Attachment LS-1
2016 ERP

Colorado PUC E-Filings System

2016 ELECTRIC RESOURCE PLAN

June 3, 2016

 Attachment LS-1
2016 ERP

Table of Contents
Executive Summary
1. Introduction....................................................................................................................................... 1
1.1 Background ..............................................................................................................2
1.2 Overview of 2013 ERP............................................................................................................ 3
2. Planning Environment .................................................................................................................. 5
2.1 Colorado Renewable Energy Standard ............................................................................ 5
2.2 Demand-Side Management .................................................................................................. 5
2.3 Economic Conditions .............................................................................................................. 6
2.4 Natural Gas Supply Trends over the Long-Term ......................................................... 6
2.5 Environmental Regulation.................................................................................................... 8
2.5.1 Greenhouse Gas Regulations........................................................................................ 9
2.5.2 Environmental Protection Agency’s Clean Power Plan ......................................... 9
2.6 Federal Tax Incentives for Wind and Solar Projects ............................................... 10
2.7 The Power Supply Market ................................................................................................. 11
3. Assumptions ......................................................................................................................................
3.1 Planning Period...................................................................................................................... 12
3.2 Resource Acquisition Period............................................................................................. 12
3.3 Planning Reserve Margin ................................................................................................... 12
3.4 Fuel and Market Gas Prices ............................................................................................... 13
3.4.1 Natural Gas Prices .......................................................................................................... 13
3.4.2 Oil Prices ............................................................................................................................ 15
3.4.3 Economy Energy Prices ............................................................................................... 15
3.4.4 Seasonal Firm Market Purchase Prices .................................................................. 16
3.5 Financial Parameters ............................................................................................................ 16
3.6 Emissions Costs....................................................................................................................... 17
3.7 Demand-Side Management ................................................................................................ 17
4. Load Forecast ................................................................................................................................. 21
4.1 Econometric Model Overview ........................................................................................... 21
4.2 Load, Economic, and Weather Data ................................................................................ 22
4.2.1 Historical Load Data ....................................................................................................... 22
4.2.2 Economic Data .................................................................................................................. 23
4.2.3 Weather Data .................................................................................................................... 23
4.2.4 Normal Weather Conditions ....................................................................................... 24
4.3 Forecast Methodology ....................................................................................................... 24
4.3.1 Peak Demand Forecast Methodology ................................................................... 24
4.3.2 Energy Forecast Methodology................................................................................. 24
4.3.3 Large Customer Growth Assumptions ................................................................. 25
4.4 Base Peak Demand and Annual Energy Forecasts .................................................. 26
4.5 Low and High Forecasts...................................................................................................... 28
4.6 Historical Peak Demand and Annual Energy.............................................................. 31
4.7 Load Forecast Comparison-2013 ERP vs. Actual vs. 2016 ERP .......................... 32

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2016 ERP

5.

6.0

7.0

8.0

4.8 Energy Capacity Sales to other Utilities & Intra-Utility Energy and
Capacity Sales and Losses .................................................................................................. 36
4.9 Load Profiles .................................................................................................................... 37
Supply-Side Resources......................................................................................................... 38
5.1 Existing Resources ......................................................................................................... 38
5.1.1 Diesels .......................................................................................................................... 40
5.1.2 Pueblo Airport Generating Station ................................................................... 40
5.1.3 Existing Purchases .................................................................................................. 40
5.1.4 Coordination Letters .............................................................................................. 41
5.1.5 Existing Renewables .............................................................................................. 41
5.1.6 Busch Ranch Wind Project ................................................................................... 41
5.1.7 Solar Resources ........................................................................................................ 41
5.1.8 1.8MW Distributed Generation Wind Facility .............................................. 42
5.1.9 Peak View Wind Project ........................................................................................ 42
5.2 Resources Options ......................................................................................................... 42
5.2.1 Conventional ............................................................................................................. 42
5.2.2 Combustion Turbine............................................................................................... 43
5.2.3 Combined Cycle ........................................................................................................ 43
5.2.4 Reciprocating Engines ........................................................................................... 44
5.2.5 Seasonal Firm Market Purchased Power ....................................................... 45
5.3 Renewables ....................................................................................................................... 45
5.3.1 PV Solar ....................................................................................................................... 45
5.3.2 Wind ............................................................................................................................. 46
5.4 Section 123 Resources ................................................................................................. 46
5.4.1 Sodium Sulfur Battery ........................................................................................... 47
5.4.2 Waste-to-Energy Facility ...................................................................................... 47
Costs and Benefits of Integration for Intermittent Renewable Energy
Resources.................................................................................................................................. 48
6.1 Integration Capacity Needs and Costs.................................................................... 48
6.2 Accreditable Capacity of Wind and Solar .............................................................. 51
Transmission Resources ..................................................................................................... 53
7.1 Local Transmission Planning Process .................................................................... 54
7.2 Regional Transmission Planning Process ............................................................. 55
7.3 Transmission Constraints ........................................................................................... 56
7.4 Transmission Projects .................................................................................................. 59
7.5 Senate Bill 07-100 Transmission Projects............................................................ 62
Future Resource Analysis and Selection....................................................................... 65
8.1 Resource Need ................................................................................................................. 65
8.2 Analysis .............................................................................................................................. 67
8.3 Base Plan Analysis and Alternative Plans ............................................................. 68
8.4 Base-with-RES Plan ....................................................................................................... 69
8.5 Alternative Plan 1 ........................................................................................................... 70
8.6 Alternative Plan 2 ........................................................................................................... 70
8.7 Retail Rate Impact Analysis ........................................................................................ 73
8.8 Scenario Analysis............................................................................................................ 75
8.9 Risk Analysis .................................................................................................................... 78

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2016 ERP

9.0
10.0
11.0

12.0

13.0
14.0
15.0
16.0
17.0

8.9.1 Stochastic Analysis ................................................................................................... 78
8.9.2 Risk Profiles ................................................................................................................ 82
8.10 Preferred Plan ............................................................................................................... 85
8.11 2018-2021 RES Compliance Plan .......................................................................... 86
Contingency Plan ................................................................................................................... 87
RFPs and Model Contracts ................................................................................................. 87
Confidential and Highly Confidential Information ................................................... 88
11.1 Public Information
11.1.1 Company Information .......................................................................................... 88
11.1.2 Purchased Generation Resource Information ............................................ 88
11.1.3 Model Input Data.................................................................................................... 88
11.1.4 Modeling Output Data .......................................................................................... 89
Confidential Information .................................................................................................... 90
12.1 Modeling Input Data ................................................................................................... 90
12.2 Modeling Output Data ................................................................................................ 91
Highly Confidential Information ...................................................................................... 92
Information that the Company will Provide Bidders .............................................. 92
Implementation of Separation Policy ............................................................................ 92
Protection of Bid Information, Modeling Inputs and Assumptions, and Bid
Evaluation Results ................................................................................................................. 92
Water Usage ............................................................................................................................. 94

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 Attachment LS-1
2016 ERP
List of Appendices
(*Found Outside the Body of this Document)
A

B
C

D
E

Estimated Net Incremental Cost Tables:
*Schedule A-1: 2016-2040 Estimated Avoided Costs and Net Incremental
Cost of 2019 60 MW Wind Resource
*Schedule A-2: 2016-2040 Estimated Avoided Costs and Net Incremental
Cost of 2026 30 MW Wind Resource
*Schedule A-3 - 2016-2040 Estimated Avoided Costs and Net Incremental
Cost of 2038 60 MW Wind Resource
*Schedule A-4: Source and Use of Funds Available for Eligible Energy
Acquisition
*WECC Standard BAL-002-WECC-2 Contingency Reserves
Econometric Load Forecast Methodology ................................................................... 98
*Schedule C-1 - Hourly Historic Demand (MW)
*Schedule C-2 - Monthly Historical Class Energy Sales Data (MWh)
*Confidential Schedule C-3 - Historical and Forecasted Economic Data
*Schedule C-4 - Historical and Forecasted Weather Data
*Schedule C-5 - Historical and Forecasted Weather Data Used for Sales
Models
*Schedule C-6 - Historical and Forecasted Variable Values for the Demand
Model
*Schedule C-7 - Variable Statistical Values for the Demand Model
*Schedule C-8 - Base Monthly Customer Class Sales Forecast & Demand
Forecast
*Schedule C-9 - Base Annual Customer Class Sales Forecast & Demand
Forecast
*Schedule C-10 - Historical and Forecasted Variable Values for Residential
use per Customer Model
*Schedule C-11 - Variable Statistical Values for Residential use per Customer
Model
*Schedule C-12 - Historical and Forecasted Variable Values for Residential
Customer Model
*Schedule C-13 - Variable Statistical Values for Residential Customer Model
*Schedule C-14 - Historical and Forecasted Variable Values for Commercial
use per Customer Model
*Schedule C-15 - Variable Statistical Values for Commercial use per Customer
Model
*Schedule C-16 - Historical and Forecasted Variable Values for Commercial
Customer Model
*Schedule C-17 - Variable Statistical Values for Commercial Customer Model
*Schedule C-18 - Historical and Forecasted Variable Values for Industrial
Sales Model
*Schedule C-19 - Variable Statistical Values for Industrial Sales Model
Load Profiles (Tables and Figures D-1 through D-36) .......................................... 103
*Technology Characterization and Busbar Cost Analysis

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2016 ERP
F
G
H
I
J
K

L
M
N
O

*Black Hills VER Integration Study
*2016 – 2040 Load and Resource Balance
General Planning Assumptions ...................................................................................... 143
Computer Models Used for Electric Resource Plan ................................................ 144
Emissions Projections (Tables J-1 through J-10) .................................................... 146
*Confidential Price Forecasts:
*Confidential Schedule K-1 -Natural Gas Price Forecast
*Confidential Schedule K-2 -Fuel Oil Price Forecast
*Confidential Schedule K-3 - Emission Costs - CO2 Scenario
*Confidential Schedule K-4 - Seasonal Firm Market Price Forecasts-AZ
PV Market-Base Plan
*Confidential Schedule K-5 - Seasonal Firm Market Price Forecasts-AZ
PV Market-High Gas Scenario
*Confidential Schedule K-6 - Seasonal Firm Market Price Forecasts-AZ
PV Market-Low Gas Scenario
*Confidential Schedule K-7 - Seasonal Firm Market Price-AZ PV
Market-CO2 Tax Scenario
*Confidential Schedule K-8 - Market Clearing Price Forecasts- CO East
Market- Base Plan
*Confidential Schedule K-9 – Market Clearing Price Forecasts- AZ PV
Market-Base Case
*Confidential Schedule K-10 – Market Clearing Price Forecasts- CO
East Market-CO2 Tax Scenario
*Confidential Schedule K-11 – Market Clearing Price Forecasts- AZ PV
Market- CO2 Tax Scenario
*Confidential Schedule K-12 – Market Clearing Price Forecasts- CO
East Market-High Gas Scenario
*Confidential Schedule K-13 – Market Clearing Price Forecasts- AZ PV
Market-High Gas Scenario
*Confidential Schedule K-14 – Market Clearing Price Forecasts- CO
East Market-Low Gas Scenario
*Confidential Schedule K-15 – Market Clearing Price Forecasts – AZ PV
Market-Low Gas Scenario
*Confidential Schedule K-16 - Projected Emissions Rates for Existing
Resources
Confidential Exhibit Cost Parameters for Waste-to-Energy PPA Price .......... 158
*Model Request for Proposal for Intermittent Resources
*Model Form Contract for Intermittent Resources
*Model Request for Proposal for Stand Alone Renewable Energy Credits

Abbreviations

........................................................................................................................ 162

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 Attachment LS-1
2016 ERP
List of Tables
Page
Table ES-1

Optimal Expansion Plans……………………………………………………………ES-5

Table 3-1
Table 3-2
Table 3-3
Table 3-4
Table 3-5

Average Annual Henry Hub Gas Price ............................................................. 14
Economy Energy Purchase Capacity Allowed by Market Value ............ 15
Financial Parameters ............................................................................................. 17
2016-2018 Demand Side Management -........................................................ 19
Adjustments to Load Forecast for DSM .......................................................... 20

Table 4-1
Table 4-2

Table 4-7
Table 4-8
Table 4-9
Table 4-10

Large Customer Load Additions and Reductions 2016- 2022 ............... 25
2013 ERP Large Customer Load Assumptions Compared to Actual
Load Additions ......................................................................................................... 26
Combined Transmission and Distribution Losses ...................................... 27
Base Load Forecast ................................................................................................. 28
Low, Base and High Load Forecasts ................................................................. 30
Seasonal Peak Demand Load Forecast Comparison – Base, Low and
High (including impacts of DSM Plans) .......................................................... 31
Historical Peak Demand and Annual Energy ................................................ 32
Historical Peak Demand ....................................................................................... 33
Peak Demand and Energy Forecast Comparison ........................................ 34
Peak Demand Forecast Comparison ................................................................ 36

Table 5-1
Table 5-2
Table 5-3
Table 5-4
Table 5-5
Table 5-6
Table 5-7

Existing Generating Facilities ............................................................................. 39
Combustion Turbine Parameters ...................................................................... 43
Combined Cycle Parameters ............................................................................... 44
Wartsila Gas Engine Parameters ....................................................................... 44
PV Solar Performance Parameters ................................................................... 45
Wind Performance Parameters ......................................................................... 46
Sodium Sulfur Battery Performance Parameters ....................................... 47

Table 6-1
Table 6-2
Table 6-3
Table 6-4
Table 6-5

Future Eligible Energy Resources Modeled .................................................. 49
Wind Integration Cost Assumptions ................................................................ 50
Net Integration Costs ............................................................................................. 51
Incremental Wind and Solar ELCC ................................................................... 53
Cost for Integration and ELCC Summary ....................................................... 53

Table 7-1
Table 7-2
Table 7-3

Transfer Path Capacity .......................................................................................... 59
Black Hills 2016 Rule 3206 Planned Transmission Projects ................. 60
Black Hills 2015 SB-100 Transmission Projects ......................................... 64

Table 8-1
Table 8-2

Load and Resource Balance (2016-2022) ..................................................... 66
Expansion Plans ....................................................................................................... 71

Table 4-3
Table 4-4
Table 4-5
Table 4-6

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2016 ERP
Table 8-3
Table 8-4
Table 8-5
Table 8-6
Table 8-7
Table 8-8
Table 8-9
Table 8-10
Table 8-11

Base-with-RES Plan Annual Capacity Factors during the Resource
Acquisition Period................................................................................................... 72
Incremental Cost/Saving of Eligible Energy Resources ........................... 75
Optimal Expansion Plans-Scenario Analysis ................................................ 77
Short-Term Volatilities.......................................................................................... 79
Mean Reversion Rates ........................................................................................... 80
Load Correlations .................................................................................................... 80
AZ-PV Market Price Correlations ...................................................................... 81
CO-East Market Price Correlations ................................................................... 81
Uncertainty Variable Range Multipliers ......................................................... 82

Table 16-1
Table 16-2

Water Resources-Existing Generating Facilities ......................................... 94
Water Resources-Potential Generating Facilities ....................................... 95

Table B-1

General Planning Assumptions .......................................................................... 96

Table D-1
Table D-2
Table D-3
Table D-4
Table D-5
Table D-6
Table D-7
Table D-8
Table D-9
Table D-10
Table D-11
Table D-12
Table D-13
Table D-14
Table D-15
Table D-16
Table D-17
Table D-18
Table D-19
Table D-20
Table D-21
Table D-22
Table D-23
Table D-24
Table D-25
Table D-26
Table D-27
Table D-28
Table D-29
Table D-30

Residential Daily Load Profiles (kW) – January ........................................ 104
Residential Daily Load Profiles (kW) – February ..................................... 105
Residential Daily Load Profiles (kW) – March ........................................... 106
Residential Daily Load Profiles (kW) – April .............................................. 107
Residential Daily Load Profiles (kW) – May ............................................... 187
Residential Daily Load Profiles (kW) – June ............................................... 109
Residential Daily Load Profiles (kW) – July ................................................ 110
Residential Daily Load Profiles (kW) – August .......................................... 111
Residential Daily Load Profiles (kW) – September .................................. 112
Residential Daily Load Profiles (kW) – October ........................................ 113
Residential Daily Load Profiles (kW) – November................................... 114
Residential Daily Load Profiles (kW) – December ................................... 115
Commercial Daily Load Profiles (kW) – January ....................................... 116
Commercial Daily Load Profiles (kW) – February.................................... 117
Commercial Daily Load Profiles (kW) – March.......................................... 118
Commercial Daily Load Profiles (kW) – April ............................................ 119
Commercial Daily Load Profiles (kW) – May .............................................. 120
Commercial Daily Load Profiles (kW) – June ............................................. 121
Commercial Daily Load Profiles (kW) – July............................................... 122
Commercial Daily Load Profiles (kW) – August ........................................ 123
Commercial Daily Load Profiles (kW) – September ................................ 124
Commercial Daily Load Profiles (kW) – October ...................................... 125
Commercial Daily Load Profiles (kW) – November ................................. 126
Commercial Daily Load Profiles (kW) – December.................................. 127
Industrial Daily Load Profiles (kW) – January ............................................ 128
Industrial Daily Load Profiles (kW) – February ......................................... 129
Industrial Daily Load Profiles (kW) – March............................................... 130
Industrial Daily Load Profiles (kW) – April ................................................. 131
Industrial Daily Load Profiles (kW) – May ................................................... 132
Industrial Daily Load Profiles (kW) – June .................................................. 133

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 Attachment LS-1
2016 ERP
Table D-31
Table D-32
Table D-33
Table D-34
Table D-35
Table D-36

Industrial Daily Load Profiles (kW) – July .................................................... 134
Industrial Daily Load Profiles (kW) – August ............................................. 135
Industrial Daily Load Profiles (kW) – September ..................................... 136
Industrial Daily Load Profiles (kW) – October ........................................... 137
Industrial Daily Load Profiles (kW) – November ...................................... 138
Industrial Daily Load Profiles (kW) – December ....................................... 139

Table J-1
Table J-2
Table J-3
Table J-4
Table J-5
Table J-6
Table J-7
Table J-8
Table J-9

Annual Projected SO2 Emissions from Existing Resources (Tons) .... 147
Annual Projected SO2 Emissions from Generic Resources (Tons) ..... 148
Annual Projected CO2 Emissions from Existing Resources (Tons) .... 149
Annual Projected CO2 Emissions from Generic Resources (Tons)..... 150
Annual Projected NOx Emissions from Existing Resources (Tons) ... 151
Annual Projected NOx Emissions from Generic Resources (Tons) .... 152
Annual Projected PM Emissions from Existing Resources (Tons) ..... 153
Annual Projected PM Emissions from Generic Resources (Tons)...... 154
Annual Projected Mercury Emissions from Existing Resources
(Tons)......................................................................................................................... 155
Annual Projected Mercury Emissions from Generic Resources
(Tons)......................................................................................................................... 156

Table J-10

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2016 ERP
List of Figures
Page
Figure ES-1
Figure ES-2
Figure ES-3
Figure ES-4
Figure ES-5

ERP Process ........................................................................................................... ES-2
2013 ERP and 2016 ERP Peak and Energy Comparison ...................... ES-3
Load and Resources Summary ....................................................................... ES-4
Base-with-RES Plan and Alternative Plans - Deterministic 25 Year
PVRR (2016-2040) ............................................................................................. ES-6
Scenarios – Deterministic PVRRs (2016-2040) ...................................... ES-9

Figure 1-1

Black Hills Service Territory .................................................................................. 2

Figure 2-1
Figure 2-2

U.S. Natural Gas Production, 1990-2040 (trillion cubic feet per year) . 7
EIA’s Different Scenarios for Generation by Fuel .......................................... 8

Figure 3-1

2009-2015 Energy Efficiency Savings............................................................. 18

Figure 6-1
Figure 6-2

Monthly Regulating Reserve Requirement ................................................... 49
Calculated Wind and Solar ELCC ....................................................................... 52

Figure 7-1
Figure 7-2
Figure 7-3

Existing Black Hills Transmission System ..................................................... 54
Existing Black Hills Transfer Path Map........................................................... 58
Planned and Conceptual Black Hills Transmission System .................... 62

Figure 8-1
Figure 8-2
Figure 8-3

Base-with-RES and Alternative Plans-Deterministic 25 PVRR (20162040) .......................................................................................................................... 72
Base-with-RES Plan and Scenarios-PVRRs (2016-2040) ........................ 78
Risk Profiles-All Plans and Scenarios .............................................................. 84

Figure D-1
Figure D-2
Figure D-3
Figure D-4
Figure D-5
Figure D-6
Figure D-7
Figure D-8
Figure D-9
Figure D-10
Figure D-11
Figure D-12
Figure D-13
Figure D-14
Figure D-15
Figure D-16

Residential Daily Load Profiles – January .................................................... 104
Residential Daily Load Profiles – February ................................................. 105
Residential Daily Load Profiles – March ....................................................... 106
Residential Daily Load Profiles – April ......................................................... 107
Residential Daily Load Profiles – May ........................................................... 108
Residential Daily Load Profiles – June........................................................... 109
Residential Daily Load Profiles – July ............................................................ 110
Residential Daily Load Profiles – August ..................................................... 111
Residential Daily Load Profiles – September ............................................. 112
Residential Daily Load Profiles – October ................................................... 113
Residential Daily Load Profiles – November .............................................. 114
Residential Daily Load Profiles – December ............................................... 115
Commercial Daily Load Profiles – January .................................................. 116
Commercial Daily Load Profiles – February ............................................... 117
Commercial Daily Load Profiles – March ..................................................... 118
Commercial Daily Load Profiles – April ........................................................ 119

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2016 ERP
Figure D-17
Figure D-18
Figure D-19
Figure D-20
Figure D-21
Figure D-22
Figure D-23
Figure D-24
Figure D-25
Figure D-26
Figure D-27
Figure D-28
Figure D-29
Figure D-30
Figure D-31
Figure D-32
Figure D-33
Figure D-34
Figure D-35
Figure D-36

Commercial Daily Load Profiles – May ......................................................... 120
Commercial Daily Load Profiles – June ......................................................... 121
Commercial Daily Load Profiles – July .......................................................... 122
Commercial Daily Load Profiles – August .................................................... 123
Commercial Daily Load Profiles – September ............................................ 124
Commercial Daily Load Profiles – October .................................................. 125
Commercial Daily Load Profiles – November............................................. 126
Commercial Daily Load Profiles – December ............................................. 127
Industrial Daily Load Profiles – January....................................................... 128
Industrial Daily Load Profiles – February.................................................... 129
Industrial Daily Load Profiles – March.......................................................... 130
Industrial Daily Load Profiles – April ............................................................ 131
Industrial Daily Load Profiles – May .............................................................. 132
Industrial Daily Load Profiles – June ............................................................. 133
Industrial Daily Load Profiles – July............................................................... 134
Industrial Daily Load Profiles – August ........................................................ 135
Industrial Daily Load Profiles – September ................................................ 136
Industrial Daily Load Profiles – October ...................................................... 137
Industrial Daily Load Profiles – November ................................................. 138
Industrial Daily Load Profiles – December.................................................. 139

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2016 ERP
Summary of Compliance with the Electric Resource Plan Reporting
Requirements
(4 CODE OF COLORADO REGULATIONS (CCR) 723-3 - PART 3 RULES
REGULATING ELECTRIC UTILITIES 4 CSR 240-22.050 (11))
3600 Electric Resource Planning
Rule
Description
Location in Report
3603 Resource Plan Filing Requirements
3603 (a)
Plan required by October 31, 2011
This plan is being filed by
June 3, 2016 in
accordance with Decision
No. C15-1071.
3603 (b)
Motions for highly confidential
Motions filed. Section
information to be filed at the same
11.0.
time
3604 Contents of the Resource Plan
3604 (a)
Identification of planning period and Sections 3.1 and 3.2
resource acquisition period
3604 (b)
Forecast developed pursuant to rule
Section 4.0
3606
3604 (c)
Evaluation of existing resources
Section 5.1
pursuant to rule 3607
3604 (d)
Evaluation of transmission resources Section 7.0
pursuant to rule 3608
3604 (e)
Reserve margins and contingency
Section 3.3 – Reserve
plans pursuant to rule 3609
Margins. Section 9.0 –
Contingency Plan
3604 (f)
Assessment for need for additional
Section 8.0
resources pursuant to rule 3610
3604 (g)
Plan for acquiring needed resources
Section 8.10
pursuant to rule 3611 and emissions
3604 (h)
Water consumption
Section 16.0
3604 (i)
Proposed RFPs and model contracts
Appendices M, N, O
3604 (j)
Identification of confidential and
See motions filed. Section
highly confidential information
11.0
3604 (k)
Three alternate plans
Section 8.0
3604 (l)
Integration of intermittent renewable Section 6.0
energy resources
3606 Electric Energy and Demand Forecasts
3606 (a) (I)
Coincident summer and winter peak Section 4.5
demand and energy by jurisdiction
3606 (a) (II)
Coincident summer and winter peak Waiver requested for peak
demand and energy by customer
demand
class
3606 (a) (III)
Sales to other utilities
Section 4.8
3606 (a) (IV)
Intra-utility capacity and energy use
Section 4.8

xi

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2016 ERP
3606 (a) (V)
3606 (a) (VI)
3606 (b)
3606 (c) (I)

System losses

Waiver requested. Section
4.4
Appendix D
Section 4.0
Section 4.0, Appendix C

Typical day load patterns
Base, low and high forecasts
End-use, econometric or other
method
3606 (c) (II)
Data by individual customers
Not applicable
3606 (d)
Data comparison – five years of
Section 4.7
historical data; last ERP
3606 (e)
Description and justification
Section 4.0
3606 (f)
Graphs; data available electronically
Section 4.0, Appendix D
3607 Evaluation of Existing Resources
3607 (a) (I)
Name and location
Section 5.1
3607 (a) (II)
Capacity
Section 5.1
3607 (a) (III)
Operational data
Section 5.1
3607 (a) (IV)
In-service dates
Sections 5.1
3607 (a) (V)
Remaining useful lives
Section 5.1
3607 (a) (VI)
Purchases
Section 5.1
3607 (a) (VII)
Wheeling and coordination
Section 5.1
agreements
3607 (a) (VIII) Emissions data
Appendix J and
Confidential Schedule K-6,
Appendix K
3607 (a) (IX)
DSM
Section 3.7
3607 (b)
Coordination with other Colorado
Section 5.1.4
jurisdictional utilities
3608 Transmission Resources
3608 (a)
Report capabilities – 115 kV and
Section 7.3
above
3608 (b)
All transmission to be built during
Section 7.4
RAP
3608 (c) (I)
Length and location
Section 7.4
3608 (c) (II)
Estimated in-service date
Section 7.4
3608 (c) (III)
Injection capacity
Section 7.5
3608 (c) (IV)
Estimated costs
Section 7.4
3608 (c) (V)
Terminal points
Section 7.4
3608 (c) (VI)
Voltage and MW rating
Section 7.4
3608 (d)
Transmission costs and benefits
Appendix (RFP)
included in bid evaluation criteria
3608 (e)
Costs for transmission required for
Not Applicable
facilities not acquired through a bid
process
3609 Planning Reserve Margins and Contingency Plan
3609 (a)
Means of assessing reliability
Section 3.3

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2016 ERP
3609 (b)
3609 (c)
3610 (a)
3610 (b) (I)
3610 (b) (II)
3610 (c)

3611 (a) – (h)

3612 (a) – (f)

3616 (a) – (f)

Reserve margins for base, load and
Section 3.3, Appendix B
high forecast and associated risks
Contingency plans
Section 9.0
3610 Assessment of Need for Additional Resources
Determine need for additional
Section 8.0
resources
Additional renewable resources
Section 8.0
required to comply with RES
Additional DSM
Section 8.0
Consideration of carbon dioxide with Not applicable
new resources
Waiver requested.
3611 Utility Plan for Meeting Need
Competitive acquisition for new
Section 8.10
resources unless waiver or other
arrangements
3612 Independent Evaluator
Selection and approval of
Waiver Requested. IE
independent evaluator
approval has been
requested in the
Application.
3616 Request(s) for Proposals
RFP and model contracts included
Appendix M, N, O
with ERP filing

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 Attachment LS-1
2016 ERP
Executive Summary
Introduction
This Electric Resource Plan (“2016 ERP”) is being filed by Black Hills Colorado
Electric (“Black Hills” or “the Company”) in compliance with the Rules of the
Colorado Public Utilities Commission (“Commission”) and its ruling in Proceeding
No.(“Planning Period”) 15V-0622E that Black Hills file this ERP on or before June 3,
2016. It covers a Planning Period of twenty-five years from January 2016 through
December 2040. The Resource Acquisition Period (“RAP”) covers the seven-year
period January 2016 through December 2022.
The analysis conducted for this 2016 ERP used industry-accepted methods to
determine the capacity necessary for expected future load growth and to determine
the cost to add Eligible energy resources sufficient to comply with the Renewable
Energy Standard (“RES”) established by C.R.S. § 40-2-124 et seq. (“the RES Statute”)
and implemented by Commission Rules 4 CCR 723-3-3650 et seq. (“the RES Rules”).
Black Hills’ 2016 ERP was developed by progressing through five fundamental
planning steps. The first element of the planning process is to prepare a load
forecast and load and resource balance to quantify resource need over time. In the
second planning step, the Company developed different resource portfolios that
meet the projected resource needs based on varying key planning assumptions. In
the third step, Black Hills performed comparative cost and risk analysis among the
different resource portfolio alternatives. This cost and risk analysis informs the
selection of a preferred portfolio which is the fourth step. Fifth and finally, the
Company selects the associated resource action plan. Throughout this process, the
Company assessed the current planning environment to develop key planning
assumptions and identify planning uncertainties. A supplemental study was also
completed to support the derivation of specific modeling assumptions.
Figure ES-1 ERP Process
Key Planning Assumptions

Supplemental Study

Resource Needs Assessment
Resource Portfolio Development
Cost and Risk Analysis
Preferred Portfolio Selection
Action Plan
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Load Forecast
The load forecast completed for the 2016 ERP indicates that growth in the Black
Hills’ service territory over the Planning Period will be slow. In addition, the effects
of the Company’s 2016-2018 Demand Side Management Plan (“2016-2018 DSM
Plan”) will reduce future retail system peak demand (MW) and retail energy sales
(MWh). The Company is aware of some known load additions and load losses,
based on discussions with existing large-demand customers and these load
additions and losses were incorporated into the load forecast.
The Company used an econometric forecasting methodology to forecast peak
demand and energy for the 2016 ERP. Black Hills gathered and refined a variety of
different types of datasets including historical load, revenue, economic, and weather
data. This data was used to develop regression models for the monthly system-level
peak demand forecast and the major customer class energy forecasts. The final
system-level monthly peak demand was computed by adding large customer loads,
including their anticipated future load growth, and accounting for the effects of
demand side management programs (“DSM”) to the load forecast produced from the
econometric methods.
The final system-level major customer class energy forecasts were computed by
adding large customer loads, including their anticipated future load growth, losses,
and accounting for the effects of the 2016-2018 DSM Plan to the energy forecasts
calculated through the regression analysis. In addition, high and low load forecasts
were developed to allow the Company to assess risk associated with uncertainty
related to load growth.
The forecast completed by the Company for the 2016 ERP indicates that peak
demand growth will be lower than was forecasted in the 2013 ERP but that energy
consumption will be similar to what was indicated in the 2013 ERP load forecast.
Detailed information related to the load forecast is found in Section 4 of this report.
Figure ES-2
2013 ERP and 2016 ERP Peak and Energy Forecast Comparison
Forecasted Annual System Energy
Usage (GWh)

Forecasted Annual System Peak
Demand (MW)
600
400

2,250.0

200

1,750.0

2013 ERP

2016 ERP

2013 ERP

3

2016 ERP

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Using the results of the load forecast analysis the Company prepared a load and
resource balance that shows that Black Hills will have sufficient capacity resources
to serve customer electricity demand including a 15 percent reserve margin beyond
the 2016-2022 RAP. Figure ES-3 shows a summary of the Company’s existing
resources and the resources that are currently under construction compared to the
Company’s peak load forecast.
Figure ES-3
Load and Resources Summary
600

Peak Demand Plus 15%

500

MW

400
300
200

Existing Resources

100
0

Resource Portfolio Evaluation
Capacity expansion and production cost modeling were conducted using an array of
conventional and renewable resources to develop resource portfolios to meet the
resource needs of the Company during the RAP and throughout the Planning Period.
These methods also allow the Company to assess the costs and benefits of various
types of resources. Three alternative plans (Base-with-RES Plan, Alternative Plan1
and Alternative Plan 2) were constructed to describe the costs and benefits of the
new utility resources required to meet the utility’s needs during the Planning Period
and to meet the requirements of the Commission’s Rules. The Base-with-RES Plan
model incorporates into the future resource portfolio all of the additional renewable
energy resources that would be required to achieve the RES requirements during
the RAP and the Planning Period. The Alternative Plans represent the costs and
benefits associated with increasing amounts of renewable energy resources or
Section 123 resources. The Base-with-RES Plan adds 60 MW of wind resources in
2019, 30 MW of wind resources in 2026, and 60 MW of wind resources in 2038. The
optimal expansion plans for the Base-with-RES Plan, Alternative Plan 1, and the
Alternative Plan 2 plans are shown in Table ES-1.

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Table ES-1
Optimal Expansion Plans
(Table only shows years where resource additions are identified,
does not show seasonal firm market purchases)

Year
2019
2026
2032
2035
2038

Base-with-RES
60 MW Wind
30 MW Wind
(2) LMS100
60 MW Wind

Alternative
Plan 1
60 MW Wind
30 MW Wind
(2) LMS100
(2) 10 MW Solar
60 MW Wind

2039
2040

Alternative Plan 2
60 MW Wind
30 MW Wind
(2) LMS100
(2) 10 MW Solar
60 MW Wind
Sodium Sulfur
Battery 10 MW
(2) Sodium Sulfur
Battery 10 MW

The deterministic present values of revenue requirements (“PVRR”) for the Basewith-RES Plan, Alternative Plan 1 and the Alternative Plan 2 plans are shown in
Figure ES-4.

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Figure ES-4
Base-with-RES and Alternative Plans
Deterministic 25 Year PVRR (2016-2040)
25 Year PVRR
$2,200

$2,100

$2,059.8

$2,060.2

$2,065.0

Million $

$2,000

$1,900

$1,800

$1,700

When recommending the addition of Eligible energy resources, the Company must
consider the net retail rate impact of this plan under the RES Rules. Rule 3661(a)
provides that “the net retail rate impact of actions taken by an investor owned
qualifying retail utility (“QRU”) to comply with the renewable energy standard shall
not exceed two percent of the total electric bill annually for each customer of that
QRU.”1
To determine the net incremental cost or savings of Eligible energy resources, the
Company compared two scenarios to estimate the cost and benefits of each system
over the ten-year RES planning period (“RES Planning Period”). The first scenario is
a “RES plan” that reflects the utility’s plans and actions to acquire new Eligible
energy resources necessary to meet the RES. The second scenario is a “No-RES
plan” which reflects the utility’s resource plan that replaces the new Eligible energy
resources in the RES plan with new non-renewable resources reasonably available.
1

Rule 3661(a) require the Company to evaluate RES Compliance over defined period of time which
for Black Hills will be 10 years.

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Net incremental cost is determined over a ten-year RES Planning Period and is the
calculated difference between the RES and No-RES over that period. See Rule
3661(f).
The portfolios were compared through computer modeling so that the benefits
associated with the addition of the wind resources selected in the Base-with-RES
Plan could be captured. Those benefits are the avoided costs of fossil fuel expense,
purchased power expense, and variable O&M production expense.2
The RES/No-RES Comparison showed that the avoided costs of the 60 MW wind
resource in 2019, 30 MW wind resource in 2026, and 60 MW wind resource in 2038
exceeded the resource costs of the same renewable resources over the ERP Planning
Period and therefore complied with the two percent retail rate impact.
Over the ten-year RES compliance period (2018-2027) identified in the Company’s
2018-2021 RES Plan the modeling shows that the 60 MW wind resource would
provide approximately $74 million of avoided cost savings from 2019 through 2027.
The resource cost of the 60 MW of wind over the same time period, 2019 through
2027, is projected to be $76 million. Thus, the addition of 60 MW of wind energy in
2019 would result in a net incremental cost of approximately $2 million over the
ten-year RES Planning Period. Over the Planning Period (2016-2040), the modeling
shows that the 60 MW wind resource in 2019 would provide approximately $305
million of avoided cost savings from 2019 through 2040. The resource cost of the
60 MW of wind resource over the same time period, 2019 through 2040, is
projected to be $235.6 million. Thus, the addition of 60 MW of wind energy in 2019
would result in a net incremental savings of approximately $69.3 million over the
25-year 2016 ERP Planning Period.
The Base-with-RES Plan also included a 30 MW wind resource in 2026 and a 60 MW
wind resource in 2038 for compliance with the RES. Over the Planning Period, the
modeling shows that the 30 MW wind resource in 2026 would provide
approximately $128 million of avoided cost savings from 2026 through 2040. The
resource cost of the 30 MW of wind resource in 2026 over the same time period,
2026 through 2040, is projected to be $157 million. Thus, the addition of 30 MW of
wind energy in 2026 would result in a net incremental cost of approximately $29
million over the time period 2026 through 2040. For the 60 MW wind resource in
2038, the modeling forecasts avoided cost savings of approximately $70 million
from 2038 through 2040. The resource cost of the 60 MW of wind resource in 2038
over the same time period, 2038 through 2040, is projected to be $63 million. Thus,
the addition of 60 MW of wind energy in 2038 would result in a net incremental
savings of approximately $7 million over the time period 2038 through 2040.
Schedules A-1, A-2, and A-3 in Appendix A shows the avoided cost calculations
associated with the proposed Eligible energy resources in 2019, 2026, and 2038
2

When the wind turbines are generating, the Company does not have to run a conventional unit or
purchase capacity and these are the avoided costs of the wind.

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Over the 25-year 2016 ERP Planning Period, the retail rate impact of the addition of
the 60 MW wind resource in 2019, 30 MW wind resource in 2026, and the 60 MW
wind resource in 2038 is shown in Schedule A-4 in Appendix A. The Company
forecasts that the RESA will have a positive balance by 2020 if the Company meets
all of its existing and authorized REC obligations and the Commission approves the
Company’s proposed 2018-2021 solar programs and the acquisition of up to 60 MW
of Eligible energy resources in 2019 through a solicitation process. Schedule A-4
also shows that over the course of the Planning Period the Company will have
sufficient RESA funds to acquire Eligible energy resources in 2026 and 2038.
These wind resource additions would not increase the RESA negative balance. The
Base-with-RES Plan that adds 60 MW of new Eligible energy resources in 2019, 30
MW new Eligible energy resources in 2026, and 60 MW of new Eligible energy
resources in 2038 complies with the RES.
Scenario Analysis
Scenario analysis was conducted using the Capacity Expansion module to derive
optimal resource expansion plans under potential future conditions. The Base-withRES plan represents expected future conditions while scenario analysis is useful for
considering uncertainties that may impact decision-making in today’s world based
on a variety of potential future conditions. The scenarios include variations in
inputs representing the significant sources of portfolio cost variability and risk. The
scenarios evaluated by the Company include an Environmental, High Load, Low
Load, High Gas Price, Low Gas Price, and NYMEX Gas Price scenario. The
deterministic PVRRs associated with these scenarios are shown in Figure ES-5.

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Figure ES-5
Scenarios – Deterministic PVRRs (2016-2040)
25 Year PVRR
$2,200
2,132.1

$2,100
2,059.8

2,059.8

2,062.9

2,059.8

2,053.2

2,060.2

2,065.0

2,009.9

Million $

$2,000

$1,900

$1,800

$1,700

Utilities must plan for future customer needs for electricity in an environment of
significant uncertainty. Thus, the analysis conducted for this 2016 ERP examined
resource needs under a variety of possible future conditions. Stochastic analysis
and risk profile compilation were among the risk techniques examined. A wide
range of uncertainties in demand, electric prices, fuel prices, supply and costs was
examined.
Preferred Plan
As a result of its evaluations, Black Hills’ Preferred Plan does not include the
addition of any new capacity resources during the RAP. However, based on the bids
that were received in the Company’s 2014 All-Source Solicitation, the Preferred Plan
does include the addition of 60 MW of wind resources as energy resources in 2019.
The Company used bid data submitted in the 2014 All-Source Solicitation as inputs
in its modeling and based on the bid prices, the forecasted cost of natural gas, and
the forecasted electric prices the model identified a 60 MW wind resource in 2019
as an economical option for energy.

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Without additional Eligible energy resources, the Company will not be able to meet
the 30 percent RES requirement in 2020. Black Hills will be able to provide all of the
retail and wholesale distributed generation (“DG”) required by the RES Statute and
RES Rules through 2025. Retail DG renewable energy credits (“REC”s) will be
provided by the resources in the Company’s solar programs and wholesale DG RECs
will be provided by the 29 MW Busch Ranch Wind Project located in Huerfano
County. The Peak View Wind Project and carry-forward RECs will provide sufficient
RECs for compliance with the RES through 2019. However, in 2020, when the RES
requirement increases to 30 percent of retail sales Black Hills’ existing Eligible
energy resources will not be able to generate enough RECs to meet this standard.
Beyond the RAP, Black Hills’ load and resource balance shows a small capacity
deficit in 2029, growing by a few megawatts each year until the beginning of 2032
when the Company’s contract for 200 MW of generation expires. The modeling for
the 2016 ERP identified an optimal portfolio to replace this expiring contract,
however, based on current Colorado Commission rules, the Company will be
required to complete at least two electric resource plans prior to considering the
appropriate replacement capacity for this contract.
Black Hills’ Action Plan to implement the Company’s Preferred Plan recommends
that the Company engage in a Phase II competitive solicitation to acquire up to 60
MW of Eligible energy resources by 2019. This solicitation will allow the Company
to determine if Eligible energy resources can be acquired at a cost that will provide
savings for customers and generate sufficient RECs such that Black Hills will comply
with the RES through 2025.
RFPs and Model Contracts
The Intermittent model Request for Proposal (“RFP”) and model contracts required
as part of this 2016 ERP are provided in Appendix M, N and O. (Waiver for other
contracts and RFPs)

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1.0

Introduction

The Company is filing its 2016 ERP pursuant to the Electric Resource Planning
Rules, 4 CCR 723-3-3600 et seq. (“ERP Rules”). The resource planning process in
Colorado typically involves two phases. The first step, otherwise known as Phase I,
includes development of a load forecast, evaluation of the utility’s current resources
(including transmission), determination of need for additional resources, and the
utility’s proposed plan for acquiring the resources to meet the identified need.
The Company’s 2016 ERP covers a seven-year RAP and a 25-year Planning Period,
conforming to the ERP Rules. Prudent utility practices were employed in the
preparation of the 2016 ERP. The Company completed a load forecast and load and
resource balance to determine resource need, capacity expansion modeling to
determine the optimum resource portfolio, production cost modeling to evaluate
cost, and stochastic analysis to evaluate the risk of each portfolio under a range of
varying conditions.
The ERP Rules require that the Company include the descriptions of at least three
alternative plans that can be used to represent the cost and benefits of the resources
necessary to meet the resource need in its ERP. One of the three plans must
represent a baseline case that complies with the RES, as well as with the demandside resource requirements under C.R.S. § 40-3.2-104. The other two plans must
include alternative combinations of resources that meet the same resource needs as
the baseline case but that include proportionately more renewable energy
resources, demand-side resources, or Section 123 resources. Following the
Commission’s Phase I decision, the utility proceeds to implement that decision.
1.0 Background
Black Hills provides electric service to several communities in Colorado, serving
more than 95,000 customers in 21 communities. In 2015, the Company sold more
than 1,959 GWh. In June 2012, the system peak was set at 400 MW, the highest load
level recorded by the Company to date. The largest communities served include
Pueblo, Cañon City, and Rocky Ford. The Company’s generating stations are located
in Pueblo and Rocky Ford. The Company’s service territory, which encompasses
parts of Crowley, Custer, El Paso, Fremont, Otero, Pueblo, and Teller counties, is
shown in Figure 1-1.

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Figure 1-1
Black Hills Service Territory

The Company currently meets electric demand and energy needs using its own
generation resources, through purchased power contracts, and via purchases from
the open market. The Company’s portfolio includes:
•
•

•
•

Pueblo Airport Generating Station (“PAGS”) natural gas-fired combustion
turbines with a total capacity of 180 MW;
Three diesel stations with a total net capacity of 30 MW;
o Rocky Ford diesels units are located in Rocky Ford, CO providing 10
MW of net capacity;
o Pueblo diesels, with a total of 10 MW of net capacity located in Pueblo,
o Airport diesels, with a total of 10 MW of net capacity located in
Pueblo;
Black Hills Colorado IPP PPA expiring at the end of 2031, which provides 200
MW of power from Black Hills Colorado IPP’s combined-cycle turbines;
An agreement with the Western Area Power Administration (“WAPA”)
between the western and eastern transmission grids to facilitate the delivery
of 5 MW of capacity and energy, extending through September 30, 2024;

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•
•

The Busch Ranch Wind Project3 in Huerfano County, which entered
commercial operation in 2012, consisting of sixteen 1.8 MW wind turbines;
A short-term PPA expiring December 31, 2016, whereby Black Hills
purchases 50 MW of firm energy.

The Company currently has two generating facilities under-construction:
•
•

A 40 MW natural gas-fired combustion turbine at PAGS that was approved in
Proceeding No. 13A-0445E; and
Peak View Wind Project, a 60 MW wind resource that will be located in
Huerfano County and Las Animas County, Colorado. The project was
approved in Proceeding No. 15A-0502E.

The Company’s power delivery system consists of approximately 585 miles of
transmission lines and 3,100 miles of distribution lines.
1.1 Overview of 2013 ERP
The Company last filed an ERP with the Colorado Public Utilities Commission on
April 30, 2013. The Company’s Preferred Plan in the 2013 ERP relied on a two-year
power purchase agreement (“PPA”) beginning in 2015, a 40 MW LM6000 as
replacement capacity for the Clark Station, a 74 MW GE 7EA in 2017, and seasonal
firm market power in several years. Based on the Preferred Plan recommendations
Black Hills filed concurrently with the 2013 ERP:
•
•
•

A request for a Certificate of Public Convenience and Necessity (“CPCN”) to
construct the 40 MW LM6000;
A CPCN to retire Pueblo 5 and 6 at the end of 2013; and
An agreement with Cargill for a two year (2015–2016) 50 MW purchase
power agreement.

On December 17, 2013, in Decision C14-0007, the Commission approved the
Company’s 2013 ERP and the 2013-2014 RES Compliance Plan with modifications.
In the same decision, the Commission granted the Company a CPCN for the LM6000
and a CPCN for the retirement of the Pueblo Units 5 and 6. In addition, the
Commission authorized Black Hills to conduct a Phase II All-Source Solicitation for
utility resources to meet the 42 MW resource4 need in 2017 and for up to 60 MW of
Eligible energy resources in 2017 or 2018.
3

The Company owns half of the 29 MW and purchases the energy produced by the remaining
turbines under a PPA that has a 25-year term.
4
One of the stipulations of the 2013 ERP Settlement Agreement included revising the Company’s
load forecast to reflect the anticipated demand and energy savings included in the Commissionapproved 2012 DSM Settlement Agreement in Proceeding No. 12A-100E. In addition, the Company
agreed to recognize two interruptible customer contracts as resources. These revisions to the
Company’s load forecast and resources resulted in a lower resource need of 42 MW in 2017, 29 MW
in 2018, and 33 MW in 2019 effectively eliminating the need for the 74 MW GE 7EA in 2017.

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As a result of Decision C14-0007, the following significant resource changes have
occurred or have been agreed to:
•
•

•

Pueblo Units 5 and 6 were retired on December 31, 2013;
The Company issued an All-Source Solicitation for up to 60 MW of Eligible
energy resources and a seasonal capacity need in 2017, 2018 and 2019
(approximately 42 MW, 29 MW and 33 MW respectively) on May 1, 2014;
and
The 40 MW LM6000 at PAGS is under construction and is expected to begin
commercial operation on December 31, 2016.

In Commission Decision C15-1182 the Commission approved a CPCN for the 60 MW
Peak View Wind Resource.

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2

Planning Environment

The environment in which utilities must plan their future resources continues to
evolve. Significant issues and concerns facing the Company during this 2016 ERP
planning cycle include:
•
•
•
•
•
•
•

Colorado Renewable Energy Standard;
Plans and programs for demand-side management;
Economic conditions globally and in Colorado;
Natural gas supply and pricing over the long-term;
Environmental regulation;
Federal production tax incentives for wind and solar project;
Power supply markets in the state and regionally.

Each of these topics is described in this section of the 2016 ERP.
2.0 Colorado Renewable Energy Standard
The RES Rules require the Company to provide specific percentages of renewable
energy and/or recycled energy according to the following schedule:
•
•

20% of its retail electricity sales in Colorado for the years 2015-2019; and
30% of its retail electricity sales in Colorado for the year 2020 and for each
following year.

Black Hills must have a certain percentage of its retail sales produced by either
wholesale DG or retail DG, regardless of technology type, according to the following
schedule:
•
•
•

1.75% of its retail electricity sales in 2015 and 2016;
2% of its retail electricity sales in 2017-2019, and
3% of its retail electricity sales in 2020 and each following year.

At least one-half of the DG requirement must be generated by retail DG systems
located on-site at customers’ facilities or premises.
2.1 Demand-Side Management
The Company will comply with the mandate of C.R.S. § 40-3.2-104 to reduce its
retail system peak demand and retail energy sales through implementation of DSM
plans. In that statute, DSM is defined as one of, or any combination of, the following
measures: energy efficiency, conservation, load management, and demand response
programs. The required goals to be achieved by 2018 are a reduction in retail
system peak demand (MW) equal to 5 percent of the 2006 level (304 MW without

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losses) and a reduction of retail energy sales (MWh) equal to 5 percent of the 2006
level (1,839 GWh without losses).
2.2 Economic Conditions
Since the Company filed its 2013 ERP, economies around the globe and the
customers in the Company’s service territory, have continued to experience a weak
recovery from the recession that began in late 2007. According to the “Economic
and Revenue Forecast” published by the Colorado Legislative Council Staff
Economics Section, activity in Colorado and the nation is expected to continue to
expand in 2016 and 2017, though at a slower pace than in the prior two years. The
weak global economy constrained domestic growth in the second half of 2015. Low
commodity prices and a strong dollar have hurt the agriculture, natural resources,
and manufacturing sectors, dampening momentum in other sectors of the economy.
5

The national unemployment rate was 4.9 percent in February 2016, the lowest rate
since February 2008 and Colorado businesses also continued to add jobs in 2015.
However, economic activity in the southern mountains region of Colorado, which
consists of Pueblo, Fremont, Custer, Huerfano, and Las Animas counties, was tepid
in 2015. Overall, regional employment was flat in 2015 over the prior year. The
area unemployment rate fell due in large part to a contracting labor force. Retail
sales rose slightly, and construction activity remained at low levels. The recession
and slow recovery have dampened the current and projected demand for electricity.
2.3 Natural Gas Supply Trends Over the Long-Term
Shale gas technology increased the amount of recoverable North American gas supply,
enabling producers to access vast supplies of shale gas. New technology that provided
lower cost access to shale gas caused total U.S. production to grow substantially from
2005 to 2015. Figure 2-1 below shows the historical and future production forecasts
for shale gas relative to other production methods.

5

“Focus Colorado: Economic and Revenue Forecast Colorado Legislative Council Staff Economics
Section,” Colorado General Assembly Legislative Council Staff,
https://www.colorado.gov/pacific/sites/default/files/MarchForecast.pdf.

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Figure 2-1
U.S. Natural Gas Production, 1990-2040
(trillion cubic feet per year)

As a result of EPA regulation to limit emissions from stationary sources, many
generation owners will close old, inefficient coal plants as opposed to investing new
capital to comply with environmental regulation. Natural gas is the lowest-cost
resource that will comply with these environmental regulations, while offering
operational flexibility, large scale capacity, and grid stability. Therefore, most
electric generation forecasts reflect continued closure of coal plants and new
capacity additions in gas generation and some in renewable energy. Over the next
few years, natural gas generation is expected to decrease as a result of generation
owners taking advantage of the production tax credit and investment tax credit
available to renewable generation investments. However, that trend is expected to
reverse and natural gas should overtake coal as the leading generation fuel by the
mid- to late-2020s. The figure below illustrates the growth in natural gas electric
generation forecasted by the U.S. Department of Energy’s Energy Information
Administration (“EIA”), published in its 2016 Annual Energy Outlook – Early
Release (“AEO2016 – Early Release”) for its reference case including
implementation of the Clean Power Plan and a case without implementation.
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Figure 2-2 – EIA's Different Scenarios for Generation by Fuel6

The AEO2016 Early Release compares electric generation capacity both with and
without implementation of the Clean Power Plan. Implementation of the Clean
Power Plan is likely to result in natural gas further replacing coal generation as a
means of compliance.
2.4

Environmental Regulation

The Environmental Protection Agency (“EPA”) has been very active since 2009 with
regard to regulations for utility-scale power plants, particularly coal-fired power
plants. Because the Company no longer has any coal-fired generation as of January
1, 2014, it will not be impacted by the Regional Haze Rule. Similarly, since the
Company does not own any oil-fired generating units and its coal-fired generation
was retired at the end of 2013, it is not affected by the EPA’s Mercury and Air Toxics
Standard (“MACT”), which was finalized on February 16, 2012. Therefore, the focus
of this section on environmental regulation is greenhouse gases.

6

Energy Information Administration, Annual Energy Outlook 2015 With Projections to 2040,
DOE/EIA-0383(2015), April 2015, 28.

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2.4.1 Greenhouse Gas Regulation
Since January 2, 2011, the EPA has been regulating greenhouse gas (“GHG”)
emissions from the largest stationary sources through the Prevention of Significant
Deterioration (“PSD”) and Title V Operating Permit Programs. GHGs are comprised
of six gases: carbon dioxide, nitrous oxide, methane, hydrofluorocarbons,
perfluorocarbons, and sulfur hexafluoride.7 Under the Greenhouse Gas Tailoring
Rule, these gases are regulated as pollutants under the major source permitting
requirements.
New or modified electric generation resources with GHG emissions above certain
levels must go through a technology-based, source-by-source review process to
demonstrate that they will use the Best Available Control Technology (“BACT”) to
control GHG emissions. This process is required before the resource can receive a
Clean Air Act permit.
On April 13, 2012, the EPA proposed GHG New Source Performance Standards for
new steam electric power plants. EPA expects that new combined cycle natural gasfired generation will meet the new standard for GHG and that new coal-fired plants
will not.
As of the end of 2011, the Company installed two utility-owned natural gas-fired
units at PAGS and contracted for power purchased from two 2x1 combined cycle
natural gas-fired units also located at PAGS.
2.4.2 Environmental Protection Agency’s Clean Power Plan (“CPP”)
The EPA proposed the Clean Power Plan (“CPP”) on June 2, 2014. The final CPP rule
was issued on August 3, 2015, published in the Federal Register on October 23,
2015 and became effective on December 22, 2015. The final CPP set specific CO2
emission reduction goals for State’s and a national CO2 emission reduction goal that
equates to a 32 percent reduction by 2030, using 2005 emission levels as a baseline.
The Clean Power Plan provides specific rate and mass goals for each State. The rule
contains guidelines for the development, submittal, and implementation of State
Implementation Plans (“SIP”). In addition, the EPA is drafting Federal
Implementation Plans (“FIP”) using rate and mass compliance approaches. States
must submit their SIP or request a two-year extension by September 6, 2016. EPA
has specified detailed actions which must be undertaken in order for a State to
qualify for an extension. If an extension is granted, States will have until September

Clean Air Act Permitting for Greenhouse Gases, U.S. Environmental Protection Agency,
http://www.epa.gov/nsr/ghgpermitting.html.
7

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6, 2018 to complete their SIP. If a state chooses to not complete a SIP it will be
subject to the requirements of EPA’S FIP.
The compliance period for the rule begins in 2022 with partial compliance
requirements during the 2022-2029 interim period and full compliance by 2030.
The rule establishes three interim “steps” or time periods (2022-2024, 2025-2027,
2028-2029), which forms a “glide path” in which emissions performance increases
as the final compliance date approaches. States may elect to set their own goals for
the three interim periods as long as they meet their “interim period” goals.
States can also choose to participate in the Clean Energy Incentive Program
(“CEIP”): a program which seeks to reward early investments in renewable energy
and energy efficiency measures that generate carbon-free electricity or reduce enduse energy demand during 2020 and/or 2021. However, projects must meet certain
criteria and construction timing.
On February 9, 2016, the U.S. Supreme Court issued an emergency order “staying”
the CPP while litigation proceeds on the legal merits of the rule. No deadlines or
other compliance obligations may be enforced while the stay is in effect. The US D.C.
Circuit Court of Appeals has been tasked to review the case and is scheduled to
begin hearing oral arguments on June 2, 2016. The D.C. court is expected to make a
final ruling on the case at the end of 2016 or the beginning of 2017. Depending on
the circuit court’s decision, either the CPP opponents or EPA could petition the
Supreme Court to hear the case which may affect State Implementation Plan
submittal dates and the initial compliance date of 2022.
2.5 Federal Tax Incentives for Wind and Solar Projects
The Energy Policy Act of 1992 originally enacted the Production Tax Credit which
has been the primary production based incentive for wind energy and has been
essential to the industry’s research and development. The original PTC legislation
expired in July 1999 but has been expanded and extended several times through
many different laws. In late 2015, Congress provided a 5-year PTC with provisions
to phase out the PTC by 2020 at a rate of 20 percent per year. The Consolidated
Appropriations Act, 2016 extended the expiration date for this tax credit to
December 31, 2019. The value of the PTC is based on the year that construction
begins rather than when the facility begins production. The phase-down begins for
wind projects commencing construction after December 31, 2016. The Act
extended the tax credit for other eligible renewable energy technologies
commencing construction through December 31, 2016. The Act applies retroactively
to January 1, 2015.
Legislation also extended the Solar Investment Tax Credit (“ITC”). This legislation,
which was signed into law on December 18, 2015, extends the 30 percent ITC for
both residential and commercial projects through the end of 2019. Previous to the
2015 extension, the 30 percent ITC was available to such facilities placed in service
on or before December 31, 2016. Changing from a deadline based solely on placed
10

 Attachment LS-1
2016 ERP
in-service to one focused on commencement of construction was intended to
provide facility developers with greater certainty. Like the PTC legislation,
provisions to lower the credits over time were included in the bill. The new
legislation decreases the credit to 26 percent for projects that begin construction in
2020, and 22 percent for projects that begin construction in 2021 before dropping
permanently to ten percent for commercial projects and zero percent for residential
projects. Projects must be placed in service before the end of 2023 to qualify for
applicable credit based on the year construction began. Projects placed into service
after 2023 will only qualify for the ten percent ITC.
2.6 The Power Supply Market
The availability and price of power through the economy and spot markets is an
important factor in how the Company operates and plans its generation. These
transactions yield economic efficiency by assuring that resources with the lowest
operating cost are serving demand in a region and by providing reliability benefits
that arise from a larger portfolio of resources. Electric prices in the WECC region
have been low for the past several months and low natural gas prices, adequate
capacity supplies and moderate load growth will likely keep prices at a low level
going forward. Some tightening of these markets is assumed through the Planning
Period and in the later years of the RAP.
Electricity markets are also faced with demand uncertainties driven by weather,
overall economic conditions, and resource supply availability. WECC publishes an
annual assessment of power supply that tracks the status of new resource additions
in the WECC region. The latest assessment, published in September 2014, indicates
that even when including only existing and under-construction units, WECC as a
whole, has ample resources through 2024, and the WECC subregion in which Black
Hills operates, the Rocky Mountain Reserve Group, has a surplus of capacity through
2021.

11

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3 Assumptions
Many data assumptions were required to complete the load forecast and modeling
for the 2016 ERP. Key assumptions described in the following paragraphs were
used in the Base-with-RES Plan, Alternative Plan 1 and Alternative Plan 2 (plans and
scenarios are described in Section 8 Future Resource Analysis and Selection).
Selected assumptions were varied in the Environmental, Low Load, High Load, High
Gas, Low Gas, and NYMEX Gas Price scenarios, such as the price of natural gas or
load growth, to test the risk associated with each specific assumption. Assumptions
must be made for the Planning Period, RAP, planning reserves, financial parameters
and DSM impact. In addition, fuel price forecasts, market price forecasts, and
emissions cost assumptions must be input into the model for all types of generic
resources as well as for existing resources.
2.1 Planning Period
The ERP Rules allow utilities to select a planning period of between twenty years
and forty years. The Company selected twenty-five years for this 2016 ERP which
covers the period 2016 through 2040. Twenty-five years was selected to allow an
adequate horizon to evaluate conventional and renewable alternatives relative to
the lives of those alternatives. Black Hills may use a longer-term planning horizon
in a future resource plan when it needs to consider a baseload resource.
2.2 Resource Acquisition Period
A seven-year RAP is proposed for this 2016 ERP. This covers the period 2016-2022.
The Company chose a seven-year RAP because that period complies with the rule
and includes the years that Black Hills has identified a need for additional Eligible
energy resources to comply with the RES. In addition, Commission Rule 3603(a)
requires that utilities file resource plans every four years. The Company will file its
next ERP on or before October 31, 2019. The RAP provides adequate time for the
acquisition of the necessary resources to meet our customers’ resource needs and
the RES through the RES Planning Period.
2.3 Planning Reserve Margin
Planning reserve is the amount of capacity that each electric utility must hold in
reserve above its annual peak load requirements. A planning reserve margin is a
percentage applied to the expected peak load to determine the minimum additional
capacity that an electric utility should plan for to ensure that it will meet its peak
load obligations in the event of an unforeseen loss of generating resources, extreme
weather, or other unexpected conditions.
For purposes of the 2016 ERP, the Company has continued to use a planning reserve
margin of 15 percent that was approved by the Commission in the Company’s 2013
12

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ERP proceeding (Proceeding No. 13A-0445E).8 The Company believes a 15 percent
reserve margin is appropriate. The Company’s peak requirement is expected to be
approximately 400 MW in 2016 and its largest single hazard is 100 MW. As a
member of the Rocky Mountain Reserve Group (“RMRG”), the Company has the
ability to rely on the RMRG for up to two hours after a forced outage of any of its
generating units. Thereafter, the Company will be required to replace the lost
capacity. According to the WECC 2014 Power Supply Assessment the Rocky
Mountain area is projected to have a surplus of capacity available in the market
through 2021.
The RMRG has adopted the WECC’s Standard BAL-002-WECC-2 for Contingency
Reserves and the group, as a whole, meets this criteria. This standard includes
reliability performance criteria such as minimum contingency reserves, types and
levels of reserves (i.e. spinning reserves), methods for measurement and
documentation of contingency reserves and compliance criteria. The entire WECC
standard is included in Appendix B of this report.
The 15 percent reserve margin was assumed for each of the base, high, and low load
forecasts.
2.4 Fuel and Market Prices
The fuel price assumptions used in the 2016 ERP were based on the ABB WECC
2015 Fall Reference Case. This is a confidential, proprietary product which can be
purchased from ABB. In order to protect ABB from public disclosure of its
proprietary product, the details of the ABB fuel price forecasts are set forth in the
Confidential Schedule K’s.
2.4.1 Natural Gas Prices
The Company used the natural gas price forecast for Henry Hub from ABB’s WECC
2015 Fall Reference Case for both existing and future natural gas-fired resources.
Basis differential and transportation costs were added to ABB’s Henry Hub forecast
to reflect the delivered price of natural gas. Table 3-1 below shows the price ranges
of Henry Hub natural gas prices from 2016 through 2040 for the Base Plan and
scenarios completed for the 2016 ERP. The natural gas price forecasts used in the
modeling are included in Confidential Schedule K-1, Appendix K.

8 In accordance with the stipulation in Proceeding No. 08A-346E, the Company undertook a study to
determine a planning reserve margin to use in its analysis in its 2013 ERP. The study was provided
in Volume II of the 2013 ERP filing. The key finding of that report was that the appropriate reserve
margin to use for planning purposes was between 20 and 30 percent. This level was determined on a
probabilistic basis with a target loss-of-load hours of no higher than one day in ten years or 2.4 hours
per year.

13

 Attachment LS-1
2016 ERP
Table 3-1
Average Annual Henry Hub Gas Price ($/MMBtu)
Scenario
Base
High Gas
Low Gas
NYMEX Price
CPP

2016
$2.94
$3.47
$2.38
$2.55
$2.90

2040
$13.43
$19.90
$ 7.52
$13.43
$13.77

The High Gas price forecast, Low Gas price forecast and CO2 gas price forecast were
all developed by ABB. ABB describes its High Gas Price forecast as a subjective view
of the 90th percentile of the natural gas price probability distribution. This
subjective view includes more stringent and costly regulation, significant bands or
moratoria on hydraulic fracturing, small or economically recoverable resources then
current median estimates, and higher cost of production then current estimates for
either geological or technical factors. ABB’s Low Gas Price forecast is described as a
subjective view of the 10th percentile of the natural gas price probability
distribution. This subjective view includes less stringent, more streamlined and less
costly regulation, larger economically recoverable resources, and lower cost of
production than current estimates. The CO2 scenario used ABB’s best estimation of
EPA’s building blocks to determine the contributions from retirements, re-dispatch
of coal to gas, renewable capacity additions, and energy efficiency improvements.
ABB found that these factors combined to lower gas demand from the power sector
beginning in 2024. Small increases in power sector gas demand were seen from
2016-2023 as a result of targeted coal retirements. Beginning in 2025 lower gas
consumption from the power sector compared with the Base Reference Case is
offset by approximately 75 percent by increased consumption from industry and
pipeline exports, and to a lesser degree LNG exports.
The NYMEX Price forecast was developed by Black Hills and is comprised of the
NYMEX natural gas price forecast and CIG Basis forecast for January 1, 2016 through
December 31, 2021 that were published as of the close of trading on December 29,
2015. The Company used ABB’s Base price forecast for the remainder of the
forecast, values for 2022 through 2040.
As the Company has moved away from coal-fired generation, its fleet is fueled
almost entirely by natural gas. On July 1, 2011, the Company filed a gas mitigation
plan (Proceeding No. 11A-580E) that includes a hedging plan to protect customers
against natural gas price spikes. The Commission approved the plan in Decision No.
C11-1132. The plan was reviewed, changes (if any) were filed, and the plan was
executed each year from 2012-2015. Early in 2016, as a result of the Company’s
Cost of Service Gas initiative, the plan was suspended until further notice. The

14

 Attachment LS-1
2016 ERP
Company is renewing its hedging plan in light of the Commission’s rejection of the
Cost of Service Gas initiative.
2.4.2 Oil Prices
The oil price forecast from ABB’s WECC 2015 Fall Reference Case for diesel was
used for the oil price forecast and is shown in Confidential Schedule K-2, Appendix
K.
2.4.3 Economy Energy Prices
Economy energy is energy (sold without capacity) that may be available in the
market from time-to-time and which is available for prices that are lower than the
incremental cost of a utility’s own resources. Economy energy is not firm energy
and, therefore, it is only available if a utility has adequate capacity to support its
load requirements. The selling party may recall an economy energy transaction at
any time. Thus, the buying party must maintain sufficient contingency reserve to
replace recalled supply.
The model was allowed to purchase up to 150 MW of economy energy every hour.
However, the pricing and the capacity allowed to be purchased varied depending on
the month of the year. Table 3-2 shows the market area used and the amount of
economy energy allowed to be purchased each month from each market area. The
market area price forecasts, included in Confidential Schedule K-8 through K-15,
Appendix K, are based on the ABB Fall 2015 Reference Case.
Table 3-2
Economy Energy Purchase
Capacity Allowed by Market Area (MW)
Month
CO-East
Palo Verde
Total
January
100
50
150
February
100
50
150
March
100
50
150
April
100
50
150
May
100
50
150
June
50
100
150
July
50
100
150
August
50
100
150
September
100
50
150
October
100
50
150
November
100
50
150
December
100
50
150

15

 Attachment LS-1
2016 ERP
2.4.4 Seasonal Firm Market Purchase Prices
The Company used the ABB Fall 2015 Reference Case energy price forecast for the
Palo Verde, Arizona (“PV”) market area plus a 20 percent premium and
transmission adder, as a proxy for seasonal firm market purchases. Seasonal firm
market purchases were assumed to be 25 MW blocks up to 75 MW through 2021
then adjusted to 25 MW blocks up to 50 MW through the remainder of the Planning
Period9. In establishing these assumptions, the Company considered both the
excess capacity in the region and capacity import limitations specific to the
Company’s system. Seasonal firm market purchases were assumed for 16 hours per
day six days a week for the summer months. The base Palo Verde, Arizona price
forecast used is included in Confidential Schedule K-4 through K-7, Appendix K.
2.5 Financial Parameters
Financial assumptions were used to develop incremental financial statements for
the Company. Cost of debt and equity, return on rate base, and interest rate
assumptions are necessary for the model to calculate the total system PVRR.
Table 3-3 presents the financial parameters used for the 2016 ERP evaluation,
including cost of debt, cost of equity, weighted-average cost of capital, income tax
rate, rate of escalation (same as inflation rate), capital structure, property tax rate,
and fixed charge rates. Tax lives of 20 years were used for combined cycle and
peaking technology. A 5-year tax life was used for solar, wind, and batteries.
The Company used a 5.29 percent cost of debt to reflect anticipated interest rates
when resources may be added in future years and a 9.83 percent return on equity
value, both of these values were approved by the Commission in the Company’s
most recent approved rate case (Proceeding No. 14AL-0393E).

9 The Company reduced the assumption for seasonal firm capacity purchases in 2022 based on data
from the WECC 2014 Power Supply Assessment. This study shows a reserve margin deficit in the
RMRG and SRSG beginning in 2022 and excess capacity in the NWPP beyond 2022. Therefore the
Company assumed that the availability of seasonal firm capacity from 2022 through the end of the
Planning Period will be reduced.

16

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2016 ERP
Table 3-3
Financial Parameters
Component
Cost of Debt
Equity
Weighted-Average Cost of Capital (WACC After-tax)
Weighted-Average Cost of Capital (WACC Before-tax)
Income Tax Rate
Rate of Escalation
Capital Structure
Equity
Debt
Property Tax Rate
20-year fixed charge rate (Solar and Wind)
30-year fixed charge rate (Combined Cycle, Peaking)

Annual Rate
(percent)
5.29
9.83
6.54
7.55
38.01
2.8
49.8
50.2
1.20
12.8839
11.0833

2.6 Emissions Costs
In February 2008, the United States Court of Appeals for the District of Columbia
Circuit vacated The Clean Air Mercury Rule (“CAMR”), so no mercury costs were
included in the modeling for the 2016 ERP. In addition, because no national
greenhouse gas regulation bill has passed the U.S. Congress and the Supreme Court
issued a stay of EPA’s CPP, no CO2 emission costs were considered in any of the
plans or scenarios, with exception to the Environmental scenario, of the 2016 ERP
or in the development of the stochastic prices. For the Environmental scenario, the
ABB WECC Fall 2015 Reference Case CO2 Tax scenario forecast for CO2 emission
costs was used. This forecast is included in Confidential Schedule K-3, Appendix K.
2.7 Demand-Side Management
DSM plans for the Company are filed for and approved outside of the ERP process.
Black Hills has administered DSM plans since 2009 helping customers to conserve
energy and save money. The graph below shows the energy efficiency savings that
were achieved from 2009 through 2015.

17

 Attachment LS-1
2016 ERP

Figure 3-1
2009 – 2015 Energy Efficiency Savings (kWh)

120,000,000

115,806,878

100,000,000
80,000,000
60,000,000
40,000,000

31,740,049
25,826,523
17,295,547 18,561,256

17,829,736

2009/2010 2010/2011 2011/2012 2012/2013

2014

20,000,000
4,553,767
0

2015

5-Year Total

Black Hills filed for approval of its 2016-2018 Electric DSM Plan for calendar years
2016, 2017 and 2018 on May 29, 2015 in Proceeding No. 15A-0424E. A Settlement
Agreement resolving the application was filed with the Commission on September
18, 2015 and a hearing on the settlement was held on September 25, 2015. Approval
of the 2016-2018 DSM Plan, as modified by the Settlement Agreement and the
recommended decision (Decision No. R15-1292), was issued on December 8, 2015.
The 2016-2018 DSM Plan is composed of three broad categories: residential
programs, non-residential programs (e.g., commercial and industrial), and special
programs. Each program has been designed to provide a variety of energy efficiency
opportunities for various customer types.
•
•

Residential programs include high efficiency lighting, high efficiency cooling,
appliance recycling, home energy comparison reports, and on-site energy
evaluations.
Commercial and industrial programs include rebates for prescriptive and
small business direct install lighting; prescriptive rebates for the purchase
and installation of pre-qualified measures, including HVAC, motors and
refrigeration; custom rebates for cost effective non-prescriptive measures
and equipment; incentives for the design and construction of new energy
efficient buildings and a self-direct program that provides rebates for costeffective non-prescriptive measures or equipment for customers with an

18

 Attachment LS-1
2016 ERP

•

aggregated peak demand higher than 1 MW in any single month and annual
energy usage of 5,000 MWh.
Special Programs include a low-income assistance program and a school
education program.

The approved 2016-2018 DSM Plan will result in the demand reductions and energy
savings shown in Table 3-4. The 2016 ERP load forecast was adjusted – reductions
in both the peak demand (MW) and energy (MWh) forecasts – to incorporate the
DSM impacts of the Company’s 2016-2018 DSM Plan.
Table 3-4
2016-2018 DSM Plan
Projected
Peak
Savings
(MW)
Plan Year 1 – 2016
5.23
Plan Year 2 – 2017
5.60
Plan Year 3 - 2018
5.82
Plan Total
16.65

19

Projected
Energy
Savings
(MWh)
18,014
19,769
20,633
58,416

 Attachment LS-1
2016 ERP
Annual load forecast adjustments to peak demand and energy are shown in Table 35.
Table 3-5
Adjustments to Load Forecast for DSM
Projected Projected
Peak
Energy
Year
Savings
Savings
(MW)*
(MWh)**
2016 (3.1)
(18,014)
2017 (8.5)
(37,783)
2018 (14.2)
(58,416)
2019 (16.7)
(58,416)
2020 (16.7)
(58,416)
2021 (16.7)
(58,416)
2022 (16.7)
(58,416)
2023 (16.7)
(58,416)
2024 (16.7)
(58,416)
2025 (16.7)
(58,416)
2026 (16.7)
(58,416)
2027 (16.7)
(58,416)
2028 (16.7)
(58,416)
2029 (16.7)
(58,416)
2030 (16.7)
(58,416)
2031 (16.7)
(58,416)
2032 (16.7)
(58,416)
2033 (16.7)
(58,416)
2034 (16.7)
(58,416)
2035 (16.7)
(58,416)
2036 (16.7)
(58,416)
2037 (16.7)
(58,416)
2038 (16.7)
(58,416)
2039 (16.7)
(58,416)
2040 (16.7)
(58,416)
*Demand adjustment reflects the expected saving achieved as of July 1 of each year.
**Energy adjustment reflects the expected savings achieved over the entire year.

20

 Attachment LS-1
2016 ERP
3

Load Forecast

The Company used an econometric forecasting methodology to forecast peak
demand and energy for the 2016 ERP. Black Hills gathered and refined a variety of
different types of datasets including historical load, revenue, economic, and weather
data. This data was used to develop regression models for the monthly system-level
peak demand forecast and the major customer class energy forecasts. The final
system-level monthly peak demand was then computed by adding large customer
loads, including their anticipated future load growth and accounting for the effects
of DSM, to the load forecast produced from the econometric methods. The final
system-level major customer class energy forecasts were computed by adding large
customer loads, including their anticipated future load growth, losses and
accounting for the effects of DSM to the energy forecasts calculated through the
regression analysis.
Base, high, and low load forecasts were developed and are described in detail in the
following sections. There are no annual sales of energy or capacity to other utilities
or intra-utility energy and capacity usage, thus the load forecast has not been
adjusted for these factors.
The Company prepared a system-level peak demand forecast rather than a major
customer class level demand forecast because historical demand data for all
customer classes has only been maintained by the Company since 2013 when the
full implementation of the automated metering infrastructure (“AMI”) system in the
Company’s service territory was completed. With only three years of historical
customer-level demand data available from the AMI dataset, the Company
determined that a system-level peak demand forecast using system-level hourly
load data from the Company’s Open Access Technology International, Inc. (“OATI”)
database and Aquila legacy systems would provide a better base for the peak
demand forecast. The OATI system included hourly load data from June 25, 2008
through December 31, 2015, and the data for January 1, 2006 through June 24, 2008
was acquired from Aquila’s data system.
Black Hills did complete major customer class energy forecasts using historical
customer class data that is maintained in the Company’s customer information
system (“CIS+”). The AMI interval consumption data available for 2014 and 2015
has been validated for overall accuracy by comparing the total kilowatt hours of
measured consumption by rate code to the total retail kilowatt hours billed in the
CIS+ billing system during 2014 and 2015, respectively. The variance between the
two datasets for 2015 is 0.09 percent.
3.0 Econometric Model Overview
Econometric modeling was used as the foundation for system level demand and
major customer class energy forecasts. The econometric models were developed
using the statistical software package Stata. Black Hills used this software to develop
21

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2016 ERP
statistical models that estimate the effect of various factors (e.g., weather) on
customer sales, the number of customers served, and system peak demand. The
explanatory factors used in these equations consist of weather, electricity prices
(calculated as average revenue per-kWh), demographic variables, and economic
variables.
The advantages of econometric forecasting models include:
•

•
•
•

The ability to estimate effects of specific drivers on sales and demand,
controlling for the effect of all other included variables. For example, the
models estimate the effect of economic conditions on sales controlling for
variations in weather conditions.
The ability to refine and adapt the models to reflect changing circumstances
over time.
The use of third-party weather, economic, and demographic data in the
forecast, removing potential concerns about biased inputs.
Providing measures of the statistical precision of the estimates, such as the
statistical significance of particular driver variables or the overall
explanatory power of the forecast model.

Econometric forecasting models reveal relationships between sales (or demand or
the number of customers served) and economic or demographic variables to
forecast future developments. The process begins by estimating the historical
relationship between sales (or demand or the number of customers served) and the
relevant drivers, which may include weather, economic conditions, demographic
trends, seasonal patterns, or retail electricity prices. The resulting estimates of the
relationship between each driver and the associated outcome (e.g., sales) are then
applied to forecasts of the drivers to develop the forecast sales, demand, or number
of customers served. The statistical models are reviewed and refined to ensure that
the estimated relationships are reasonable (i.e., correctly signed and of reasonable
magnitude).
3.1 Load, Economic, and Weather Data
3.1.1 Historical Load Data
The Company used historical system-level hourly load data that has been
maintained since 2006. The data set was reviewed to ensure accuracy and any data
anomalies were replaced by averaging data from the hour before and the hour after
to create a new value for missing or erroneous data. One large customer’s load was
removed from the Company’s historical load data prior to modeling. The Company
excluded this customer’s load from the historical data because it is a significant
percentage of the Company’s total load and is not expected to increase. Therefore
the Company did not want the growth rates calculated through the regression
analysis applied to this large load. Black Hills subtracted this large customer’s
22

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2016 ERP
hourly peak data from the system historical data, creating a new “base” historical
dataset. This “base” historical dataset was used in the regression analysis. The
excluded data for the one large customer was added back into the demand forecast
after the models runs were complete.
The major customer class energy forecasts were developed using historic sales and
customer count data that has been maintained by the Company in its (“CIS+”). Sales
data by rate identification from 2006 through 2015 was gathered, reviewed, and
aggregated into three major customer classes: residential, commercial, and
industrial. Similar to the hourly load data, the Company created a “base” historical
sales dataset. The Company removed the historical data of three large customers as
well as lighting service and Company-use data prior to conducting the sales forecast
modeling. The lighting and Company-use sectors were removed to ensure that the
customer class sales growth rates were not skewed by the historical growth
patterns of these sectors and the Company did not want the growth rates calculated
through the regression analysis applied to the three large customer loads.
The excluded data for the three large customers, lighting, and Company-use was
added into the aggregated sales forecast after the major class forecast models were
complete. The historical load and sales data used in the peak demand and sales
models is included in Schedule C-1 and Schedule C-2, Appendix C, respectively.
3.1.2 Economic Data
Economic and demographic historical and forecast data were obtained from Woods
& Poole Economics, Inc. (“W&P”) for Pueblo and Fremont Counties for the years
1969 through 2050. Though this dataset includes several economic variables, Black
Hills determined that the relevant variables for the Company’s load forecasts were
Gross Regional Product (“GRP”), Number of Households, Nonfarm Employment,
Manufacturing Employment, Total Employment, Mean Real Household Total
Personal Income, Net Earnings, Total Earnings, Total Retail Sales Including Eating
and Drinking Places Sales, and Total Personal Income. Each of these variables was
tested in the regression analysis.
In addition, historical electric price data was gathered from the Company’s FERC
Form 1, page 304 filings reflecting the average annual price of electricity, on a
dollars per-kWh basis, for each of Black Hills’ customer classes. The historical and
forecasted economic data and historical price data used in the peak demand and
sales models are included in Confidential Schedule C-3, Appendix C.
3.1.3 Weather Data
Historical weather data was collected from the NOAA National Climatic Data
Center’s (“NCDC”) Pueblo Airport weather station. The historical hourly
23

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2016 ERP
temperature data was used to calculate heating degree days (“HDD”) and cooling
degree days (“CDD”) using a 60 degree Fahrenheit threshold. The heating degree
hours (“HDH”) and cooling degree hours (“CDH”) were calculated using 50 degree
and 70 degree Fahrenheit thresholds, respectively. The HDD, CDD, HDH, and CDH
data were used for both historical and normal weather forecasting purposes. The
monthly CDD daily average was based upon the monthly average of total CDD. The
historical weather data used in the peak demand and sales models is included in
Schedule C-4 and Schedule C-5, Appendix C respectively.
3.1.4 Normal Weather Conditions
The weather variables in the energy and demand forecasts are set to reflect
“normal” conditions, which is interpreted as the average weather conditions over 20
years. In the energy model, the average of the sum of the cooling degree days over
the available time period was used to calculate normal weather for each month. In
the peak demand model, each month is determined to be either a predominantly
cooling- or heating-peak month, and then only the relevant peak-hours for each
month and year are averaged. Those averages are averaged again for each month
and used as normalized peak weather conditions.
3.2 Forecast Methodology
Multiple combinations of the variables described above were tested in the
development of the energy and demand forecasts. The models were refined to
ensure that the estimates were logically reasonable (e.g., sales increase with CDDs)
and statistically significant (or approaching statistical significance). Normal
weather conditions are used to forecast energy and demand.
3.2.1 Peak Demand Forecast Methodology
The Company’s system demand forecast is a system-level forecast inclusive of
residential, commercial, industrial, and lighting sectors. Each month’s peak hours
from 2006 to 2015 were used to model the monthly peak demand forecast. The
peak demand model was estimated using Ordinary Least Squares (“OLS”). The
resulting estimates were used in combination with normal weather and forecasted
economic conditions to forecast peak demands.
3.2.2 Energy Forecast Methodology
To complete the energy forecast, the Black Hills system was disaggregated into
three major customer classes: residential, commercial and industrial. The
residential customer class is an aggregation of all of Black Hills’ residential rate IDs.
The commercial class is an aggregation of Black Hills’ small and large general
24

 Attachment LS-1
2016 ERP
service rate IDs, and the Company’s large power service rate IDs constitute the
industrial class.
Summaries of the final energy and demand equations are described in Appendix C.
Also included in Schedules C-1, C-2, Confidential C-3, Appendix C are the historical
and forecasted values for variables that were used in the models. The resulting
forecasts, monthly and annual, for the residential and customer use per customer
(“UPC”), number of customer and sales models and the industrial sales model are in
Schedules C-8 and C-9, Appendix C, respectively..
3.2.3 Large Customer Growth Assumptions
The Company periodically reviews the growth plans of the largest customers in its
service territory. These expected load increases can be uncertain and depend to a
great extent on economic conditions. Table 4-1 shows anticipated large customer
load additions and reductions (with confidence factor applied) for the period 2016
through 2022. This information was compiled based on information gathered by
the Company’s economic development personnel and adjusted by a confidence
factor depending on the level of certainty expressed by the customer that the
growth will actually occur. These annual changes in large customer loads are
reflected in the peak demand and energy load forecasts.
Table 4-1
Large Customer Load Additions and Reductions 2016 - 2022
Customer
Load 2016 2017 2018 2019 2020
2021
Factor (MW) (MW) (MW) (MW) (MW) (MW)
Large Customer A
85%
1.5
1.6
2.5
1.6
1.3
Large Customer B
68%
6.4
-6.3
Large Customer C
55%
1.7

2022
(MW)
-5

The economic downturn and the uncertainty of the PTC legislation have had a
significant impact on several of the larger customers in the Company’s service
territory in the past few years. Of significance in this area are the changes in shorter
term forecasts associated with several of the Company’s large volume customers.
Since the Company’s 2013 ERP filing, the reductions in the short-term load forecasts
for these customers has amounted to more than 15 megawatts of reduction or delay
in forecasted demand growth. Table 4-2 shows a comparison between the large
volume customer load addition assumptions used in the 2013 ERP and the actual
load growth experienced by these customers in 2013 through 2015. While the
forecast for this small group of customers continues to reflect moderate growth in
the near term (11-12 additional megawatts over the period, 2016-2020), the
anticipated rate of growth has slowed during that time period. In addition, one
large customer expects significant load reduction in 2021 and 2022.

25

 Attachment LS-1
2016 ERP
Table 4-2
2013 ERP Large Customer Load Additions Assumptions
Compared to Actual Load Additions
Projected
Peak Growth

Customer 1
Customer 2
Customer 3
Customer 4
Customer 5
Annual Total
Additions

2013

2014

2015

2013 ERP
Projected
Additions
(MW)

Actual
2013
Additions
(MW)

2013 ERP
Projected
Additions
(MW)

Actual
2014
Additions
(MW)

2013 ERP
Projected
Additions
(MW)

Actual
2015
Additions
(MW)

1.9
5.4
0
0
0.8

0.8
1.0
0
0
0

9.5
0.0
0
0.9
0

0.4
1.5
0
0
0

1.9
9.9
9
0
0

3.5
0.6
0
0
0

8.1

1.8

10.4

1.9

20.8

4.1

Industrial Agriculture is a growing customer segment comprised primarily of
cannabis-related farming activities. In contrast to the trends characterizing Black
Hills’ aforementioned industrial customer class, growth in both the number and
overall energy demand associated with the industrial agriculture loads has been
trending upwards since early 2013. While the Company will continue to monitor
growth of this new and growing industry and its energy needs, it is the Company’s
belief that the lack of sufficient load and growth data combined with the
uncertainties surrounding the new industry’s legal and regulatory environment
preclude the Company’s ability to accurately forecast future growth patterns at this
time.
3.3 Base Peak Demand and Annual Energy Forecasts
The final base system-level monthly peak demand forecast was computed by adding
the one large customer and anticipated future load growth of other large customers
into the load forecast calculated by the regression analysis. The final system-level
major customer class energy forecasts were computed by adding large customer
loads, including their anticipated future load growth, lighting service, and Companyuse to the energy forecasts calculated through the regression analysis.
Combined transmission and distribution losses were also added into the annual
energy forecast for each major customer class. Losses were estimated by calculating
a weighted loss percentage for each aggregated major class. The class level
transmission and distribution losses are shown in Table 4-3. Separate system loss
estimates cannot be made for transmission and distribution because the forecast
was not developed at the transmission and distribution voltage level. The peak
demand and energy forecast values for the Base load forecast are shown in Table 44.
26

 Attachment LS-1
2016 ERP
Table 4-3
Combined Transmission and Distribution Losses
Major Sales
Class

Line Loss Class

Average
Estimated
Losses

Residential
Commercial

Residential
Large General Service
- Primary
Large General Service
- Secondary
Small General Service
Large Power Service Primary
Large Power Service Secondary
Large Power Service Transmission
Large Customer 1

5.82%
4.03%

Industrial

Large
Customer 1
Large
Customer 2
Large
Customer 3
Lighting
Company Use
Auxiliary*

Aggregated
Customer Class
Weighted
Losses by Class

Nonaggregated
Customer
Class Sales
Losses
5.82%

5.67%

5.82%
5.82%
4.03%

4.11%

5.82%
2.21%
4.03%

4.03%

Large Customer 2

4.03%

4.03%

Large Customer 3

2.21%

2.21%

5.82%
5.82%

5.82%
5.82%

Total System

0.63%

Station Use
* Auxiliary Total System and Station Use MWh consists of 2014 totals

27

 Attachment LS-1
2016 ERP
Table 4-4
Base Load Forecast

Year
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040

Peak Demand*
(MW)
395
395
394
397
401
401
397
398
401
404
406
409
411
414
416
419
421
423
426
428
430
432
435
437
439

Annual Energy*
(MWh)
2,037,488
2,065,684
2,084,666
2,123,907
2,156,324
2,157,010
2,145,097
2,152,368
2,173,886
2,194,817
2,216,110
2,237,165
2,258,860
2,280,431
2,300,541
2,319,801
2,338,428
2,356,329
2,374,779
2,393,173
2,411,213
2,427,570
2,443,671
2,460,146
2,476,553

Losses
(MWh)
109,783
111,025
112,432
113,806
115,091
116,167
117,207
118,356
119,599
120,808
122,038
123,255
124,509
125,756
126,916
128,026
129,097
130,126
131,188
132,246
133,283
134,221
135,142
136,086
137,025

*Peak Demand and Annual Energy Forecast values includes impacts of DSM Plans
and losses.

3.4 Low and High Forecasts
The base load forecast is assumed to represent the expected midpoint of possible
future outcomes, meaning that a future year’s actual load may deviate from the
midpoint projections. To evaluate the impact of these potential deviations, low, and
high load forecasts were developed.
The Company prepared low and high load forecasts in addition to its base load
forecast as required by Rule 3606(b). For the high and low load forecasts, the
28

 Attachment LS-1
2016 ERP
Company developed an 80 percent confidence interval band around the base
demand and sales forecasts. Black Hills had a relatively short historical time series
of data over which to examine variations in demand and sales for the service
territory. So the Company opted to base the degree of variability in the forecast on
GRP and nonfarm employment data rather than historical demand and energy
usage. GRP and nonfarm employment historical data was available from the Woods
and Poole dataset for the time period 1969 through 2013.
The peak demand model provided an estimate of the effect of changes in GRP and
nonfarm employment on changes in peak demand, along with a standard error
associated with the estimate. These two uncertainties (in GRP and nonfarm
employment over time and in the estimated effect of GRP and nonfarm employment
on peak demand) are combined to produce the confidence interval around the
demand and sales forecasts. The following steps were used to develop the
confidence interval.
1. Calculate the average annual 10-year percentage change in GRP and nonfarm
employment for each 10-year window between 1969 and 2013, producing
35 separate percentage change values.
2. From the peak demand model, obtain the estimated coefficient associated
with the GRP and nonfarm employment variables.
3. Multiply each of the 35 GRP and nonfarm employment 10-year growth rates
by the corresponding coefficient from the peak demand model, then add the
resulting values. The sum represents the 10-year average growth rate in
peak demand during the 10-year window in question.
4. Calculate the mean and standard deviation of the growth rate of demand
across these 35 observations.
5. Calculate the coefficient of variation (“CV”) from this mean and standard
deviation by dividing the standard deviation by the mean. This represents
the historical relationship between mean load growth and the variability of
load growth.
6. Calculate the forecast standard error of load growth by multiplying the CV by
the forecast percentage load growth (as forecast from the peak demand
forecast model).
7. The high and low scenarios are simulated as the 90th and 10th percentile
values (respectively) from a normal distribution with a mean equal to the
forecast growth rate in peak demand and the standard deviation equal to the
value derived in Step 7.
8. These high and low percentages are applied to the demand and sales forecasts in
each of the forecast months, beginning in 2017.
The values for the base, low and high load forecasts, including the effects of DSM are
shown in Table 4-5.

29

 Attachment LS-1
2016 ERP
Table 4-5
Low, Base and High Load Forecasts

Year
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040

Peak Demand (MW)
Low
Base
High
395
395
395
392
395
398
387
394
400
387
397
406
388
401
414
385
401
417
378
397
417
376
398
422
376
401
428
376
404
434
375
406
440
375
409
446
375
411
452
374
414
458
374
416
464
374
419
470
373
421
476
373
423
481
373
426
487
372
428
493
372
430
498
372
432
504
371
435
510
371
437
516
370
439
521

Energy (GWh)
Low
Base
High
2,037
2,037
2,037
2,041
2,066
2,089
2,032
2,085
2,138
2,046
2,124
2,204
2,054
2,156
2,263
2,030
2,157
2,291
1,993
2,145
2,307
1,976
2,152
2,344
1,973
2,174
2,395
1,969
2,195
2,445
1,966
2,216
2,497
1,963
2,237
2,550
1,959
2,259
2,604
1,956
2,280
2,659
1,953
2,301
2,711
1,949
2,320
2,761
1,946
2,338
2,810
1,945
2,356
2,858
1,942
2,375
2,907
1,941
2,393
2,957
1,939
2,411
3,007
1,938
2,428
3,053
1,936
2,444
3,098
1,934
2,460
3,145
1,933
2,477
3,191

Table 4-6 shows the total system summer and winter peak demand forecast for each
year of the Planning Period.

30

 Attachment LS-1
2016 ERP
Table 4-6
Seasonal Peak Demand Load Forecast Comparison – Base, Low and High
(including impacts of DSM Plans)

Year
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040

Peak Summer Demand
(MW)
High
Base
Low
395
395
395
398
395
392
400
394
387
406
397
387
414
401
388
417
401
385
417
397
378
422
398
376
428
401
376
434
404
376
440
406
375
446
409
375
452
411
375
458
414
374
464
416
374
470
419
374
476
421
373
481
423
373
487
426
373
493
428
372
498
430
372
504
432
372
510
435
371
516
437
371
521
439
370

Peak Winter Demand
(MW)
High
Base
Low
320
320
320
321
319
316
323
317
312
329
321
314
335
325
315
334
321
308
334
318
303
339
320
303
344
322
302
349
324
302
354
326
302
359
328
301
363
330
301
368
332
301
373
334
301
377
336
300
382
338
300
387
340
300
391
342
299
396
344
299
400
346
299
405
348
299
410
349
298
414
351
298
419
353
298

3.5 Historical Peak Demand and Annual Energy
The Company has historically experienced annual peaks in the summer. Peak
demand and annual energy for the period 2011-2015 are provided on Table 4-7.
Since 2011, the summer peak has experienced an average annual growth rate of
0.04 percent and the historical annual energy experienced an average annual
growth rate of 1.76 percent.
31

 Attachment LS-1
2016 ERP

Year

Table 4-7
Historical Peak Demand and Annual Energy
Peak Demand
Annual Energy*
Load
Summer % Change
GWh
% Change Factor (%)
(MW)

2011
2012
2013
2014
2015
Average Annual
0.04
Growth (%)
* Annual energy includes transmission and distribution losses.

1.76

3.6 Load Forecast Comparison – 2013 ERP versus Actual and versus
2016 ERP
A comparison of actual peak demand and energy sales and the forecasts from the
2013 ERP and this 2016 ERP is shown in Table 4-9. In the 2013 ERP, the annual
energy growth was projected at 0.92 percent over the 2013-2037 period, compared
to the 0.82 percent growth rate projection in the current plan over the 2016-2040
time period. The annual summer and winter peak demand growth over the 20132037 period was forecasted at 1.09 percent and 1.16 percent, respectively, in the
2013 ERP, compared to the summer and winter 2016 ERP growth rates projected to
be 0.44 percent and 0.41 percent, respectively, as shown in Table 4-9.
The economic downturn and the uncertainty of the PTC legislation have had a
significant impact on several of the larger customers in the Company’s service
territory in the past few years. The changes in short-term anticipated load growth
associated with several of the Company’s large volume customers had a significant
impact on the Company’s 2016 ERP load forecast as compared to the 2013 ERP load
forecast. Since the Company’s 2013 ERP filing, the reductions in the short-term load
forecasts for these customers has amounted to more than 15 megawatts of
reduction in forecasted demand growth.
The load forecast methodology used for the 2013 ERP relied upon historical load
data from 2006 through 2012 that showed a steadily increasing peak demand (with
exception to one year) over this time period with an all-time system peak occurring
in the last year of the historical data. Table 4-8 shows the actual summer peak
demand for 2006 through 2015.

32

 Attachment LS-1
2016 ERP
Table 4-8
Historical Peak Demand
Peak Demand
Year
(MW)
% Change
2006
360
2007
375
4.17
2008
376
0.27
2009
365
-2.93
2010
384
5.21
2011
392
2.08
2012
400
2.04
2013
381
-4.75
2014
384
0.79
2015
392
2.08
Average Annual
1.77
Growth (%)

In 2013 the Company experienced a peak demand of 381 MW, 19 MW lower than the
previous year’s peak demand of 400 MW. Though demand has continued to increase
since 2013, demand has not yet reached the peak that was set in 2012.
The Company compared the 2016 ERP load forecast to the 2013 ERP load forecast
and found that the primary reasons that the 2016 ERP demand forecast is lower
than estimated for the 2013 ERP are: (1) the anticipated effects of the Company’s
2016-2018 DSM Plan; (2) the sizable revisions to the large customer load projection
since the 2013 ERP; and (3) the fact that the econometric analysis used in this 2016
ERP included three additional years of historical data. This is important because the
Company’s historical system peak occurred in 2012, the last year of data used in the
2013 ERP load forecast analysis. The lower annual system peaks in 2013, 2014, and
2015 are accounted for in the 2016 ERP load forecast.
Table 4-9 depicts the load forecast comparison between the 2013 ERP and the 2016
ERP.

33

 Attachment LS-1
2016 ERP
Table 4-9
Peak Demand and Energy Forecast Comparison
Summer Peak
Annual Energy
Demand including
Winter Peak Demand
including DSM (GWh)
DSM (MW)
including DSM (MW)
Year
2013 ERP 2016 ERP 2013 ERP 2016 ERP 2013 ERP 2016 ERP
2011
1,915*
1,915*
392*
392*
297*
297*
2012
1,928*
1,928*
400*
400*
284*
284*
2013
1,928*
1,928*
381*
381*
289*
289*
2014
1,960*
1,960*
384*
384*
298*
298*
2015
2,052*
2,052*
392*
392*
303*
303*
2016
2,177
2,037
450
395
339
320
2017
2,192
2,066
457
395
345
319
2018
2,098
2,085
445
394
332
317
2019
2,098
2,124
449
397
335
321
2020
2,116
2,156
454
401
339
325
2021
2,135
2,157
458
401
341
321
2022
2,154
2,145
462
397
344
318
2023
2,173
2,152
466
398
347
320
2024
2,192
2,174
470
401
350
322
2025
2,212
2,195
474
404
353
324
2026
2,231
2,216
479
406
356
326
2027
2,251
2,237
483
409
359
328
2028
2,271
2,259
487
411
362
330
2029
2,291
2,280
491
414
365
332
2030
2,311
2,301
496
416
369
334
2031
2,332
2,320
500
419
372
336
2032
2,352
2,338
504
421
375
338
2033
2,373
2,356
509
423
378
340
2034
2,394
2,375
513
426
382
342
2035
2,416
2,393
518
428
385
344
2036
2,437
2,411
523
430
388
346
2037
2,459
2,428
527
432
392
348
2038
2,444
435
349
2039
2,460
437
351
2040
2,477
439
353
Average Annual Growth
20132037
0.92%
1.09%
1.16%
2016 –
2040
0.82%
0.44%
0.41%
*Actual

Table 4-9 reflects the load forecast that was filed with the 2013 ERP on April 30,
2013, however, the load forecast was reduced as a part of the 2013 ERP Settlement
Agreement. The levels of DSM modeled for the Company’s 2013 ERP Baseline 1
with RES Plan (the base plan in which the 30 percent of energy sales provided by
34

 Attachment LS-1
2016 ERP
renewable resources by 2020 requirement is achieved) and which is reflected in
Table 4-9, assumed compliance with C.R.S. § 40-3.2-104(2), requiring a 5 percent
reduction in both retail system peak demand and retail energy sales from 2006
levels by 2018 due to DSM measures. These savings levels were lower than what
was approved by the Commission in Proceeding No. 12A-100E, the Company’s
Electric DSM Plan for 2012-2015. As a part of the Settlement Agreement for the
2013 ERP, the Company agreed to reduce the load forecast to reflect the anticipated
demand and energy savings included in the Commission-approved 2012 DSM
Settlement Agreement in Proceeding No. 12A-100E. The revised 2013 ERP peak
demand forecast is compared to the 2016 ERP peak demand forecast in Table 4-10.

35

 Attachment LS-1
2016 ERP
Table 4-10
Peak Demand Forecast Comparison

Year
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040

Summer Peak
Demand including
DSM (MW)
2013 ERP
Revised
2016 ERP
392*
392*
400*
400*
381*
381*
384*
384*
392*
392*
437
395
443
395
432
394
436
397
440
401
444
401
448
397
453
398
456
401
461
404
465
406
469
409
474
411
478
414
482
416
487
419
491
421
495
423
500
426
504
428
509
430
514
432
435
437
439

*Actual values

3.7 Energy and Capacity Sales to Other Utilities and Intra-Utility Energy
and Capacity Sales and Losses
Pursuant to Rule 3606(a)(III), the Company must provide a forecast of annual
energy and capacity sales to other utilities; and capacity sales to other utilities at the
time of coincident summer and winter peak demand. The Company does not have
any energy or capacity contracts with other utilities.
36

 Attachment LS-1
2016 ERP
Pursuant to Rule 3606(a)(IV), the Company must provide a forecast of annual intrautility energy and capacity use at the time of coincident summer and winter peak
demand. The Company does not have any intra-utility energy or capacity contracts.
3.8 Load Profiles
Typical day load patterns for each major customer class presented for peak day,
average day, and representative average off-peak days for each calendar month are
provided in Appendix D. The major customer classes represented by these load
profiles are the same as the major customer classes used for the load forecast.
These monthly load shapes were developed from data acquired from the Company’s
AMI system for the year 2015 and reflect average customer use for each major class.

37

 Attachment LS-1
2016 ERP
4.0 Supply-Side Resources
4.1

Existing Resources

The Company’s generation resources consist of two existing natural gas combustion
turbines, three diesel plants, and 50 percent ownership of the Busch Ranch Wind
Project. The Peak View Wind Project, and a 40 MW LM6000 that will be located at
PAGS are currently under construction.
The Rocky Ford diesels are located in Rocky Ford, the Pueblo diesels, Airport
diesels, and the three PAGS combustion turbines are located in Pueblo, and the
Busch Ranch Wind Project, and Peak View Wind Project are located in Huerfano
County and Las Animas County, Colorado. The data used for modeling these units
are shown in Table 5-1. This table provides information on unit operating
parameters for each of the generating facilities. Nameplate capacity is considered to
be equivalent to rated capacity. The summer capacity is what is considered
dependable capacity. The annual availability is projected to range between 80
percent and 90 percent for all units and is dependent on the timing of major
overhauls as well as actual forced outages.

38

 Nameplate
Capacity
(MW)
90
90
40
10.0
10.0
10.0
14.5

Average
Heat Rate
(Btu/kWh)
Nat Gas
9152
Nat Gas
9152
Nat Gas
9915
#2 Oil
10,626
#2 Oil
10,626
#2 Oil
10,626
Wind
N/A

Fuel
Type

Probable
Retirement
Date
2047
2047
2057
2041
2041
2041
2037

39

PAGS CT 1
2012
PAGS CT 2
2012
PAGS LM6000
2017
1
Pueblo Diesels
1963
Airport Diesels1
1964
1
RF Diesels
1964
Busch Ranch
2012
Wind Project
Peak View
2016
60
13.8
0.0
0.0
Wind
N/A
2041
Wind Project
Total Capacity
267.1
Notes: 1. There are five 2 MW diesel units at Pueblo and Rocky Ford. There are four 2.5 MW diesel units at the Airport.

Unit Name

Year
Installed

Table 5-1
Existing Generating Facilities
Summer
Forced
Scheduled
Capacity
Outage
Outage
(MW)
Rate (%) Rate (%)
90
2.0
2.0
90
2.0
2.0
40
2.0
2.0
10
9.46
0.5
10
9.46
0.5
10
9.46
0.5
3.3
0.0
0.0

Attachment LS-1
2016 ERP

 Attachment LS-1
2016 ERP

3.8.1

Diesels

The diesel units commonly known as the Pueblo, Rocky Ford, and the Airport
Diesels are used for peaking, to support the transmission system, and to provide
system reserves. There are five 2 MW diesel units at each of Pueblo and Rocky Ford.
There are four 2.5 MW diesel units at the Airport Station.
3.8.2 Pueblo Airport Generating Station
Two LMS100 natural gas-fired combustion turbines at PAGS began commercial
operation on January 1, 2012.
The Company is currently constructing a 40 MW LM6000 at PAGS that was
approved by the Commission in Decision No. C14-0007. The LM6000 is
replacement capacity for the Company’s 42 MW Clark Station that was retired
pursuant to the Clean Air-Clean Jobs Act (“CACJA”). The LM6000 is expected to
begin commercial operation on December 31, 2016.
3.8.3 Existing Purchases
The Company purchases 200 MW of firm power from Black Hills Colorado IPP, LLC.
This power is generated by two natural gas-fired combined cycle units at PAGS that
began commercial operation on January 1, 2012. This contract expires at the end of
2031. The Company also has one agreement in place for the purchase of 50 MW of
firm energy. This contract expires at the end of 2016.
Firm power is also purchased through what is referred to as the MPS Agreement.
The Company has a letter agreement in place with the former Missouri Public
Service (“MPS”). MPS provides capacity and energy to the Western Area Power
Administration (“Western”) on the eastern grid. In exchange, Western provides
capacity and energy to the Company on the western grid. This agreement expires
on September 30, 2024 and provides 5 MW of firm capacity and energy to the
Company.

-

-

40

 Attachment LS-1
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3.8.4 Coordination Letters
Pursuant to Section 3607(b) of the ERP Rules, utilities must coordinate their ERP
plan filings such that the amount of electricity purchases and sales between utilities
during the Planning Period is reflected uniformly in their respective plans. The
Company does not have any contracts of this type. Therefore no coordination
letters are provided in this 2016 ERP.
3.8.5 Existing Renewables
The Company’s existing renewable resources include the Busch Ranch Wind Project,
modest amounts of on-site solar in the form of photovoltaics (“PV solar”) installed
by customers, a 120 kW Community Solar Garden, and the Vestas 1.8 MW wind
turbine. The Peak View Wind Facility is currently under-construction with
commercial operation expected in late 2016.
3.8.6 Busch Ranch Wind Project
The Busch Ranch Wind Project in eastern Huerfano County, Colorado began
commercial operation in October 2012. The project consists of sixteen 1.8 MW wind
turbines. The Company owns half of the 29 MW and purchases the energy produced
by the remaining turbines under a PPA that has a 25-year term from the date of
commercial operation of the facility. The Company proposed the project pursuant
to Rule 3660(h) of the RES Rules.
3.8.7 Solar Resources
The Company has offered solar on-site distributed generation programs to
residential, commercial, and industrial customers since 2006. The programs offered
have changed over the years but generally the Company has offered capacity in
small (under 10 kW), medium (10 kW to 100 kW), and large (over 100 kW)
increments to customers who connect a PV solar system to the Company’s grid.
These customers have received rebates, production based incentives and bill credits
and the Company is entitled to the RECs generated by these systems over the first
twenty years of the solar installation’s operation. In addition to the RECs acquired
through the solar on-site program, Black Hills has a contractual obligation with the
winning bidder of a 2007 competitive acquisition process to acquire the RECs from
a large class (100 kW to 2 MW) solar on-site installation and a recently installed 120
kW Community Solar Garden (“CSG”).
41

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3.8.8 1.8 MW Distributed Generation Wind Facility
Vestas Towers A/S, a wind turbine manufacturer with a facility located in the
Company’s service territory in Pueblo, Colorado, has installed on its site a wind
turbine (V100 1.8 MW) with a prototype blade system technology in order to test
and demonstrate the ability of the system to generate energy with low wind
velocity. The generation from this facility is used by Vestas at their manufacturing
facility, however, Black Hills has an agreement with Vestas for the RECs from this
facility. That agreement expires in June 2030.
3.8.9 Peak View Wind Project
The Peak View Wind Project is currently under construction in Huerfano County
and Las Animas County, Colorado. The project, when complete, will consist of 34 GE
1.7-100 class wind turbines. The Peak View Wind Project is expected to produce
energy at an estimated net capacity factor of approximately 41 percent. Black Hills
filed a CPCN Application for the Peak View Wind Project on June 23, 2015 in
Proceeding No. 15A-0502E. The Company has entered into a Build Transfer
Agreement with Invenergy Colorado LLC. Upon commercial operation of the plant,
targeted for November 2016, ownership rights would transfer from Invenergy to
Black Hills. The project was approved by the Commission in Decision No. C15-1182.
4.2 Resources Options
Conventional resources, renewable energy resources, and purchased power
alternatives were analyzed in the evaluation of the resource options for this 2016
ERP.
4.2.1 Conventional
A variety of conventional supply-side resource options were available for selection
in the modeling. These include natural gas-fired simple-cycle combustion turbines,
natural gas-fired combined-cycle units, and reciprocating engines. The natural gasfired resources were assumed to be built in the Pueblo area at a “greenfield” or
undeveloped site. All capital cost estimates used in the modeling are order-ofmagnitude overnight estimates with an accuracy level of ±25 percent. All estimates
are based on an engineering, procurement, and construction (“EPC”) method of
contracting. EPC capital cost estimates are exclusive of owner’s cost and only
consider “inside-the-fence” physical assets. Inside-the-fence physical assets begin
with interconnects at the plant boundary (fuel, water, etc.) and end at the high side
of the generator step-up transformer.
An important consideration for siting any generating facility is accessibility to
transmission. For natural gas-fired units, additional consideration needs to be given
42

 Attachment LS-1
2016 ERP
to accessibility to natural gas pipelines and availability of natural gas from the
natural gas pipeline.
Performance parameter and cost values for modeling conventional resources came
from a 2012 study conducted by Black & Veatch for the Company (see Appendix E).
This study was reviewed and updated to reflect changes in capital cost and
operations and maintenance cost trends since 2012. All resources examined in the
Black & Veatch study, with the exception of the 2x1 LMS 100 conversion to
combined-cycle unit with duct firing, 3x1 GE LM6000 PF Sprint, and the 100 MW
coal unit, were evaluated as resource options in this 2016 ERP.
5.2.2 Combustion Turbine
Combustion turbines (“CT”) typically burn natural gas and/or No. 2 fuel oil and are
available in a wide variety of sizes and configurations. CTs are generally used for
peaking and reserve purposes because of their relatively low capital costs, higher
full-load heat rate, and the higher cost of fuel when compared to conventional
baseload capacity. Many CTs have the added benefit of providing quick-start and
black-start capability in certain configurations. In this analysis, technology options
modeled for CTs included LMS 100 (an aeroderivative turbine), LM6000, and a GE
6FA. Parameters used to model each of these CT options are shown on Table 5-2.
Table 5-2
Combustion Turbine Parameters
Parameter
LMS 100
LM6000
Earliest feasible installation
11/2019
8/2019
Size, MW (net) - summer
90.2
39.6
Full load heat rate, Btu/kWh
8,946
9,445
SO2 Emission Rate, lb/MWh
0.0104
0.0113
NOx Emission Rate, lb/MWh
0.1872
0.2038
CO2 Emission Rate, lb/MWh
1164
1237
Fixed O&M, $/kW-year (2016 $)
18.32
40.26
Variable O&M, $/MWh (2016 $)
6.61
7.14
Forced Outage Rate, %
2.00
2.00
Maintenance Outage Rate, %
2.00
2.00
Capital Cost, $/kW (2016 $)
1,130
1,591

GE 6FA
6/2019
64.5
10,999
0.0136
0.2457
1505
25.30
18.78
2.00
2.00
1,366

5.2.3 Combined Cycle
In a combustion turbine combined-cycle (“CC”) facility, the hot exhaust gases from
the combustion turbine pass through a heat recovery steam generator (“HRSG”).
The steam generated by the HRSG is expanded through a steam turbine which, in
turn, drives an additional generator. CCs typically burn natural gas and are
available in a wide variety of sizes and configurations. Parameters for two different
CC configurations were included in the modeling; 1x1 CC with duct firing and 2x1
43

 Attachment LS-1
2016 ERP
CC. These are shown on Table 5-3. The 1x1 CC consists of one gas turbine
generator, one steam turbine generator, and one HRSG. The 2x1 CC configuration
has two gas turbine-generators and two HRSGs that supply steam through a
common header to a separate single steam turbine-generator.
Table 5-3
Combined Cycle Parameters
1x1 with
Parameter
Duct Firing
2x1
Earliest feasible installation
9/2019 9/2019
Size, MW (net) - summer
55.1
102.2
Full load heat rate, Btu/kWh
7,660
7,316
SO2 Emission Rate, lb/MWh
0.0080 0.0078
NOx Emission Rate, lb/MWh
0.0879 0.0860
CO2 Emission Rate, lb/MWh
775
854
Fixed O&M, $/kW-year (2016 $)
38.94
27.20
Variable O&M, $/MWh (2016 $)
7.44
7.44
Forced Outage Rate, %
2.00
2.00
Maintenance Outage Rate, %
2.00
2.00
Capital Cost, $/kW (2016 $)
2,184
1,845
5.2.4 Reciprocating Engines
A Wartsila 34 SG lean-burn natural gas engine was modeled in two configurations –
as an individual unit (9.1 MW) and in a group of ten units (total capacity 91.4 MW).
This four stroke, gas-ignited spark engine can be used for baseload, intermediate
and peaking operation. Parameters used to model an individual and group of ten
Wartsila gas engines are shown on Table 5-4.
Table 5-4 Wartsila Gas Engine Parameters
Parameter
One Unit
Ten Units
Earliest feasible installation
6/2019
6/2019
Size, MW (net) - summer
9.1
91.4
Full load heat rate, Btu/kWh
8,982
8,982
SO2 Emission Rate, lb/MWh
0.0096
0.0096
NOx Emission Rate, lb/MWh
0.1736
0.1736
CO2 Emission Rate, lb/MWh
1127
1127
Fixed O&M, $/kW-year (2016 $)
94.07
21.80
Variable O&M, $/MWh (2016 $)
13.49
11.66
Forced Outage Rate, %
2.00
2.00
Maintenance Outage Rate, %
2.00
2.00
Capital Cost, $/kW (2016 $)
2,184
1,806

44

 Attachment LS-1
2016 ERP

5.2.5 Seasonal Firm Market Purchased Power
The Company assumed that, due to its small size relative to the market, it will be
able to purchase seasonal firm market power during the summer months. This
measure covers any peak demand shortfall and defers the need to install new
resources until the need for capacity extends to multiple months. The product
would be seasonal firm market power available 6 x 16 (six days per week, sixteen
hours per day, 7 am – 11 pm). The model is able to select the seasonal firm market
power in 25 MW blocks, up to a total of 75 MW (three blocks) through 2021 and 50
MW (two blocks) beginning in 2022. This seasonal firm market power is priced at
the cost of energy at Palo Verde plus a 20 percent premium and transmission adder.
The 25-MW block size was selected based on the minimum size of the blocks of
power typically available for this type of product in the market.
4.3 Renewables
The renewable energy resource technologies that were modeled in this 2016 ERP
include PV and wind. A sodium sulfur battery and a waste-to-energy facility were
modeled as Section 123 resources. Data for performance and cost parameters for
solar and wind technology were gathered from the Company’s 2014 All-Source
Solicitation.
5.3.1 PV Solar
The Company included three sizes of PV solar facilities for selection in the modeling:
a 10 MW, 30 MW and 60 MW option. A PV solar cell is made of semiconducting
material, typically wafer-based crystalline silicon technology, configured such that
when sunlight hits the cells, the electrons flow through the material and produce
electricity. Using data from the bids that were obtained during the Company’s 2014
All-Source Solicitation, Black Hills developed performance parameter and cost
assumptions for future PV solar resources. Parameters used to model PV solar,
which assume a PPA for solar energy, are included Table 5-5. Recent legislation
related to ITC levels for 2016 through 2022 were included in the development of the
PV solar cost assumptions.
Table 5-5
PV Solar Performance Parameters
Parameter
10 MW
30 MW
Earliest feasible year of installation
5/2018
5/2018
Size, MW (net) - summer
10
10
45

60 MW
5/2018
10

 Attachment LS-1
2016 ERP
Capacity Factor, %
Accreditable Capacity, %
Forced Outage Rate, %
Maintenance Outage Rate, %
PPA Energy Cost (First Year $/MWh)

29
37
0.00
0.00
65.35

29
37
0.00
0.00
76.21

29
37
0.00
0.00
50.92

5.3.2 Wind
Wind turbines use their blades to collect the kinetic energy of the wind. The blades
are connected to a drive shaft that turns an electric generator to produce electricity.
Similar to the PV solar performance parameters and costs, the Company used data
from bids that were obtained during the Company’s 2014 All-Source Solicitation to
develop future wind resource assumptions. Recent legislation related to PTC levels
for 2016 through 2022 were included in the development of the wind resource cost
assumptions. Parameters used to model wind in this 2016 ERP, which assume a
PPA for wind, are shown in Table 5-6.
Table 5-6
Wind Performance Parameters
Parameter
30 MW 60 MW
Earliest feasible year of installation
5/2018 5/2018
Forced Outage Rate, %
0.00
Maintenance Outage Rate, %
0.00
Capacity Factor, %
36.01
36.01
Accreditable Capacity, %
20
20
PPA Energy Cost (First Year $/MWh)
56.21
40.08
4.4 Section 123 Resources
As defined in the RES Rules, Section 123 resources mean new energy technology or
demonstration projects, including new clean energy or energy efficient technologies
under C.R.S. § 40-2-123 (1)(a), and C.R.S. § 40-2-123 (1)(c), and Integrated
Gasification Combined Cycle projects under C.R.S. § 40-2-123(2). Energy storage
systems, including sodium sulfur batteries, have the potential to enhance a utility’s
ability to integrate renewable energy technologies and increase electric grid
reliability and asset use. Energy storage systems allow a utility to move energy
production to the hour during the day when it is most beneficial.10 Energy storage
systems have significant locational flexibility and, if sited on the distribution system,
could provide voltage support and congestion relief, among other benefits.

10

Electricity Energy Storage Technology Options: A White Paper Primer on Applications, Costs, and
Benefits, 1020676, Electric Power Research Institute, Technical Update, December 2010.

46

 Attachment LS-1
2016 ERP
Much demonstration work still needs to be done with energy storage systems.
These demonstration or pilot projects are expected to provide the needed data and
information on the robustness of such systems, including performance and
durability, life cycle costs, and risks. In addition, such projects are expected to
increase interest in such technologies, resulting in lower initial capital costs and
increased deployment and adoption of energy storage systems.11
Because of the need for demonstration projects to validate the operational
characteristics, costs, and risks associated with the deployment of energy storage
technologies, including sodium sulfur batteries, the Company characterizes sodium
sulfur batteries as a Section 123 resource. The Company also characterizes a
proposed waste-to-energy facility as Section 123 resources.
5.4.1 Sodium Sulfur Battery
Sodium sulfur (“NAS”) batteries consist of liquid (molten) sulfur at the positive
electrode and liquid (molten) sodium at the negative electrode. The electrodes are
separated by a solid beta alumina ceramic electrolyte. The battery is hermetically
sealed with an environment of approximately 300o C. NAS battery round trip
efficiency ranks relatively high amongst electrochemical batteries. Depending on
parasitic loads, operating temperatures, and applications, efficiency ranges from 70
to 90 percent. NAS batteries typically are charged at night, when demand and costs
are relatively low, and are discharged during peak demand when energy costs are
relatively high. This effectively shifts some of the peak demand to the hour during
the day when it is most beneficial and allows utilities to reduce costs. NAS battery
performance parameters are shown in Table 5-7.

Table 5-7
Sodium Sulfur Battery Performance Parameters
Parameter
Value
Size, MW (net) – summer and winter
10
Efficiency (%)
75
Lifetime Cycles
4,500
Capital Cost, $/kW (2012 $)
3,775
5.4.2 Waste-to-Energy Facility
A waste-to-energy facility was proposed within the Company’s service territory and
the developer submitted a bid for this project in the Company’s 2014 All-Source
Solicitation. Using data submitted in the bid the Company derived modeling
parameters for a 10 MW facility, with an 80 percent capacity factor that would
operational in 2019 with PPA pricing. Because this was the only project of its kind

11

Id.

47

 Attachment LS-1
2016 ERP
bid into the 2014 All-Source Solicitation the cost parameter used in the 2016 ERP
modeling is confidential pending its public disclosure pursuant to Rule 3613(j).
5.0 Costs and Benefits of Integration for Intermittent Renewable Energy
Resources
To comply with the RES requiring that in 2020 thirty percent of retail electricity
sales come from renewable energy, the Company anticipates developing or
procuring new wind and solar resources in the coming years. The impact that higher
levels of wind and solar penetration will have on Black Hills’ operation is important
to understand so that appropriate steps can be taken to assure that grid stability is
not compromised. In 2015, the Company contracted with Black & Veatch to perform
a renewable integration analysis on the Black Hills system. The objectives of the
study were two-fold; first, to determine the incremental costs of providing
regulation and ancillary services for the integration of future wind and solar
resources (referred to in this analysis as Variable Energy Resources, or “VER”s) at
various penetration levels; and second, to determine the accreditable capacity of
wind and solar resources for reliability planning purposes. The study is contained
in Appendix F.
The incremental regulation costs for the Company to integrate future levels of
renewable resources were determined by first calculating the additional amounts of
regulating reserves required to maintain a certain reliability level. Black Hills and
Black & Veatch used a conservative approach that mimics existing reliability
standards. Specifically, the analysis proxies the NERC Control Performance Standard
2 (CPS2) approach to calculating incremental regulation requirements.
Black & Veatch calculated the accreditable capacity of future levels of wind and solar
resources utilizing an Effective Load Carrying Capability (“ELCC”) analysis to
determine the percentage of the nameplate capacity that can be counted on for
reserve margin planning purposes.
5.1 Integration Capacity Needs and Costs
In order to assess the impact of additional wind and solar resources on the
Company’s operations, assumptions were made for the amount, type, and location of
future renewable resources. Locations were chosen based on areas that have
received the greatest commercial interest during recent renewable resource RFPs.
The cases assessed are outlined in Table 6-1; each location refers to a specific
electrical substation in the Black Hills system.

48

 Attachment LS-1
2016 ERP

Type
Wind
Wind
Wind
Wind
Solar
Solar

Table 6-1
Future Eligible Energy Resources Modeled
Total
Incremental
Location
MW (AC)
Rattlesnake Butte (“RB”)
60
RB 60 MW, Lamar 30 MW
90
RB 60 MW, Lamar 60 MW
120
RB 90 MW, Lamar 60 MW
150
Burnt Mill 30 MW
30
Burnt Mill 30 MW, Nyberg 30 MW
60

Figure 6-1 depicts the monthly average 10-minute incremental reserve
requirements for 98 percent NERC CPS2 compliance for various levels of wind and
solar resource additions in 2020. The incremental regulating reserve requirements
increase more drastically as the renewable levels approach 30 percent of the peak
load. Wind and solar integration calculations were performed separately to
distinguish the cost of integration independently for each of these resources.
Figure 6-1
Monthly Regulating Reserve Requirement

The integration cost components of the additional regulation reserve requirements
were separated into the underlying energy and capacity components. The cost of
integration relative to energy was derived from a NERC CPS2 analysis which
calculated the ACE deviation at 10 minutes intervals. The cost of regulation was then
modeled in the ABB Planning & Risk (“PAR”) production cost model to determine
the system energy cost differential of providing regulation up and regulation down.

49

 Attachment LS-1
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Flex Reserve Service is needed to maintain generation and load balance in the event
of a reduction of online wind generation of 100 MW or more in 30 minutes due to
reductions in wind speed. This can be met by:
•
•

Generating units that are on-line but unloaded, and/or
Generating units off-line but capable of starting in 30 minutes.

Black Hills has around 450 MW of existing resources that can qualify to provide Flex
Reserves. Black Hills is long on Flex Reserve capacity, therefore, the additional cost
to the Company to integrate more wind and solar if there is no other use for this
Flex Reserve capacity is zero. Black Hills is subject to Public Service Company of
Colorado (“Public Service”) Schedule 3 and Schedule 16 tariff rules. The criteria of
these FERC tariffs make it difficult for the Company to self‐regulate wind capacity.
Public Service Schedules 3 and 16 tariff costs were included in the 2016 ERP
modeling for wind only. Based on these tariffs, the Company used the wind
integration adders shown in Table 6-2 for 30 MW and 60 MW wind options,
escalating at 2.5% annually, in its 2016 ERP modeling.
Table 6-2
Wind Integration Cost Assumptions ($/MWh)

30 MW Wind
60 MW Wind

2018
$4.46
$4.66

Adding the Regulation Energy and Flexible Reserve costs produces a net integration
cost that captures the majority of the expected costs for new wind and solar projects
in the Black Hills service territory. This is shown in Table 6-3. While there are a
number of other factors that can be taken into account when performing a wind and
solar integration analysis, such as increased O&M from regulating units, ancillary
service charges, and more frequent ramping of baseload units, these items are
difficult to quantify and are typically considered a small overall factor in most
integration studies. The scenarios represent incremental additions on top of Black
Hills’ existing Busch Ranch Wind Project. The 60 MW wind scenario represents a 90
MW total wind analysis.

50

 Attachment LS-1
2016 ERP
Table 6-3
Net Integration Costs

Incremental Renewable
Scenario
60MW Wind
90 MW Wind
120 MW Wind
30 MW Solar
60 MW Solar

Energy Cost
($/MWh)
$1.23
$0.95
$0.97
$0.96
$1.22

Schedule 16
Capacity
Cost
($/MWh)
$4.25
$4.07
$4.11
-

Total
($/MWh)
$5.48
$5.02
$5.08
$0.96
$1.22

The Company used the solar integration adders shown in Table 6-3 (energy cost
$/MWh) for 30 MW and 60 MW solar options, escalating at 2.5% annually, in its
2016 ERP modeling.
5.2 Accreditable Capacity of Wind and Solar
Black & Veatch calculated the accreditable capacity of future levels of wind and solar
resources utilizing an ELCC analysis to determine the percentage of the nameplate
capacity that can be counted on for reserve margin planning purposes. The ELCC
approach takes into consideration the load carrying capability for all 8760 hours in
a year. For this analysis Black & Veatch used the ELCC methodology that compares
the reliability impact of wind and solar resources to a “perfect generator.” ELCC is
calculated based on a monthly Loss of Load Expectation (“LOLE”) metric. LOLE is
the amount of time during which system capacity is unable to meet system load.
The calculated ELCC at different wind and solar penetrations on the BHCE system
can be seen in Figure 6-2.

51

 Attachment LS-1
2016 ERP
Figure 6-2
Calculated Wind and Solar ELCC

The solar ELCCs are higher than those for wind since their load profiles provide
peak output closer to times when Black Hills most needs the power. As more and
more solar comes onto the system, the benefit of similar output profiles leads to
diminishing returns since the hours that incremental solar can contribute to key
loss of load hours is limited. Conversely, wind has a much different output profile,
leading to a somewhat flatter ELCC contribution as more capacity is placed on the
system. Table 6-4 shows the ELCC for incremental wind and solar additions to the
Black Hills system.

52

 Attachment LS-1
2016 ERP
Table 6-4
Incremental Wind and Solar ELCC
Total Incremental
Type
(MW)
ELCC (%)
Wind
30
30
Wind
60
27
Wind
90
23
Wind
120
20
Wind
150
19
Solar
30
45
Solar
60
37
Solar
90
31
Solar
120
27
Solar
150
23

A summary of the key findings from the analysis completed by Black & Veatch can
be seen in Table 6-5. This table shows the regulating capacity reserves over the
current base case as calculated by the CPS2 model, the energy cost from the PAR
analysis, the capacity cost per Public Service Schedule 16, and the results of the
ELCC analysis. Note that each case represents incremental additions on top of the
status quo; thus, the 60 MW wind resource represents a 90 MW total wind analysis
from the ELCC results presented in Section 4 of the Variable Integration Cost Study.
Table 6-5
Cost for Integration and ELCC Summary
Case

60 MW
90 MW
120 MW
30 MW
60 MW

Energy
Capacity
Total
Cost
Cost*
Cost
($/MWh) ($/MWh) ($/MWh)
Wind Cases
$1.23
$4.25
$0.95
$4.07
$0.97
$4.11
Solar Cases
$0.96
-$1.22
--

ELCC
(%)

$5.48
$5.02
$5.08

23%
20%
19%

$0.96
$1.22

45%
37%

* Uses PSCo Schedule 16 for wind cases, assumes zero cost for self-regulation of solar
cases due to excess of flexible capacity.

6.0 Transmission Resources
The Company’s service territory generally follows the Arkansas River Valley in
Colorado from the Royal Gorge, west of Cañon City, to La Junta, east of Pueblo. The
53

 Attachment LS-1
2016 ERP
major load centers are the cities of Pueblo and Cañon City with significant smaller
load centers in the Cripple Creek area and the area near Rocky Ford.
The Company’s transmission system also follows the Arkansas River and consists of
585 miles of 115-kV transmission lines. The Company’s transmission system and
the interconnection to neighboring entities is shown in Figure 7-1.
Figure 7-1
Existing Black Hills Transmission System

6.1 Local Transmission Planning Process
Black Hills recognizes the importance of stakeholder involvement throughout the
transmission planning process, and considers a stakeholder to be any person, group
or entity that has an expressed interest in participating in the planning process, is
affected by the transmission plan, or can provide meaningful input to the process
that may affect the development of the final plan.
Stakeholders are encouraged to participate in Black Hills’ transmission planning
through the regular meetings held by the Transmission Coordination and Planning
54

 Attachment LS-1
2016 ERP
Committee (“TCPC”) as part of the annual study process under FERC Order No. 890.
The TCPC is an advisory committee consisting of individuals or entities that are
interested in providing input to Black Hills’ Transmission Plan. The TCPC study
process consists of a comprehensive evaluation of the Black Hills and surrounding
transmission systems for critical scenarios throughout the 10-year planning
horizon. Stakeholders are notified of the initial meeting at the start of the study
cycle and invited to participate. An opportunity is provided to comment on the
scope of the study at this point in the process. Relevant system modeling data is
requested from the stakeholders, as well as any economic study or alternative
scenario requests. The study cases are compiled the data and study scope are
finalized. A stakeholder meeting is held to review preliminary study results and
discuss potential solutions to any identified problems. This process allows the TCPC
to develop a comprehensive transmission plan to meet the needs of all interested
parties. A final stakeholder meeting is held as necessary to approve the study report
and Local Transmission Plan (“LTP”). Following each meeting, contact information
for the transmission planner performing the study is provided to allow for ongoing
questions or comments regarding the study process. Updates on the progress of the
TCPC study efforts are also provided to regional planning groups, such as the CCPG,
to promote involvement from a larger stakeholder body.
6.2 Regional Transmission Planning Process
In addition to the local TCPC planning process, Black Hills participates in a wider
planning effort at the sub-regional and regional levels through the Western
Electricity Coordinating Council (“WECC”), WestConnect, and the Colorado
Coordinated Planning Group (“CCPG”). WECC is the forum responsible for
coordinating and promoting BES reliability in the entire Western Interconnection.
The WECC includes committees that focus on transmission planning. The
Transmission Expansion Planning Policy Committee (“TEPPC”) prepares economic
models and performs high-level assessments of transmission congestion and
expansion needs on an interconnection-wide basis. The Planning Coordination
Committee (“PCC”) is responsible for preparing reliability models and performs
assessments of the interconnection based on performance standards developed by
the North American Electric Reliability Corporation (“NERC”).
WestConnect is one of four planning “regions” within WECC established for regional
transmission planning to comply with FERC Order No. 1000, Transmission Planning
and Cost Allocation by Transmission Owning and Operating Public Utilities (“Order
1000”). At the end of 2015, WestConnect had 23 members, including 15
Transmission Owners, 5 Independent Transmission Developers, and one Key
Interest Group. The WestConnect footprint includes 9 western states. One additional
Transmission Owner is expected to join WestConnect in 2016. WestConnect
includes 3 sub-regional planning groups (“SPGs”): CCPG, Southwest Area
Transmission Group (“SWAT”), and Sierra Subregional Planning Group (“SSPG”).

55

 Attachment LS-1
2016 ERP
CCPG, which was formed in 1991, is a planning forum that cooperates with state and
regional agencies to ensure a high degree of reliability in planning, development and
operation of the transmission system in the Rocky Mountain Region. Many CCPG
participants are involved in specialized work groups and subcommittees—for
example, the Conceptual Planning Work Group (“CPWG”) and the TPL Studies Work
Group—which are responsible for conducting technical, environmental, and cost
studies for specific projects, focused geographic areas and/or expansion needs.
Black Hills and the other Transmission Planners in Colorado have a long history of
coordinated transmission planning with each other. Given the integrated nature and
ownership of the transmission grid in Colorado, coordinated transmission planning
has been commonplace in Colorado before it was a requirement.
Internally, and through WestConnect and CCPG, each company performs annual
system assessments to verify compliance with reliability standards to determine
related system improvements and to demonstrate adherence to the standards and
criteria set forth by NERC and WECC. Compliance is certified annually. During the
coordinated planning process, a wide range of factors and interests are considered
by the companies, including, but not limited to:
•
•
•
•
•
•

the needs of network transmission service customers to integrate loads
and resources;
transmission infrastructure upgrades necessary to interconnect new
generation resources;
the minimum reliability standard requirements promulgated by NERC
and WECC;
bulk electric system considerations above and beyond the NERC and
WECC minimum reliability standard requirements;
transmission system operational flexibility, which supports economic
dispatch of interconnected generation resources; and
various regional and sub-regional transmission projects planned by other
utilities and stakeholders.

This comprehensive internal, regional, and sub-regional planning process ensures
that transmission plans continue to be carefully coordinated with all TPs in the State
of Colorado.
6.3 Transmission Constraints
There are constraints that exist on the Black Hills transmission system under
circumstances of high power transfers combined with certain contingency events.
These constraints should be taken into consideration when exploring locations for
future generation interconnections. Transfer capability limitations on the Black Hills
transmission system as they pertain to the future siting of resources are dependent
on the location and size of the resource being proposed. For resources sited
external to the Black Hills transmission system, limitations may exist at the
56

 Attachment LS-1
2016 ERP
interfaces between the Black Hills transmission system and the interconnection
points with neighboring transmission systems as shown in Figure 7-2. Proposed
resources that are sited within the Black Hills system may experience transfer
limitations between the resource location and Black Hills’ load centers. These
limitations are generally evaluated on an individual basis within the generator
interconnection request process. The transfer capability of the transfer paths on the
BHCE transmission system is shown in Table 7-1.
The transmission system is currently constrained when transferring power from the
Pueblo area into the Cañon City load center, or through the system from Portland to
West Cañon. The conceptual West Station-West Cañon 115 kV project will address
this constraint in the 2019-2020 timeframe. Siting future generation on the 115 kV
or 69 kV system between Portland and Cañon City may help to alleviate this
constraint.
There is also a general constraint on the Black Hills transmission system when
moving power from Baculite Mesa toward Midway under contingency conditions.
There are limited paths for energy to flow out of Baculite Mesa, and following the
loss of the Baculite Mesa-West Station 115 kV double circuit, the remaining outlet
paths become constrained. Future generation additions at Baculite Mesa may
require transmission upgrades to maintain unrestricted transfers under
contingency conditions. This constraint is dependent on surrounding system
conditions as well as the nature of the generation dispatched at Baculite Mesa.
Another constraint on the Black Hills transmission system can exist when
transferring power to the La Junta/Rocky Ford area under contingency conditions.
The loss of the single 115 kV line feeding the area via the Boone substation requires
the area load to be served from the single 69 kV line also terminating at Boone. By
adding generation in the La Junta/Rocky Ford area, the likelihood of realizing a
transmission constraint following a transmission outage is reduced.

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Attachment LS-1
2016 ERP

Table 7-1
Transfer Path Capability
Path Name

Total Transfer
Capability
(MW)

Midway-BHCE
BHCE-Midway
BHCE-TSGT
BHCE-Reader
Reader-BHCE
BHCE-Cañ on City West
Cañ on City West-BHCE
BHCE-Boone
Boone-BHCE

295
181
74
560
560
46
237
176
176

Available
Transfer
Capability
(MW)
45
58
69
360
295
46
222
176
141

6.4 Transmission Projects
Black Hills annually files with the Commission its planned projects for the next three
years in compliance with Rule 3206. In that report, Black Hills identifies the
expected in-service date, estimated project cost, location, and compliance with
corona noise and magnetic field requirements. The planned and conceptual projects
submitted in Black Hills’ 2016 compliance filing are shown in Table 7-2.
The Company does not have any proposed transmission additions that are the result
of Section 210 of the Federal Power Act or other federal open access transmission
(i.e. interconnection or transmission service) requirements.

59

 Length and
Location

Huerfano
County, CO

9 miles;
Boone, CO

4 miles;
Pueblo, CO

Pueblo, CO

La Junta, CO

Cripple Creek,
CO

Penrose, CO

Project

Rattlesnake
Butte 115 kV
Expansion

Boone-Nyberg
115 kV Line

Baculite MesaFountain Lake
115 kV Rebuild

Fountain Lake
115 kV
Substation

La Junta Area
Upgrades

Arequa Gulch
115 kV Capacitor

Portland 115/69
kV Transformer
Replacement
Q4 2018

Q4 2018

Q1 2018

Q1 2017

Jan 2017

Oct.
2016

Sept.
2016

Inservice
Date

$2.5M

$1.5M

$6.2M

$10.5M

$2.0M

$8.7M

$1.85M

Estimated
cost
(millions)

60

Portland

Arequa
Gulch

La Junta,
Rocky Ford

Baculite
Mesa,
Fountain
Lake
New
Fountain
Lake
substation

Boone,
Nyberg

Rattlesnake
Butte

Terminal
Points

115/69
kV; 80
MVA

C13-0879

C15-0590

C09-1240

115 kV &
69 kV; 50
MVA
115 kV; 12
MVAR

C07-0553

C11-0749

C13-0879

C15-1182

Commission
Decision/
Proceeding

115 kV, 80
MVA

115 kV;
221 MVA

115 kV;
221 MVA

115 kV;
221 MVA

Voltage
and MW
Rating
Description

Replace existing 25 MVA Portland
transformer with 80 MVA unit.
Improve reliability and add additional
transformation capacity.

Improved voltage support

Convert Rattlesnake Butte substation
to a ring bus configuration and add a
terminal to interconnect a wind
generation project.
Increased reliability and capacity by
rebuilding the existing circuit with
larger conductor and adding a second
circuit, both on monopole steel
structures within existing ROW.
Rebuild existing 115 kV line between
Baculite Mesa and new Ftn. Lake
substation. Increased reliability and
gen interconnection.
New 115/69 kV substation at Belmont
Tap on the West Station - Overton 69
kV line. Additional voltage support
and load growth capacity.
Add transformation capacity at
BHCE’s Boone and La Junta
substations; Add a 69 kV capacitor.
Improved reliability in the La Junta
area.

Black Hills 2016 Rule 3206 Planned Transmission Projects

Table 7-2

Attachment LS-1
2016 ERP

 Q4 2019

Pueblo, CO

40 miles;
Pueblo, CO
Cañon City,
CO

Cañon City,
CO

West Station 115
kV Upgrade

West StationWest Canon 115
kV Line

North Cañon 115
kV Substation
Q4 2020

Q4 2018

Length and
Location

Project

Inservice
Date

$9.9M

$27.1M

$7.8M

Estimated
cost
(millions)

61

North
Cañon

West
Station,
West Cañon

West
Station

Terminal
Points

N/A

N/A

115/69
kV; 80
MVA

TBD

Commission
Decision/
Proceeding

115 kV;
221 MVA

115 kV;
239 MVA

Voltage
and MW
Rating

Description
Expand the West Station 115 kV
substation and relocate existing line
terminals from the old part of the
substation to the new expanded area
to increase thermal ratings. Increased
reliability and transfer capability.
New transmission line connecting
West Station and West Cañon with
load service substation at North
Cañon. Increased reliability and load
service.
New 115/69 kV substation at North
Cañon on the planned West Station –
West Cañon 115 kV line. Increased
reliability and load service.

Black Hills 2016 Rule 3206 Planned Transmission Projects

Table 7-2 cont.:

Attachment LS-1
2016 ERP

 Attachment LS-1
2016 ERP
All of the projects shown in Table 7-2 and included in Black Hills’ 2016 Rule 3206
filing are tentatively planned to be complete and in service during the RAP. Figure 73 shows the current Black Hills transmission system with the planned projects in
Table 7-2 identified by dashed lines.
Figure 7-3
Planned and Conceptual Black Hills Transmission System

6.5 Senate Bill 07-100 Transmission Projects
Colorado Senate Bill 07-100 (“SB-100”), codified at § 40-2-126(2), requires rate
regulated Colorado utilities to perform the following on or before October 31 of
each odd-numbered year:

(a) Designate Energy Resource Zones (“ERZ”s);
(b) Develop plans for the construction or expansion of transmission facilities
necessary to deliver electric power consistent with the timing of the
development of beneficial energy resources located in or near such zones;
62

 Attachment LS-1
2016 ERP
(c) Consider how transmission can be provided to encourage local ownership
of renewable facilities, whether through renewable energy cooperatives
as provided in Colo. Rev. Stat. § 7-56-210, or otherwise; and
(d) Submit proposed plans, designations, and applications for Certificates of
Public Convenience and Necessity to the Commission for simultaneous
review.
The Black Hills 2015 SB-100 Study evaluated resource injections from three ERZs to
determine resource injection capability. The point of injection for the evaluated
ERZs is shown below:
•
•
•

Nyberg 115 kV substation (ERZ-2);
West Cañon 230 kV substation (ERZ-4); and
Rattlesnake Butte 115 kV substation (ERZ-5).

The 2015 SB-100 Report identified several projects that would increase the ability
of the transmission system to accommodate resource injections12 from each ERZ.
These projects are described in Table 7-3. The resource injections shown in Table
7-3 are non-simultaneous injections that do not take into consideration potential
injections from other ERZs.

12

The 2015 BHCE SB-100 Report considered single contingency events when identifying resource
injection capability. Injection capability would be reduced when considering all applicable events as
identified in NERC Reliability Standard TPL-001-4.

63

 Attachment LS-1
2016 ERP
Table 7-3
Black Hills 2015 SB-100 Transmission Projects

ERZ

2

2016 Max
Injection
(MW)

250

4

200

4

330

5

219

5

219

Facility Upgrade Description

Facility
Upgrade
Cost ($)

New Max
Injection
(MW)

Incremental
Injection
Capability
(MW)

Replacing the limiting conductor
on the Nyberg-Airport Memorial
115 kV line (5 miles of 336 ACSR
30/7 Oriole) with 795 ACSR
Drake increases the rating from
119 MVA to 221 MVA. This project
is conceptual and no in-service
date has been assigned.

$1.75M

420

170

$12M

330

130

$27.1M

400

70

$7.8M

221

2

$7.8M

221

2

Add a second 230/115 kV
transformer (100 MVA+) at the
West Cañon substation. This
project is conceptual and no inservice date has been assigned.
Add a new 40-mile 115 kV
transmission line (221 MVA)
connecting West Station and West
Cañon. The tentative in-service
date for this conceptual project is
Q4 2019.
The planned upgrade of the West
Station 115 kV substation (Q4
2018) will increase the thermal
rating of the Hyde Park-West
Station 115 kV line from 120 MVA
to 221 MVA. This is one limitation
to achieving a 221 MVA rating on
the Reader-Rattlesnake Butte 115
kV line.
By upgrading terminal equipment
on the Pueblo Plant-Reader 115
kV line, the line’s rating would
increase from 160 MVA to 182
MVA. This is the other limitation
to achieving a 221 MVA rating on
the Reader-Rattlesnake Butte 115
kV line. This project is conceptual
and no in-service date has been
assigned.

64

 Attachment LS-1
2016 ERP

7.0 Future Resource Analysis and Selection
7.1 Resource Need
The Company developed a load and resource balance to compare its annual peak
demand with the annual peak capability of existing resources. The load and
resource balance highlights the year in which forecast load exceeds resources and
indicates a need for additional generation. The load and resource balance takes into
account the planning reserve requirement. Based on the peak demand forecast
developed by the Company, including the impacts of the DSM plan and expected
large customers load additions, the Company does not have a capacity deficit during
the RAP. Based on modest load growth during the entire Planning Period and the
addition of the PAGS LM6000 and Peak View Wind Project in 2017, the first year
that Black Hills has a capacity deficit is 2029.
The load and resource balance for the RAP, which includes the base load forecast
and existing resources, purchases and interruptible contracts, is shown in Table 8-1.
The load and resource balance for the entire Planning Period is included in
Appendix G.

65

 Attachment LS-1
2016 ERP

Peak Demand
DSM
Net Peak Demand

Table 8-1
Load and Resource Balance (2016-2022)
2016 2017
2018
2019
2020
398
403
408
413
418
(3.1)
(8.5)
(14.2) (16.7) (16.7)
395
395
394
396
401

Existing Resources **
Pueblo Diesels
10
Airport Diesels
10
Rocky Ford
10
Diesels
PAGS 1
90
PAGS 2
90
Busch Ranch
3.3
Ownership*
Peak View
Wind
PAGS LM6000
Total Resources
213

2022
414
(16.7)
397

10
10
10

10
10
10

10
10
10

10
10
10

10
10
10

10
10
10

90
90
3.3

90
90
3.3

90
90
3.3

90
90
3.3

90
90
3.3

90
90
3.3

13.8

13.8

13.8

13.8

13.8

13.8

40
267

40
267

40
267

40
267

40
267

40
267

200
5

200
5

200
5

200
5

3.3

3.3

3.3

3.3

5

5

5

5

Purchases and Interruptible Contracts
PAGS PPA
200
200
200
MPS
5
5
5
Busch Ranch
3.3
3.3
3.3
PPA*
Cargill Purchase
50
Interruptible
9.5
9.5
5
Contracts
Total Resources
and Purchases

2021
417
(16.7)
401

481.2

485.0

480.5

480.5

480.5

480.5

480.5

15% Reserve
Margin (MW)

59.3

59.2

59.1

59.5

60.1

60.1

59.6

Total Capacity
Requirement
(Peak plus
Reserves

454.6

454.0

452.9

456.0

461.0

460.7

456.6

19.5

19.8
4.9

23.9
6.0

Total Resources minus Total Capacity Requirement
In MW
26.5
31.0
27.6
24.5
% Excess
6.7
7.9
7.0
6.2

4.9

*23% of Busch Ranch and Peak View capacity counts as accredited capacity.
**Summer rated capacity shown.

66

 Attachment 
2016 ERP

7.2 Analysis

67

Attachment LS-1
2016 ERP
The Company undertook capacity expansion and production costing modeling to
determine the portfolio of future resources that meets the needs of its customers
over the Planning Period in the least cost manner. Subsequent to those analyses, the
Company conducted scenario analysis and risk analysis.
Capacity expansion modeling is a process used to determine the appropriate type,
size, and timing for economic resource additions for utilities. The utility’s existing
generation resources and future resource alternatives are input into a capacity
expansion model with a forecasted load. The model simulates utility operation and
“serves” the forecasted load with the utility’s existing resources and economically
“selects” additional resources from the list of available resource alternatives. The
typical criterion for evaluation is the expected present value of revenue
requirements (“PVRR”) subject to meeting load plus reserves and various resource
planning constraints, such as Colorado’s RES.
Production cost modeling simulates the hourly operation of the resources available
to a utility and is used to forecast system cost and risk exposure. A production cost
model includes an hourly dispatch model, with a load forecast and fixed resources to
serve that load. The model simulates a load every hour, then economically serves
that load with the available resources, and captures the associated cost. Production
cost modeling can also be completed using multiple iterations with changing
variables. This form of modeling provides a measure of risk associated with the
modeled plan subject to changing variables.
Scenario analysis was conducted during which the Capacity Expansion module was
used to derive optimal resource expansion plans. The scenarios include variations
in inputs representing the significant sources of portfolio cost variability and risk.
These inputs include the load forecast, the price of natural gas, and potential
enactment of carbon tax or other similar mechanisms.
Utilities must plan for the future electricity needs of their customers in an
environment of significant uncertainty. Thus, the analysis conducted for this 2016
ERP examined resource needs under a variety of possible future conditions.
Stochastic analysis and risk profile compilation were among the risk techniques
examined. The stochastic analysis conducted by ABB examined a wide range of
uncertainties that resulted in 50 unique future scenarios for price determination
and evaluation of a given portfolio of resources. The scenarios are driven by
variations in market price drivers (e.g. peak demand and energy forecast, natural
gas price, oil price, coal price, unit availability and capital costs) and take into
account statistical distributions, correlations and volatilities. Cumulative
probability distributions, also known as risk profiles, were used to visually assess
the results of the stochastic analysis.
All of the deterministic modeling used in the 2016 ERP analysis was performed by
Black Hills using ABB’s System Optimizer and PAR. The Company retained ABB to
68

 Attachment LS-1
2016 ERP
provide analytical services in support of the 2016 ERP. ABB reviewed the capacity
expansion and production cost modeling completed by the Company. This included
verifying input data from ABB’s 2015 WECC Fall Reference Case and reviewing
modeling results. Using the Company’s modeling results, ABB used its Strategic
Planning powered by MIDAS Gold® Corporate Finance module to model the financial
and risk simulations.
7.3 Base Plan Analysis and Alternative Plans
Rule 3604(k) of the Commission’s ERP Rules requires that the resource plan
contain:
Descriptions of at least three alternate plans that can be used to represent
the costs and benefits from increasing amounts of renewable energy
resources, demand-side resources, or Section 123 resources as defined in
paragraph 3602(q) potentially included in a cost-effective resource plan. One
of the alternate plans shall represent a baseline case that describes the costs
and benefits of the new utility resources required to meet the utility’s needs
during the planning period that minimize the net present value of revenue
requirements and that complies with the Renewable Energy Standard, 4 CCR
723-3-3650 et seq., as well as with the demand-side resource requirements
under § 40-3.2-104, C.R.S. The other alternate plans shall represent
alternative combinations of resources that meet the same resource needs as
the baseline case but that include proportionately more renewable energy
resources, demand-side resources, or Section 123 resources. The utility shall
propose a range of possible future scenarios and input sensitivities for the
purpose of testing the robustness of the alternate plans under various
parameters.
The process used to determine the base resource portfolio for the Company over the
planning horizon began by examining plans to meet the RES as well as alternative
plans with higher levels renewable energy resources or Section 123 resources. As
per the Rules, one of these plans shall represent a base case that describes the costs
and benefits of the new utility resources required to meet the utility’s needs during
the planning period that minimizes the PVRR and that complies with the RES as well
as the demand-side resources requirements. The Company refers to this plan as the
Base-with-RES Plan.

7.4 Base-with-RES Plan
The Base-with-RES Plan was developed in accordance with the following RES
requirements:
69

 Attachment LS-1
2016 ERP
Rule 3604(k) states that the baseline case and alternative plans must comply with
the RES. That standard requires the Company to provide specific percentages of
renewable energy and/or recycled energy according to the following schedule:
•
•

20% of its retail electricity sales in Colorado for the years 2015-2019; and
30% of its retail electricity sales in Colorado for the year 2020 and for each
following year.

Black Hills must also have a certain percentage of its retail sales produced by either
wholesale DG or retail DG, regardless of technology type, according to the following
schedule:
•
•
•

1.75% of its retail electricity sales in 2015 and 2016;
2% of its retail electricity sales in 2017-2019; and
3% of its retail electricity sales in 2020 and each following year.

At least one-half of the DG requirement must be generated by retail DG systems
located on-site at customers’ facilities or premises.
The Colorado RES currently includes credit multipliers for four types of projects.
These multipliers cannot be combined. Two of these types of projects are relevant
to the Company.
1. For purposes of compliance with the renewable energy standard, each
kilowatt-hour of eligible energy generated by an early eligible energy
resource shall be counted as 1.25 kilowatt-hours of eligible energy. Busch
Ranch Wind Project qualifies as an early Eligible energy resource.
2. Electricity generated at a “community-based project” – a project not greater
than 30 MW in capacity that is located in Colorado and owned by individual
residents of a community or by an organization or cooperative that is
controlled by individual residents, or by a local government entity or tribal
council – can receive 150 percent credit for RES-compliance purposes.
The Capacity Expansion model was used to develop the Base-with-RES Plan by using
constraint logic to select the optimal resources to meet the RES throughout the
Planning Period. The constraint logic was set up to require the model to
economically select renewable technologies to meet the RES. The following
assumptions were made:
•
•

All existing conventional resources were included as available resources
Eligible energy resources included:
o Busch Ranch PPA and Ownership – 29.04 MW.
o Peak View Wind Project – 60 MW.
o Existing and authorized On-site Solar – 15 MW.
o Vestas 1.8 MW wind turbine.
70

 Attachment LS-1
2016 ERP
•
•
•
•
•
•
•
•

No carbon tax.
DSM demand and energy reductions in compliance the Company’s 20162018 DSM Plan. The impacts of the DSM plan were assumed to reduce load.
Firm seasonal market purchases up to 75 MW (2016-2021).
Firm seasonal market purchases up to 50 MW after 2021.
RECs were used for compliance with the RES.
Eligible energy resource options included 30 MW and 60 MW generic wind
resources and 10 MW, 30 MW and 60 MW solar resources.
A waste-to-energy resource and a sodium sulfur battery were included as
Section 123 resource options.
DG requirements were met with existing on-site solar and CSG, proposed
solar on-site and CSG in the Company’s 2018 – 2021 RES Compliance Plan,
the Vestas 1.8 MW wind turbine and the Busch Ranch Wind Project.
7.5 Alternative Plan 1

Pursuant to Rule 3604(k) two alternative plans were modeled. The first alternative
plan (labeled Alternative Plan 1) incorporated the same assumptions as included in
the Base-with-RES Plan and the following assumption:
•

The level of required Eligible energy resources or Section 123 resources was
increased such that the increased requirement could be fulfilled by adding a
single facility.
7.6 Alternative Plan 2

The second alternative plan (labeled Alternative Plan 2) incorporated the same
assumptions as included in the Base-with-RES Plan and the following assumptions:
•

The level of required Eligible energy resources or Section 123 resources was
increased such that the increased requirement could be fulfilled by adding
multiple facilities.

The resources added over the Planning Period for each of these three plans are
shown in Table 8-2. The deterministic PVRRs for these plans are shown in Figure 81.

71

 Attachment LS-1
2016 ERP

Year
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034

Table 8-2
Expansion Plans
Base-with-RES
Alternative
Plan 1

Alternative Plan
2

60 MW Wind

60 MW Wind

60 MW Wind

30 MW Wind

30 MW Wind

30 MW Wind

(2) LMS100, 25
MW SFMP
25 MW SFMP
25 MW SFMP

(2) LMS100, 25
MW SFMP
25 MW SFMP
25 MW SFMP
(2) 10 MW Solar,
25 MW SFMP
25 MW SFMP
25 MW SFMP
60 MW Wind, 25
MW SFMP

(2) LMS100, 25
MW SFMP
25 MW SFMP
25 MW SFMP
(2) 10 MW Solar,
25 MW SFMP
25 MW SFMP
25 MW SFMP
60 MW Wind, 25
MW SFMP
Sodium Sulfur
Battery 10 MW,
25 MW SFMP
(2) Sodium Sulfur
Battery 10 MW,
25 MW SFMP

2035
2036
2037

25 MW SFMP
25 MW SFMP
50 MW SFMP
60 MW Wind, 25
2038
MW SFMP

2039

25 MW SFMP

25 MW SFMP

2040

25 MW SFMP

25 MW SFMP

SFMP denotes seasonal firm market power of 25, 50 or 75 MW

72

 Attachment LS-1
2016 ERP
Figure 8-1
Deterministic 25 Year PVRR (2016-2040)
25 Year PVRR
$2,200

$2,100

$2,059.8

$2,060.2

$2,065.0

Million $

$2,000

$1,900

$1,800

$1,700

Source: ABB Advisors

Projected annual capacity factors for existing resources in the Base-with-RES Plan
are shown in Table 8-3. Projected emission rates for the Company’s existing
resources are found in Confidential Schedule K-16, Appendix K.
Table 8-3
Base-with-RES Plan
Annual Capacity Factors during the Resource Acquisition Period (%)
2016 2017 2018 2019 2020 2021 2022
PAGS CT 1
3.2
3.3
4.7
3.7
3.9
3.9
3.7
PAGS CT 2
0.8
0.5
0.4
0.3
0.5
0.8
0.6
PAGS LM6000
0.0
0.1
0.1
0.2
0.2
0.1
0.1
Pueblo Diesels
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Airport Diesels
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Rocky Ford Diesels
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Busch Ranch Wind
38
38
38
38
38
38
38
Project
Peak View Wind
41.5
41.5
41.5
41.5
41.5
41.5
41.5
Project

73

 Attachment LS-1
2016 ERP
7.7 Retail Rate Impact Analysis
The Base-with-RES Plan that was modeled incorporates all of the additional
renewable energy resources that would be required to achieve the RES
requirements during the RAP and the Planning Period. This plan adds 60 MW of
wind resources in 2019, 30 MW of wind resources in 2026, and 60 MW of wind
resources in 2038. However, in order to determine whether this plan complies with
the RES, it is necessary to consider the net retail rate impact of this plan under the
RES Rules. Rule 3661(a) provides that “the net retail rate impact of actions taken by
an investor owned QRU [qualifying retail utility] to comply with the renewable
energy standard shall not exceed two percent of the total electric bill annually for
each customer of that QRU.”
The Company collects a monthly 2 percent surcharge from each of its customers
which funds the RESA. This is the money available to pay the net incremental cost of
Eligible energy resources. At the end of 2015, the Company had a negative balance
in its RESA account of $4,043,450 due to funds advanced by the Company to acquire
Eligible energy resources. The Company receives interest at its weighted-average
cost of capital on this negative balance.
As was demonstrated in the Company’s Peak View Wind Project proceeding, wind
resources can be added for no or very little net incremental cost. When wind energy
is being generated, the utility generates less from conventional resources. As a
result, the utility avoids the cost of fuel (natural gas in the Company’s case),
economy energy purchases, and variable O&M costs. Natural gas prices are
forecasted to remain low for a number of years which means that the avoided costs
of wind will remain low. The Peak View Wind Project modeling showed that the
avoided costs associated with the conventional generation that was displaced by the
wind resource was greater than the cost of the wind resource.
To determine the net incremental cost of Eligible energy resources, the Company
compared two scenarios to estimate the costs and benefits of each system over the
RES Planning Period. (Rule 3661(h)). The first scenario is a “RES plan” that reflects
the utility’s plans and actions to acquire new Eligible energy resources necessary to
meet the RES. This is the Base-with-RES Plan in this 2016 ERP. The second scenario
is a “No-RES plan,” which reflects the utility’s resource plan that models the new
Eligible energy resources in the RES plan with new nonrenewable resources
reasonably available. Net incremental cost is determined over a ten-year RES
Planning Period. Rule 3661(f). In the Company’s 2018-2021 RES Compliance Plan
(Attachment LS-2), the Company has presented a RES/No-RES comparison for the
addition of the 60 MW of wind resources in 2019. To determine the net incremental
cost of the 60 MW of wind in the Base-with-RES Plan, the following two portfolios
were compared:

74

 Attachment LS-1
2016 ERP
o Base-with-RES and 2019 60 MW Wind Resource - This plan
includes the 60 MW Wind Resource in 2019 and the Eligible energy
resources that have been locked-down in prior proceedings.
o No-RES Plan - This model includes all of the Company’s existing
conventional resources and the Eligible energy resources that have
been locked-down in prior proceedings.
The portfolios were compared through computer modeling so that the benefits
associated with the addition of wind could be captured. Those benefits are the
avoided costs of fossil fuel expense, purchased power expense, and variable O&M
production expense.
The RES/No-RES Comparison showed that the addition of 60 MW of wind energy in
2019 would provide approximately $74 million of avoided cost savings over the tenyear RES Planning Period, from 2019 through 2027. The cost of the 60 MW of wind
over the same time period, 2019 through 2027, is projected to be $76 million. Thus,
the addition of 60 MW of a wind resource in 2019 would result in a net incremental
cost of approximately $2 million over the ten year RES Planning Period. Over the
2016 ERP Planning Period (2016-2040), the modeling shows that the 60 MW wind
resource in 2019 would provide approximately $305 million of avoided cost savings
from 2019 through 2040. The cost of the 60 MW of wind resource over the same
time period, 2019 through 2040, is projected to be $235.6 million. Thus, the
addition of 60 MW of wind energy in 2019 would result in a net incremental savings
of approximately $69.3 million over the 25 year 2016 ERP Planning Period.
The Company used the same methodology to calculate the net incremental cost of
the two other Eligible energy resources identified in the Base-with-RES Plan – a 30
MW wind resource in 2026 and 60 MW wind resource in 2038. The Company
compared a model that included only the 30 MW wind resource in 2026 to a model
that excluded all Eligible energy resources except Eligible energy resources that
have been locked-down in previous proceedings. This same comparison was
completed for the 60 MW wind resource in 2038. Tables A-1, A-2, A-3, and A-4 in
Appendix A shows the avoided cost calculations associated with the proposed
Eligible energy resources in 2019, 2026, and 2038, the impact of the proposed
resources on the Company’s RESA balance and the Company’s REC compliance with
the addition of the proposed Eligible energy resources.

75

 Attachment LS-1
2016 ERP
Table 8-4
Incremental Cost/Saving of Eligible Energy Resources
($ Million)
Avoided Costs Incremental
Costs
Cost/Saving
2019 60 MW
Wind
$304.9 $235.6
$69.3
2026 60 MW
Wind
$127.8 $157.3
$29.4
2038 60 MW
Wind
$70.7
$63.4
$7.3
Over the 25-year 2016 ERP Planning Period, the retail rate impact of the addition of
the 60 MW wind resource in 2019, 30 MW wind resource in 2026, and the 60 MW
wind resource in 2038 is shown in Schedule A-4 in Appendix A. The Company
forecasts that the RESA will have a positive balance by 2020 if the Company meets
all of its existing and authorized REC obligations and the Commission approves the
Company’s proposed 2018-2021 solar programs and the acquisition of up to 60 MW
of Eligible energy resources in 2019 through a solicitation process. Schedule A-4
also shows that over the course of the 2016 ERP Planning Period the Company will
have sufficient RESA funds to acquire Eligible energy resources in 2026 and 2038.
7.8 Scenario Analysis
Scenario analysis was conducted during which the Capacity Expansion module was
used to derive optimal resource expansion plans. The scenarios include variations
in inputs representing the significant sources of portfolio cost variability and risk.
These scenarios and a brief description of the scenario variables are listed below:
1. Base-with-RES Plan
• See Section 8.4 above
2. Environmental Scenario
• Same build assumptions as Base-with-RES Plan
• CO2 emissions prices from ABB’s CO2 Tax scenario
• Gas and market prices from ABB’s CO2 Tax scenario
3. High Load Scenario
• Same build assumptions as Base-with-RES Plan
• High load forecast
4. Low Load Scenario
• Same build assumptions as Base-with-RES Plan
• Low load forecast
5. High Gas Scenario
• Same build assumptions as Base-with-RES Plan
• Gas and market prices from ABB’s High Gas scenario
6. Low Gas Scenario
76

 Attachment LS-1
2016 ERP
• Same build assumptions as Base-with-RES Plan
• Gas and market prices from ABB’s Low Gas scenario
7. NYMEX Gas Price Scenario
• Same build assumptions as Base-with-RES Plan
• Gas prices from the Company’s NYMEX gas price forecast (describe in
Section 3.4.1)

The electric market price, natural gas price and CO2 tax cost forecasts used during
the scenario analysis are included in Confidential Schedules K-1 through K-15,
Appendix K.
Capacity Expansion modeling results (resource portfolios) for these scenarios are
shown in Table 8-5. The PVRRs for the scenario analysis are shown on Figure 8-2.

77

 Attachment LS-1
2016 ERP

Year

Basewith-RES

Table 8-5
Optimal Expansion Plans – Scenario Analysis
Low
High
Low Gas
High Gas
NYMEX
Load
Load
Price
Price
Gas Price

Environ
mental

2016
2017
60 MW
Wind

2018
2019
2020
2021
2022
2023

60 MW
Wind

60 MW
Wind

2024
2025

2026

30 MW
Wind

30 MW
Wind

2027
2028
2029
2030
2031

2032
2033
2034
2035
2036
2037

2038
2039
2040

(2)
LMS100,
25 MW
SFMP
25 MW
SFMP
25 MW
SFMP
25 MW
SFMP
25 MW
SFMP
50 MW
SFMP
60 MW
Wind, 25
MW SFMP
25 MW
SFMP
25 MW
SFMP

LMS100,
50 MW
SFMP
50 MW
SFMP
50 MW
SFMP
50 MW
SFMP
50 MW
SFMP
50 MW
SFMP
60 MW
Wind, 50
MW SFMP
50 MW
SFMP
50 MW
SFMP

60 MW
Wind

25 MW
SFMP
25 MW
SFMP
30 MW
Wind, 25
MW SFMP
25 MW
SFMP
50 MW
SFMP
50 MW
SFMP
50 MW
SFMP
50 MW
SFMP

(3)
LMS100

25 MW
SFMP
25 MW
SFMP
25 MW
SFMP
60 MW
Wind, 25
MW SFMP
25 MW
SFMP
50 MW
SFMP

60 MW
Wind

30 MW
Wind

60 MW
Wind

60 MW
Wind

30 MW
Wind

30 MW
Wind

30 MW
Wind

(2)
LMS100, 25
MW SFMP
25 MW
SFMP
25 MW
SFMP
25 MW
SFMP
25 MW
SFMP
50 MW
SFMP
60 MW
Wind, 25
MW SFMP
25 MW
SFMP
25 MW
SFMP

(2) LMS100,
25 MW
SFMP
25 MW
SFMP
25 MW
SFMP
25 MW
SFMP
25 MW
SFMP
50 MW
SFMP
60 MW
Wind, 25
MW SFMP
25 MW
SFMP
25 MW
SFMP

(2)
LMS100,
25 MW
SFMP
25 MW
SFMP
25 MW
SFMP
25 MW
SFMP
25 MW
SFMP
50 MW
SFMP
60 MW
Wind, 25
MW SFMP
25 MW
SFMP
25 MW
SFMP

25 MW
SFMP
65 MW 6FA,
LMS100, 50
MW SFMP
50 MW
SFMP
50 MW
SFMP
50 MW
SFMP
50 MW
SFMP
50 MW
SFMP
60 MW
Wind, 50
MW SFMP
50 MW
SFMP
50 MW
SFMP

SFMP denotes seasonal firm market power of 25, 50 or 75 MW

78

 Attachment LS-1
2016 ERP

Figure 8-2
Base-with-RES Plan and Scenarios – Deterministic PVRRs (2016-2040)
25 Year PVRR
$2,300
$2,200
$2,132.1

Million $

$2,100

$2,059.8

$2,059.8 $2,062.9

$2,059.8

$2,053.2

$2,060.2

$2,065.0

$2,009.9

$2,000
$1,900
$1,800
$1,700

Source: ABB Advisors

7.9 Risk Analysis
Utilities must plan for future customer needs for electricity in an environment of
significant uncertainty. Thus, the analysis conducted for the 2016 ERP examined
uncertainty under a variety of possible future conditions. Stochastic analysis and
risk profile compilation were among the risk techniques examined.
8.9.1

Stochastic Analysis

ABB’s PAR includes a stochastic model to simulate volatility in electricity, fuel
prices, and loads. PAR has a regression tool that uses historical data to calculate the
stochastic properties of these variables, including their volatility, short-term meanreversion, and the correlations among the random time-series. Monte Carlo
simulation is then performed, with sample random draws from specified
distributions. The power simulation model uses these random paths to optimize
commitment and dispatch along each random path. Also the PAR stochastic
modeling framework allows the user to specify for each stochastic entity either a
normal or lognormal distribution. Load was modeled with a normal distribution
79

 Attachment LS-1
2016 ERP
while the market prices and natural gas prices were modeled with a lognormal
distribution.
In PAR three different variable types are set as stochastic variables: load, market
price and natural gas price. Stochastic runs were performed using 50 iterations in
PAR for the 9 different scenarios. The model calculated the short-term volatilities,
shown in Table 8-6 and mean reversion rates shown in Table 8-7 for the stochastic
variables. Correlations between the variables were also calculated and are shown in
Table 8-8, Table 8-9 and Table 8-10. During the stochastic evaluations, the prices
and associated uncertainties provide sufficient information about the market to
allow for proper evaluation of alternatives. For example, high gas prices would
generally result in high on-peak prices for market power.
Table 8-6
Short-Term Volatilities

January
February
March
April
May
June
July
August
September
October
November
December

BHCE Load
0.039
0.036
0.038
0.036
0.043
0.060
0.053
0.043
0.056
0.038
0.037
0.036

Electric
Price AZPV
0.083
0.169
0.126
0.123
0.087
0.136
0.115
0.077
0.059
0.119
0.057
0.228

(Source: ABB Advisors)

80

Electric
Prices
CO-East
0.199
0.088
0.117
0.050
0.094
0.146
0.174
0.105
0.117
0.092
0.077
0.221

Natural Gas Price
0.083
0.140
0.116
0.086
0.032
0.0670
0.071
0.019
0.052
0.158
0.092
0.198

 Attachment LS-1
2016 ERP
Table 8-7
Mean Reversion Rates

January
February
March
April
May
June
July
August
September
October
November
December

BHCE
Load
0.241
0.112
0.252
0.203
0.287
0.175
0.220
0.222
0.162
0.282
0.179
0.198

Electric
Price AZPV
0.000
0.000
0.299
0.101
0.300
0.101
0.488
0.559
0.000
0.608
0.000
0.113

Electric
Prices
CO-East
0.000
0.124
0.323
0.055
0.000
0.220
0.327
0.377
0.481
0.350
0.000
0.509

Natural Gas
Price
0.000
0.014
0.202
0.000
0.257
0.000
0.311
0.077
0.092
0.389
0.000
0.000

(Source: ABB Advisors)

Table 8-8
Load Correlations

January
February
March
April
May
June
July
August
September
October
November
December

Electric
Price
AZ-PV
0.763
-0.534
0.389
-0.477
0.300
0.817
0.540
-0.118
0.171
-0.266
-0.033
-0.118

Electric
Prices
CO-East
-0.181
0.155
-0.618
-0.259
-0.830
-0.506
0.571
-0.452
0.211
-0.469
-0.671
-0.158

(Source: ABB Advisors)

81

Natural Gas
Price
0.245
-0.614
-0.252
-0.517
-0.129
-0.225
0.761
-0.308
-0.360
-0.275
-0.048
0.249

 Attachment LS-1
2016 ERP
Table 8-9
AZ-PV Market Price Correlations
AZ-PV Correlations
Electric Prices
Natural Gas
CO-East
Price
January
0.219
0.480
February
0.301
0.951
March
-0.400
0.388
April
0.323
0.822
May
-0.107
0.443
June
-0.243
-0.299
July
-0.282
0.113
August
-0.122
0.535
September
0.668
0.480
October
0.847
0.956
November
-0.270
0.623
December
0.591
0.895
(Source: ABB Advisors)

Table 8-10
CO-East Market Price Correlations
CO-East Correlations
Natural Gas
Price
January
0.643
February
0.318
March
0.328
April
0.560
May
0.539
June
0.440
July
0.847
August
0.252
September
0.528
October
0.728
November
0.236
December
0.584
(Source: ABB Advisors)

In addition, using Strategic Planning’s Stratified Monte Carlo sampling program,
ABB created 50 future scenarios for portfolio capital cost evaluation. Uncertainty
82

 Attachment LS-1
2016 ERP
draws were made for the capital cost of the resource additions in the portfolio
evaluation. These capital cost draws were combined with the uncertainty draws
from the PAR runs. The uncertainties shown in Table 8-11 were examined in the
2016 ERP and resulted in 50 future scenarios for price development and portfolio
evaluation.
Table 8-11
Uncertainty Variable Range Multipliers

Uncertainty
Combustion Turbine Capital Cost
Combined Cycle Capital Cost
Reciprocating Engine Capital Cost
Wind Capital Cost
Battery Capital Cost

Uncertainty
Range
Multiplier
1.0-1.10
1.0-1.10
1.0-1.15
1.0-1.20
0.9-1.10

Each scenario was designed to maintain a minimum 15 percent reserve margin.
However, when exposed to demand uncertainty in the risk analysis, there are
certain scenarios where the expansion plan did not meet the 15 percent reserve
margin. In the stochastic scenarios that had a reserve margin deficit, the installed
capacity (“ICAP”) price from the 2015 WECC Fall Reference case was used as a proxy
for the capacity market to meet the target 15 percent reserve margin
8.9.2 Risk Profiles
During the stochastic analysis, the expansion plans optimized for each case remain
the same. The analysis examines the costs of each expansion plan assuming 50
different “futures” and tabulates the PVRR expected for each of those 50 futures. A
risk profile for each expansion plan is then constructed using all 50 of those “future”
PVRR points. For this study, ABB combined the Company’s PAR production results
with the Strategic Planning financial results. Figure 8-3 shows the risk profiles for
the Base-with-RES Plan, Alternative Plan 1, Alternative Plan 2, and the scenarios. In
Figure 8-3, with the exception of the low load and low gas scenario, the Base-withRES plan is the closest to the left and has the lowest PVRR in all years. The capacity
expansion module develops resource expansion plans over a 30-year horizon.
Because the study period was only 25 years, the impact of the capacity deficit for the
Low Load scenario in later years was not included in the PVRR, therefore making it a
lower cost plan than the Base-with-RES Plan. The Low Gas scenario builds one LMS
100 and a 6FA unit in 2032 instead of two LMS 100s included in the Base-with-RES
Plan. The PVRRs in these two cases are within 0.3 percent of each other. The

83

 Attachment LS-1
2016 ERP
benefit of the LMS 100 over the 6FA is not fully captured in the study period because
they were both brought on-line late in the study period.
One can view the risk profile to determine the probability that PVRR will be a
particular value. Using the Base-with-RES Plan as an example in Figure 8-3, there is
a 90 percent probability that PVRR could be as much as $2.112 billion with an
expected value of $2.068 billion. From the prior deterministic simulation, the PVRR
value was $2.060 billion under “base case” conditions. The $8 million difference
between the expected value and the deterministic value is “real option value” or
extrinsic value. This reflects the risk of the Base-with-RES Plan with future
uncertainty.

84

 Cumulative Probability

$2,050

$2,100

$2,150

Nymax Gas

Low Load

85

Base Alt 1

High Gas

Base Alt 2

Low Gas

Present Value of Revenue Requirements (Millions $)

$2,000

Environmental

$1,950

Base

$1,900

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Risk Profiles

Figure 8-3
Risk Profiles – All Plans and Scenarios

High Load

$2,200

$2,250

Attachment LS-1
2016 ERP

 Attachment LS-1
2016 ERP
7.10 Preferred Plan
As a result of the load and resource balance, capacity expansion, production cost
modeling, retail rate impact evaluations and risk analysis, Black Hills is
recommending a Preferred Plan that does not include the addition of any new
capacity resources during the RAP. However, based on bid pricing that were
received in the Company’s 2014 All-Source Solicitation, the Preferred Plan does
include the addition of 60 MW of wind resources in 2019. The Company used bid
data submitted in the 2014 All-Source Solicitation as inputs in its modeling and
based on the bid prices, the forecasted cost of natural gas and the forecasted electric
prices the model identified a 60 wind resource in 2019 as an economical option for
energy. In addition, with the acquisition of 60 MW of wind resources in 2019 Black
Hills will be able to acquire all of the RECs required by the RES Statute and RES
Rules through 2025. By 2026, the Company will likely need to acquire additional
Eligible energy resources in order to stay in compliance with the RES. However,
based on the Commission’s current electric resource planning and RES rules these
additions will be considered in future resource plans.
Beyond the RAP, Black Hills’ load and resource balance shows a small capacity
deficit in 2029 growing by a few megawatts each year until the beginning of 2032
when the Company’s contract for 200 MW of generation expires. The modeling
identified an optimal portfolio to replace this expiring contract, however, based on
current Commission electric resource planning rules the Company will be required
to complete at least two resource plans prior to considering the appropriate
replacement capacity for this contract.
Black Hills’ Preferred Plan recommends that the Company engage in a Phase II
competitive solicitation to acquire up to 60 MW of Eligible energy resources by
2019. This solicitation and subsequent analysis will allow the Company to
determine if Eligible energy resources can be acquired at a cost that will provide
savings for customers, meet the requirements of the retail rate impact and generate
sufficient RECs such that Black Hills will comply with the RES standard through
2025.
The planning assumptions used in this 2016 ERP will underlie the evaluation of
proposals received in response to a Company solicitation in a Phase II of this 2016
ERP Proceeding. The Company has included a list of General Planning Assumptions
in Appendix H that were used in the 2016 ERP modeling and will be used in a
solicitation process. These assumptions represent “base case” assumptions.
Sensitivity analysis will be performed in which certain of these assumptions are
altered in accordance with any Commission directives. The Company has indicated
in the General Planning Assumptions table those assumptions that will be updated
for the evaluation of proposals.

86

 Attachment LS-1
2016 ERP
7.11 2018 through 2021 RES Compliance Plan
The Company is filing, concurrently with this 2016 ERP, its 2018 through 2021 RES
Compliance Plan. The RES Plan is being filed by Black Hills pursuant to the RES
established by the RES Statute and implemented by the RES Rules. The RES Plan
details how the Company will comply with the RES Rules covering compliance years
2018 through 2021, the RAP and the 10-year RES Compliance Period.

87

 Attachment LS-1
2016 ERP
8.0

Contingency Plan

Rule 3609(c) requires the utility to develop contingency plans for the RAP and to
provide, under seal, a description of its proposed contingency plans for the
acquisition of (1) additional resources if actual circumstances deviate from the most
likely estimate of future resource needs developed pursuant to Rule 3610, or (2)
replacement resources in the event that resources are not developed in accordance
with a Commission-approved plan under Rule 3617. 13
Black Hills’ Preferred Plan does not include the addition of any new capacity
resources during the RAP therefore a contingency plan for the acquisition of new
resources or replacement resources in the event that resources are not developed in
accordance with a Commission-approved plan is not required. Black Hills has
sufficient existing resources and resources that are currently under construction to
supply the projected capacity and energy needs of its customer through the RAP.
9.0 RFPs and Model Contracts
As required by Rule 3604(i), the Company is filing in Volume III of this 2016 ERP the
proposed RFP(s) and model contracts the Company intends to use to solicit Section
123 resources and Eligible energy resources discussed in Section 6.3 of this 2016
ERP.

13

Black Hills is not filing contingency plans under seal because the 2016 ERP does not include the
addition of any new capacity resources during the RAP. To the extent this would require a waiver of
Rule 3609(c), such a waiver is hereby requested.

88

 Attachment LS-1
2016 ERP
10.0 Confidential and Highly Confidential Information
As required by Rule 3604(j), the Company must provide a list of the information
related to the resource plan proceeding that the utility claims is confidential and a
list of the information related to the resource plan proceeding that the utility claims
is highly confidential. The utility shall also list the information that it will provide to
owners or developers of a potential resource under Rules 3613(a) and (b). The
utility shall further explicitly list the protections it proposes for bid prices, other bid
details, information concerning a new resource that the utility proposes to build and
own as a rate base investment, other modeling inputs and assumptions, and the
results of bid evaluation and selection. The protections sought by the utility for
these items shall be specified in the motion(s) submitted under Rule 3603(b). For
good cause shown, the utility may seek to protect additional information as
confidential or highly confidential by filing the appropriate motion under Rule 1100
of the Commission’s Rules of Practice and Procedure in a timely manner.
10.1 Public Information
The following information that is relevant to the 2016 ERP is, or will be, public
information as the result of the Company filing the information in Phase I or Phase II
of the 2016 ERP or as the result of a prior filing with the Commission, the State of
Colorado or with federal agencies:
11.1.1
•
•
•
•
•
•
•

Company Information
Annual Sales
Annual Revenue
Resource Need for RAP
RES Status
RESA
o Balance
o Forecast
Peak Demand and Energy Forecast
o Annual / Monthly Peak Demand
o Annual / Monthly Total Energy Demand
Total DSM Costs

11.1.2 Purchased Generation Resource Information
•
•
•

Capacity
Contract Duration
Contract Modification Terms

11.1.3 Model Input Data

89

 Attachment LS-1
2016 ERP
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•

Depreciation and Amortization Expense
Capacity
Average Heat Rate
Fuel Type
Expected Retirement Date
Contract Duration
Contract Modification Terms
Inflation/Escalation Rate
Federal Tax Rate
State Tax Rate
Discount Rate
Weighted-Average Cost of Capital
Wind Integration Costs
Solar Integration Costs
DSM Forecast
Reserve Margin Requirements
Spinning Reserve Requirement
In-Service Dates
Unit Capacities
PPA In-service Dates
PPA Retirement Dates
PPA Capacities
Generic Resources
o Name Plate Capacity
o Summer Peak Capacity
o Capital Costs
o Book Life
o Fixed O&M
o Variable O&M
o Heat Rate Curves
o Forced Outage Rates
o Typical Annual Maintenance Requirements
o CO2 Emission Rate
o NOx Emission Rate
o SO2 Emission Rate
o PPA Pricing if applicable

11.1.4 Modeling Output Data
•
•
•
•

Annual System Capacity Obligation
Total System Capacity
Capacity Additions (Expansion Plans)
Capacity Retirements
90

 Attachment LS-1
2016 ERP
•
•
•
•
•

•
•

Total Emissions by Type
Unit Emissions by Type
Total Fuel Consumed
Capacity Factors
System Emissions
o CO2
o SO2
o NOx
o Mercury
Average Cost per-kWh modeling output
Total System Present Value of Revenue Requirements

The models developed for the Company’s 2016 ERP contain thousands of data
points that were used to represent the Company’s system. Model inputs are not
contained in files that would be easily understood or manipulated. Specific
questions concerning data input will receive an informative response. Worksheets
developed by the Company for provision to the modeling vendor and output
worksheets can be provided.
11.0 Confidential Information
The Company will seek to protect the following proprietary information as
confidential information:
11.1 Modeling Input Data
•
•
•
•
•
•
•
•
•
•
•

•
•
•
•

Hourly Load Patterns
Monthly On/Off Peak Market Prices
Market Emission Assumptions
Unit Variable O&M
Unit Fixed O&M
Fuel Costs
Unit Contribution to Spinning Reserve
SO2 Pricing
NOx Pricing
CO2 Pricing
Unit Emission Rates
o SO2
o NOx
o CO2
o Mercury
PPA Capacity Pricing (subject to contractual limitations)
PPA Energy Pricing (subject to contractual limitations)
PPA Energy Schedules (subject to contractual limitations)
PPA Contribution to Spinning Reserves
91

 Attachment LS-1
2016 ERP
•

•
•
•

PPA Emission Rates
o CO2
o SO2
o NOx
o Mercury
Hourly Wind Patterns
Hourly Solar Patterns
Generic Renewable Resources
o Capital Costs
o Fixed O&M
o Variable O&M
11.2 Modeling Output Data

•
•
•
•
•
•
•
•
•
•
•

•
•
•
•
•
•
•
•

Unit Level Generation
Unit Level Capacity Factors
Unit Level Fuel Consumed
Unit Level Average Heat Rate
Unit Level Total Variable O&M
Unit Level Fixed O&M
Fuel Cost
o Coal Cost Projection
o Gas Cost Projection
Capacity
Energy Purchased
Cost of Energy Purchased
Unit Level Emissions
o NOx
o SO2
o CO2
o Mercury
PPA Maximum Capacities
PPA Summer Accredited Capacities
PPA Accredited Capacities
PPA Generation
PPA Capacity Factors
PPA Total Energy Payments (subject to contractual limitations)
PPA Total Capacity Payments (subject to contractual limitations)
PPA Emissions
o NOx
o SO2
o CO2
o Mercury

92

 Attachment LS-1
2016 ERP
12.0 Highly Confidential Information
The Company considers the following proprietary information as highly confidential
information:
•
•
•
•
•
•
•

Unit Level Delivered Fuel Costs
Hourly Market Price Data
Unit Level Heat Rate Curves
Unit Detailed Maintenance Schedules
Bid Information of any sort (from the Company and from other entities)
Pricing and any other commercially sensitive information regarding a PPA
Certain Modeling Files

The Company believes that disclosure of the items listed above can cause
irreparable harm to the Company’s trading operations, the Company’s ability to
solicit cost-effective resources, and, ultimately, the Company’s customers. The
Company will seek to limit access in accordance with the Commission’s rules.
13.0 Information that the Company will Provide Bidders
The Company will provide the following information developed by the Company to
bidders with respect to their own bids after initial bid screening and before Phase II
modeling:
•

Transmission Interconnection Costs

14.0 Implementation of Separation Policy
The Company will implement a separation policy in the event that one of its
affiliates decides to submit a bid in a Phase II acquisition process.
15.0 Protection of Bid Information, Modeling Inputs and Assumptions, and
Bid Evaluation Results
The Company will seek to protect all bid information and bid evaluation results
(including Company self-build proposals) that would reveal specific bid pricing or
other bid information, as highly confidential information in accordance with the
Commission’s rules, until completion of the resource acquisition process, i.e. until
the last contract for a resource that meets a portion of the 2016 ERP resource need
is signed. In accordance with Commission Rule 3613(k), upon completion of the
resource acquisition process, the Company will post on its website the following bid
information:
•
•

Bidder Name
Bid Price (Utility Cost for Utility–Owned Bid Proposals)
93

 Attachment LS-1
2016 ERP
•
•
•
•

Generation Technology Type
Size of Facility
Contract Duration (Expected Useful Life of Utility Resource)
Purchase Option Details as relevant

In accordance with the ERP Rule 3613(j), within fourteen months after
completion of the resource acquisition process, the Company will make public
confidential information that was redacted from testimony and reports by refiling the testimony or reports in an un-redacted form. If any Company highly
confidential modeling inputs and assumptions listed above under highly
confidential information are entered into the record in any manner, the
Company will seek to indefinitely continue the confidentiality protections
ordered by the Commission.

94

 Attachment LS-1
2016 ERP
16.0 Water Usage
The Company’s generation facilities vary in their water consumption. Table 16-1
identifies the actual gallons consumed by the Company’s existing facilities in 2015
and the gallons consumed per MWh, also known as water intensity, for the current
generation fleet.
Table 16-1
Water Resources – Existing Generating Facilities
Unit Name
Fuel
2015
Annual
Water Intensity
Type
MWh
Water
(gallons/MWh)
Consumption
(gallons)
PAGS CT 1
Nat Gas
29,413
8,443,512
287.1
PAGS CT 2
Nat Gas
79,438
22,810,726
287.2
Pueblo Diesels
#2 Oil Negligible
Negligible
Negligible
Airport Diesels
#2 Oil Negligible
Negligible
Negligible
Rocky Ford
#2 Oil Negligible
Negligible
Negligible
Diesels
Annual Total
31,254,238
Table 16-2 shows the expected annual water consumption for conventional
resources that were included in the analysis of future resources in this 2016 ERP.
Water consumption values were forecasted assuming a 5 percent capacity factor
and 30 percent capacity factor for some of the possible resources and a 30 percent
and 70 percent capacity factor was assumed for some resources.

95

 Attachment LS-1
2016 ERP
Table 16-2
Water Resources – Potential Generating Facilities
Annual Water
Annual Water
Consumption
Consumption
Fuel
Unit Name
5% Capacity
30% Capacity
Type
Factor
Factor
(Gallons)
(Gallons)
LMS 100
Nat Gas
5,870,000
35,200,000
LM 6000
Nat Gas
1,470,000
8,810,000
GE 6FA
Nat Gas
1,390,000
8,340,000
Wartsila (1
Nat Gas
130,000
790,000
unit)
Wartsila (10
Nat Gas
530,000
3,150,000
units)
Annual Water
Annual Water
Consumption
Consumption
Fuel
Unit Name
30% Capacity
70% Capacity
Type
Factor
Factor
(Gallons)
(Gallons)
CC 1x1 with DF
Nat Gas
62,200,00
145,000,000
CC 2x1
Nat Gas
95,300,000
222,000,000

96

 Attachment LS-1
2016 ERP
Appendix A
Estimated Net Incremental Cost Tables

Schedule A-1: 2016-2040 Estimated Avoided Costs and Net Incremental Cost of
2019 60 MW Wind Resource
Schedule A-2: 2016-2040 Estimated Avoided Costs and Net Incremental Cost of
2026 30 MW Wind Resource
Schedule A-3: 2016-2040 Estimated Avoided Costs and Net Incremental Cost of
2038 60 MW Wind Resource
Schedule A-4: Source and Use of Funds Available for Eligible Energy Acquisition

97

 Attachment 
2016 ERP

Appendix 
WECC Standard BAL-OOZ-WECC-Z Guidance Document

98

Attachment LS-1
2016 ERP
Appendix C
Econometric Load Forecast Methodology
Multiple combinations of variables were tested in the development of the energy
and demand forecasts. The models were refined to ensure that the estimates were
logically reasonable (e.g., sales increase with CDDs) and statistically significant (or
approaching statistical significance). Normal weather conditions are used to
forecast energy and demand. Summaries of the final energy and demand equations
are described in this appendix.
Peak Demand Forecast Methodology
The Company’s system demand forecast is a system-level forecast inclusive of
residential, commercial, industrial, and lighting sectors. Each month’s peak hours
from 2006 to 2015 were used to model the monthly peak demand forecast. The
peak demand model was estimated using Ordinary Least Squares (“OLS”). The
resulting estimates were used in combination with normal weather and forecasted
economic conditions to forecast peak demands. The model is as follows:
         
     
              
              
              
          
   
        
              
               !"
       #$ %&'()(*  
23
    + ,
- . /    0(43   (    +   (,
   .

Where:
  
      

   

   

   

   

       

!"

 + ,
- . /

 +   (,


= natural logarithm;
= demand use without identified large volume customers;
= cooling degree days, 60-degree threshold;
= peak-hour cooling degree hours, 70-degree threshold;
= heating degree days, 60-degree threshold;
= peak-hour heating degree hours, 50-degree threshold;
= monthly cooling degree daily average, 60-degree threshold;
= gross regional product;
= nonfarm employment; and
= indicator variables for each month.

In this equation, a and the b’s are estimated parameters; et is the error term; and t
indexes observations. As the Black Hills service territory experiences hot and coldweather peaks, the CDD, HDD, and MnthCDD variables account for cooling and
heating buildup, while the CDH and HDH variables account for peak-hour cooling
and heating requirements. The month variables account for seasonal differences in
peak demands not due to weather, and the GRP and Nonfarmemp variables reflect
how peak demand changes with economic conditions. The historical and forecasted
values for these variables are included in Schedule C-6, Appendix C. The variable

99

 Attachment LS-1
2016 ERP
statistical values are included in Schedule C-7, Appendix C. Schedule C-8 in
Appendix C provides the resulting monthly demand forecast.
Residential Sales Forecast
The residential sales forecast is the product of a forecast of residential use per
customer (“UPC”) and a forecast of the number of residential customers served.
Black Hills evaluated the following potential drivers of residential UPC: the
residential price of electricity, personal income per household, heating degree days,
cooling degree days, and the month of the year. Higher electricity prices and lower
income may result in less electricity use, while colder than normal winters (more
heating degree days) and hotter than normal summers (more cooling degree days)
may result in more electricity consumption.
The final residential UPC model is as follows:
   5/ 
     
              
              
     6 7      /   89  _12 =
    
23
0(43   (    +   (,
   .

Where:
  
= natural logarithm;
5/ 

= residential energy use per customer;
   

= cooling degree days, 60-degree threshold;
   

= heating degree days, 60-degree threshold;
/   89  _12 =
 = 12-month moving average of real personal income per
household; and
 +   (,

= indicator variables for each month.
In this equation, a and the b’s are estimated parameters; et is the error term; and t
indexes observations. As the Black Hills service territory experiences hot- and coldweather peaks, the CDD and HDD variables account for cooling- and heating-related
usage. The month variables account for seasonal differences in residential energy
use per customer not due to weather, and the personal income variable reflects how
residential UPC changes with income levels. The historical and forecasted values for
these variables are included in Schedule C-10, Appendix C.14 The variable statistical
values are included in Schedule C-11, Appendix C.

14

All models except the system peak demand model are estimated using the Prais-Winsten estimator.
(The system peak demand model is estimated using OLS. The use of Prais-Winsten in that model was
prevented by the inclusion of multiple observations in some months.) The Prais-Winsten estimator is
intended to account for the effects of first-order serial correlation in the error term, which means
that the error in a specific period of time is related to the error in the prior period of time. A failure to
account for serial correlation (as in OLS estimation) does not bias the estimated coefficients, but it
can result in inefficient estimates (higher standard errors).

100

 Attachment LS-1
2016 ERP
The model for the number of customers is as follows:
    5> >
     
       ?@         8_12 =
     .

Where:
   = natural logarithm;
 5> >

= residential customers; and
   8_12 =

= 12-month moving average of the number of households.
In this equation, a and the b’s are estimated parameters; et is the error term; and t
indexes observations. The number of households variable explains the growth of
the residential customers. The historical and forecasted values for these variables
are included in Schedule C-12, Appendix C. The variable statistical values are
included in Schedule C-13, Appendix C.
The residential UPC and number of customers forecasts are converted from logs to
levels and then multiplied together to produce the forecast residential sales as
follows:
 >
 .>
     exp E   5/ 
  F   exp E    5> >
  F
Schedule C-8 and Schedule C-9, in Appendix C provides the resulting monthly and
annual residential sales forecast, respectively.
Commercial Sales Forecast
The commercial sales forecast was completed using the same methodology as the
residential sales forecast, by multiplying the commercial UPC model results by the
number of commercial customers model results. Black Hills evaluated the following
drivers of commercial UPC: the price of electricity, employment, regional economic
expansion or contraction (“GRP”), weather and the month of the year.
The final commercial UPC model is as follows:
   5/ 
     
              
              
               !"_12 =
       *'G7)  
23
   /-9 .
     0(43   (    +   (,
   .

Where:
  
5/ 

   

   

!"_12 =

/-9 .

 +   (,


= natural logarithm;
= commercial energy use per customer;
= cooling degree days, 60-degree threshold;
= heating degree days, 60-degree threshold;
= 12-month moving average of gross regional product;
= average commercial revenue per-kWh sold; and
= indicator variables for each month.
101

 Attachment LS-1
2016 ERP
In this equation, a and the b’s are estimated parameters; et is the error term; and t
indexes observations. As the Black Hills service territory experiences hot- and coldweather peaks, the CDD and HDD variables account for cooling- and heating-related
usage. The month variables account for seasonal differences in commercial energy
use per customer not due to weather. The gross regional product variable reflects
how energy changes with economic conditions, and the price variable demonstrates
how changes in retail energy prices affect commercial energy consumption. The
historical and forecasted values for these variables are included in Schedule C-14,
Appendix C. The variable statistical values are included in Schedule C-15, Appendix
C. Schedule C-8 and C-9, in Appendix C provides the resulting annual commercial
sales amounts, respectively.
The model for the number of commercial customers served is as follows:
23

    5> >
     
               !"_12 =
       
G()    9 .
   0(43   (  
 +   (,
   .

Where:
  
 5> >

!"_12 =

 9 .

 +   (,


= natural logarithm;
= commercial customers;
= 12-month moving average of gross regional product;
= monthly time trend; and
= indicator variables for each month.

In this equation, a and the b’s are estimated parameters; et is the error term; and t
indexes observations. The month variables account for seasonal differences in
commercial customers not due to weather. The gross regional product variable
reflects how commercial customers change with economic conditions, and the time
trend variable captures the historical trend of the commercial customer count,
controlling for the other included variables. The historical and forecasted values for
these variables are included in Schedule C-16, Appendix C. The variable statistical
values are included in ScheduleC-17, Appendix C.
The resulting commercial sales forecast is as follows:
 >
 .>
     exp E   5/     
  F   exp E    5> >
  F
Schedule C-8 and Schedule C-9, in Appendix C provides the resulting monthly and
annual commercial sales forecast, respectively.

102

 Attachment LS-1
2016 ERP
Industrial Sales Forecast
The industrial sales forecast was developed from a single model. Variables that
could impact industrial sales include the price of electricity, weather, employment,
regional economic expansion or contraction and the month of the year. Higher
electricity prices can result in less electricity use, while higher employment as well
as colder than normal winters (more heating degree days) and hotter than normal
summers (more cooling degree days) could result in more electricity consumption.
The final industrial sales model is as follows:
23

   >
 .>    
     
              
               !"_12 =
     0(43   (  
 +   (,
   .

Where:
  
>
 .>    

   

!"_12 =

 +   (,


= natural logarithm;
= industrial sales without identified large volume customers;
= cooling degree days, 60-degree threshold;
= 12-month moving average of gross regional product; and
= indicator variables for each month.

In this equation, a and the b’s are estimated parameters; et is the error term; and t
indexes observations. As the Black Hills service territory experiences hot-weather
peaks, the CDD variable accounts for cooling-related usage. The month variables
account for seasonal differences in industrial energy use not due to weather, and the
gross regional product variable reflects how energy changes with economic
conditions. The historical and forecasted values for these variables are included in
Schedule C-18, Appendix C. The variable statistical values are included in Schedule
C-19, Appendix C. Schedule C-8 and C-9, Appendix C provides the resulting monthly
and annual industrial sales forecast, respectively.

103

 Attachment LS-1
2016 ERP
Appendix D
Load Profiles
Table D-1 and Figure D-1 Residential Daily Load Profiles (kW) – January
Table D-2 and Figure D-2 Residential Daily Load Profiles (kW) – February
Table D-3 and Figure D-3 Residential Daily Load Profiles (kW) – March
Table D-4 and Figure D-4 Residential Daily Load Profiles (kW) – April
Table D-5 and Figure D-5 Residential Daily Load Profiles (kW) – May
Table D-6 and Figure D-6 Residential Daily Load Profiles (kW) – June
Table D-7 and Figure D-7 Residential Daily Load Profiles (kW) – July
Table D-8 and Figure D-8 Residential Daily Load Profiles (kW) – August
Table D-9 and Figure D-9 Residential Daily Load Profiles (kW) – September
Table D-10 and Figure D-10 Residential Daily Load Profiles (kW) – October
Table D-11 and Figure D-11 Residential Daily Load Profiles (kW) – November
Table D-12 and Figure D-12 Residential Daily Load Profiles (kW) – December
Table D-13 and Figure D-13 Commercial Daily Load Profiles (kW) – January
Table D-14 and Figure D-14 Commercial Daily Load Profiles (kW) – February
Table D-15 and Figure D-15 Commercial Daily Load Profiles (kW) – March
Table D-16 and Figure D-16 Commercial Daily Load Profiles (kW) – April
Table D-17 and Figure D-17 Commercial Daily Load Profiles (kW) – May
Table D-18 and Figure D-18 Commercial Daily Load Profiles (kW) – June
Table D-19 and Figure D-19 Commercial Daily Load Profiles (kW) – July
Table D-20 and Figure D-20 Commercial Daily Load Profiles (kW) – August
Table D-21 and Figure D-21 Commercial Daily Load Profiles (kW) – September
Table D-22 and Figure D-22 Commercial Daily Load Profiles (kW) – October
Table D-23 and Figure D-23 Commercial Daily Load Profiles (kW) – November
Table D-24 and Figure D-24 Commercial Daily Load Profiles (kW) – December
Table D-25 and Figure D-25 Industrial Daily Load Profiles (kW) – January
Table D-26 and Figure D-26 Industrial Daily Load Profiles (kW) – February
Table D-27 and Figure D-27 Industrial Daily Load Profiles (kW) – March
Table D-28 and Figure D-28 Industrial Daily Load Profiles (kW) – April
Table D-29 and Figure D-29 Industrial Daily Load Profiles (kW) – May
Table D-30 and Figure D-30 Industrial Daily Load Profiles (kW) – June
Table D-31 and Figure D-31 Industrial Daily Load Profiles (kW) – July
Table D-32 and Figure D-32 Industrial Daily Load Profiles (kW) – August
Table D-33 and Figure D-33 Industrial Daily Load Profiles (kW) – September
Table D-34 and Figure D-34 Industrial Daily Load Profiles (kW) – October
Table D-35 and Figure D-35 Industrial Daily Load Profiles (kW) – November
Table D-36 and Figure D-36 Industrial Daily Load Profiles (kW) – December

104

 Attachment LS-1
2016 ERP
Figure D-1
Residential Daily Load Profiles – January

RESIDENTIAL - January
2.00

kW

1.50

1.00

0.50

0.00
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour
System Peak Day

Average Weekday

Average Weekend/Holidays

Table D-1
Residential Daily Load Profiles (kW) - January
Hour

System Peak Day

Average Weekday

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24

0.8105
0.7795
0.7720
0.7800
0.8124
0.8836
0.9767
1.0318
1.0220
0.9734
0.9308
0.9110
0.8807
0.8465
0.8255
0.8437
0.9425
1.1388
1.2176
1.2077
1.1859
1.1196
1.0161
0.9163

0.7564
0.7251
0.7132
0.7151
0.7420
0.8115
0.9226
0.9554
0.9078
0.8915
0.8740
0.8599
0.8418
0.8156
0.8051
0.8279
0.9280
1.0955
1.1477
1.1287
1.1002
1.0304
0.9203
0.8174

105

Average
Weekend/Holidays
0.9383
0.8937
0.8671
0.8557
0.8597
0.8820
0.9223
0.9883
1.0461
1.0805
1.0917
1.0883
1.0587
1.0250
1.0059
1.0128
1.0923
1.2385
1.2680
1.2451
1.2095
1.1428
1.0406
0.9364

 Attachment LS-1
2016 ERP
Figure D-2
Residential Daily Load Profiles – February

RESIDENTIAL - February
2.00

kW

1.50

1.00

0.50

0.00
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour
System Peak Day

Average Weekday

Average Weekend/Holidays

Table D-2
Residential Daily Load Profiles (kW) - February
Hour

System Peak Day

Average Weekday

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24

0.7788
0.7646
0.7578
0.7596
0.7849
0.8496
0.9478
0.9923
0.9876
0.9747
0.9611
0.9779
0.9757
0.9678
0.9870
1.0248
1.1128
1.2380
1.3131
1.2915
1.2459
1.1602
1.0412
0.9397

0.7235
0.6947
0.6841
0.6847
0.7114
0.7816
0.8943
0.9124
0.8541
0.8264
0.8031
0.7856
0.7671
0.7430
0.7369
0.7561
0.8302
0.9698
1.0638
1.0584
1.0353
0.9723
0.8686
0.7721

106

Average
Weekend/Holidays
0.6980
0.6641
0.6482
0.6442
0.6557
0.6877
0.7479
0.8306
0.8893
0.9233
0.9327
0.9336
0.9241
0.9083
0.9055
0.9269
0.9755
1.0576
1.1106
1.0860
1.0567
0.9929
0.8938
0.7949

 Attachment LS-1
2016 ERP
Figure D-3
Residential Daily Load Profiles – March

RESIDENTIAL - March
2.00

kW

1.50

1.00

0.50

0.00
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour
System Peak Day

Average Weekday

Average Weekend/Holidays

Table D-3
Residential Daily Load Profiles (kW) - March
Hour

System Peak Day

Average Weekday

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24

0.8030
0.7722
0.7644
0.7674
0.7945
0.8643
0.9879
1.0316
0.9989
0.9838
0.9638
0.9385
0.9293
0.9175
0.9145
0.9454
1.0291
1.1587
1.2677
1.2663
1.2398
1.1656
1.0479
0.9450

0.6376
0.6048
0.5919
0.5943
0.6183
0.6841
0.7927
0.8243
0.7778
0.7584
0.7438
0.7302
0.7172
0.6989
0.6916
0.7044
0.7590
0.8393
0.8984
0.9455
0.9522
0.8961
0.7938
0.6940

107

Average
Weekend/Holidays
0.6432
0.6081
0.5876
0.5897
0.5988
0.6288
0.6861
0.7582
0.8029
0.8189
0.8128
0.8020
0.7887
0.7709
0.7572
0.7654
0.7954
0.8431
0.8868
0.9343
0.9398
0.8796
0.7789
0.6786

 Attachment LS-1
2016 ERP
Figure D-4
Residential Daily Load Profiles – April

RESIDENTIAL - April
2.00

kW

1.50

1.00

0.50

0.00
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour
System Peak Day

Average Weekday

Average Weekend/Holidays

Table D-4
Residential Daily Load Profiles (kW) - April
Hour

System Peak Day

Average Weekday

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24

0.5238
0.4847
0.4654
0.4604
0.4798
0.5432
0.6476
0.6578
0.6120
0.6003
0.5952
0.6048
0.6202
0.6305
0.6544
0.6986
0.7814
0.8593
0.8890
0.8905
0.9242
0.8786
0.7600
0.6333

0.5495
0.5142
0.4988
0.4987
0.5202
0.5867
0.7005
0.7231
0.6790
0.6631
0.6520
0.6465
0.6411
0.6299
0.6299
0.6506
0.7094
0.7749
0.8089
0.8428
0.8806
0.8298
0.7230
0.6180

108

Average
Weekend/Holidays
0.5771
0.5391
0.5213
0.5162
0.5250
0.5554
0.6177
0.6920
0.7467
0.7732
0.7766
0.7768
0.7730
0.7571
0.7480
0.7585
0.7865
0.8220
0.8367
0.8699
0.9047
0.8457
0.7413
0.6344

 Attachment LS-1
2016 ERP
Figure D-5
Residential Daily Load Profiles – May

RESIDENTIAL - May
2.00

kW

1.50

1.00

0.50

0.00
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour
System Peak Day

Average Weekday

Average Weekend/Holidays

Table D-5
Residential Daily Load Profiles (kW) - May
Hour

System Peak Day

Average Weekday

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24

0.5269
0.4874
0.4660
0.4622
0.4788
0.5361
0.6401
0.6642
0.6306
0.6248
0.6239
0.6223
0.6200
0.6086
0.6094
0.6327
0.7058
0.7723
0.8013
0.8064
0.8540
0.8272
0.7165
0.5973

0.5437
0.5046
0.4855
0.4804
0.4981
0.5579
0.6606
0.6866
0.6564
0.6546
0.6547
0.6559
0.6561
0.6486
0.6518
0.6749
0.7375
0.8032
0.8270
0.8357
0.8668
0.8287
0.7221
0.6119

109

Average
Weekend/Holidays
0.5570
0.5153
0.4940
0.4835
0.4891
0.5145
0.5646
0.6242
0.6848
0.7297
0.7497
0.7613
0.7613
0.7548
0.7591
0.7788
0.8181
0.8507
0.8455
0.8477
0.8805
0.8460
0.7376
0.6235

 Attachment LS-1
2016 ERP
Figure D-6
Residential Daily Load Profiles – June

RESIDENTIAL - June
2.00

kW

1.50

1.00

0.50

0.00
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour
System Peak Day

Average Weekday

Average Weekend/Holidays

Table D-6
Residential Daily Load Profiles (kW) - June
Hour

System Peak Day

Average Weekday

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24

0.7786
0.6870
0.6328
0.6009
0.5934
0.6189
0.6697
0.7294
0.7816
0.8752
1.0035
1.1569
1.3098
1.4306
1.5292
1.6293
1.7229
1.8045
1.7908
1.6825
1.5851
1.4606
1.2532
1.0531

0.6935
0.6227
0.5803
0.5552
0.5531
0.5856
0.6420
0.6880
0.7138
0.7702
0.8466
0.9411
1.0388
1.1186
1.1906
1.2553
1.3157
1.3592
1.3405
1.2698
1.2155
1.1479
0.9872
0.8218

110

Average
Weekend/Holidays
0.7171
0.6400
0.5927
0.5623
0.5486
0.5571
0.5960
0.6689
0.7601
0.8578
0.9602
1.0690
1.1765
1.2637
1.3083
1.3320
1.3573
1.3708
1.3459
1.2879
1.2250
1.1538
0.9965
0.8341

 Attachment LS-1
2016 ERP
Figure D-7
Residential Daily Load Profiles – July

RESIDENTIAL - July
2.00

kW

1.50

1.00

0.50

0.00
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour
System Peak Day

Average Weekday

Average Weekend/Holidays

Table D-7
Residential Daily Load Profiles (kW) - July
Hour

System Peak Day

Average Weekday

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24

0.8180
0.7280
0.6735
0.6370
0.6267
0.6474
0.6931
0.7498
0.8133
0.9152
1.0557
1.2182
1.3654
1.4738
1.5699
1.6650
1.7629
1.8377
1.8437
1.7536
1.6435
1.5113
1.2776
1.0584

0.7683
0.6895
0.6392
0.6087
0.6010
0.6262
0.6737
0.7233
0.7666
0.8358
0.9282
1.0371
1.1473
1.2321
1.3041
1.3657
1.4208
1.4456
1.4025
1.3272
1.2615
1.1862
1.0288
0.8650

111

Average
Weekend/Holidays
0.7778
0.6927
0.6394
0.6035
0.5861
0.5903
0.6265
0.7045
0.8104
0.9300
1.0693
1.2269
1.3792
1.4996
1.5557
1.5439
1.5177
1.5093
1.4622
1.3709
1.2822
1.1865
1.0768
0.9491

 Attachment LS-1
2016 ERP
Figure D-8
Residential Daily Load Profiles – August

RESIDENTIAL - August
2.00

kW

1.50

1.00

0.50

0.00
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour
System Peak Day

Average Weekday

Average Weekend/Holidays

Table D-8
Residential Daily Load Profiles (kW) - August
Hour

System Peak
Day

Average
Weekday

Average
Weekend/Holidays

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24

0.7368
0.6712
0.6341
0.6094
0.6100
0.6383
0.6944
0.7450
0.7912
0.8761
0.9847
1.1296
1.2761
1.3949
1.5036
1.5847
1.6874
1.7823
1.7840
1.6826
1.5871
1.4289
1.2038
1.0074

0.7467
0.6739
0.6327
0.6025
0.5981
0.6280
0.6868
0.7193
0.7425
0.8049
0.8936
1.0071
1.1285
1.2374
1.3365
1.4215
1.5065
1.5595
1.5218
1.4269
1.3487
1.2163
1.0352
0.8689

0.8022
0.7219
0.6687
0.6336
0.6158
0.6184
0.6492
0.7197
0.8232
0.9364
1.0650
1.2064
1.3479
1.4669
1.5624
1.6313
1.6582
1.6312
1.5490
1.4468
1.3670
1.2442
1.0666
0.9001

112

 Attachment LS-1
2016 ERP
Figure D-9
Residential Daily Load Profiles – September

RESIDENTIAL - September
2.00

kW

1.50

1.00

0.50

0.00
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour
System Peak Day

Average Weekday

Average Weekend/Holidays

Table D-9
Residential Daily Load Profiles (kW) - September
Hour

System Peak Day

Average Weekday

Average
Weekend/Holidays

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24

0.6882
0.6265
0.5865
0.5643
0.5646
0.6041
0.6869
0.6993
0.6957
0.7370
0.8186
0.9310
1.0606
1.1901
1.3121
1.4255
1.5472
1.6153
1.5637
1.4148
1.3143
1.1636
0.9762
0.8181

0.6562
0.5948
0.5581
0.5379
0.5399
0.5805
0.6655
0.6810
0.6614
0.6861
0.7311
0.8018
0.8900
0.9812
1.0767
1.1792
1.2925
1.3525
1.3209
1.2579
1.1841
1.0532
0.8924
0.7457

0.6927
0.6222
0.5765
0.5482
0.5365
0.5413
0.5702
0.6283
0.7008
0.7742
0.8519
0.9505
1.0650
1.1751
1.2814
1.3801
1.4621
1.5067
1.4480
1.3568
1.2630
1.1125
0.9421
0.7844

113

 Attachment LS-1
2016 ERP
Figure D-10
Residential Daily Load Profiles – October

RESIDENTIAL - October
2.00

kW

1.50

1.00

0.50

0.00
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour
System Peak Day

Average Weekday

Average Weekend/Holidays

Table D-10
Residential Daily Load Profiles (kW) - October
Hour

System Peak
Day

Average Weekday

Average
Weekend/Holidays

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24

0.5624
0.5137
0.4866
0.4711
0.4680
0.4828
0.5223
0.5943
0.6696
0.7293
0.7747
0.8265
0.8979
0.9777
1.0667
1.1557
1.2209
1.2505
1.2371
1.2063
1.1070
0.9832
0.8224
0.6806

0.5422
0.5040
0.4846
0.4787
0.4945
0.5504
0.6516
0.6850
0.6533
0.6499
0.6496
0.6544
0.6621
0.6654
0.6812
0.7175
0.7942
0.8733
0.9273
0.9370
0.8973
0.8225
0.7151
0.6115

0.5628
0.5204
0.4971
0.4856
0.4883
0.5092
0.5562
0.6327
0.7001
0.7384
0.7540
0.7676
0.7823
0.7948
0.8168
0.8532
0.8963
0.9346
0.9651
0.9635
0.9148
0.8370
0.7303
0.6250

114

 Attachment LS-1
2016 ERP
Figure D-11
Residential Daily Load Profiles – November

RESIDENTIAL - November
2.00

kW

1.50

1.00

0.50

0.00
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour
System Peak Day

Average Weekday

Average Weekend/Holidays

Table D-11
Residential Daily Load Profiles (kW) - November
Hour

System Peak
Day

Average
Weekday

Average
Weekend/Holidays

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24

0.8441
0.8009
0.7759
0.7652
0.7693
0.7933
0.8444
0.9312
1.0118
1.0687
1.1029
1.1311
1.1533
1.1504
1.1508
1.1971
1.2916
1.3804
1.3445
1.2901
1.2623
1.1634
1.0454
0.9032

0.6566
0.6289
0.6176
0.6208
0.6475
0.7171
0.8243
0.8320
0.7795
0.7554
0.7391
0.7260
0.7147
0.6985
0.6984
0.7258
0.8374
1.0026
1.0356
1.0156
0.9879
0.9215
0.8212
0.7292

0.7455
0.6948
0.6757
0.6701
0.6824
0.7242
0.7984
0.9104
1.0101
1.0814
1.1219
1.1292
1.0922
1.0287
0.9857
0.9720
1.0094
1.0785
1.0861
1.0726
1.0542
0.9992
0.9054
0.8193

115

 Attachment LS-1
2016 ERP
Figure D-12
Residential Daily Load Profiles – December

RESIDENTIAL - December
2.00

kW

1.50

1.00

0.50

0.00
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour
System Peak Day

Average Weekday

Average Weekend/Holidays

Table D-12
Residential Daily Load Profiles (kW) - December
Hour

System Peak
Day

Average Weekday

Average
Weekend/Holidays

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24

0.8807
0.8481
0.8344
0.8374
0.8618
0.9096
0.9833
1.0341
1.0242
1.0200
1.0091
0.9975
0.9777
0.9615
0.9579
0.9787
1.1052
1.3038
1.3395
1.3082
1.2713
1.2000
1.1002
0.9732

0.8036
0.7674
0.7517
0.7516
0.7752
0.8379
0.9374
0.9680
0.9241
0.9013
0.8782
0.8571
0.8336
0.8073
0.8018
0.8334
0.9686
1.1632
1.2002
1.1912
1.1690
1.1014
0.9865
0.8745

0.8645
0.8165
0.7907
0.7824
0.7926
0.8249
0.8897
0.9895
1.0556
1.0921
1.1055
1.1117
1.0862
1.0421
1.0130
1.0182
1.0908
1.2048
1.2239
1.2102
1.1890
1.1227
1.0170
0.9071

116

 Attachment LS-1
2016 ERP
Figure D-13
Commercial Daily Load Profiles – January

COMMERCIAL - January
14.00
12.00

kW

10.00
8.00
6.00
4.00
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour
System Peak Day

Average Weekday

Average Weekend/Holidays

Table D-13
Commercial Daily Load Profiles (kW) - January
Hour

System Peak
Day

Average Weekday

Average
Weekend/Holidays

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24

6.1376
6.1261
6.0457
6.2040
6.4215
6.9293
7.6661
8.1668
8.6650
8.9056
9.1134
9.0290
8.9893
9.0052
8.8358
8.6361
8.3828
8.4246
8.1037
7.8140
7.5394
7.1290
6.7021
6.4638

5.9112
5.8699
5.8538
5.8683
6.0924
6.5651
7.3650
8.0170
8.5445
8.7653
8.8651
8.7866
8.6784
8.6698
8.5581
8.3921
8.1631
8.0820
7.7733
7.4676
7.1941
6.8066
6.3705
6.1065

6.1852
6.1266
6.0926
6.0867
6.2159
6.4338
6.7587
6.7821
6.8392
6.8796
6.9539
6.8803
6.7874
6.6939
6.6577
6.6124
6.7920
7.2278
7.1621
6.9531
6.8179
6.6051
6.3418
6.1093

The System Peak Day was January 22, 2015

117

 Attachment LS-1
2016 ERP
Figure D-14
Commercial Daily Load Profiles – February

COMMERCIAL - February
14.00
12.00

kW

10.00
8.00
6.00
4.00
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour
System Peak Day

Average Weekday

Average Weekend/Holidays

Table D-14
Commercial Daily Load Profiles (kW) - February
Hour

System Peak
Day

Average Weekday

Average
Weekend/Holidays

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24

6.2907
6.2925
6.2357
6.2962
6.4552
6.9201
7.6581
8.1050
8.6603
8.9019
8.9794
9.0182
8.8777
8.9510
8.8629
8.8397
8.5332
8.3425
8.2453
8.0405
7.7541
7.4096
6.9530
6.7682

5.9743
5.9196
5.8805
5.8736
6.1019
6.6009
7.4185
7.9612
8.5285
8.7102
8.8161
8.7575
8.6799
8.6998
8.5941
8.4392
8.1539
7.9680
7.8635
7.5771
7.2743
6.8920
6.4158
6.1484

5.8534
5.7674
5.7322
5.7384
5.8418
6.0496
6.3988
6.3973
6.5716
6.7459
6.8910
6.9232
6.8930
6.8435
6.8366
6.8278
6.8965
7.1206
7.1846
6.9967
6.7895
6.5143
6.2278
5.9922

The System Peak Day was February 26, 2015

118

 Attachment LS-1
2016 ERP
Figure D-15
Commercial Daily Load Profiles – March

COMMERCIAL - March
14.00
12.00

kW

10.00
8.00
6.00
4.00
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour
System Peak Day

Average Weekday

Average Weekend/Holidays

Table D-15
Commercial Daily Load Profiles (kW) - March
Hour

System Peak
Day

Average Weekday

Average
Weekend/Holidays

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24

6.0820
5.9627
5.9032
5.8864
6.0657
6.5342
7.3239
7.8994
8.5725
8.8382
8.9456
8.8235
8.6558
8.7224
8.6356
8.4555
8.0881
7.8735
8.0208
7.7576
7.4265
7.0738
6.6639
6.4530

5.5457
5.4522
5.4162
5.4204
5.5938
6.1050
6.8338
7.3841
7.9296
8.1300
8.2714
8.2506
8.1949
8.2758
8.2462
8.0275
7.6876
7.2974
7.1289
7.1674
6.8933
6.5314
6.0385
5.7368

5.4205
5.3280
5.3113
5.3067
5.4216
5.6298
5.9358
5.9290
6.0557
6.2129
6.3771
6.4283
6.4477
6.4390
6.3998
6.3848
6.3906
6.4091
6.3807
6.4497
6.2712
5.9908
5.6929
5.4728

The System Peak Day was March 4, 2015

119

 Attachment LS-1
2016 ERP
Figure D-16
Commercial Daily Load Profiles – April

COMMERCIAL - April
14.00
12.00

kW

10.00
8.00
6.00
4.00
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour
System Peak Day

Average Weekday

Average Weekend/Holidays

Table D-16
Commercial Daily Load Profiles (kW) - April
Hour

System Peak
Day

Average Weekday

Average
Weekend/Holidays

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24

5.4462
5.3928
5.2545
5.2771
5.3881
5.8041
6.3829
7.1949
7.8706
8.3393
8.7786
8.9583
9.1603
9.3814
9.4760
9.3763
8.9635
8.2991
7.8937
7.6537
7.4926
6.9820
6.3886
5.9339

5.4302
5.2893
5.2346
5.2443
5.4279
5.9029
6.5354
7.1898
7.8157
8.0510
8.2501
8.3049
8.3163
8.4343
8.4181
8.2194
7.8384
7.2989
6.9532
6.9359
6.8453
6.4436
5.9383
5.6382

5.2654
5.1851
5.1318
5.1298
5.1908
5.4653
5.7037
5.6787
5.8950
6.1041
6.2827
6.3353
6.3415
6.4210
6.3785
6.2947
6.2432
6.1945
6.1352
6.2342
6.2012
5.9934
5.7384
5.3734

The System Peak Day was April 30, 2015

120

 Attachment LS-1
2016 ERP
Figure D-17
Commercial Daily Load Profiles – May

COMMERCIAL - May
14.00
12.00

kW

10.00
8.00
6.00
4.00
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour
System Peak Day

Average Weekday

Average Weekend/Holidays

Table D-17
Commercial Daily Load Profiles (kW) - May
Hour

System Peak
Day

Average Weekday

Average
Weekend/Holidays

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24

5.4377
5.2673
5.1564
5.1734
5.3743
5.8263
6.4774
7.2846
7.9977
8.4184
8.5750
8.5381
8.5887
8.7077
8.7228
8.5398
8.1129
7.5785
7.2402
6.9690
7.0215
6.5078
6.0299
5.6859

5.4100
5.2998
5.2212
5.2179
5.4051
5.8372
6.4323
7.1799
7.8611
8.2218
8.4396
8.4955
8.4986
8.6233
8.5960
8.3884
7.9277
7.4179
7.0672
6.8987
6.8753
6.4731
5.9718
5.6506

5.2187
5.1213
5.0480
5.0676
5.1555
5.3488
5.4124
5.6732
5.9015
6.1618
6.3764
6.5200
6.6052
6.6688
6.6632
6.5222
6.4382
6.2981
6.1718
6.0924
6.2310
5.9869
5.6378
5.3867

The System Peak Day was May 13, 2015

121

 Attachment LS-1
2016 ERP
Figure D-18
Commercial Daily Load Profiles – June

COMMERCIAL - June
14.00
12.00

kW

10.00
8.00
6.00
4.00
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour
System Peak Day

Average Weekday

Average Weekend/Holidays

Table D-18
Commercial Daily Load Profiles (kW) - June
Hour

System Peak
Day

Average Weekday

Average
Weekend/Holidays

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24

6.4368
6.2396
6.0553
6.0186
6.1986
6.5206
7.2672
8.2322
9.2830
9.9907
10.6721
11.0298
11.3552
11.5966
11.6251
11.5141
11.0537
10.4057
9.7030
9.2678
9.0235
8.4948
7.7845
7.3010

6.0796
5.9042
5.7713
5.7190
5.8577
6.2249
6.8161
7.7271
8.6231
9.2289
9.7370
10.0290
10.2355
10.4933
10.5340
10.3640
9.8940
9.2159
8.5980
8.2167
8.0309
7.6044
6.9396
6.4526

6.0483
5.8421
5.6997
5.6144
5.6392
5.7559
5.9128
6.2995
6.8041
7.3248
7.7876
8.1066
8.2864
8.3879
8.3709
8.2328
8.1079
7.9867
7.7374
7.4888
7.3856
7.1134
6.6306
6.2089

The System Peak Day was June 30, 2015

122

 Attachment LS-1
2016 ERP
Figure D-19
Commercial Daily Load Profiles –July

COMMERCIAL - July
14.00
12.00

kW

10.00
8.00
6.00
4.00
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour
System Peak Day

Average Weekday

Average Weekend/Holidays

Table D-19
Commercial Daily Load Profiles (kW) - July
Hour

System Peak
Day

Average Weekday

Average Weekend/Holidays

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24

6.8716
6.5769
6.3947
6.3227
6.4322
6.9052
7.4925
8.4856
9.5434
10.3344
11.1053
11.4957
11.7535
12.0227
12.0552
11.8646
11.4009
10.6573
10.2268
9.7114
9.4828
8.9058
8.0655
7.4763

6.5007
6.3065
6.1483
6.0648
6.2275
6.6692
7.2389
8.1056
9.0223
9.6648
10.1875
10.4670
10.6334
10.8553
10.8718
10.6995
10.2209
9.4336
8.8070
8.4519
8.3249
7.8948
7.2494
6.7778

6.4300
6.1727
6.0038
5.9243
5.9118
6.0655
6.1772
6.5530
7.1031
7.6582
8.1465
8.5299
8.7428
8.9100
8.8942
8.6869
8.4944
8.3267
8.0622
7.8197
7.7920
7.5082
7.0469
6.6820

The System Peak Day was July 23, 2015

123

 Attachment LS-1
2016 ERP
Figure D-20
Commercial Daily Load Profiles – August

COMMERCIAL - August
14.00
12.00

kW

10.00
8.00
6.00
4.00
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour
System Peak Day

Average Weekday

Average Weekend/Holidays

Table D-20
Commercial Daily Load Profiles (kW) - August
Hour

System Peak
Day

Average Weekday

Average
Weekend/Holidays

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24

6.3823
6.1814
6.0379
6.0162
6.1286
6.6034
7.1996
8.1430
9.1000
9.7939
10.4248
10.8481
11.1087
11.4591
11.6141
11.5397
11.1335
10.4179
9.8857
9.4290
9.1090
8.4249
7.6651
7.2856

6.4137
6.2140
6.0530
5.9832
6.1281
6.6026
7.2814
8.1006
9.0320
9.6958
10.2706
10.6219
10.8050
11.1141
11.2043
11.0879
10.6071
9.8306
9.1770
8.7975
8.5574
7.9538
7.2372
6.7939

6.5022
6.2684
6.0664
5.9798
6.0010
6.2196
6.3766
6.6661
7.1890
7.7193
8.2231
8.6124
8.8046
8.9674
9.0600
9.0483
8.9416
8.6363
8.2430
8.0267
7.9228
7.5015
7.0025
6.6149

The System Peak Day was August 12, 2015

124

 Attachment LS-1
2016 ERP
Figure D-21
Commercial Daily Load Profiles – September

COMMERCIAL - September
14.00
12.00

kW

10.00
8.00
6.00
4.00
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour
System Peak Day

Average Weekday

Average Weekend/Holidays

Table D-21
Commercial Daily Load Profiles (kW) - September
Hour

System Peak
Day

Average Weekday

Average
Weekend/Holidays

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24

6.4333
6.2642
6.1271
5.9905
6.0879
6.5382
7.3477
8.2061
9.2971
9.9856
10.6635
11.1658
11.3677
11.7025
11.7443
11.5256
11.0297
10.2980
9.5352
9.0480
8.6966
8.0664
7.2666
6.8462

6.1520
5.9548
5.7809
5.7343
5.8619
6.3410
7.1451
7.8651
8.7784
9.4473
10.0042
10.3677
10.5632
10.8472
10.9109
10.7943
10.2912
9.5521
8.9317
8.7023
8.2691
7.6562
6.9639
6.5278

6.0606
5.8813
5.7101
5.6604
5.7061
5.9283
6.1842
6.2702
6.5965
7.1074
7.5725
7.9637
8.2390
8.4352
8.5838
8.6794
8.6274
8.4474
8.0481
7.9162
7.6491
7.2034
6.6778
6.2950

The System Peak Day was September 1, 2015

125

 Attachment LS-1
2016 ERP
Figure D-22
Commercial Daily Load Profiles – October

COMMERCIAL - October
14.00
12.00

kW

10.00
8.00
6.00
4.00
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour
System Peak Day

Average Weekday

Average Weekend/Holidays

Table D-22
Commercial Daily Load Profiles (kW) - October
Hour

System Peak Day

Average Weekday

Average Weekend/Holidays

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24

5.3914
5.2530
5.1443
5.0415
5.0563
5.2679
5.5745
5.6618
5.9187
6.2806
6.7100
6.9534
7.1127
7.2385
7.4396
7.5775
7.6112
7.5036
7.3416
7.0824
6.7112
6.3987
6.0036
5.7227

5.3201
5.1998
5.0912
5.0754
5.2391
5.7137
6.5118
7.1824
7.7791
8.0947
8.3770
8.5133
8.5933
8.7864
8.8011
8.6529
8.2555
7.7214
7.5109
7.2948
6.9026
6.4497
5.9073
5.5984

5.2704
5.1348
5.0092
4.9645
5.0055
5.2127
5.5594
5.6521
5.8081
6.0404
6.2991
6.5029
6.6047
6.7219
6.7740
6.8506
6.8180
6.6907
6.6678
6.5834
6.3036
5.9875
5.6666
5.3962

The System Peak Day was October 11, 2015

126

 Attachment LS-1
2016 ERP
Figure D-23
Commercial Daily Load Profiles – November

COMMERCIAL - November
14.00
12.00

kW

10.00
8.00
6.00
4.00
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour
System Peak Day

Average Weekday

Average Weekend/Holidays

Table D-23
Commercial Daily Load Profiles (kW) - November
Hour

System Peak Day

Average Weekday

Average Weekend/Holidays

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24

5.9725
5.8903
5.8891
5.9000
5.9795
6.1553
6.3457
6.2841
6.4337
6.6259
6.8001
6.8195
6.7534
6.6945
6.6559
6.6775
6.8720
7.0697
6.8849
6.7470
6.6271
6.3986
6.1592
5.9627

5.6257
5.5455
5.4751
5.5185
5.7291
6.2408
6.9837
7.4884
8.0362
8.2679
8.4324
8.4497
8.3936
8.4775
8.3860
8.2077
7.9780
7.8564
7.5356
7.2670
6.9821
6.6049
6.1653
5.8968

5.7786
5.5880
5.5190
5.5065
5.6107
5.8549
6.0988
6.0443
6.1133
6.2341
6.3061
6.3161
6.2978
6.2599
6.2425
6.2401
6.4098
6.6553
6.5834
6.4407
6.3167
6.1252
5.8999
5.7742

The System Peak Day was November 29, 2015

127

 Attachment LS-1
2016 ERP
Figure D-24
Commercial Daily Load Profiles – December

COMMERCIAL - December
14.00
12.00

kW

10.00
8.00
6.00
4.00
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour
System Peak Day

Average Weekday

Average Weekend/Holidays

Table D-24
Commercial Daily Load Profiles (kW) - December
Hour

System Peak Day

Average Weekday

Average Weekend/Holidays

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24

6.0795
6.0698
6.0910
6.1899
6.3461
6.8467
7.5203
7.9693
8.3134
8.4011
8.4520
8.2849
8.1591
8.2944
8.1748
7.9932
7.9357
7.9457
7.7396
7.4968
7.2902
6.9696
6.6321
6.4712

6.0585
5.9792
5.9336
5.9581
6.1608
6.6645
7.4701
8.0312
8.5397
8.6743
8.7392
8.6364
8.4801
8.4882
8.3693
8.1857
8.1017
8.0390
7.7167
7.4536
7.2096
6.8864
6.4722
6.2138

5.8120
5.7880
5.7868
5.8048
5.8878
6.1620
6.4848
6.3803
6.3529
6.3128
6.2839
6.2756
6.1859
6.1471
6.1112
6.1041
6.3501
6.7130
6.6180
6.4908
6.3963
6.2402
6.0466
5.8925

The System Peak Day was December 28, 2015

128

 Attachment LS-1
2016 ERP
Figure D-25
Industrial Daily Load Profiles – January

INDUSTRIAL - January
4500.00
4000.00

kW

3500.00
3000.00
2500.00
2000.00
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour
System Peak Day

Average Weekday

Average Weekend/Holidays

Table D-25
Industrial Daily Load Profiles (kW) - January
Hour

System Peak Day

Average Weekday

Average Weekend/Holidays

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24

3066.2622
3201.9763
3500.7375
3476.7156
3470.2708
3522.0518
3589.5496
3629.7546
3663.3147
3675.7437
3789.0095
3679.1277
3784.4541
3760.3313
3814.7563
3807.9985
3684.0122
3810.2844
3683.4512
3720.5557
3728.2419
3747.6709
3722.7773
3733.8755

2911.9204
2906.1221
2949.6667
2950.1143
2998.8562
3029.5430
3031.0901
3037.6775
3072.9136
3115.6877
3105.5767
3115.8926
3052.9238
3076.9373
3060.1250
2999.7231
2969.0645
3049.3508
2966.7446
2944.7979
3011.5378
3013.3330
3002.0969
3015.5820

2527.8721
2548.3433
2546.7576
2549.1670
2578.7813
2595.7407
2592.1528
2557.8086
2529.2747
2536.8362
2578.4404
2568.4907
2541.8286
2557.1250
2623.0881
2668.8892
2670.2375
2781.7114
2734.6934
2535.6680
2707.4316
2768.7427
2782.7002
2755.3501

129

 Attachment LS-1
2016 ERP
Figure D-26
Industrial Daily Load Profiles – February

INDUSTRIAL - February
4500.00
4000.00

kW

3500.00
3000.00
2500.00
2000.00
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour
System Peak Day

Average Weekday

Average Weekend/Holidays

Table D-26
Industrial Daily Load Profiles (kW) - February
Hour

System Peak Day

Average Weekday

Average Weekend/Holidays

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24

2598.4182
2582.0984
2667.4939
2609.3853
2659.8533
2660.2998
2553.3953
2591.8250
2843.2483
3106.8555
3116.9709
3129.6570
3053.4224
3132.0212
3192.0378
3063.2788
3105.6785
3159.3418
3501.1501
3373.8164
3072.7097
3059.0847
3095.8232
3262.4749

3162.1091
3130.5420
3118.0200
3130.0483
3103.3030
3101.9753
3136.9985
3137.5901
3178.6833
3232.9233
3213.0647
3224.6431
3152.7666
3149.9783
3129.9421
3131.4670
3139.3738
3168.0852
3185.1846
3169.0090
3200.2771
3178.4783
3188.6445
3224.5732

3167.4209
3201.0537
3199.4048
3220.3672
3235.9551
3216.3052
3164.2017
3101.7078
3123.5142
3162.8252
3166.8906
3159.1597
3108.9690
3130.4077
3147.2202
3103.9653
3093.6328
3124.7520
3064.4219
3075.1479
3078.0884
3100.7988
3132.6421
3079.6248

130

 Attachment LS-1
2016 ERP
Figure D-27
Industrial Daily Load Profiles – March

INDUSTRIAL - March
4500.00
4000.00

kW

3500.00
3000.00
2500.00
2000.00
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour
System Peak Day

Average Weekday

Average Weekend/Holidays

Table D-27
Industrial Daily Load Profiles (kW) - March
Hour

System Peak Day

Average Weekday

Average Weekend/Holidays

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24

3093.5022
3073.2864
3069.5352
3151.6750
3111.9551
3154.8394
3165.0061
3212.8936
3318.8025
3471.3040
3482.1208
3459.7346
3290.6865
3232.4854
3022.6155
2828.2075
2843.9731
3144.2275
3206.6475
3176.9478
3229.5789
3136.6948
3134.2617
3207.7534

2602.1416
2593.6355
2602.3650
2589.0913
2644.3245
2663.1699
2663.9895
2684.4771
2740.7415
2786.4402
2787.5046
2743.2263
2645.1418
2641.4185
2656.9934
2651.5183
2604.0049
2585.3259
2556.6924
2602.2207
2610.4055
2588.0491
2618.2546
2647.1030

2670.3142
2644.7329
2631.4531
2611.2080
2655.4441
2605.8281
2519.5146
2474.6743
2445.6582
2450.7649
2473.9355
2560.8298
2496.7578
2473.3008
2470.5088
2403.2949
2420.8899
2412.8069
2396.6582
2477.3389
2509.5713
2530.1226
2518.5464
2542.6777

131

 Attachment LS-1
2016 ERP
Figure D-28
Industrial Daily Load Profiles – April

INDUSTRIAL - April
4500.00
4000.00

kW

3500.00
3000.00
2500.00
2000.00
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour
System Peak Day

Average Weekday

Average Weekend/Holidays

Table D-28
Industrial Daily Load Profiles (kW) - April
Hour

System Peak Day

Average Weekday

Average Weekend/Holidays

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24

3390.5461
3378.0000
3381.7251
3386.0154
3435.9866
3424.1987
3289.1152
3368.0444
3488.4272
3537.7671
3594.3933
3611.5615
3535.6030
3577.4807
3506.8813
3476.5427
3516.2100
3486.9060
3379.0229
3461.2102
3531.3755
3459.5632
3451.8005
3374.7786

2535.3276
2525.3618
2503.7209
2476.0793
2492.3550
2521.3816
2541.7207
2611.3206
2709.3000
2723.3738
2780.8086
2792.0913
2723.8467
2751.4709
2736.9128
2722.5354
2649.3186
2601.4106
2456.3518
2481.1318
2519.4109
2529.8254
2570.6965
2596.8330

2473.9028
2542.5784
2552.8364
2550.3999
2589.5286
2630.7256
2538.2637
2523.4321
2540.5654
2578.3865
2526.5859
2529.6362
2570.0342
2589.9077
2571.4250
2600.5508
2577.0496
2530.2070
2443.6211
2480.8376
2525.5806
2450.1646
2432.3892
2506.6709

132

 Attachment LS-1
2016 ERP
Figure D-29
Industrial Daily Load Profiles – May

INDUSTRIAL - May
4500.00
4000.00

kW

3500.00
3000.00
2500.00
2000.00
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour
System Peak Day

Average Weekday

Average Weekend/Holidays

Table D-29
Industrial Daily Load Profiles (kW) - May
Hour

System Peak Day

Average Weekday

Average Weekend/Holidays

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24

3411.5088
3481.0435
3551.9917
3523.6213
3623.8206
3591.3826
3490.9985
3593.0176
3734.4180
3528.0159
2993.8535
2915.1987
2895.4502
3108.2043
3543.2517
3696.5400
3269.3511
3292.7356
3372.2788
3120.9031
3640.7473
3684.5347
3621.8181
3631.6152

3014.2954
3050.0002
3079.3015
3113.8633
3180.6157
3140.9307
3074.1052
3103.5938
3136.6638
3130.2415
3120.6157
3074.7292
3007.3887
2981.6995
3043.9790
3047.2942
3021.1636
3026.8899
2993.0928
2983.9458
3072.1543
3012.5933
3005.9358
3029.7808

3008.0615
3183.2993
3176.1851
3123.7822
3191.4946
3256.9790
3280.1416
3206.5000
3038.7141
2885.0337
2858.2751
2855.1633
2861.3545
2839.7334
3046.7935
3054.3384
2901.8901
2812.1633
2689.0957
2679.7014
2616.3352
2629.3843
2659.3745
2667.2573

133

 Attachment LS-1
2016 ERP
Figure D-30
Industrial Daily Load Profiles – June

INDUSTRIAL - June
4500.00
4000.00

kW

3500.00
3000.00
2500.00
2000.00
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour
System Peak Day

Average Weekday

Average Weekend/Holidays

Table D-30
Industrial Daily Load Profiles (kW) – June
Hour

System Peak Day

Average Weekday

Average Weekend/Holidays

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24

3246.7825
3695.1804
3722.6287
3760.6597
3771.4309
3810.5696
3763.3557
3885.6567
3968.9199
3992.4656
4116.9175
4134.3545
3852.5701
3592.2212
3599.6421
3496.2998
3537.0264
3900.6575
3709.3569
3660.9722
3695.9978
3754.6406
3767.9495
3694.3279

3297.5654
3363.8926
3386.1228
3405.3662
3393.8193
3359.7231
3294.8538
3404.8716
3486.6311
3514.0339
3524.4868
3519.3394
3459.5251
3507.5779
3513.5928
3468.7153
3425.9316
3409.7131
3329.5867
3290.6853
3325.5220
3323.6538
3354.7976
3338.2090

3504.8240
3506.0972
3463.4858
3389.6328
3442.6997
3375.7056
3360.1860
3412.9644
3438.3772
3480.5090
3516.8022
3499.6340
3447.0259
3513.5610
3573.8547
3456.4858
3474.6865
3544.9290
3432.2832
3236.3337
3335.3643
3342.5735
3294.7727
3297.4614

134

 Attachment LS-1
2016 ERP
Figure D-31
Industrial Daily Load Profiles – July

INDUSTRIAL - July
4500.00
4000.00

kW

3500.00
3000.00
2500.00
2000.00
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour
System Peak Day

Average Weekday

Average Weekend/Holidays

Table D-31
Industrial Daily Load Profiles (kW) - July
Hour

System Peak Day

Average Weekday

Average Weekend/Holidays

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24

3691.8208
3655.2961
3665.2393
3621.9460
3652.4082
3577.0010
3545.8208
3628.5964
3700.2456
3726.0073
3837.3264
3836.0122
3903.6929
4063.8398
4146.3789
4104.0195
3999.3240
4019.0295
3937.2878
3975.8892
4008.8076
3994.0237
3862.4197
3896.1152

3329.4099
3306.4319
3303.2532
3314.6614
3349.8333
3343.5999
3309.2603
3321.9177
3366.9790
3401.0486
3412.2175
3410.7307
3339.4958
3335.7361
3328.7319
3342.6506
3394.1697
3365.2825
3300.0239
3289.7166
3401.9060
3391.3098
3338.2195
3305.4238

3588.9211
3597.2290
3590.4463
3623.3208
3632.4131
3617.1782
3393.2568
3313.6206
3419.6716
3413.0234
3427.6533
3440.9968
3494.4199
3742.6179
3648.9419
3723.9609
3688.8184
3691.5630
3611.5466
3545.7925
3591.7725
3593.6978
3612.2642
3587.9248

135

 Attachment LS-1
2016 ERP
Figure D-32
Industrial Daily Load Profiles – August

INDUSTRIAL - August
4500.00
4000.00

kW

3500.00
3000.00
2500.00
2000.00
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour
System Peak Day

Average Weekday

Average Weekend/Holidays

Table D-32
Industrial Daily Load Profiles (kW) - August
Hour

System Peak Day

Average Weekday

Average Weekend/Holidays

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24

3276.0210
3278.8318
3275.1487
3246.5293
3305.6741
3303.2798
3225.3694
3274.0032
3307.6477
3412.9268
3455.8953
3428.4377
3427.3147
3493.3169
3533.7305
3566.4082
3774.7280
3952.7903
3793.7844
3660.2864
3514.5090
3619.3005
3787.0369
3756.7402

3344.1538
3338.6479
3358.6729
3343.0186
3382.7603
3424.6660
3432.6252
3455.4211
3530.7368
3578.6152
3595.7212
3583.4365
3497.5554
3476.0366
3522.2813
3496.7820
3468.6733
3463.9871
3367.0642
3337.7495
3461.6738
3465.5852
3403.4685
3372.7070

3423.6423
3447.2122
3455.3208
3465.8862
3463.4346
3470.9443
3391.1030
3361.3379
3416.1333
3497.8110
3551.7017
3623.9775
3644.4878
3639.7554
3720.9980
3675.6064
3698.7598
3635.2942
3551.8164
3472.7041
3583.3264
3544.1191
3501.9893
3493.0864

136

 Attachment LS-1
2016 ERP
Figure D-33
Industrial Daily Load Profiles – September

INDUSTRIAL - September
4500.00
4000.00

kW

3500.00
3000.00
2500.00
2000.00
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour
System Peak Day

Average Weekday

Average Weekend/Holidays

Table D-33
Industrial Daily Load Profiles (kW) - September
Hour

System Peak Day

Average Weekday

Average Weekend/Holidays

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24

3525.2588
3582.0750
3573.9824
3542.1555
3625.3313
3603.6724
3379.6414
3149.7266
3214.3618
3219.5063
3193.0688
3325.6160
3443.5347
3543.4746
3608.7275
3593.6479
3603.7751
3455.4651
3312.2097
3321.3804
3754.0681
3736.1707
3648.9514
3663.4297

3109.3289
3124.2244
3131.3574
3168.8369
3211.3669
3207.5735
3137.5090
3136.6123
3200.2473
3254.0227
3332.5615
3358.4734
3345.1616
3373.4626
3390.2739
3393.0439
3339.9946
3286.2317
3265.8743
3272.3806
3321.3120
3294.3162
3194.4514
3190.1904

3492.8230
3492.4172
3467.6919
3454.2871
3449.4146
3450.6821
3453.7817
3401.0967
3411.9946
3421.3452
3444.1646
3434.0483
3262.9844
3082.0293
3092.8757
3094.9211
3127.3638
3107.6167
3212.6953
3155.5684
3158.4277
3237.0713
3196.0796
3173.4067

137

 Attachment LS-1
2016 ERP
Figure D-34
Industrial Daily Load Profiles – October

INDUSTRIAL - October
4500.00
4000.00

kW

3500.00
3000.00
2500.00
2000.00
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour
System Peak Day

Average Weekday

Average Weekend/Holidays

Table D-34
Industrial Daily Load Profiles (kW) - October
Hour

System Peak Day

Average Weekday

Average Weekend/Holidays

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24

2977.6750
3067.6321
3102.2842
3053.9280
3125.2761
3154.0200
3183.8320
3175.4519
3179.3984
3117.6221
3195.9839
3218.6509
3235.2261
3300.7607
3275.2251
3262.4719
3249.6206
3209.4363
3167.7251
2829.1875
2794.0413
2782.4009
2581.2688
2448.3293

2757.5681
2747.1042
2770.2546
2777.7307
2801.1328
2816.9124
2802.3975
2765.1724
2831.1174
2847.9358
2860.0852
2852.8584
2808.5410
2803.6057
2819.7046
2832.6912
2830.0027
2818.5178
2797.0085
2798.1790
2783.6624
2780.9370
2748.5764
2721.3687

2642.4395
2714.2273
2820.7476
2809.6243
2851.7297
2884.0708
2885.6150
2821.4941
2809.6301
2824.1943
2835.6226
2863.3335
2795.1978
2786.3213
2819.0088
2798.0405
2842.7813
2834.6714
2790.8848
2767.7734
2804.3237
2794.3887
2775.8096
2777.2671

138

 Attachment LS-1
2016 ERP
Figure D-35
Industrial Daily Load Profiles – November

INDUSTRIAL - November
4500.00
4000.00

kW

3500.00
3000.00
2500.00
2000.00
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour
System Peak Day

Average Weekday

Average Weekend/Holidays

Table D-35
Industrial Daily Load Profiles (kW) - November
Hour

System Peak Day

Average Weekday

Average Weekend/Holidays

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24

2666.0325
2853.4690
3118.0078
3118.1313
3150.6230
3138.2043
3094.4741
3065.8965
3107.1794
3134.7004
3127.2129
3070.3679
3181.3782
3187.9333
3242.7253
3231.2756
3173.5637
3286.4202
3162.8538
3185.8018
3222.6467
3212.6741
3205.7566
3234.8342

2813.1760
2814.2192
2780.2927
2761.9600
2789.1309
2727.1055
2634.9309
2705.0491
2782.1387
2760.2739
2801.5793
2776.6565
2737.0857
2762.2859
2792.0771
2771.6304
2737.6694
2821.6785
2774.2336
2781.6145
2852.0359
2892.8220
2896.3350
2877.2871

2732.4053
2700.4067
2743.1362
2756.4907
2774.8792
2791.7627
2815.4099
2783.8098
2766.3044
2773.9775
2768.9634
2756.7500
2728.8584
2709.2427
2701.9922
2689.6399
2711.6641
2747.7605
2574.4373
2507.9541
2544.5342
2534.4722
2527.6694
2570.1421

139

 Attachment LS-1
2016 ERP
Figure D-36
Industrial Daily Load Profiles – December

INDUSTRIAL - December
4500.00
4000.00

kW

3500.00
3000.00
2500.00
2000.00
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour
System Peak Day

Average Weekday

Average Weekend/Holidays

Table D-36
Industrial Daily Load Profiles (kW) - December
Hour

System Peak Day

Average Weekday

Average Weekend/Holidays

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24

3203.9041
3179.5378
3178.9331
3173.5901
3193.9897
3225.5417
3234.7896
3258.1226
3265.5376
3326.5771
3320.2087
3245.5464
3233.9170
3269.7405
3269.5322
3244.0569
3266.4827
3411.6399
3322.4856
3199.1497
3183.3689
3119.0710
3115.5254
3126.5994

2777.3477
2767.1428
2777.6052
2764.8601
2784.4099
2790.9341
2756.8984
2715.8967
2683.4714
2718.8149
2684.2517
2635.1689
2625.6062
2643.9375
2666.2083
2645.9492
2666.6941
2741.5681
2704.1699
2700.8396
2747.4321
2734.5715
2742.1292
2747.4463

2955.9924
2914.8701
2918.2588
2892.8633
2909.5254
2964.7183
2984.2461
2949.6096
2935.6355
2930.6396
2862.9297
2864.3501
2868.3735
2846.3877
2856.4004
2917.7085
2955.7224
3002.7783
2950.6487
2950.3428
2951.4070
2930.1597
2902.5979
2962.6443

140

 Attachment 
2016 ERP

Appendix 
Technology Characterization and Busbar Cost Analysis

141

Attachment 
2016 ERP

Appendix 
Black Hills Variable Energy Resource Integration Study

142

Attachment 
2016 ERP

Appendix 
2016 -2040 Load and Resource Balance

143

Attachment LS-1
2016 ERP
Appendix H
General Planning Assumptions
The planning assumptions shown on Table H-1 will underlie the evaluation of
proposals received in response to any Company solicitation in Phase II of these
2016 ERP proceedings. Note that the following is not a complete listing of all
assumptions that will be applied in the evaluation process. In addition, the
assumptions noted below represent “base case” assumptions. Sensitivity analysis
will be performed in which certain of these assumptions are altered in accordance
with Commission directives.
Table H-1
General Planning Assumptions
Item
2016 ERP Assumption
Capacity credit for solar
37%
Capacity credit for wind
20%
CO2 price forecast
$0/ton
Conventional resource options
See Section 5.0
considered
Cost of integrating renewable
See Section 6
resources
DSM forecast
See Section 3.7
Emissions costs
Confidential
Financial parameters
See Table 3-3
General inflation rate
2.8%
Interconnection costs applied to
Varies by resource
bids
Load forecast
See Section 4.0
Market prices
Confidential
Natural gas prices
Confidential
Owned unit operating
See Table 5-1
characteristics and costs
Owned unit retirement dates
See Table 5.1
Planning period
25 years
Planning reserve margin
15% minimum
Power purchase contracts
Varies by resource
Renewable resource options
See Section 5.0
considered
Resource Acquisition Period
7 years
Seasonal firm market purchases
See Section 3.4.4
Spinning reserve requirement
Rocky Mountain
Reserve Group
requirements

144

Updated in Phase II
No
No
No
No
No
If appropriate
No
Yes, as appropriate
Yes
Yes
No
Yes
Yes
No
No
No
No
No
No
No
No
No

 Attachment LS-1
2016 ERP
Appendix I
Computer Modules Used for the Electric Resource Plan
Black Hills uses System Optimizer to produce unique resource portfolios across a range
of different planning assumptions. System Optimizer’s core logic handles both
Capacity Expansion and Emissions Compliance decisions. The optimized capacity
plan can be fed into the Planning and Risk module for a detailed analysis without
changing base data or scenario data.
The System Optimizer model operates by minimizing operating costs for existing
and prospective new resources, subject to system load balance, reliability and other
constraints. Over the 25-year planning horizon, it optimizes resource additions
subject to resource costs and capacity constraints (summer peak loads plus a
planning reserve margin). In the event that a retirement of an existing generating
resource or contract expiration is assumed for a given planning scenario, System
Optimizer will select additional resources as required to meet summer peak loads
inclusive of a target planning reserve margin. To accomplish these optimization
objectives, System Optimizer performs a time-of-day least cost dispatch for existing
and potential planned generation, while considering cost and performance of
existing contracts within Black Hills’ transmission system. Resource dispatch is
based on a representative-week method. Dispatch determines optimal electricity
flows between zones and includes spot market transactions for system balancing.
The model minimizes the system PVRR, which includes the net present value cost of
existing contracts, spot market purchase costs, generation costs (fuel, fixed and
variable operation and maintenance, decommissioning, emissions, unserved energy,
and unmet capacity), and amortized capital costs for potential new resources.
The PAR software was used to analyze, report, and estimate the optimal dispatch of
a generation portfolio against either a market price or a load requirement.
PAR’s uncertainty modeling technology allows for the flexibility to perform either
scenario or Monte Carlo simulation analysis of electricity based portfolios, thereby
fitting in with either traditional analytical methods, or those based on modern
financial theory.
PAR is driven by ABB’s PROSYM chronological calculation engine for modeling
power systems, developed for over 20 years to meet the needs of utilities facing
portfolio planning decisions. PROSYM’s engine is used to simulate a portfolio’s
operation by reflecting detailed unit operating constraints like start-up costs, ramp
rate restrictions, minimum up and down times, and other plant dynamics, providing
a credible analysis of asset valuation and risk exposure.
Strategic Planning powered by MIDAS Gold® was utilized to measure and analyze
the consumer value of competition. Strategic Planning includes multiple modules for
an enterprise-wide strategic solution. These modules are: Markets, Capacity
145

 Attachment LS-1
2016 ERP
Expansion, Portfolio, Financial, and Risk. The financial and risk modules were the
only modules used for the 2016 ERP.
The financial module allows the user the ability to model other financial aspects
regarding costs exterior to the operation of units and other valuable information
that is necessary to properly evaluate the economics of a generation fleet. The
financial module produces bottom-line financial statements to evaluate profitability
and earnings impacts.
Risk module provides users the capability to perform stochastic analyses on all
other modules and review results numerically and graphically. Stochastics may be
performed on both production and financial variables providing flexibility not
available in other models.
Strategic Planning has the functionality of developing probabilistic price series by
using a four-factor structural approach to forecast prices that captures the
uncertainties in regional electric demand, resources and transmission. Using a Latin
Hypercube-based stratified sampling program, Strategic Planning generates
regional forward price curves across multiple scenarios. Scenarios are driven by
variations in a host of market price “drivers” (e.g. demand, fuel price, availability,
hydro year, capital expansion cost, transmission availability, market electricity
price, reserve margin, emission price, electricity price and/or weather) and takes
into account statistical distributions, correlations, and volatilities for three time
periods (i.e. Short-Term hourly, Mid-Term monthly, and Long-Term annual) for
each transact group. By allowing these uncertainties to vary over a range of
possible values a range or distribution of forecasted prices are developed.
Stratified sampling can be thought of as “smart” Monte Carlo sampling. Instead of
drawing each sample from the entire distribution – as in Monte Carlo sampling – the
sample space is divided into equal probability ranges and then a sample is take from
each range.

146

 Attachment LS-1
2016 ERP
Appendix J
Emissions Projections
Table J-1
Table J-2
Table J-3
Table J-4
Table J-5
Table J-6
Table J-7
Table J-8
Table J-9
Table J-10

Annual Projected SO2 Emissions from Existing Resources (Tons)
Annual Projected SO2 Emissions from Generic Resources (Tons)
Annual Projected CO2 Emissions from Existing Resources (Tons)
Annual Projected CO2 Emissions from Generic Resources (Tons)
Annual Projected NOx Emissions from Existing Resources (Tons)
Annual Projected NOx Emissions from Generic Resources (Tons)
Annual Projected PM Emissions from Existing Resources (Tons)
Annual Projected PM Emissions from Generic Resources (Tons)
Annual Projected Mercury Emissions from Existing Resources (Tons)
Annual Projected Mercury Emissions from Generic Resources (Tons)

147

 Attachment LS-1
2016 ERP
Table J-1
Annual Projected SO2 Emissions from
Existing Resources (Tons)
Year
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040

Base-withRES
0.09
0.08
0.11
0.09
0.10
0.11
0.10
0.12
0.11
0.15
0.17
0.19
0.19
0.21
0.24
0.25
2.52
2.51
2.52
2.57
2.58
2.49
2.52
2.52
2.52

148

Alt Plan
1
0.09
0.08
0.11
0.09
0.10
0.11
0.10
0.11
0.12
0.16
0.16
0.18
0.18
0.20
0.23
0.24
1.73
1.74
1.77
1.75
1.71
1.82
1.56
1.60
1.58

Alt Plan
2
0.09
0.08
0.11
0.09
0.10
0.11
0.10
0.11
0.12
0.16
0.16
0.18
0.18
0.20
0.23
0.24
1.73
1.74
1.77
1.75
1.71
1.82
1.56
1.58
1.56

 Attachment LS-1
2016 ERP
Table J-2
Annual Projected SO2 Emissions
from Generic Resources (Tons)
BaseAlt 1
Alt 2
Year with-RES
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
0.76
0.75
0.35
2032
0.72
0.72
0.34
2033
0.70
0.70
0.33
2034
0.69
0.68
0.30
2035
0.70
0.72
0.34
2036
0.66
0.67
0.31
2037
0.81
0.81
0.37
2038
0.77
0.76
0.35
2039
0.77
0.77
0.35
2040

149

 Attachment LS-1
2016 ERP
Table J-3
Annual Projected CO2 Emissions from
Existing Resources (Tons)
Year
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040

Base-withRES
17,711
16,529
22,639
18,242
19,838
21,013
19,853
22,909
24,431
31,240
32,891
36,184
36,835
40,666
46,180
47,799
344,821
347,169
353,458
367,424
367,126
375,514
330,863
339,216
340,035

150

Alt Plan
1
17,711
16,529
22,639
18,242
19,838
21,013
19,853
22,909
24,431
31,240
32,891
36,184
36,835
40,666
46,180
47,799
345,283
347,522
354,223
350,890
342,469
364,059
311,026
320,682
315,399

Alt Plan
2
17,711
16,529
22,639
18,242
19,838
21,013
19,853
22,909
24,431
31,240
32,891
36,184
36,835
40,666
46,180
47,799
345,283
347,522
354,223
350,890
342,469
364,059
311,026
316,711
312,989

 Attachment LS-1
2016 ERP
Table J-4
Annual Projected CO2 Emissions from
Generic Resources (Tons)
Year
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040

Base-withRES
151,070
144,416
140,566
137,386
139,585
131,176
161,468
154,872
154,712

151

Alt
Plan 1
151,070
144,416
140,566
137,386
139,585
131,176
161,468
154,872
154,712

Alt Plan 2
149,404
144,111
139,742
135,876
143,615
133,935
162,533
151,039
152,376

 Attachment LS-1
2016 ERP
Table J-5
Annual Projected NOX Emissions from Existing
Resources (Tons)
Year
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040

Base-with
RES
2.21
2.08
2.85
2.31
2.51
2.65
2.50
2.90
3.09
3.98
4.17
4.60
4.68
5.17
5.87
6.09
44.01
44.15
44.52
46.27
46.34
47.30
42.55
43.47
42.85

Alt Plan 1
2.21
2.08
2.85
2.31
2.51
2.65
2.50
2.90
3.09
3.98
4.17
4.60
4.68
5.17
5.87
6.09
44.05
44.20
44.62
44.15
43.17
45.97
39.25
40.35
39.74

152

Alt
Plan 2
2.21
2.08
2.85
2.31
2.51
2.65
2.50
2.90
3.09
3.98
4.17
4.60
4.68
5.17
5.87
6.09
44.05
44.20
44.62
44.15
43.17
45.97
39.25
39.84
39.40

 Attachment LS-1
2016 ERP
Table J-6
Annual Projected NOX Emissions from
Generic Resources (Tons)
Year
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040

Base-withRES
18.88
18.05
17.57
17.17
17.45
16.40
20.18
19.36
19.34

153

Alt
Plan 1
18.68
18.01
17.47
16.98
17.95
16.74
20.32
19.03
19.37

Alt
Plan 2
18.68
18.01
17.47
16.98
17.95
16.74
20.32
18.88
19.05

 Attachment LS-1
2016 ERP
Table J-7
Annual Projected PM Emissions from
Existing Resources (Tons)
Year
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040

Base-withRES
0.59
0.56
0.76
0.62
0.67
0.71
0.67
0.77
0.83
1.07
1.11
1.23
1.25
1.38
1.57
1.63
11.69
11.72
11.89
12.36
12.39
12.64
11.23
11.47
11.45

154

Alt
Plan 1
0.59
0.56
0.76
0.62
0.67
0.71
0.67
0.77
0.83
1.07
1.11
1.23
1.25
1.38
1.57
1.63
11.70
11.73
11.92
11.79
11.54
12.29
10.49
10.78
10.62

Alt Plan
2
0.59
0.56
0.76
0.62
0.67
0.71
0.67
0.77
0.83
1.07
1.11
1.23
1.25
1.38
1.57
1.63
11.70
11.73
11.92
11.79
11.54
12.29
10.49
10.64
10.53

 Attachment LS-1
2016 ERP
Table J-8
Annual Projected PM Emissions from
Generic Resources (Tons)
Year
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040

Base-withRES

Alt
Plan 1

Alt
Plan 2

5.04
4.81
4.69
4.58
4.65
4.37
5.38
5.16
5.16

4.98
4.80
4.66
4.53
4.79
4.46
5.42
5.08
5.17

4.98
4.80
4.66
4.53
4.79
4.46
5.42
5.03
5.08

155

 Attachment LS-1
2016 ERP
Table J-9
Annual Projected Mercury Emissions from Existing
Resources (Tons)
Year
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040

Base-withRES
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0

156

Alt 1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0

Alt 2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0

 Attachment LS-1
2016 ERP
Table J-10
Annual Projected Mercury Emissions from Generic
Resources (Tons)
Year
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040

Base-withRES
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0

157

Alt 1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0

Alt 2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0

 Attachment LS-1
2016 ERP
Appendix K
Confidential Price Forecasts
Confidential Schedule K-1 Natural Gas Price Forecast
Confidential Schedule K-2 Fuel Oil Price Forecast
Confidential Schedule K-3 Emission Costs (ABB WECC 2015 Fall Reference Case
CO2 Scenario)
Confidential Schedule K-4 Seasonal Firm Market Price Forecasts- AZ PV Market
Base Case
Confidential Schedule K-5 Seasonal Firm Market Price Forecasts- AZ PV Market
High Gas Case
Confidential Schedule K-6 Seasonal Firm Market Price Forecasts- AZ PV Market
Low Gas Case
Confidential Schedule K-7 Seasonal Firm Market Price Forecasts- AZ PV Market
CO2 Tax Case
Confidential Schedule K-8 Market Clearing Price Forecasts- CO East Market- Base
Case
Confidential Schedule K-9 Market Clearing Price Forecasts- AZ PV Market- Base
Case
Confidential Schedule K-10 Market Clearing Price Forecasts- CO East Market- CO2
Tax Case
Confidential Schedule K-11 Market Clearing Price Forecasts- AZ PV Market- CO2 Tax
Case
Confidential Schedule K-12 Market Clearing Price Forecasts- CO East Market- High
Gas Case
Confidential Schedule K-13 Market Clearing Price Forecasts- AZ PV Market- High
Gas Case
Confidential Schedule K-14 Market Clearing Price Forecasts- CO East Market- Low
Gas Case
Confidential Schedule K -15 Market Clearing Price Forecasts- AZ PV Market- Low
Gas Case
Confidential Schedule K-16 Projected Emissions Rates for Existing Resources

158

 Attachment LS-1
2016 ERP
Appendix L
Confidential Exhibit
Cost parameters for Waste-to-Energy PPA Price
Waste-toAnnual
Energy PPA Escalation
Year
Price
Rate
Assumption
(%)
2019
$47.50
2.5
*Assumed 2019 first year of operation.

159

 Attachment 
2016 ERP

Appendix 
Model Request for Proposal for Intermittent Resources

160

Attachment LS-1
2016 ERP

Appendix 
Model Form Contract for Intermittent Resources

161

Attachment LS-1
2016 ERP

Appendix O
Model Request for Proposal for Stand Alone Renewable Energy Credits

162

 Attachment LS-1
2016 ERP

Abbreviations
AMI – Advanced Metering Infrastructure
AEO2016 Early Release - 2016 Annual Energy Outlook, Early Release
BACT – Best Available Control Technology
Black Hills - Black Hills Colorado Electric
Btu – British Thermal Unit
CACJA – Clean Air - Clean Jobs Act
CAIR – Clean Air Transport Rule
CAMR – Clean Air Mercury Rule
CC – Combined Cycle
CCPG – Colorado Coordinated Planning Group
CCR – Code of Colorado Regulations
CDD – Cooling Degree Days
CDH – Cooling Degree Hours
CEIP - Clean Energy Incentive Program
CIS+ - Customer Information System
CO2 – Carbon dioxide
Commission – Colorado Public Utilities Commission
CPCN – Certificate of Public Convenience and Necessity
CPP – Clean Power Plan
CPWG – Conceptual Planning Work Group
C.R.S. – Colorado Revised Statutes
CSG- Community Solar Garden
CT – Combustion Turbine
CV – Coefficient of Variation
DG – Distributed Generation
DSM – Demand-Side Management
EIA – Energy Information Administration
ELCC – Effective Load Carrying Capability
EPA – Environmental Protection Agency
EPC – Engineering, Procurement and Construction
ERP – Electric Resource Plan
ERZ – Energy Resource Zone
FERC – Federal Energy Regulatory Commission
FIP – Federal Implementation Plans
GHG – Greenhouse Gas
GRP - Gross Regional Product
GWh – Gigawatthour
HDD – Heating Degree Days
HDH – Heating Degree Hours
HRSG – Heat Recovery Steam Generator
ITC – Investment Tax Credit
163

 Attachment LS-1
2016 ERP
JDA – Joint Dispatch Agreement
kV – Kilovolt
kW – Kilowatt
kWh – Kilowatthour
LOLE – Loss of Load Expectation
LOLH – Loss of Load Hours
LTP - Local Transmission Plan
MACT – EPA’s Mercury and Air Toxics Standard
MPS – Missouri Public Service
MW – Megawatt
MWh – Megawatthour
NAS – Sodium Sulfur
NCDC - NOAA National Climatic Data Center
NERC – North American Electric Reliability Corporation
OLS – Ordinary Least Squares
Order 1000 – FERC Order No. 1000
PAGS – Pueblo Airport Generation Station
PAR- Planning & Risk
PCC – Planning Coordination Committee
PPA – Power Purchase Agreement
PSD – Prevention of Significant Deterioration
PSCo – Public Service Company of Colorado
PV solar– Photovoltaics
PV - Palo Verde, Arizona
PVRR – Present Value of Revenue Requirements
QRU – Qualifying Retail Utility
RAP – Resource Acquisition Period
RB – Rattlesnake Butte Substation
REC – Renewable Energy Credit
RES – Renewable Energy Standard
RESA – Renewable Energy Standards Account
RFP – Request for Proposals
RMRG – Rocky Mountain Reserve Group
SIP – State Implementation Plan
SPG- Sub regional Planning Group
SSPG – Sierra Sub regional Planning Group
SWAT – Southwest Area Transmission Group
TCPC - Transmission Coordination and Planning Committee
TEPPC – Transmission Expansion Planning Policy Committee
The Company – Black Hills Colorado Electric
The RES Rules - Commission Rules 4 CCR 723-3-3650 et seq.
The RES Statute – C.R.S., § 40-2-124 et seq.
UPC – Use per Customer
VER – Variable Energy Resources
W&P – Woods & Poole Economics, Inc.
WACC – Weighted-Average Cost of Capital
164

 Attachment LS-1
2016 ERP
WAPA - Western Area Power Administration
WECC – Western Electricity Coordinating Council
Western – Western Area Power Administration

165