1.46 2.09 0.26 4.64 2.50 2.05 0.42 0.53 0.53 0.08 0.12 0.18 0.04 1.29 1.27 3.44 3.61 2.36 1.06 0.50 0.48 1.39 0.47 0.76 2.49 0.22 N/A N/A 9.63 N/A 5.07 18.98 1.66 N/A 4.91 2.10 5% 2% 5% 9% 9% 9% 9% 9% 9% 44% 72% 50% 90% 82% 82% 15% 15% 90% 85% 64% 59% 26% 26% 37% 37% 60% 28% 25% 25% 4% 12% 12% 34% 37% 40% 43% 34% 37% 21% 21% 21% 12% 12% 12% $1.26 $$1.26 $0.69 $$0.69 $0.69 $$0.76 $0.62 $2.56 $0.22 $0.30 $0.34 $2.88 $1.02 $1.02 $4.34 $5.86 $7.71 $0.99 $0.25 $0.25 $0.09 $0.09 $0.16 $0.38 $1.36 $1.36 $0.05 $0.40 $0.67 $1.15 $$11.48 $$0.29 $$0.26 $0.07 $0.27 $$0.48 $0.25 10 10 10 10 10 10 10 10 10 20 19 18 15 15 15 15 15 15 15 10 10 10 10 10 10 12 13 20 20 2 10 10 14 14 14 14 14 14 12 12 12 15 15 15 89 Study of Potential for Electric Energy Savings New Orleans, Louisiana ES Freezer Fridge and Freezer Removal ES Air Purifier ENERGY STAR Pool Pump ENERGY STAR Pool Pump Tier 2 Power Strip ES Desktop Computer Efficient Windows (Cooling) Efficient Windows (Heating) Window Attachments (Cooling) Window Attachments (Heating) Attic Insulation, E (Cooling) Attic Insulation, E (Heating) Attic Insulation, G Air Sealing, E (Cooling) Air Sealing, E (Heating) Air Sealing, G LED Screw-in Lamp (18) LED Screw-in Lamp (19) LED Screw-in Lamp (20) LED Screw-in Lamp (21) ES LED Downlight Fixture (18) ES LED Downlight Fixture (19) ES LED Downlight Fixture (20) ES LED Downlight Fixture (21) LED DI (18) LED DI (19) LED DI (20) LED DI (21) Occupancy Sensors Smart LED Screw-in Lamp Ext Motion Sensor Net Zero Energy Home Energy Efficient New Home - Single Family ENERGY STAR Manufactured Home Energy Efficient New Home - Multi Family Home Energy Reports Q3, Electric Conservation Voltage Reduction ES LED PAR/Flood Lamp, Ext (18) ES LED PAR/Flood Lamp, Ext (19) ES LED PAR/Flood Lamp, Ext (20) ES LED PAR/Flood Lamp, Ext (21) Optimal Energy, Inc. MD RET MD MD RET RET MD MD MD RET RET RET RET RET RET RET RET MD MD MD MD MD MD MD MD RET RET RET RET RET MD RET MD 1.32 3.17 13.90 0.40 0.30 1.68 3.23 4.19 N/A 1.14 N/A 2.48 N/A 1.84 2.23 N/A 1.87 1.88 1.42 0.96 0.48 2.27 1.72 1.16 0.58 1.01 0.76 0.52 0.26 1.00 0.14 0.99 0.70 10% 100% 73% 69% 79% 51% 50% 10% 10% 9% 11% 21% 21% 21% 8% 8% 8% 82% 82% 82% 82% 88% 88% 88% 88% 82% 82% 82% 82% 40% 10% 40% 100% $0.29 $0.09 $0.02 $0.17 $1.15 $0.32 $0.06 $0.51 $$0.81 $$0.95 $$0.95 $0.53 $$0.53 $0.10 $0.10 $0.10 $0.10 $0.09 $0.09 $0.09 $0.09 $0.16 $0.16 $0.16 $0.16 $0.44 $5.16 $0.30 $1.62 11 8 9 10 10 10 4 25 25 10 10 20 20 20 11 11 11 4 3 2 1 4 3 2 1 15 15 15 15 10 16 10 30 MD 3.37 35% $0.34 30 MD 2.17 27% $0.52 30 MD 1.59 37% $0.71 30 RET RET RET RET RET RET 0.98 56.53 1.79 1.35 0.92 0.46 2% 2% 82% 82% 82% 82% $0.04 $0.02 $0.07 $0.07 $0.07 $0.07 1 30 4 3 2 1 90 Study of Potential for Electric Energy Savings New Orleans, Louisiana Table 52 Commercial Measure Level Information Measure ESTAR Room AC Exterior Canopy/Soffit LED Exterior Canopy/Soffit LED Exterior Wall Pack LED Exterior Wall Pack LED Improved Ext Lgt Design Heat Pump Water Heater Heat Pump Water Heater High Volume Low Speed Fans Mini Split Ductless HP-Cool Mini Split Ductless HP-Heat Mini Split Ductless HP-Cool Mini Split Ductless HP-Heat Optimized unitary HVAC distribution/control system Optimized chiller distribution/control system Int Ltg Controls Exit Sign Retrofit High Bay LED Incand. Over 100W Ret, Fixt. Incand. Over 100W Ret, Lamp Incand. Up to 100W Ret, Fixt. Incand. Up to 100W Ret, Lamp LED Troffers Com LED Tube Replacement Lamps Refrigerated Case LED Stairwell Occupancy Sensors LED Street Lighting VSD, Chilled Water Pump VSD, Heating Hot Water Pump VSD, Condenser Water Pump VSD, HVAC Fan VSD, Cooling Tower Fan Demand Control Ventilation-Cool Demand Control Ventilation-Heat Demand Control Ventilation-Vent Screw-Based LED Retrofit duct sealing fan energy Retrofit duct sealing cool Retrofit duct sealing HS fan Retrofit duct sealing HS cool Optimal Energy, Inc. MD RET MD RET MD MD MD RET MD MD MD RET RET 2.24 0.64 0.94 0.39 1.74 2.13 0.22 0.04 1.57 0.93 3.16 N/A N/A % Savings 9% 78% 77% 78% 76% 42% 35% 40% 82% 47% 72% 47% 72% MD 0.87 30% $1.02 15 MD RET RET RET RET RET RET RET RET RET RET RET RET RET RET RET RET RET RET RET RET RET RET RET RET RET 0.55 4.55 2.61 0.36 0.84 2.50 0.80 2.30 0.49 3.85 2.03 0.81 2.25 0.84 2.14 0.84 1.86 0.35 38.02 N/A N/A 1.18 2.53 N/A 1.16 N/A 20% 34% 97% 43% 74% 76% 71% 72% 52% 58% 73% 92% 65% 43% 48% 43% 26% 25% 10% 18% 10% 13% 13% 7% 51% 23% $1.02 $0.06 $0.25 $0.71 $0.51 $0.04 $0.48 $0.05 $0.81 $0.07 $0.22 $0.77 $0.20 $0.54 $0.21 $0.54 $0.24 $1.27 $0.18 $$$0.15 $1.49 $$0.89 $- 15 8 16 11.3 11.3 3.4 11.3 3.4 11.3 11.3 10 14.4 15 15 15 15 15 15 15 15 15 3.4 15 15 15 15 Market TRC $/kWh (annual) $0.23 $0.34 $0.51 $0.19 $0.84 $0.23 $0.86 $8.62 $0.22 $0.24 $$0.83 $- Measur e Life 9 10.2 10.2 10.2 10.2 15 10 10 15 15 15 15 15 91 Study of Potential for Electric Energy Savings New Orleans, Louisiana Ground Source HP (Heating) Ground Source HP (Cooling) Ground Source HP (Heating) Ground Source HP (Cooling) HE Clothes Washer, elec DHW HE Clothes Washer, elec DHW Ozone Laundry System Office Equipment Controls Window Film Cool Roof Cool Roof HE Kitchen Equipment HP Window Glaze (Cooling) HP Window Glaze (Heating) High Efficiency HP (Heating) High Efficiency HP (Cooling) High Efficiency HP (Heating) High Efficiency HP (Cooling) High Efficiency AC High Efficiency AC HP Tune Up (Heating) HP Tune Up (Cooling) AC Tune Up Commercial Faucet Aerator (Elec WH) ECM Blower Motors Conservation Voltage Reduction Building Management System - Elec Heat Retrocommissioning/Calibrate Sensors - Electric Heat Integrated bldg design -Elec Advanced RTU Control - Elec Heat Advanced RTU Control - Gas Heat Advanced RTU Control - Elec Heat Advanced RTU Control - Gas Heat Network Connected LEDs High Efficiency Chiller vs DX System Replace Pneumatic contols with DDC - Elec Heat Replace Pneumatic contols with DDC - Gas Heat Integrated bldg design -Gas Retrocommissioning/Calibrate Sensors - Gas Heat Building Management System - Gas Heat HP Window Glaze Gas Data Center Retrofit Chiller Tune-Up Optimal Energy, Inc. MD MD RET RET MD RET RET RET RET MD RET MD MD MD MD MD RET RET MD RET RET RET RET RET RET RET RET N/A N/A 0.48 0.06 7.57 1.81 2.95 1.11 0.80 2.37 0.22 208.42 16.13 N/A N/A N/A 6.76 0.81 4.18 0.50 N/A 4.07 4.16 20.15 2.31 57.53 0.39 33% 49% 38% 56% 28% 20% 91% 29% 5% 32% 32% 27% 6% 24% 55% 32% 59% 42% 30% 40% 18% 10% 10% 55% 61% 2% 18% $$1.69 $$11.23 $0.47 $3.18 $21.95 $0.11 $0.46 $0.31 $3.50 $0.12 $0.05 $$$0.14 $$0.87 $0.20 $1.25 $$0.13 $0.14 $0.01 $0.50 $0.02 $1.27 20 20 20 20 11 11 20 3.2 10 20 20 12 20 20 15 15 15 15 15 15 10 10 10 10 15 30 15 RET 1.79 16% $0.17 8 MD MD MD RET RET RET MD RET RET MD RET RET MD RET RET 1.76 1.09 0.98 1.28 1.15 0.44 0.28 0.48 2.90 1.59 1.85 0.35 13.63 6.17 2.00 31% 9% 9% 9% 9% 47% 35% 15% 15% 31% 16% 18% 6% 22% 5% $0.50 $0.48 $0.50 $0.48 $0.50 $1.31 $2.72 $1.02 $1.24 $0.56 $0.18 $1.34 $0.05 $0.12 $0.11 30 15 15 15 15 15 20 15 15 30 8 15 20 20 5 92 Study of Potential for Electric Energy Savings New Orleans, Louisiana Cooler Night Cover High Efficiency Chiller High Efficiency Chiller Replace Cooler and Freezer Door Gaskets Reach-in Storage Refrigerator HE Small Walk-In Refrigeration Retrofit Strip Curtains Pre-Rinse Sprayers Control System for Hospitality Control System for Hospitality ENERGY STAR Pool Pump ENERGY STAR Pool Pump Compressed Air Compressed Air Optimal Energy, Inc. RET MD RET RET MD MD RET RET RET RET MD MD RET MD RET 0.51 1.10 0.15 0.76 1.55 5.47 1.36 2.45 4.80 6.17 5.65 2.24 0.33 1.90 1.74 7% 14% 22% 3% 37% 40% 32% 15% 32% 19% 19% 69% 69% 22% 22% $0.32 $0.56 $3.44 $0.18 $0.31 $0.10 $0.36 $0.05 $0.12 $0.08 $0.08 $0.17 $0.17 $0.23 $0.23 5 10 10 5 12 13 13 4 10 8 8 10 10 10 10 93 BEFORE THE COUNCIL OF THE CITY OF NEW ORLEANS EX PARTE: IN RE: 2018 TRIENNIAL INTEGRATED RESOURCE PLAN OF ENTERGY NEW ORLEANS, INC. ) ) ) ) DOCKET NO. UD-17-03 APPENDIX F HIGHLY SENSITIVE PROTECTED MATERIALS INTENTIONALLY OMITTED JULY 2019 BEFORE THE COUNCIL OF THE CITY OF NEW ORLEANS EX PARTE: IN RE: 2018 TRIENNIAL INTEGRATED RESOURCE PLAN OF ENTERGY NEW ORLEANS, INC. ) ) ) ) DOCKET NO. UD-17-03 APPENDIX G Entergy New Orleans, LLC Technical Meeting Presentations JULY 2019 title style ENO 2018 IRP Technical Meeting #1 January 22, 2018 1 Goals and Agenda of Technical Meeting #1 title style Goals • As described in the Initiating Resolution (R‐17‐430), the main purpose of this meeting is for ENO, the Advisors,  and Intervenors to discuss Planning Scenarios and Strategies with a view towards reaching consensus on the   Scenarios and Strategies to be used in developing the 2018 IRP.  – As such, per the Initiating Resolution, the meeting shall be treated as a settlement negotiation and subject  to all applicable procedural and evidentiary protections.  • ENO will present its reference and alternative Planning Scenarios and its least‐cost/reference Planning Strategy. • Prior to the meeting, Intervenors should have discussed among themselves their priorities regarding Planning  Scenarios and Strategies. • Should the parties not agree that the proposed Scenarios and/or Strategies, or any Scenarios and/or Strategies  developed during Technical Meeting #1, will adequately capture the Intervenors’ point of view, the Intervenors  shall prepare and submit, with the Advisors’ assistance as needed, their proposed Planning Scenario and/or  Strategy before Technical Meeting #2. Agenda 1. 2018 IRP Objectives 2. Analytical Framework 3. Inputs and Assumptions 4. Resource Options a. Supply‐Side Resources b. DSM Potential Study (Navigant) 5. Timeline 2 title style Section 1 2018 IRP Objectives 3 ENO’s planning process seeks to accomplish three key objectives title style Reliability Serve customers’ needs  reliably Cost  Risk Mitigation Serve customers’ needs at  the lowest reasonable  cost Mitigate exposure to risks  that may affect customer  cost or reliability  Achieve these objectives while considering  known utility regulatory policy goals of the Council  4 In the 2018 IRP, ENO will consider the ongoing evolution of the  title style utility industry    The Changing Utility Industry Customer  Preferences ENO’s planning processes  seek to address changing  customer needs. Planning  processes and tools will  continue to evolve to help  identify customer needs and  wants.  5 Resource  Alternatives Grid  Modernization Ever advancing technology  provides new opportunities  to meet future customer  needs reliably and affordably.  Planning processes strive to  understand these  technological changes in  order to enable us to design  optimal portfolios of  resources and services. ENO’s distribution planning  process will need to  accommodate the integration  of distributed energy  resources safely and securely  so they can be interoperable  with the grid. title style Section 2 Analytical Framework 6 Analytic Process to Create and Value Portfolios  title style Development of Planning Scenarios and Strategies Market Modeling Portfolio Development Development of  assumptions and  inputs for Scenarios  Projection of MISO  and Strategies market outside of  ENO for each  Scenario 7 Total Relevant Supply Cost Action Plan Construction of  resource portfolios  for each  Scenario/Strategy  combination Production costs and  fixed costs are  determined for each  portfolio under each  Identify action plan  Scenario/Strategy  that   balances  combination  reliability, cost, and  risk title style ENO Planning Scenarios‐‐Assumptions Scenario 1 (Reference) Scenario 2 Scenario 3 Peak Load & Energy Growth Reference Low High Natural Gas Prices Reference Low  High Reference (60 years) Accelerated (50 years) Accelerated (55 years) 12% by 2028 54% by 2038 54% by 2028 91% by 2038 31% by 2028 88% by 2038 MISO Market Additions Renewables / Gas Mix  34% / 66% 50% / 50% 50% / 50% CO2 Price Forecast Reference High Reference Market Coal & Legacy Gas  Deactivations  Magnitude of Coal & Legacy Gas Deactivations If necessary, a fourth Stakeholder Scenario will be modeled. 8 title style ENO Planning Strategies‐‐Assumptions Strategy 1 (Reference) Strategy 2 Strategy  3 Least Cost Planning 0.2/2% DSM Goal TBD Resource Portfolio Criteria  and Constraints Meet 12% long‐term  Planning Reserve Margin  (PRM) target using least‐ cost resource portfolio of  supply and DSM resources Include a portfolio of DSM  programs that meet the  Council’s stated 2% goal  and determine remaining  needs Description Design a portfolio that  Assess demand‐ and  includes a set of potential  supply‐side alternatives to  DSM programs intended  to meet the Council’s  meet projected capacity  stated 2% goal and  needs with a focus on  total relevant supply costs considers additional  supply‐side alternatives Objective If necessary, an Stakeholder Planning Strategy will be modeled.  9 MISO Market Modeling and Total Relevant Supply Cost Calculation title style ❶ Market Model Set‐Up • Develop projection of MISO market outside ENO for each Scenario – 16% reserve margin target (based on MISO summer peak load and Resource Adequacy process) – Build out MISO resource pool to achieve target fuel mix per Scenario ❷ Initial Production Cost Simulation • Using AURORA production cost model, simulate MISO market to generate market price curve (i.e.,  LMPs) for each Scenario  ❸ Development of Portfolios using either AURORA or Manual Process • Use AURORA capacity expansion model to select demand‐ and supply‐side alternatives to create ENO  portfolios for each Scenario/Strategy combination – 12% long term reserve margin (based on ENO long term planning assumption) – Portfolio addition decisions based on maximizing market value of supply additions • If the capacity expansion model is unable to select resources required by a particular Strategy  consistent with identified resource needs, develop manual portfolios using defined constraints and  professional judgment ❹ Final Production Cost Simulations and Total Relevant Supply Cost Calculations • Compute variable supply costs for each portfolio in each of the Scenarios/Strategies using detailed  MISO Zonal Model in AURORA • Calculate Total Relevant Supply Cost for each portfolio – Includes: variable supply costs, cost of DSM programs, incremental non‐fuel fixed costs, and  capacity purchases 10 Assessment of Portfolio Performance Across Scenarios title style • • Portfolios developed for each Scenario/Strategy combination will be tested across all other  Scenarios to assess performance in a range of possible outcomes The total relevant supply cost of each of the Scenario/Portfolio combinations represents the  present value of fixed and variable costs to customers in 2018$ ILLUSTRATIVE ONLY—Actual number of Scenario/Portfolio combinations TBD Portfolios Strategy 1 (Reference) Strategy 2 (2% DSM Goal) Strategy 3 (TBD) Scenarios Port 1 Port 2 Port 3 Port 4 Port 5 Port 6 Port 7 Port 8 Port 9 Port  10 Port  11 Port  12 Scenario 1 R11 R12 R13 R14 R15 R16 R17 R18 R19 R110 R111 R112 Scenario 2 R21 R22 R23 R24 R25 R26 R27 R28 R29 R210 R211 R212 Scenario 3 R31 R32 R33 R34 R35 R36 R37 R38 R39 R310 R311 R312 Scenario 4 R41 R42 R43 R44 R45 R46 R47 R48 R49 R410 R411 R412 Note: “R” = resulting total relevant supply cost 11 title style Section 3 Inputs and Assumptions 12 2018 IRP Inputs and Assumptions title style Input/Assumption Present at  Technical  Meeting # Scenarios & Strategies 1 Gas Price Forecast 1 CO2 Price Forecast 1 Capacity Value 1 Supply‐Side Resource Alternative Costs 2 Load Forecast 2 ENO’s Long‐Term Capacity Need 2 Input Sensitivities 2 DSM Potential Study Results 3 13 MISO Market  Modeling Portfolio  Development Total Relevant  Supply Costs                     Gas Forecast title style Nominal  $/mmbtu $12 $10 $8 High $6 Ref Low $4 $2 $0 Case 2018 2025 2030 2037 Low $2.67 $3.12 $3.68 $4.34 Reference $3.08 $4.48 $5.49 $7.16 High $3.55 $6.48 $7.74 $10.05 14 CO2 Forecast title style Nominal               $/Short Ton $80 $70 $70 $60 $50 $38 $40 $27 $30 $20 $10 $4 $0 High 15 Ref Low ENO Capacity Value title style Levelized Cost of a New‐Build CT  $/kW‐Yr $130 $123 $110 $90 $85 $70 16 title style Section 4a Supply‐Side Resource Options 17 Technology Assessment Process and Purpose title style • Generation technology cost and performance are a  necessary input to resource planning and portfolio  development.   • In an effort to minimize operational and economic  risk, ENO prefers technologies that are proven on a  commercial scale.  Some technologies identified lack  the commercial track record to demonstrate their  technical and operational feasibility. • The process to evaluate generation technologies  has two main steps – an initial screening level  analysis and a more detailed economic analysis. • The technology screening analysis identifies  generation technology alternatives which are  expected to reasonably meet primary planning  objectives of reliability, cost, and risk mitigation.   Economic modeling parameters are developed for the  identified technologies.   • The technology assessment includes technologically  mature alternatives that are expected to be  operational in or around the Entergy regulated  service territory. • Technologies that are eliminated as a result of the  initial screen will continue to be monitored and  changes in technology assessments will be  incorporated in future IRPs, when appropriate. 18 Identified Supply‐Side Resource Alternatives title style Technology Deployment Over Time Innovation, R&D Conventional  Gas Fired Maturing Established Microturbines Frame CT and CCGT Internal  Combustion Engine Generation IV  Nuclear Solid Fuel Small Modular  Reactor (SMR) Generation III  Nuclear Integrated  Gasification (IGCC) MSW Plasma  Torch Offshore Wind Underground  Pumped Hydro Retained for further evaluation 19 Onshore Wind Landfill Gas Geothermal Solar Thermal Renewable Battery Generation II  Nuclear Biomass ‐ Stoker Boiler Biomass ‐ CFB Ocean and  Tidal Power Energy  Storage Supercritical  Coal Aeroderivative CT Solar PV Flywheel Pumped  Storage Hydro title style Section 4b DSM Potential Study (Navigant Presentation) 20 title style Section 5 Timeline 21 Current Timeline title style Description Target Date Status Public Meeting #1‐ Process Overview September 2017 Technical Meeting #1 Material Due Technical Meeting #1 Technical Meeting #2 Material Due Technical Meeting #2 Technical Meeting #3 Material Due Technical Meeting #3 IRP Inputs Finalized Optimized Portfolio Results Due Technical Meeting #4 Material Due Technical Meeting #4 File IRP Report Public Meeting #2 Material Due Public Meeting #2 ‐ Present IRP Results Intervenors and Advisors Questions & Comments Due ENO Response to Questions and Comments Due Public Meeting #3 Material Due Technical Meeting #5 Material Due Public Meeting #3 ‐ Public Response Technical Meeting #5 ENO File Reply Comments Advisors File Report January 2018 January 2018 March 2018 April 2018 May 2018 June 2018 June 2018 October 2018 October 2018 November 2018 January 2019 January 2019 February 2019 February 2019 February 2019 February 2019 February 2019 March 2019 March 2019 May 2019 June 2019    ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ 22 title style ENO 2018 IRP Technical Meeting #2 UPDATED September 14, 2018 1 Goals and Agenda of Technical Meeting #2 title style Goals • As described in the Initiating Resolution (R-17-430), the main purpose of this meeting is for ENO, the Advisors, and Intervenors to attempt to reach consensus on the Scenarios and Strategies that were initially discussed in Technical Meeting #1 (and which have been refined as described in this presentation), or • To discuss the Planning Scenario and/or Strategies that have been prepared by the Intervenors and provided to the parties in advance of this Technical Meeting Agenda 1. Analytical Framework and Portfolio Development 2. ENO Capacity Need and Supply Alternatives 3. IRP Inputs and Assumptions 4. Timeline and Next Steps 2 title style Meeting #1—Follow Ups Technical • Proposed Planning Scenarios – Add narrative descriptions – Consider impact of 50/50 renewables-to-gas buildout on LMPs – Consider CO2 pricing adjustments to create better range of macro market futures • Proposed Planning Strategies – Propose ideas for Strategy 3 for group discussion, possible consensus building, per IRP Rules, Sec. 7 (D)2 • IRP Modeling – Further discussion of portfolio development process • Inputs Workbook – Produce workbook with relevant IRP modeling inputs – Transition from BP18U to BP19 3 title style Section 1 Analytical Framework and Portfolio Development 4 Analytic title style Process to Create and Value Portfolios Development of Planning Scenarios and Strategies Market Modeling Portfolio Development Development of assumptions and inputs for Scenarios and Strategies Total Relevant Supply Cost Projection of MISO market outside of ENO for each Scenario 5 Construction of resource portfolios for each Scenario/Strategy combination Action Plan Production costs and fixed costs are determined for each portfolio under each Identify action plan Scenario/Strategy that balances combination reliability, cost, and risk title style Scenario Purpose and Drivers Proposed IRP analytics rely on macro market Scenarios designed to allow for the assessment of the total production cost and risk of resource portfolios across a reasonable range of possible future outcomes. The three proposed Scenarios for the ENO 2018 IRP are: Scenarios Scenario 1 (Moderate Change Over Time) Scenario 2 (Customer Driven Change) Key Drivers • • • • • • • Scenario 3 (Policy Driven Change) • • Flat/declining usage per customer (UPC) in residential and commercial sectors due to increases in energy efficiency and other customer adopted measures UPC declines partially offset by industrial growth and growth in residential and commercial customer counts Renewables and gas replace retiring capacity to promote fuel diversity in long-term resource planning Low peak load growth and natural gas prices tied to slumping demand Growth rate of residential and commercial demand and energy usage decreased due to strong customer preferences for EE and DERs Capacity additions in the MISO market are weighted towards gas-fired generation due to low gas and CO2 prices Growth rate of residential and commercial customer demand and energy usage increased through economic development and moderated energy efficiency gains Political and economic pressure on coal and legacy gas plants accelerates retirements High CO2 pricing along with economic factors drive the replacement of retiring capacity with portfolio of equal amounts of renewables complemented with battery storage and gas-fired technology to replace retiring capacity 6 Development of ENO Proposed Planning Scenarios – Update title style MISO Market Outside of New Orleans • Aurora market model testing has shown negative Locational Marginal Prices (LMPs), over an extended period of time as a result of the 50/50 renewables-to-gas market additions originally proposed for the MISO market – These negative LMPs could result in the suppression of renewable resource additions in portfolios designed for ENO – Because it is not realistic to expect the MISO market to experience negative LMPs over an extended period of time, it was necessary to reconsider this assumption • Based on this testing, two of the three Scenarios proposed at Technical Meeting #1 were modified as shown on following slide: – To mitigate the impact that negative LMPs would have on the results and to encourage a range of market prices, ENO: • Adjusted the second Scenario to reflect a 25%/75% renewables-to-gas mix for MISO Market additions, and adjusted the CO2 pricing assumption • Adjusted the third Scenario to incorporate battery deployment to address the possibility of negative LMPs due to the 50/50 renewables-to-gas addition assumption This helps ensure that the market model doesn’t preclude any resource type because of negative LMPs • 7 title ENOstyle Proposed Planning Scenarios – Assumptions Scenario 1 (Moderate Change) Scenario 2 (Customer Driven) Scenario 3 (Policy Driven) Peak Load & Energy Growth Medium Low High Natural Gas Prices Medium Low High Market Coal & Legacy Gas Deactivations 60 years 55 years (Modified from 50 years) 50 years (Modified from 55 years) 12% by 2028 54% by 2038 31% by 2028 88% by 2038 54% by 2028 91% by 2038 34% / 66% 25% / 75% (Modified from 50%/50%) 50% / 50% Medium Low (Modified from High) High (Modified from Medium) Magnitude of Coal & Legacy Gas Deactivations2 MISO Market Additions Renewables / Gas Mix CO2 Price Forecast 1. 2. Highlighted cells indicate a change since Technical Meeting #1 "Magnitude of Coal & Legacy Gas Deactivation" driven by "Market Coal and Legacy Gas deactivation" assumptions (e.g. 55 Years; 31%/88%) and were likewise swapped between Scenarios 2 and 3. Percentages based on BP18U for MISO South; to be adjusted for BP19 8 ENOstyle Proposed Planning Strategies– Update title Proposed Strategy 3: Renewables, Storage, and DSM Alternative • Policy-driven, and possible consensus/reference, strategy under which incremental capacity needs are exclusively met through a diverse array of renewables, battery storage, and DSM 9 Energy Efficiency Energy Storage Demand Response Renewables ENOstyle Proposed Planning Strategies--Assumptions title Objective Capacity Portfolio Criteria and Constraints Description DSM Input Case Strategy 11 Strategy 22 Strategy 33 Least Cost Planning 0.2/2% DSM Goal Renewables, Storage & DSM Alternatives Meet 12% Long-term Planning Reserve Margin (PRM) target using leastcost resource portfolio Include a portfolio of DSM programs that meet the Council’s stated 2% goal Meet peak load need + 12% PRM target using DSM, solar, and battery resources Assess demand- and supply-side alternatives to meet projected capacity needs with a focus on total relevant supply costs Assess portfolio of DSM programs that meet Council’s stated 0.2/2% goal along with consideration of additional supply-side alternatives Assess demand- and supplyside alternatives to meet projected capacity needs with a focus on adding solar and batteries Navigant Base Navigant 2% To be discussed 1 Least Cost Strategy – required by IRP Rules Sec. 7(D)1 2 Policy Goal Strategy – required by IRP Rules Sec. 7(D)3 3 Proposed Consensus/Reference Strategy – required by IRP Rules Sec. 7(D)2 10 Optimized title style Portfolio Design • Aurora Capacity Expansion Algorithm Portfolios – Used to identify least cost portfolios for each Strategy across a range of Scenarios. • Aurora Production Cost Modeling – Select portfolios are later tested across Scenarios in the Aurora Production Cost model in order to calculate the variable supply costs for each portfolio/Scenario combination. Benefits Aurora Capacity Expansion • • • • • • Capable of finding least cost portfolios given inputs and constraints 3rd party model-based portfolio development Considers multiple market and cost inputs Simultaneously considers multiple competing constraints Captures intermittent resource attributes Consistent application of algorithm 11 Challenges • • • • • Dependent on and sensitive to changes to inputs in ways that can be unpredictable May not account for qualitative benefits and considerations May not account for all stakeholder preferences Application of constraints without judgment can result in less appropriate resource selection Lack of transparency for validation and explanation of results Optimized Portfolio Design title style Least Cost Strategy Driver Which Strategy? 2% DSM Renewables, Storage, DSM Stakeholder Design Process Aurora Capacity Expansion Software Total Fixed Costs For each Scenario, Aurora Capacity Expansion creates the least cost portfolio of demand- and supply-side resources to meet the identified need (peak load + 12% PRM) Total Variable Production Costs Using Aurora’s Production Cost Modeling, select portfolios are analyzed across Scenarios Total Supply Costs Total supply costs (fixed + variable production costs) calculated for select portfolios/scenario combinations for comparison and analysis Sensitivity Analysis Least cost portfolios subjected to sensitivity analysis to examine effects of differing fuel prices, CO2 costs, etc. 12 Optimized Portfolio Design Illustrative title style Strategy 1 Least Cost Strategy 2 2% DSM Strategy 3 Renewables, Storage, DSM Strategy 4 (Stakeholder) Scenario 1 Portfolio Portfolio Portfolio Portfolio Scenario 2 Portfolio Portfolio Portfolio Portfolio Scenario 3 Portfolio Portfolio Portfolio Portfolio Scenario 4 (Stakeholder) Portfolio Portfolio Portfolio Portfolio 13 Optimized Portfolio Design Illustrative title style Strategy 1 Least Cost Strategy 2 2% DSM Strategy 3 Renewables, Storage, DSM Strategy 4 (Stakeholder) Scenario 1 Portfolio Portfolio Portfolio Portfolio Scenario 2 Portfolio Portfolio Portfolio Portfolio Scenario 3 Portfolio Portfolio Portfolio Portfolio Scenario 4 (Stakeholder) Portfolio Portfolio Portfolio Portfolio NOTE: In this example, all 7 of the select portfolios would be tested across the 4 Scenarios in the Production Cost Model, generating 28 Total Relevant Supply Cost Results 14 title style Section 2 ENO Capacity Need and Supply Alternatives 15 ENO’s Long-Term Capacity Need title style ENO’s existing and planned capacity portfolio over the 20 year planning period MW 800 700 Union 1 600 500 400 300 200 100 0 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 NOPS ENO Solar Requirements Assumptions: • Requirements based on non-coincident peak and a 12% reserve margin • ENO Solar additions modeled with 50% effective capacity (100 MW nameplate) 16 DSM Resources title style • • • • Same process for DSM evaluation as in 2015 IRP; including additional step to enable selection of DSM options that are cost-effective after year 1 DSM programs will be evaluated based on the characteristics and attributes provided in the potential studies. – Demand Response programs described by an average annual load reduction and annual program costs will be evaluated through spreadsheet models outside of the Aurora model based on capacity value net of fixed program costs. – Energy Efficiency programs described by an hourly load reduction profile and annual program costs. Programs determined to be economic (i.e. positive net benefits) will be selected in the first year. – ENO’s capacity position (surplus/deficit) will be adjusted to reflect the capacity contribution of selected Demand Response programs. Programs not considered economic in year one will be evaluated by AURORA alongside supply side resources in future years (future program inputs to be provided following initial run). – DSM programs with hourly load reduction profiles will be evaluated alongside supply side resources in the portfolio design in order to identify the most economic combination of DSM programs and supply side resources. 17 Supply-Side Technology Resources title style • The supply-side technology assessment analyzes potential supply-side generation solutions that could help ENO serve customers’ needs reliably and at the most reasonable cost, including renewables, energy storage, and natural gas technologies. • ENO’s technology assessment for the 2018 IRP explores in detail the challenges, opportunities, and costs of generation alternatives to be considered when designing resource portfolios to meet the capacity needs of customers. – Renewable energy resources, especially solar, have emerged as viable economic alternatives. – Trend to smaller, more modular resources (such as battery storage) provides opportunity to reduce risk and manage peak demand. – Deployment of intermittent generation has increased the need for flexible, diverse supply alternatives. New smaller scale supply alternatives will better address locational, site specific reliability requirements while continuing to support overall grid reliability. 18 Renewable Resource Assumptions (Solar PV & Wind) title style Levelized Real Cost of Electricity (2019$/MWh-AC) 1 2019 2020 2021 2022 2023 2026 2029 2032 2035 2038 Solar Tracking 2 $53.39 $49.64 $46.71 $44.35 $43.86 $43.79 $42.28 $40.51 $39.10 $37.82 Onshore Wind 3 $44.82 $46.12 $48.65 $48.19 $48.14 $47.32 $44.35 $42.21 $41.47 $41.46 Other Modeling Assumptions Solar Wind Fixed O&M (2017$/kW-yr-AC) $16 $36.01 Useful Life (yr) 30 25 MACRS Depreciation (yr) 5 5 Capacity Factor 26% 36% DC:AC 1.35 N/A Hourly Profile Modeling Software 1. 2. 3. PlantPredict NREL SAM Year 1 levelized real cost for a project beginning in the given year ITC normalized over useful life and steps down to 10% by 2023 PTC steps down to 40% by 2020 and expires thereafter Levelized Real Cost of Electricity (2019$/MWh) 1 $55 $50 $45 $40 $35 $30 2019 2021 2023 2025 2027 2029 2031 2033 2035 2037 Tracking Solar Onshore wind Source: The capital cost assumptions for Wind and Solar are based on a confidential IHS Markit forecast. 19 Grid-Scale title style Battery Storage Alternatives As battery storage technology continues to improve it is important to assess the costs and benefits associated with its deployment to meet long-term needs in the proper context. Battery storage includes a range of unique attributes that should be considered, such as: • • • • • • • • The ability to store energy for later commitment and dispatch (energy and capacity value) Ability to discharge in milliseconds and fast ramping capability (ancillary services) Potential deferral of transmission and distribution upgrades Rapid construction (on the order of months) Modular deployment provides potential scalability Portability and capability to be redeployed in different areas Small footprint (typically less than an acre), allowing for flexible siting Low round-trip losses compared to other storage technologies (such as compressed air) These attributes should be considered in the appropriate context, not all of which is well understood at this time, including but not limited to: • • • • • Batteries are not a source of electric generation Useful life can be much shorter than other grid-scale investments (replacement cost) Market rules not yet established to govern participation in wholesale markets Discharge less electricity than required to charge due to losses Cost of environmentally sound disposal 20 Battery title styleStorage Assumptions Levelized Real Fixed Cost (2019$/kW-yr) 1 2019 2020 2021 2022 2023 2026 2029 2032 2035 2038 $177 $163 $155 $146 $143 $132 $122 $113 $105 $96 Battery Storage Other Modeling Assumptions 1. 2. 3. Levelized Real Fixed Cost (2019$/kW-yr) 1 Battery Storage $200 Energy Capacity : Power 2 4:1 $180 Fixed O&M (2017$/kW-yr) $9.00 $160 Useful Life (yr) 3 10 $140 MACRS Depreciation (yr) 7 $120 AC-AC efficiency 90% $100 Hourly Profile Modeling Software Aurora $80 2019 2021 2023 2025 2027 2029 2031 2033 2035 2037 Battery Storage Year 1 levelized real cost for a project beginning in the given year Current MISO Tariff requirement for capacity credit Assumes daily cycling, no module replacement cost, full depth of discharge Source: The capital cost assumptions for Battery Storage is based on a confidential IHS Markit forecast. 21 title Gasstyle resource assumptions Summer Capacity [MW] Capital Cost [2017$/kW] Fixed O&M [2017$/kW-yr] Variable O&M [2017 $/MWh] Heat Rate* [Btu/kWh] Expected Capacity Factor [%] 1x1 501JAC 605 $1,244 $16.70 $3.14 6,300 80% 501JAC 346 $809 $2.37 $13.35 9,400 10% Aeroderivative Combustion Turbine (Aero CT) LMS100PA 102 $1,543 $5.86 $2.90 9,400 20% Reciprocating Internal Combustion Engine (RICE) 7x Wartsila 18V50SG 128 $1,545 $31.94 $7.30 8,400 30% Technology Combined Cycle Gas Turbine (CCGT) Simple Cycle Combustion Turbine (CT) *Heat Rate based on full load without duct firing 22 title style Section 3 Inputs and Assumptions 23 2018 IRP Inputs and Assumptions title style Input/Assumption MISO Market Modeling Portfolio Development Total Relevant Supply Costs Scenarios & Strategies    Gas Price Forecast*    CO2 Price Forecast*    Capacity Value*   Supply-Side Resource Alternative Costs*   Load Forecast*   ENO’s Long-Term Capacity Need*   DSM Potential Study Results   *Updated to Business Plan 19 Inputs since Technical Meeting #1 24 Gasstyle Price Forecast title Nominal $/MMBtu $10 $9 $8 High Mid Low $7 $6 $5 $4 $3 $2 $1 $0 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 Case 2019 2026 2031 2038 Low $2.52 $2.86 $3.32 $3.83 Medium $2.79 $4.15 $5.09 $6.41 High $3.09 $5.64 $6.89 $8.80 25 Coalstyle Price Forecast title Nominal $/MMBtu $5 $4 $3 $2 $1 $0 Independence 26 White Bluff CO2style Price Forecast title Nominal $/Short Ton $40 $35 $35 $30 $25 $20 $18 $18 $15 $10 $5 $3 $0 $0 Low 27 Mid High Capacity title style Value Forecast $/kW-Yr Levelized Cost of a New-Build CT $140 $120 $114 $100 $104 $96 $80 $87 $78 $60 $40 $20 $0 28 Peak Load & Energy Forecast title style 3 demand forecasts were created for the ENO IRP: a low, medium, and high (GWh) (MW) Energy Peak Load 1,250 6,600 1,230 6,400 1,210 1,190 6,200 1,170 6,000 1,150 1,130 5,800 1,110 1,090 5,600 High 5,400 2019 2022 10 Year CAGR (%) Mid 2025 2028 2031 1,070 Low 2034 2037 High 1,050 2019 Mid Low 2022 2025 2028 2031 2034 2037 Peak Load (MW) 2019 2024 2029 2033 2038 2019 – 2028 2029 – 2038 Low - 0.28% 0.26% Low 1,158 1,130 1,114 1,127 1,130 Medium 0.08% 0.41% Medium 1,175 1,171 1,162 1,179 1,191 High 0.20% 0.42% High 1,181 1,182 1,177 1,196 1,207 29 title style Section 4 Timeline and Next Steps 30 Current title style Timeline Description Target Date Status Public Meeting #1- Process Overview September 2017 Technical Meeting #1 Material Due Technical Meeting #1 Technical Meeting #2 Material Due Technical Meeting #2 Technical Meeting #3 Material Due Technical Meeting #3 IRP Inputs Finalized Optimized Portfolio Results Due Technical Meeting #4 Material Due Technical Meeting #4 File IRP Report Public Meeting #2 Material Due Public Meeting #2 - Present IRP Results Public Meeting #3 Material Due Technical Meeting #5 Material Due Public Meeting #3 - Public Response Technical Meeting #5 Intervenors and Advisors Questions & Comments Due ENO Response to Questions and Comments Due Advisors File Report January 2018 January 2018 August 2018 September 2018 November 2018 November 2018 December 2018 April 2019 April 2019 April 2019 July 2019 July 2019 August 2019 August 2019 August 2019 September 2019 September 2019 September 2019 October 2019 December 2019      - 31 title style Appendix 32 title Technical style Meeting Purpose Technical Meeting Purpose Technical Meeting 1 (January 22nd) The purpose of this meeting will be to discuss Planning Scenarios and Strategies. ENO should be prepared to present its Reference (and two alternative) Planning Scenarios, the Least Cost Planning Strategy, and the Utility’s proposed Reference Planning Strategy. Technical Meeting 2 (September 14th) Technical Meeting 3 (November 19th – November 30th) Technical Meeting 4 (April 22nd – May 3rd) Technical Meeting 5 (August 28th – September 11th) 33 The purpose of this meeting is to either confirm the consensus Scenario and Strategy or to confirm that ENO is prepared to include the Stakeholder Scenario and Strategy pursuant to the discussions of Technical Meeting 1. Purpose is to finalize the Planning Scenarios and Strategies by all parties and lock down of all IRP inputs. The results of the DSM Potential Studies will be provided in the input format required for modeling in the IRP. This meeting will also contain the initial discussion of scorecard metrics. The purpose of this meeting is to review the Optimized Resource Portfolios, finalize the Scorecard Metrics, and conduct an initial discussion regarding Energy Smart Program budgets and savings goals. For this meeting, ENO should prepare initial proposed Energy Smart Program budgets, and savings goals for discussion. The purpose of this meeting is to discuss Energy Smart implementation for Program Years 10-12. title style ENO 2018 IRP Docket No. UD-17-03 Technical Meeting #3 November 28, 2018 1 Goals and Agenda of Technical Meeting #3 title style Goals • As described in the Initiating Resolution (R-17-430), the main purpose of this meeting is for ENO, the Advisors, and Intervenors to finalize the Planning Scenarios and Strategies, lock down all of the IRP inputs, provide the results of the DSM Potential Studies, and engage in an initial discussion regarding scorecard metrics. Agenda 1. Planning Scenarios and Strategies—Discussion and Decision 2. Navigant DSM Potential Study Results—Presentation by Navigant 3. Scorecard Metrics—Initial Discussion 2 title style Meeting #2—Follow Ups Technical • • • DSM Input Files – HSPM workpapers and supporting files for Navigant Study • ENO provided to Advisors and Intervenors on 10/1/18 – Requirements for Aurora input files necessary to model Optimal study results in IRP • Call to discuss w/ENO, Advisors, and Optimal on 10/18/18 – DSM Program Input files from Optimal • Provided by Optimal on 11/13/18 Proposed Planning Scenarios – Information on DER assumptions in ENO load forecasts • ENO provided on 10/17/18 – Intervenors to develop proposed Scenario #4 • Consensus Stakeholder Scenario provided by AAE on 11/13/18 Proposed Planning Strategies – Intervenors to develop proposed Strategy #4 • Notes on strategy ideas provided by AAE on 11/13/18 3 title style Section 1 Planning Scenarios and Strategies 4 title Proposed style Planning Scenarios – Assumptions Scenario 1 (Moderate Change) Scenario 2 (Customer Driven) Scenario 3 (Policy Driven) Scenario 4 (Stakeholder) Peak Load & Energy Growth Medium Low High Low Natural Gas Prices Medium Low High High Market Coal & Legacy Gas Deactivations 60 years 55 years 50 years 50 years Magnitude of Coal & Legacy Gas Deactivations1 17% by 2028 57% by 2038 31% by 2028 73% by 2038 46% by 2028 76% by 2038 46% by 2028 76% by 2038 34% / 66% 25% / 75% 50% / 50%2 50%/50%2 Medium Low High High (start 2022) MISO Market Additions Renewables / Gas Mix CO2 Price Forecast 1. 2. "Magnitude of Coal & Legacy Gas Deactivation" driven by "Market Coal and Legacy Gas deactivation" assumptions (e.g. 55 Years; 31%/73%) for BP19 Includes storage to support market LMPs 5 ENOstyle Proposed Planning Strategies--Assumptions title Objective Capacity Portfolio Criteria and Constraints Description DSM Input Case Strategy 11 Strategy 22 Strategy 33 Least Cost Planning 0.2/2% DSM Goal Renewables, Storage & DSM Alternatives Meet 12% Long-term Planning Reserve Margin (PRM) target using leastcost resource portfolio Include a portfolio of DSM programs that meet the Council’s stated 2% goal Meet peak load need + 12% PRM target using DSM, solar, and battery resources Assess demand- and supply-side alternatives to meet projected capacity needs with a focus on total relevant supply costs Assess portfolio of DSM programs that meet Council’s stated 0.2/2% goal along with consideration of additional supply-side alternatives Assess demand- and supplyside alternatives to meet projected capacity needs with a focus on adding solar and batteries Navigant Base Navigant 2% To be discussed 1 Least Cost Strategy – required by IRP Rules Sec. 7(D)1 2 Policy Goal Strategy – required by IRP Rules Sec. 7(D)3 3 Proposed Consensus/Reference Strategy – required by IRP Rules Sec. 7(D)2 6 Intervenor title style Planning Strategy Notes (11/13/18) Council DSM Strategy Portfolio Criteria & Constraints Strategies for Consideration Optimal DSM Program Renewables Replacement (ENO Scenario 3) Distributed/ Resilience Scenario 4 (Stakeholder) Stakeholders believe ENO must run Optimal plan similar to ENO’s proposed “Strategy 2” to get clear picture of the impact of Optimal’s program on its own. Over course of time horizon, all deactivated ENO fossil assets are replaced with renewable energy resources and Demand Side Management. Priority on significant resources distributed in Orleans Parish, including microgrids/smart grid technology. Intended to build a resilient distribution level system that also provides every day reliable energy services to residents /businesses. Customer sited/owned resources are a priority. Description This strategy acknowledges and attempts to capture ENO’s “smart cities” and grid modernization upgrades described in Council Dockets UD-18-01 and UD-18-07 DSM input Optimal Program level DSM Optimal Program level DSM Optimal Program Level DSM + higher DR (per AEMA letter) Intervenors did not find firm consensus, on a “stakeholder strategy” considering a lack of clarity on ENO’s strategy inputs, including DSM input. Many Intervenors are interested in the strategies above, but are unsure how the priorities are developed as inputs for Aurora modeling. 7 title style Section 2 DSM Potential Study Results (Separate Deck for Navigant Presentation) 8 title style Section 3 Scorecard Metrics (Separate Excel File with Draft Scorecard Format) 9 title style Section 4 Timeline and Next Steps 10 Current title style Timeline Description Public Meeting #1- Process Overview Technical Meeting #1 Material Due Technical Meeting #1 Technical Meeting #2 Material Due Technical Meeting #2 Technical Meeting #3 Material Due Technical Meeting #3 IRP Inputs Finalized Optimized Portfolio Results Due Technical Meeting #4 Material Due Technical Meeting #4 File IRP Report Public Meeting #2 Material Due Public Meeting #2 - Present IRP Results Public Meeting #3 Material Due Technical Meeting #5 Material Due Public Meeting #3 - Public Response Technical Meeting #5 Intervenors and Advisors Questions & Comments Due ENO Response to Questions and Comments Due Advisors File Report 11 Target Date Status September 2017 P P P P P P P - January 2018 January 2018 August 2018 September 14, 2018 November 14, 2018 November 28, 2018 December 7, 2018 April 8, 2019 April 2019 April 2019 July 2019 July 2019 August 2019 August 2019 August 2019 September 2019 September 2019 September 2019 October 2019 December 2019 1 ENOL 2018 IRP Technical Meeting #4 Docket No. UD-17-03 May 1, 2019 Entergy New Orleans, LLC Goals and Agenda of Technical Meeting #4 Goals • The Initiating Resolution (R-17-430) contemplates several goals for this Technical Meeting: • First, the parties need to review and discuss the Optimized Resource Portfolios selected through the Aurora capacity expansion modeling, then reach consensus on the subset of portfolios to be carried through the total supply cost analysis and cross testing; • Next, the parties need to finalize the Scorecard Metrics initially presented at Technical Meeting #3; • Finally, there will be an initial discussion regarding Energy Smart Program budgets and savings goals for Program Years 10-12. Agenda 1. 2. 3. 4. 5. Optimized Resource Portfolio Discussion Scorecard Metrics Discussion Risk Assessment Discussion Energy Smart Program Discussion Next Steps and Timeline 2 Technical Meeting #3- Follow Ups • Technical Meeting #3 occurred on November 28, 2018. • Strategies and Scenarios • The Parties discussed Planning Scenarios and Strategies and reached consensus on 3 Scenarios and 5 Strategies. • On December 4, 2018, the Council’s Advisors circulated slides summarizing the consensus achieved on the Planning Scenarios and Strategies and requested that the Parties disclose any desired modifications, or objections, to the Strategies and Scenarios on or before December 6, 2018. When none were submitted, the Scenarios and Strategies became final for use in modeling. • DSM Inputs • To enable Aurora to optimize selection of programs from the DSM cases used in Strategies 1 and 5, SPO required additional data files beyond those originally provided. Navigant provided these for Strategy 1 in late January and Optimal provided these for Strategy 5 in mid-February. • Score Card Draft Template • At Technical Meeting #3, ENO presented a draft Score Card for initial review and comment. As of the date these materials were submitted (April 17, 2019), no written comments or feedback have been received. 3 44 Section 1 Optimized Resource Portfolios 5 Analytic Process to Create and Value Portfolios Development of Planning Scenarios and Strategies Market Modeling Portfolio Development Development of assumptions and inputs for Scenarios and Strategies Reviewed & finalized inputs, Strategies and Scenarios at Technical Conference #3 Projection of MISO market outside of ENOL for each Scenario Developed and executed market modeling based upon agreed upon Scenarios & Scenarios Total Relevant Supply Cost Action Plan Construction of resource portfolios for each Scenario/Strategy combination Produced optimized portfolios through Aurora’s capacity expansion based on agreed upon Strategies & Scenarios. Results summarized within the following slides. Production costs and fixed costs are determined for each portfolio under each Scenario/Strategy combination (Recommendations included on following slides) Recommendations for Total Supply Cost analysis and sensitivities are included within the following slides. Identify action plan that balances reliability, cost, and risk Review of the Scorecard is included within the following slides. 6 IRP Planning Scenarios Scenarios finalized at ENOL IRP Technical Meeting #3 Scenario 1 (Moderate Change) Scenario 2 (Customer Driven) Scenario 3 (Stakeholder) Peak Load & Energy Growth Medium High Low Natural Gas Prices Medium Low High Market Coal & Legacy Gas Deactivations 60 years 55 years 50 years 17% by 2028 57% by 2038 31% by 2028 73% by 2038 46% by 2028 76% by 2038 34% / 66% 25% / 75% 50% / 50%2 Medium Low High (Start 2022) Magnitude of Coal & Legacy Gas Deactivations1 MISO Market Additions Renewables / Gas Mix CO2 Price Forecast 1. "Magnitude of Coal & Legacy Gas Deactivation" driven by "Market Coal and Legacy Gas deactivation" assumptions (e.g. 55 Years; 31%/73%) 2. Included storage to support market LMPs 7 IRP Planning Strategies Strategies finalized at ENOL IRP Technical Meeting #3 Objective Capacity Portfolio Criteria and Constraints Description DSM Input Case Strategy 1 Strategy 2 Strategy 3 Strategy 4 Strategy 5 Least Cost Planning 0.2/2% DSM Goal Optimal Program Achievable DSM Navigant High DSM Stakeholder Strategy Meet 12% Long-term Planning Reserve Margin (PRM) target using least-cost resource portfolio Include a portfolio of DSM programs that meet the Council’s stated 2% goal Meet peak load need + Meet peak load need + Meet peak load need + 12% PRM target using 12% PRM target using 12% PRM target using Navigant High Case Optimal Program Level Optimal Program Level DSM and resources DSM and resources DSM, renewables, and selected by model selected by model energy storage Assess demand- and Assess portfolio of DSM Assess portfolio of DSM Assess portfolio of DSM Assess demand and programs that meet supply-side alternatives from Optimal Program from Navigant High Supply-side alternatives Council’s stated 0.2/2% to meet projected Achievable case along case along with to meet projected goal along with capacity needs with a capacity need with a with consideration of consideration of consideration of focus on total relevant additional supply side additional supply side focus on adding additional supply-side supply costs alternatives alternatives renewables and storage alternatives Navigant Base (Optimized) Navigant 2% Optimal Program Achievable Navigant High Optimal Program Achievable (Optimized) 8 Strategy 1 - Capacity Expansion Portfolios 187 SCENARIO 3 SCENARIO 2 SCENARIO 1 200 194 159 60 100 346 160 200 340 346 Resource M 501 J CT Solar Battery Battery Battery Year 2033 2033 2033 2034 2035 Installed Cap (MW) 346 200 20 20 20 Resource M 501 J CT Battery Battery Battery Solar Year 2033 2033 2034 2038 M501 CT Installed Cap (MW) 346 120 20 20 Battery Wind Resource Solar Battery Wind Battery Year 2033 2033 2034 2038 DSM * *DSM value represents last year’s (2038) peak reduction throughout study period, inclusive of EE and DR contribution Resource MW capacity amounts represent installed capacity Indicates initial recommendation for further Total Supply Cost evaluations Installed Cap (MW) 100 320 200 20 9 Strategy 2 - Capacity Expansion Portfolios SCENARIO 2 SCENARIO 1 SCENARIO 3 200 230 230 230 346 20 Year 2033 2033 2038 40 100 346 Resource M 501 J CT Solar Battery 346 Installed Cap (MW) 346 200 20 Resource M 501 J CT Battery Battery Battery Solar Year 2033 2033 2035 2038 M501 CT Installed Cap (MW) 346 60 20 20 Battery Wind Resource M 501 J CT Battery Year 2033 2038 DSM * *DSM value represents last year’s (2038) peak reduction throughout study period, inclusive of EE and DR contribution Resource MW capacity amounts represent installed capacity Indicates initial recommendation for further Total Supply Cost evaluations Installed Cap (MW) 346 40 10 Strategy 3 - Capacity Expansion Portfolios SCENARIO 3 SCENARIO 2 SCENARIO 1 100 100 278 278 278 260 346 346 200 40 Resource M 501 J CT Solar Year 2033 2034 Installed Cap (MW) 346 100 Resource M 501 J CT Battery Solar Year 2033 2034 M501 CT Installed Cap (MW) 346 40 Battery Wind Resource Solar Battery Battery Wind Year 2033 2033 2034 2038 DSM * *DSM value represents last year’s (2038) peak reduction throughout study period, inclusive of EE and DR contribution Resource MW capacity amounts represent installed capacity Indicates initial recommendation for further Total Supply Cost evaluations Installed Cap (MW) 100 240 20 200 11 Strategy 4 - Capacity Expansion Portfolios SCENARIO 3 SCENARIO 2 SCENARIO 1 100 214 214 300 214 20 346 346 Resource M 501 J CT Solar Battery Solar Solar Year 2033 2033 2033 2034 2038 340 200 100 Installed Cap (MW) 346 100 20 100 100 Resource M 501 J CT Battery Battery Battery Solar Year 2033 2033 2034 2035 M501 CT Installed Cap (MW) 346 60 20 20 Battery Wind Resource Solar Battery Battery Battery Wind Year 2033 2033 2034 2035 2037 DSM * *DSM value represents last year’s (2038) peak reduction throughout study period, inclusive of EE and DR contribution Resource MW capacity amounts represent installed capacity Indicates initial recommendation for further Total Supply Cost evaluations Installed Cap (MW) 100 300 20 20 200 12 Strategy 5 - Capacity Expansion Portfolios SCENARIO 3 SCENARIO 2 SCENARIO 1 100 272 278 400 270 260 400 300 Resource Battery Solar Battery Battery Year 2033 2033 2034 2038 200 Installed Cap (MW) 240 400 40 20 Resource Battery Battery Solar Year 2033 2034 M501 CT Installed Cap (MW) 360 40 Battery Wind Resource Solar Battery Battery Wind Year 2033 2033 2034 2038 DSM * *DSM value represents last year’s (2038) peak reduction throughout study period, inclusive of EE and DR contribution Resource MW capacity amounts represent installed capacity Indicates initial recommendation for further Total Supply Cost evaluations Installed Cap (MW) 100 240 20 200 13 Capacity Expansion Portfolio Selections Capacity Expansion Portfolio Selection Strategy 1 Strategy 2 Strategy 3 Strategy 4 Scenario 1 Scenario 2 Scenario 3 Indicates initial recommendation for further Total Supply Cost evaluations Strategy 3, Scenario 3 Portfolio is identical to Strategy 5, Scenario 3 Portfolio Strategy 5 14 Strategy 1 & 5 Energy Efficiency Selections Under Strategies 2-4, all DSM programs identified in the selected DSM Input cases contributed towards meeting ENOL’s supply needs. Strategy 1 (Navigant Base DSM) Program Strategy 5 (Optimal Program Achievable DSM) Scenario 1 Scenario 2 Scenario 3 Program Scenario 1 Scenario 2 ü Scenario 3 Com Behavior ü ü ü Home Energy Services ü Large C&I ü ü ü Res HVAC ü Not Selected ü Small C&I ü ü ü ü ü ü Consumer Products ü ü 2033 2033 ü Res Efficient Products Res Lighting Not Selected Not Selected ü Efficient New Homes Not Selected Not Selected ü Appliance Recycling ü ü ü CVR- Res ü ü ü Small Business DI ü ü ü Commercial Prescriptive ü ü ü Commercial Custom ü ü ü Retro commissioning ü ü ü New Construction ü ü ü CVR – C&I ü ü ü HPwES HVAC ü ü ü ü 2033 Low Income and Multi Family ü Res Behavior ü ü ü School Kits ü ü ü ü 2033 ü ü 2033 ü 2033 ü 15 Demand Response Programs Under each Strategy, all Demand Response programs identified through the selected DSM Input case were assumed to be economic and contributed to meeting ENOL’s supply needs. Navigant Demand Response Program DLC-thermostat- HVAC Description Control of cooling load using a PCT. Optimal Demand Response Program RES DLC/ADR Voluntary opt-in dynamic pricing offer Dynamic Pricing w/o Enabling Tech with enabling technology. Dynamic Pricing with Enabling Tech DLC-Switch-HVAC C&I Curtailment-Manual HVAC Control Voluntary opt-in dynamic pricing offer without enabling technology. Res- Pricing- PTR Control of cooling load using a load control switch. Firm capacity reduction Commitment. $/kW payment based on contracted capacity plus $/kWh payment based on energy reduction during an event. Large Cust SOP Description Reduce residential peak demand during load control events through remotely controlled programs and software. Pay-for-performance incentive programs that pay participants to reduce energy use during certain hours of selected days when a peak event is called. The customer is paid to allow the utility to curtail load for a maximum number of times during set periods, usually with 24 hour advance notice. 16 Section 2 Risk Assessment Proposed Risk Assessments Following agreement at Technical Meeting #4 on the subset of 5-6 portfolios to be carried through the total relevant supply cost analysis, selected portfolios will be passed through two rounds of risk analysis to comply with Section 8 of the IRP rules: 1.Primary Risk Analysis: Cross-Testing A.Time Necessary to Complete: 2 days per portfolio a) Cross-testing determines how each portfolio’s total supply costs change under the assumptions of the 3 Scenarios. 2. Secondary Risk Analysis: Additional Sensitivities on Variable Supply Cost Inputs A. ENOL proposes analyzing variations for two key inputs: i. Gas Price ii. CO2 Forecast B. Next, portfolios would be analyzed using one of two possible Alternative Sensitivity Evaluation methods: i. Probabilistic Assessment: variable supply cost simulation based on a distribution of possible outcomes around two individual inputs. a) Use a single Scenario (recommend Scenario 1) b) 29 days required to complete (assumes four portfolios, which is the limit possible under current timeline) OR i. Deterministic Assessment: variable supply cost simulations based on a high or low forecast of a single or multiple inputs a) 2 days per portfolio per scenario to complete 17 18 Proposed Risk Assessments All estimated dates assume a May 6th start date with no schedule modifications. Working Days 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 Preliminary Risk Analysis: Cross Testing Portfolio 1 Portfolio 2 Portfolio 3 Portfolio 4 Portfolio 5 Portfolio 6 Total Supply Cost Analysis ~May 29th completion date* Additional Risk Sensitivities Probabilistic Scenario Portfolio 1, Variable 1 Portfolio 2, Variable 1 Portfolio 3, Variable 1 Portfolio 4, Variable 1 Portfolio 1, Variable 2 Portfolio 2, Variable 2 Portfolio 3, Variable 2 Portfolio 4, Variable 2 Total Supply Cost Analysis ~June 12th completion date* Deterministic Scenario Portfolio 1 Portfolio 2 Portfolio 3 Portfolio 4 Total Supply Cost Analysis ~May 22nd completion date* 19 Section 3 Scorecard Metrics 20 Proposed Scorecard Scoring Parameters / Descriptions Scoring Criteria Cost and Risk Expected Value (Average Cost Across Futures) Downside Risk (Maximum Cost - Expected Cost) Upside Potential (Expected Value - Lowest Cost) Operational Flexibility Flexible Resources (MW of Ramp) Quick-Start Resources (MW of Quick-Start)1 UCAP/ICAP Ratio (UCAP/ICAP) Environmental Impact CO2 Intensity (Tons CO2/GWh) Groundwater Usage (% of Portfolios with Groundwater Usage) Policy Goals/Sustainability 100% Low Carbon (% of Carbon Free Energy from New Resource)2 255 MW Solar Added (Total Solar MW in Portfolio) 3.3% Annual Energy Savings (CAGR over 20 Years) Economic Impact Macroeconomic Factor (To be developed) Notes: 1. Quick-Start includes supply and demand side dispatchable 2. Carbon-Free Resources include Energy Efficiency Scoring Weight 50.0% 20.0% 15.0% 15.0% 20.0% 6.7% 6.7% 6.7% 20.0% 10.0% 10.0% 5.0% 1.7% 1.7% 1.7% 5.0% 5.0% 1 4 7 10 ≤ 2.50 ≤ 2.50 ≤ 2.50 2.51 - 5.00 2.51 - 5.00 2.51 - 5.00 5.01 - 7.50 5.01 - 7.50 5.01 - 7.50 > 7.50 > 7.50 > 7.50 ≤ 2.50 ≤ 2.50 ≤ 2.50 2.51 - 5.00 2.51 - 5.00 2.51 - 5.00 5.01 - 7.50 5.01 - 7.50 5.01 - 7.50 > 7.50 > 7.50 > 7.50 ≤ 2.50 < 33% 2.51 - 5.00 > 33% 5.01 - 7.50 >66% > 7.50 = 100% < 33% < 150 MW < 1.0% > 33% > 200MW > 1.0% >66% >225 MW >2.0% = 100% ≥ 255 MW ≥ 3.3% ≤ 2.50 2.51 - 5.00 5.01 - 7.50 > 7.50 21 Section 4 Energy Smart Program 22 Energy Smart Implementation Plan Timeline 23 DSM Program Matrix 24 Program Year 10-12—Potential New Measures 25 Savings Potential Comparison 26 Section 5 Timeline and Next Steps 27 Current Timeline Description Public Meeting #1- Process Overview Technical Meeting #1 Material Due Technical Meeting #1 Technical Meeting #2 Material Due Technical Meeting #2 Technical Meeting #3 Material Due Technical Meeting #3 IRP Inputs Finalized Optimized Portfolio Results Due Technical Meeting #4 Material Due Technical Meeting #4 File IRP Report Public Meeting #2 Material Due Public Meeting #2 - Present IRP Results Public Meeting #3 Material Due Technical Meeting #5 Material Due Public Meeting #3 - Public Response Technical Meeting #5 Intervenors and Advisors Questions & Comments Due ENOL Response to Questions and Comments Due Advisors File Report Energy Smart PY 10-12 Implementation Plan Filed Target Date Status September 2017 January 2018 January 2018 August 2018 September 14, 2018 November 14, 2018 November 28, 2018 December 7, 2018 April 8, 2019 April 17, 2019 May 1, 2019 July 19, 2019 July 2019 August 2019 August 2019 August 2019 September 2019 September 2019 September 2019 October 2019 December 2, 2019 December 9, 2019 P P P P P P P P P P P - 28 Appendix 29 Renewable Resource Assumptions (Solar PV & Wind) Levelized Real Cost of Electricity (2019$/MWh-AC) 1 2019 2020 2021 2022 2023 2026 2029 2032 2035 2038 Solar Tracking 2 $53.39 $49.64 $46.71 $44.35 $43.86 $43.79 $42.28 $40.51 $39.10 $37.82 Onshore Wind 3 $44.82 $46.12 $48.65 $48.19 $48.14 $47.32 $44.35 $42.21 $41.47 $41.46 Levelized Real Cost of Electricity (2019$/MWh) 1 Other Modeling Assumptions $55 Solar Wind Fixed O&M (2017$/kW-yr-AC) $16 $36.01 Useful Life (yr) 30 25 MACRS Depreciation (yr) 5 5 Capacity Factor 26% 36% DC:AC 1.35 N/A Hourly Profile Modeling Software PlantPredict NREL SAM $50 1. 2. 3. $45 $40 $35 $30 2019 2021 Year 1 levelized real cost for a project beginning in the given year ITC normalized over useful life and steps down to 10% by 2023 PTC steps down to 40% by 2020 and expires thereafter Source: The capital cost assumptions for Wind and Solar are based on a confidential IHS Markit forecast. 2023 2025 2027 Tracking Solar 2029 2031 Onshore wind 2033 2035 2037 30 Battery Storage Assumptions Levelized Real Fixed Cost (2019$/kW-yr) 1 2019 2020 2021 2022 2023 2026 2029 2032 2035 2038 $177 $163 $155 $146 $143 $132 $122 $113 $105 $96 Battery Storage Levelized Real Fixed Cost (2019$/kW-yr) 1 Other Modeling Assumptions Battery Storage $200 $180 Energy Capacity : Power 2 4:1 Fixed O&M (2017$/kW-yr) $9.00 Useful Life (yr) 3 10 MACRS Depreciation (yr) 7 $120 AC-AC efficiency 90% $100 Hourly Profile Modeling Software Aurora $80 1. 2. 3. $160 $140 2019 Year 1 levelized real cost for a project beginning in the given year Current MISO Tariff requirement for capacity credit Assumes daily cycling, no module replacement cost, full depth of discharge Source: The capital cost assumptions for Battery Storage is based on a confidential IHS Markit forecast. 2021 2023 2025 2027 2029 Battery Storage 2031 2033 2035 2037 31 Gas Resource Assumptions Summer Capacity [MW] Capital Cost [2017$/kW] Fixed O&M [2017$/kW-yr] Variable O&M [2017 $/MWh] Heat Rate* [Btu/kWh] Expected Capacity Factor [%] 1x1 501JAC 605 $1,244 $16.70 $3.14 6,300 80% 501JAC 346 $809 $2.37 $13.35 9,400 10% Aeroderivative Combustion Turbine (Aero CT) LMS100PA 102 $1,543 $5.86 $2.90 9,400 20% Reciprocating Internal Combustion Engine (RICE) 7x Wartsila 18V50SG 128 $1,545 $31.94 $7.30 8,400 30% Technology Combined Cycle Gas Turbine (CCGT) Simple Cycle Combustion Turbine (CT) *Heat Rate based on full load without duct firing 32 Gas Price Forecast Price Forecast Case 2019 2026 2031 2038 Low $2.52 $2.86 $3.32 $3.83 Medium $2.79 High $3.09 $4.15 $5.09 $5.64 $6.41 $6.89 $8.80 Coal Price Forecast 33 CO2 Price Forecast 34 Capacity Value Forecast 35 36 Peak Load & Energy Forecast 3 demand forecasts were created for the ENO IRP: a low, medium, and high 10 Year CAGR (%) 2019 – 2028 2029 – 2038 Peak Load (MW) 2019 2024 2029 2033 2038 Low - 0.28% 0.26% Low 1,158 1,130 1,114 1,127 1,130 Medium 0.08% 0.41% Medium 1,175 1,171 1,162 1,179 1,191 High 0.20% 0.42% High 1,181 1,182 1,177 1,196 1,207 37 38 Effective Load Carrying Capability for Solar Generation Effective Load Carrying Capability (ELCC): Effective Load Carrying Capability (ELCC) is defined as the amount of incremental load a resource, such as wind or solar, can dependably and reliably serve, while also considering the probabilistic nature of generation shortfalls and random forced outages as driving factors to load not being served. • ELCC has been used in the determination of capacity value for generation resources as far back as 1966 1. • MISO currently uses ELCC to determine the capacity value for wind. The first ELCC-capacity credit in MISO was applied when wind achieved 8% Penetration, or 10 GW Nameplate. • According to the MISO PY 2019/20 Loss of Load Expectation (LOLE) Study, there is roughly 0.6 GWs of solar active in MISO Market. However, the penetration of solar is expected to increase significantly over the planning horizon. • MISO along with other balancing authorities have applied or expect to apply in the future an ELCC approach to determining solar capacity value • California Public Utility Commission Currently employs this method. • PJM is currently studying the implementation of this method. Note 1: Garver, L.L.; , "Effective Load Carrying Capability of Generating Units," Power Apparatus and Systems, IEEE Transactions on, vol.PAS-85, no.8, pp.910-919, Aug. 1966 Note 2: *RIIA is MISO’s Renewable Integration Impact Assessment; https://cdn.misoenergy.org/20180605%20RIIA%20Workshop%20Presentation213125.pdf 39 Solar Generation Modeling Assumptions • Solar Capacity Credit within IRP Evaluation: – For the purpose of calculating Total Supply Cost solar will receive 50% Capacity Credit1 – Consistent with the curve reviewed in the MISO Renewable Integration Impact Study (RIIA), for the purpose of capacity expansion beginning in year 2031 solar received decreasing credit towards peak demand based on increasing solar penetration. ENOL Capacity Expansion Solar Credit Curves 70% 60% MISO RIIA2 50% ENOL solar credit, by tranche 40% 30% 20% 10% 0% 0% 10% 20% 30% 40% 50% 60% 70% Nameplate Solar % of Entity Peak 80% 90% 100% Note 1: Consistent with MISO’s current solar capacity credit methodology. Note 2: *RIIA is MISO’s Renewable Integration Impact Assessment; https://cdn.misoenergy.org/20180605%20RIIA%20Workshop%20Presentation213125.pdf CERTIFICATE OF SERVICE Docket No. UD-17-03 I hereby certify that I have served the required number of copies of the foregoing report upon all other known parties of this proceeding, by the following: electronic mail, facsimile, overnight mail, hand delivery, and/or United States Postal Service, postage prepaid. Lora W. Johnson, CMC, LMMC Clerk of Council Council of the City of New Orleans City Hall, Room 1E09 1300 Perdido Street New Orleans, LA 70112 Erin Spears, Chief of Staff Bobbie Mason Connolly Reed Council Utilities Regulatory Office City of New Orleans City Hall, Room 6E07 1300 Perdido Street New Orleans, LA 70112 David Gavlinski Council Chief of Staff New Orleans City Council City Hall, Room 1E06 1300 Perdido Street New Orleans, LA 70112 Sunni LeBeouf City Attorney Office City Hall, Room 5th Floor 1300 Perdido Street New Orleans, LA 70112 Norman White Department of Finance City Hall, Room 3E06 1300 Perdido Street New Orleans, LA 70112 Jonathan M. Rhodes, Director of Utilities, Mayor’s Office City Hall – Room 2E04 1300 Perdido Street New Orleans, LA 70112 (504) 658-4928 - Office Hon. Jeffery S. Gulin 3203 Bridle Ridge Lane Lutherville, GA 21093 Clinton A. Vince, Esq. Presley R. Reed, Jr., Esq. Emma F. Hand, Esq. Herminia Gomez Dee McGill Dentons US LLP 1900 K Street, NW Washington, DC 20006 Basile J. Uddo, Esq. J.A. “Jay” Beatmann, Jr. c/o Dentons US LLP The Poydras Center 650 Poydras Street, Suite 2850 New Orleans, LA 70130-6132 Victor M. Prep Joseph W. Rogers Cortney Crouch Legend Consulting Group 6041 South Syracuse Way, Suite 105 Greenwood Village, CO 80111 Errol Smith, CPA Bruno and Tervalon 4298 Elysian Fields Avenue New Orleans, LA 70122 Brian L. Guillot Vice-President, Regulatory Affairs Entergy New Orleans, LLC Mail Unit L-MAG-505B 1600 Perdido Street New Orleans, LA 70112 Timothy S. Cragin, Esq Harry M. Barton, Esq. Alyssa Maurice-Anderson, Esq. Karen Freese, Esq. Entergy Services, LLC Mail Unit L-ENT-26E 639 Loyola Avenue New Orleans, LA 70113 Polly S. Rosemond Seth Cureington Derek Mills Keith Wood Kevin Boleware Entergy New Orleans, LLC Mail Unit L-MAG-505B 1600 Perdido Street New Orleans, LA 70112 Joseph J. 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Kenton Parsons Christian J. Rgodes Shelly Ann McGlathery Roedel, Parsons, Koch, Blache, Balhoff & McCollister 8440 Jefferson Highway, Suite 301 Baton Rouge, LA 70809 Corey G. Dowden Lower Nine House of Music 1025 Charbonnet St. New Orleans, LA 70117 Andreas Hoffman Green Light New Orleans 8203 Jeannette Street New Orleans, LA 70118 Nathan Lott Brady Skaggs Miriam Belblidia The Water Collaborative of Greater New Orleans 4906 Canal Street New Orleans, LA 70119 Jason Richards Angela Morton Joel Pominville American Institute of Architects 1000 St. Charles Avenue New Orleans, LA 70130 Jeffery D. Cantin Gulf States Renewable Energy Industries Association 400 Poydras Street, Suite 900 New Orleans, LA 70130 Monique Harden Deep South Center for Environmental Justice 3157 Gentilly Boulevard, Suite 145 New Orleans, LA 70122 Andreanecia Morris Trayshawn Webb Greater New Orleans Housing Alliance 4460 S. Carrollton Avenue, Suite 160 New Orleans, LA 70119 Elizabeth Galante Ben Norwood PosiGen 819 Central Avenue, Suite 201 Jefferson, LA 70121 Katherine Hamilton Advanced Energy Management Alliance 1200 18th Street NW, Suite 700 Washington DC 20036 Cliff McDonald Jeff Loiter Optimal Energy 10600 Route 116, Suite 3 Hinesburg, VT 05461 New Orleans, Louisiana, this 19th day of July, 2019. ___________________________________ Harry M. Barton