•

Entergy Services, LLC
639 Loyola Avenue (70113)
P.O. Box 61000
New Orleans, LA 70161-1000
Tel 504 576 6571
Fax 504 576 5579

~Entergy

Timothy S. Cragin
Assistant Gen eral Counsel
Legal Services • Regulatory

October 31 , 2018

Bv Hand Delivery
Ms. Lora W. Johnson, CMC, LMMC
Clerk of Council
City Hall - Room 1E09
1300 Perdido Street
New Orleans, LA 70112

Re: Resolution Directing Entergy New Orleans, Inc. to Investigate and Remediate
Electric Service Disruptions and Complaints and to Establish Minimum
Electric Reliability Performance Standards and Financial Penalty Mechanisms
CNO Docket No. UD-17-04

Dear Ms. Johnson:
Please find enclosed for your further handling an original and three copies of Quanta
Technology, LLC's Assessment of Entergy New Orleans, LLC's ("ENO") Distribution
Reliability Improvement Initiatives. Please file an original and two copies into the record of the
above-referenced matter and return a date-stamped copy to our courier.
Thank you for your assistance with this matter.
Sincerely,

~
TSC\rdm
Enclosures
cc:

Official Service List (UD-17-04 via electronic mail)

 Assessment of Distribution
Reliability Improvement Initiatives

PREPARED FOR:

Entergy New Orleans
(ENO)

REPORT DATE:

October 31, 2018

PREPARED BY:

Julio Romero Agüero, PhD
Julio@Quanta-technology.com
Victor Romero, PE
VRomero@Quanta-technology.com
Bill Snyder
Bsnyder@Quanta-technology.com
B
Bill
QUANTA TECHNOLOGY, LLC

4020 WESTCHASE BOULEVARD, SUITE 300, RALEIGH, NC 27607 USA
Toronto   Oakland   Chicago   Boston   San Clemente
www.Quanta-Technol ogy.com
Quanta Technology, LLC is a wholly-owned subsidiary of Quanta Services, Inc. (NYSE: PWR)

© 2018 QUANTA TECHNOLOGY, LLC

 E NTERGY N EW O RLEANS – R ELIABILITY ASSESSMENT

EXECUTIVE SUMMARY
In August 2017 the New Orleans City Council Established Docket UD-17-04 – “RESOLUTION DIRECTING
ENTERGY NEW ORLEANS, INC. TO INVESTIGATE AND REMEDIATE ELECTRIC SERVICE DISRUPTIONS AND
COMPLAINTS AND TO ESTABLISH MINIMUM ELECTRIC RELIABILITY PERFORMANCE STANDARDS AND
FINANCIAL PENALTY MECHANISMS.” As part of the effort to cooperate with the City Council’s resolution,
Entergy New Orleans (ENO) committed to retain the services of a nationally-recognized firm to consult
with ENO’s distribution reliability team and provide third-party expertise and a national perspective on
their current reliability improvement plan. In August 2018 ENO engaged Quanta Technology LLC (Quanta)
to perform an assessment of ENO’s reliability performance and improvement plans and actions.
Quanta conducted a review of ENO’s distribution reliability program and a comparison of its distribution
reliability practices versus industry leading practices and those of a selected group of high performing
utility peers. ENO’s distribution reliability performance had declined in the last five years, and its 2017 key
distribution reliability indices (SAIFI and SAIDI) were close to the borderline between 3rd and 4th quartile
of the 2017 IEEE Annual Distribution Reliability Benchmark. ENO has increased its reliability spending in
the last three years and has planned further investments in key infrastructure, technologies, and systems
to stop and reverse this trend and improve reliability performance. As shown in Table 1, these activities
are starting to yield positive results and have helped ENO improve its reliability performance during 2018.
Year-to-date key indices and overall distribution outage data show a reduction in the number of customer
interruptions and customer minutes of interruption.
Table 1 – Actual CI 2017 and 2018 through October 21

ENO System
Distribution View
Transmission View
Customer View

2017 YTD
Actual CI
281,119
42,387
323,506

2018 YTD
Actual CI
204,324
81,054
285,378

Actual Diff
(76,795)
38,667
(38,128)

Actual %
Diff
-27.3%
91.2%
-11.8%

In 2018, ENO has experienced significantly higher customer interruptions due to transmission and
substation outages than in the previous two years. Thus far in 2018, transmission and substation outages
have accounted for 28.5% of the total CI. Through the same period in 2017, transmission and substation
outages accounted for approximately 13.1% of total CI.
The results of Quanta’s assessment indicate that ENO’s distribution reliability program includes adequate
components to continue addressing existing and short-term needs in this area. If investments in
distribution reliability and grid modernization continue as planned, it would be expected that ENO’s
distribution reliability indices improve to 2nd quartile performance. It should be recognized, however, that
reliability indices are the outcome of a combination of numerous internal and external distribution system
variables. Due to the unpredictability of these variables (e.g., future weather patterns), it is not possible
to forecast the exact value of distribution reliablity indices for upcoming years.
The improvements in ENO reliability will not be immediate, since some investments (e.g., BACKBONE
program) are essentially needed to stabilize performance and prevent further decline of reliability indices,
while others (e.g., deployment of smart reclosers) are largely intended to improve performance, and most

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importantly, because of the legacy construction and design of ENO’s distribution grid coupled with aging
infrastructure. The overall effort to achieve reliability improvement on the ENO system should be viewed
as a long-term initiative, as opposed to quick fixes.
ENO’s proposed reliability program is largely intended to address foundational infrastructure needs (e.g.,
replace legacy construction and aging assets). These investments, combined with deployment of
intelligent infrastructure, and processes enhancements that are part of ENO’s grid modernization, AMI
(Advanced Metering Infrastructure) and ADMS (Advanced Distribution Management System) programs,
are expected to improve ENO’s distribution reliability performance. It is worth noting that there is a
synergistic relationship between these different programs, i.e., they complement and reinforce each
other.
There are important opportunities to improve ENO’s reliability program that are described in greater
detail in the recommendations section. Specific recommended improvements include considering a
broader set of reliability metrics to start preparing to account for forthcoming needs, further deployment
of intelligent infrastructure (e.g., smart reclosers/switches), greater utilization of data/grid analytics
solutions and techniques to perform more detailed root-cause analyses and enhance asset management,
implementation of advanced distribution planning techniques, particularly model-based reliability
analysis, and periodic inspection and condition assessment of all its distribution grid assets. It’s worth
noting that some of these improvements are already being considered and/or implemented by ENO to
various degrees as part of its grid modernization, AMI, and ADMS programs. In general, there is a valuable
opportunity to accelerate improvement of reliability performance by expediting, to the extent possible
within regulatory requirements, the implementation of the components of the reliability program
discussed in this report, along with the implementation of these other programs. This needs to be carefully
coordinated at project management and implementation levels. It is recommended that ENO evaluates
this opportunity if there is a need to achieve reliability improvement objectives faster.
The rest of the report is organized as follows, section 1 discusses project drivers, scope and objectives;
section 2 presents a distribution reliability tutorial intended to set the stage, particularly for readers who
may not be fully familiar with some of the concepts discussed in the document; section 3 discusses the
overall approach used by Quanta to execute the project; section 4 discusses the current state of ENO’s
system in terms of distribution reliability and assesses the individual components of ENO’s reliability
improvement program; section 5 discusses industry leading practices pertaining to distribution reliability,
including a description of the reliability improvement benefits associated with grid modernization
programs; section 6 provides recommendations to improve ENO’s distribution reliability program; and
section 7 presents the conclusions of this project. Finally, there are five appendices included with relevant
technical information about specific topics included in the report.

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TABLE OF CONTENTS
EXECUTIVE SUMMARY ........................................................................................................... ii
List of Figures ............................................................................................................................................. v
List of Tables ............................................................................................................................................. vi
List of Acronyms ...................................................................................................................................... vii

1

INTRODUCTION .............................................................................................................. 1

2

DISTRIBUTION RELIABILITY – A BRIEF TUTORIAL .............................................................. 2
2.1
2.2
2.3
2.4

3

PROJECT APPROACH AND METHOD ................................................................................ 9
3.1
3.2

4

Definition or explanation of the broad concepts of distribution reliability..................................... 2
Distribution reliability standards and metrics in the US power industry ........................................ 2
Variables in reliability reporting ...................................................................................................... 4
How reliability reporting can/should be used in evaluating utility performance............................ 6

Scope of Work .................................................................................................................................. 9
Approach and Methodology ............................................................................................................ 9
3.2.1 Project Team .......................................................................................................................... 9
3.2.2

Project Approach & Methodology ....................................................................................... 10

3.2.3

Data Requests ...................................................................................................................... 10

3.2.4

Benchmarking ...................................................................................................................... 11

3.2.5

Analysis, Evaluation, and Conclusions ................................................................................. 11

DISTRIBUTION RELIABILITY AT ENTERGY NEW ORLEANS ................................................ 12
4.1

4.2

Current State .................................................................................................................................. 12
4.1.1 Legacy Distribution Design ................................................................................................... 14
4.1.2

Infrastructure ....................................................................................................................... 16

4.1.3

Metrics and Outage Data ..................................................................................................... 19

Primary Reliability Improvement Programs and Initiatives ........................................................... 22
4.2.1 FOCUS Program .................................................................................................................... 22
4.2.2

4.3

BACKBONE Program ............................................................................................................. 25

Other Efforts to Improve Reliability ............................................................................................... 26
4.3.1 R1 Program........................................................................................................................... 26
4.3.2

Pole Program ........................................................................................................................ 27

4.3.3

Infrared Inspections ............................................................................................................. 27

4.3.4

Underground Cable Renewal Program ................................................................................ 27

4.3.5

Vegetation Management Program ...................................................................................... 28

4.3.6

Basic Insulation Level (BIL) Enhancements .......................................................................... 28

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4.3.7

Sectionalizing Program ......................................................................................................... 28

4.3.8

Storm Hardening .................................................................................................................. 29

4.3.9

Internal Program .................................................................................................................. 29

4.3.10 Cross-Company Reliability Improvement Collaboration ...................................................... 29

5

INDUSTRY PERFORMANCE AND TRENDS ....................................................................... 31
5.1
5.2
5.3

5.4

6

5.3.2

Reliability programs ............................................................................................................. 47

5.3.3

Asset management .............................................................................................................. 52

Grid Modernization – Industry Trends and Activities .................................................................... 54
5.4.1 Grid Modernization – Reliability Benefits ............................................................................ 57

ENO RELIABILITY PERFORMANCE IMPROVEMENT ......................................................... 66
6.3

7

Reliability reports and metrics ....................................................................................................... 31
Benchmark of distribution reliability practices.............................................................................. 37
Peer comparisons .......................................................................................................................... 43
5.3.1 Metrics ................................................................................................................................. 43

Potential Improvement Efforts ...................................................................................................... 67
6.3.1 Metrics ................................................................................................................................. 67
6.3.2

Reliability Programs ............................................................................................................. 68

6.3.3

Asset Management .............................................................................................................. 73

CONCLUSIONS .............................................................................................................. 74

APPENDIX A: PROJECT TEAM ............................................................................................... 76
APPENDIX B: RELIABILITY INDICES........................................................................................ 80
APPENDIX C: SIMILARITY METRIC ........................................................................................ 81
APPENDIX D: INDUSTRY SURVEY .......................................................................................... 83
APPENDIX E: GRID MODERNIZATION TECHNOLOGIES........................................................... 88

List of Figures
Figure 1 – State Reporting Requirements and Practices for Utility-Reported Reliability Information......... 5
Figure 2 - Project Approach ........................................................................................................................ 10
Figure 3 - ENO 2018 Distribution SAIFI Performance - As of 10/21/2018 .................................................. 13
Figure 4 – Example of Deteriorated Legacy Construction .......................................................................... 15
Figure 5 – Decay Hazard Zones ................................................................................................................... 18
Figure 6 – CI and CMI Trend ........................................................................................................................ 20
Figure 7 – Proportional DLIN CI Increases................................................................................................... 21
Figure 8 – Proportional DLIN CMI Increases ............................................................................................... 21

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Figure 9 - FOCUS Life Cycle Process ............................................................................................................ 23
Figure 10 - FOCUS Algorithm Method ........................................................................................................ 24
Figure 11 – BACKBONE Inspections ............................................................................................................ 25
Figure 12 – Reliability Projects .................................................................................................................... 26
Figure 13 – SAIDI and SAIFI Quartiles from 2018 IEEE Annual Benchmark (without MED). ....................... 34
Figure 14 – Historical SAIDI and SAIFI Quartiles from 2018 IEEE Annual Benchmark (without MED)........ 35
Figure 15 – Historical SAIDI and SAIFI Quartiles from 2018 IEEE Annual Benchmark (with MED). ............ 36
Figure 16 – Historical SAIDI and SAIFI Quartiles from 2016 NRECA Annual Benchmark (without MED). .. 37
Figure 17 – SAIDI and SAIFI values for overhead (OH) and underground (UG) transmission and distribution
systems. ...................................................................................................................................................... 39
Figure 18 – Benchmark Comparisons. ........................................................................................................ 40
Figure 19 – Survey of Reliability Metrics used for Regulatory and Internal Reporting. ............................. 43
Figure 20 – Adjusted CEMI5 and MAIFIE reported by IOUs in Florida ......................................................... 45
Figure 21 – Utility Practices for Defining Sustained Interruptions. 3 .......................................................... 46
Figure 22 – Major Event Identification and Exclusion Methodology. 30 ..................................................... 47
Figure 23 – Cost per Mile: Converting Overhead to Underground Distribution19 ...................................... 50
Figure 24 – Grid Modernization Index by State42 ....................................................................................... 55
Figure 25 – Total Number of Grid Modernization Actions by Quarter.36 ................................................... 56
Figure 26 – Legislative and Regulatory Grid Modernization (Q2 2018). 36.................................................. 57
Figure 27 – Smarter Energy Infrastructure Features & Grid Modernization Technologies. ....................... 58
Figure 28 – Devices and Systems that Support Distribution Automation Applications.47 .......................... 59
Figure 29 – Conceptual Example of FLISR Operation.................................................................................. 60
Figure 30 – Comparison of Fault Recovery Timelines for Different Operation Modes in FLISR
Implementation. ......................................................................................................................................... 61
Figure 31 – DA Asset Deployments by Participating Utilities Under SGIG Projects.47 ................................ 62
Figure 32 – Reliability and Outage Management Results from DA Investments from the SGIG program.47
.................................................................................................................................................................... 63
Figure 33 – CMI Avoided by DA Operations.47 ............................................................................................ 64
Figure 34 – ComEd System SAIFI improvement since the beginning of the implementation of grid
modernization program47 ........................................................................................................................... 65
Figure 35 – ComEd System Customers Reliability Complaints decrease since implementation of grid
modernization program 47 ........................................................................................................................... 65
Figure 36 – Reliability benefits (SAIDI reduction) from single-phase tripping ............................................ 90
Figure 37 – ComEd loop distribution automation schemes using a combination of modern reclosing
technologies, ............................................................................................................................................... 91
Figure 38 – PSE&G advanced loop scheme using multiple reclosers and high speed peer-to-peer
communication ........................................................................................................................................... 91
Figure 39 – Conceptual description of the reliability benefits associated with a FLISR implementation. . 93
Figure 40 – Expected reliability improvements due to implementation of portfolio of projects .............. 96
Figure 41 – Project prioritization ................................................................................................................ 96

List of Tables
Table 1 – Actual CI 2017 and 2018 through October 21 ............................................................................... ii
Table 2 - ENO SAIFI as of October 21 Each Year ........................................................................................ 12
Table 3 – ENO Year End DLIN Reliability Indices ......................................................................................... 12

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Table 4 –Distribution Transformers ............................................................................................................ 16
Table 5 – Transformer Failure Rates ........................................................................................................... 16
Table 6 – ENO Equipment Failures 2013-17 ............................................................................................... 17
Table 7 – Entergy Vegetation Clearance Specifications .............................................................................. 28
Table 8 - Summary of Distribution Reliability Indices based on EIA Data (2015). ...................................... 32
Table 9 - Prioritized list of utilities to benchmark based on results of similarity analysis .......................... 42
Table 10 – Reliability Improvement Initiatives ........................................................................................... 48
Table 11 – Reliability Improvement Survey of 12 Utilities .......................................................................... 49
Table 12 – Estimated Costs to Convert OH to UG in New Orleans ............................................................. 50
Table 13 - Summary of Grid Modernization Actions (Q2 2018).36 .............................................................. 57
Table 14 - 2016 thru Mid-2018 Reliability Expenditures ............................................................................ 66
Table 15 – Summary of Changes in Distribution Reliability ........................................................................ 93
Table 16 – Utilities Deploying Distribution Reliability Devices and Systems as Part of SGIG ..................... 94

List of Acronyms
ADMS: Advanced Distribution Management System
AMI: Advanced Metering Infrastructure
BIL: Basic Impulse Insulation Level
CAIDI: Customer Average Interruption Duration Index
CEMIn: Customers Experiencing n Multiple Interruptions
CI: Customer Interruption
CIS: Customer Information Systems (CIS)
CMI: Customer-Minutes of Interruption
DER: Distributed Energy Resources
FLISR: Fault Location, Isolation and Service Restoration
IEEE: Institute of Electrical and Electronics Engineers
IVR: Interactive Voice Response (IVR)
MAIFI: Momentary Average Interruption Frequency Index
MAIFIE: Momentary Average Interruption Event Frequency Index
MED: Major Event Day
OMS: Outage Management System

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PUC: Public Utilities Commission
PV-DG: Photovoltaic Distributed Generation
SAIDI: System Average Interruption Duration Index
SAIFI: System Average Interruption Frequency Index
SCADA: Supervisory Control and Data Acquisition

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1

INTRODUCTION

In August 2017 the New Orleans City Council Established Docket UD-17-04 – “RESOLUTION DIRECTING
ENTERGY NEW ORLEANS, INC. TO INVESTIGATE AND REMEDIATE ELECTRIC SERVICE DISRUPTIONS AND
COMPLAINTS AND TO ESTABLISH MINIMUM ELECTRIC RELIABILITY PERFORMANCE STANDARDS AND
FINANCIAL PENALTY MECHANISMS.” As part of the effort to cooperate with the City Council’s resolution,
Entergy New Orleans (ENO) committed to retain the services of a nationally-recognized firm to consult
with Entergy’s distribution reliability team and provide third-party expertise and a national perspective
on their current reliability improvement plan.
In August 2018 ENO engaged Quanta Technology LLC (Quanta) to perform an assessment of ENO’s
reliability performance and improvement plans and actions. Quanta Technology is a nationally-recognized
consulting firm with expertise in all aspects of transmission and distribution operations, reliability, and
planning, as well as other technical areas of utility operations and management.
ENO and Quanta reached verbal agreement on the scope of work and contractual terms during the week
of August 13, 2018. At that time the Quanta consultants began to develop a list of data requests to be
submitted to ENO so that the required information could be collected in advance of an anticipated project
site meeting during the week of August 27. The formal contract was executed on August 22 and a project
kickoff web conference was held on August 23. The Quanta team then spent several days on site at ENO
during the week of August 27 to begin interviews with ENO personnel and to gather additional operational
information.
In addition to meeting with ENO personnel, it was deemed appropriate for Quanta to have a discussion
with the City Council’s Technical and Legal Advisors to review the work scope, project approach and to
respond to any questions the Advisors had about the proposed work. A conference call was held on August
31 between ENO, Quanta, and the Advisors for that purpose. In addition to discussion of the work scope,
the group reached consensus on the expected timing of a report from Quanta. A final report date of
October 31 was agreed.
This report provides information on many aspects of distribution reliability. A brief discussion of the
general concepts of distribution reliability is provided as well as detailed discussion of the methods and
approach used by Quanta, the current state of ENO reliability programs as assessed by Quanta, industry
reliability performance information as determined through industry research, opportunities for reliability
improvement as assessed by Quanta, and final conclusions and recommendations. Quanta’s ultimate
objective in this engagement is to provide a thorough assessment of the current efforts being made by
ENO to improve reliability and how those efforts are expected to produce results.

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2

DISTRIBUTION RELIABILITY – A BRIEF TUTORIAL

This section is intended to present a brief introduction to distribution reliability in general. The objective
is to inform the non-technical reader on basic reliability concepts, metrics, standards, etc. so that the
underlying principles of distribution reliability in subsequent sections are understood. Therefore, this
section purposely avoids introducing formulae to describe relevant concepts in favor of description of a
more narrative description of those concepts. Appendix B of this report includes the respective
mathematical formulations, moreover, publicly available technical references on this topic are provided
in this section.

2.1

Definition or explanation of the broad concepts of distribution reliability

Distribution reliability studies the impact of service interruptions on the continuity of supply provided to
customers, specifically, focusing on analyzing and estimating the frequency and duration of interruptions
experienced by customers over a defined period of time, as well as on understanding the underlying rootcauses of these interruptions. This is done by collecting and analyzing historical data of distribution system
events and calculating and studying performance metrics. An interruption for reliability analysis purposes
is defined as the total loss of electric power on one or more normally energized conductors to one or more
customers connected to the distribution portion of the system. This does not include any of the power
quality issues such as: sags, swells, impulses, or harmonics.
Distribution reliability studies both planned and unplanned interruptions, i.e., interruptions that result
from planned and unplanned outages. An outage is defined as the loss of ability of a component to deliver
power. Distribution reliability pays special attention to unplanned interruptions to understand, prevent,
alleviate and address the root-causes and impacts of these events.
Distribution reliability focuses on studying interruptions caused by outages on distribution facilities, i.e.,
between circuit breakers and customer revenue meters. This includes primary (medium-voltage) and
secondary (low-voltage) lines, service transformers, protective and switching devices, and voltage
regulation and control equipment. However, outages may occur on generation, transmission, substations,
or customer facilities and result in the interruption of service to one or more customers. While generally
a small portion of the number of interruption events, these interruptions can affect a large number of
customers and may last for a long time. Utilities collect data from interruptions caused by outages on
generation, transmission and substations to understand how they may impact the overall customer
experience, and take necessary measures to address them, if needed. Distribution reliability does not
include behind-the-meter outages that only affect individual customer facilities.

2.2

Distribution reliability standards and metrics in the US power industry

Distribution reliability can be evaluated through a variety of metrics, commonly known as “distribution
reliability indices”. Distribution reliability indices evaluate the impact of service interruptions on the
continuity of supply provided by a distribution system. The distribution system under analysis may consist
of a service transformer, a distribution feeder, a set of feeders served by a substation, or all the feeders
in a utility service territory.
Distribution reliability indices evaluate the impact of sustained and momentary interruptions on
continuity of supply. Sustained interruptions are defined as those that have a duration greater than a

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predefined threshold, common thresholds used in North America include 1 min., 3 min., and 5 min.
Momentary interruptions are those with durations shorter or equal to these thresholds.
The most common distribution reliability indices evaluate system performance as a function of customers
affected by service interruptions and total number of customers served by a distribution system. The IEEE
Guide for Electric Power Distribution Reliability Indices (IEEE Std. 1366-2012), which is the most widely
used reference in this area, defines a variety of distribution reliability indices. The most common reliability
indices used by the utility industry are based on the number of customers affected by sustained and
momentary interruptions, which are known as customer-based indices. They include1:






Sustained Interruption Indices
o SAIFI: the System Average Interruption Frequency Index (SAIFI) indicates how often the
average customer experiences a sustained interruption over a predefined period of time.
Mathematically this is expressed as the sum of Customers Interrupted (CI) divided by the total
number of customers served. SAIFI is a normalized metric; its units are interruptions per
customer.
o SAIDI: the System Average Interruption Duration Index (SAIDI) indicates the total duration of
interruption for the average customer during a predefined period of time. Mathematically
this is expressed as the sum of Customer Minutes of Interruption (CMI) divided by the total
number of customers served. SAIDI is a normalized metric; its units are minutes per customer
or hours per customer.
o CAIDI: the Customer Average Interruption Duration Index (CAIDI) represents the average time
required to restore service. Mathematically this is equivalent to SAIDI divided by SAIFI. CAIDI
units are minutes per interruption or hours per interruption.
o CEMIn: The Customers Experiencing Multiple Interruptions Index (CEMIn) indicates the ratio
of individual customers experiencing “n” or more sustained interruptions to the total number
of customers served.
o ASAI: The Average Service Availability Index (ASAI) represents the fraction of time (often in
percentage) that a customer has received power during the defined reporting period.
Momentary Interruption Indices
o MAIFIE: The Momentary Average Interruption Event Frequency Index (MAIFIE) indicates the
average frequency of momentary interruption events. This index does not include the events
immediately preceding a sustained interruption.
Other indices: there are other reliability indices included in IEEE Std. 1366-2012 that are calculated
based on the load affected by sustained and momentary interruptions, which are known as loadbased indices, however, they are not commonly used for external reporting. Additionally there are
other indices not included in IEEE Std. 1366-2012, but that are used in specific jurisdictions (e.g., some
states and municipalities) to evaluate particular aspects of distribution reliability.

It is worth noting that CI and CMI are frequently used as a proxies for SAIFI and SAIDI, particularly when
comparing the performance of feeders or the expected benefits derived from reliability improvement
projects. For instance, if two feeders have the same SAIFI values, a 10% reduction in this metric may be
1

This list includes the most commonly used reliability indices. IEEE Std. 1366-2012 includes additional reliability
indices that are used in specific cases

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interpreted as equally impactful for both feeders. However, if the first feeder has twice the number of
customers as the second one, then the 10% reduction in the former will benefit a larger number of
customers and, therefore, will be more impactful from that standpoint. This cannot be concluded from
the review of SAIFI values only. However, if CI is used instead, the feeder with the greater number of
customers will also have larger initial CI and CI reduction values. Therefore, this feeder may be assigned
greater priority for implementation of reliability improvement projects. The same also holds true for SAIDI
and CMI.
Reliability indices evaluate distribution system performance within “reasonable design and/or operational
limits”. Events that exceed these conditions are considered “major events” and are excluded from the
calculation of reliability indices. IEEE Std. 1366-2012 defines an approach to identify and exclude a Major
Event Day (MED), this methodology is commonly known as the “IEEE 2.5 Beta method”. The method
consists of analyzing the daily SAIDI values for five sequential years to identify a Major Event Day threshold
(TMED). Any day with daily SAIDI greater than the threshold value TMED that occurs during the subsequent
reporting period is classified as a MED and consequently excluded from the calculation of reliability
indices. The reasoning behind this approach is that distribution systems are not designed to withstand
extreme conditions associated with major events such as earthquakes, tornados, ice storms, forest fires,
major hurricanes, major flooding, etc.
There are other major event definitions used by the industry, examples include2:



A severe storm, flood, or civil disturbance that requires three or more days to restore service
The following criteria are met: 1) The National Weather Service has issues a severe watch or warning
for the area. 2) Extensive mechanical damage has been sustained. 3) More than 10% of customers are
out of service at some time during or immediately after the storm. 4) At least 1% of customers are out
of service 24 hours after the beginning of the storm.
A certain percentage of customers in a specific area (e.g., utility service territory) experience an
interruption



2.3

Variables in reliability reporting

Reliability reporting is vital to understand distribution system performance, root-causes, and to identify
solutions to address relevant issues. Utilities generally prepare daily, monthly and annual distribution
reliability reports, the objective of these reports vary in scope. Daily reports are tactical and provide
important information to decision makers about the overall operation of the distribution system, which
is valuable to manage internal and external relationships with key stakeholders, particularly with
customers, local authorities and regulators. Monthly reports are intended to identify system trends,
effectiveness of executed and ongoing projects and programs, and may be used to allocate emergent
resources and investments to areas that require urgent attention. Finally, annual reports are generally
used for planning purposes (e.g., to identify areas of the system that require attention, such as worst
performing feeders, and to ascertain needed projects and programs to improve or maintain reliability
levels), and for regulatory reporting. For instance, Figure 1 shows the results of a survey of state reporting
requirements and practices for utility-reported reliability information.

2

R.E. Brown, Electric Power Distribution Reliability, 2nd Edition, CRC Press

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Figure 1 – State Reporting Requirements and Practices for Utility-Reported Reliability Information3

Reliability reports usually include the values of key reliability indices used by a utility (e.g., SAIDI, SAIFI,
CAIDI, CMI, CI, etc.) for each distribution feeder and overall service territory. Reliability indices included
in reports are generally calculated for the period under analysis and preceding periods (e.g., days of the
same week/month for daily reports; months of the same year for monthly reports; or last 3 to 5 years for
annual reports). Moreover, reliability indices are calculated for various categories of interruption and
outage events. Examples comprise:




All events included: this includes planned and unplanned interruptions, along with major events and
interruptions caused by generation, transmission, substation and distribution outages. The purpose
of this type of report is to understand the overall customer experience, regardless of root-cause,
magnitude and responsibility.
Distribution events only: this typically includes interruptions caused by unplanned distribution
outages, excluding major events. The purpose of this type of report is to understand reliability
performance due to outages in the distribution grid that are within reasonable design and/or
operational limits.

3

“35 PUCs, including DC, require that reliability information be reported routinely. An additional four state PUCs receive reliability information
from utilities though not in response to a formal reporting requirement. Thirty-seven state PUCs, including DC, make publicly available or
summarize in publicly available documents the reliability information they collect from utilities”. J. Eto, K.H. LaCommare, Tracking the Reliability
of the U.S. Electric Power System: An Assessment of Publicly Available Information Reported to State Public Utility Commissions
https://emp.lbl.gov/publications/tracking-reliability-us-electric

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Utilities may also include combinations of the aforementioned categories of interruption and outage
events, for instance, interruptions caused by planned and unplanned outages, excluding major events,
etc. Additionally, utilities may report reliability indices by root-cause category, for instance, CI and CMI (or
SAIFI and SAIDI) due to weather events, equipment failures, or vegetation issues. These types of reports
allow a utility to identify main root-cause contributors to reliability performance, and to allocate resources
and investments to address these issues, if needed. Finally, utilities generally prepare a list of worst
performing feeders based on a specific metric (e.g., bottom 5% feeders in terms of SAIFI, SAIDI, CI, CMI,
etc.). This report may also include root-cause contribution to reliability performance. The purpose of this
type of report is to identify the areas of the system that need urgent attention to allocated needed
resources and investments for reliability improvement.
An important aspect to consider when computing reliability indices is the accuracy and uncertainty
associated with outage data collection. Historically, outage data collection involved a significant amount
of manual work and a variety of data sources with different levels of accuracy and uncertainty. Many
utilities are transitioning into automating most of the data collection process through the implementation
of grid modernization initiatives and deployment of automated systems, such as Outage Management
Systems (OMS), Customer Information Systems (CIS), Interactive Voice Response (IVR) systems, Advanced
Metering Infrastructure (AMI), smart phone applications and social media. However, in distribution
systems without AMI, customer trouble calls remain an important data source, and therefore, key
interruption information, such as start time, may need to be estimated, rather than automatically
reported. For this reason, utilities may initially observe an increase in distribution reliability indices after
implementing OMS or AMI systems, which help minimize some of the inaccuracies and uncertainties
associated with outage data collection.

2.4

How reliability reporting can/should be used in evaluating utility performance

Distribution reliability reports provide important insights regarding system performance. However, since
reports generally use average reliability indices, it is important to understand the limitations of these
metrics to avoid common pitfalls and misconceptions when using them to evaluate performance2.






SAIDI and SAIFI: reductions in SAIFI and SAIDI are proportional to the number of affected customers.
Reliability analyses based on SAIFI and SAIDI tend to allocate investments to feeders with greater
number of customers, because projects benefit more customers, i.e., the benefit/cost ratio is higher.
This can make improvement project justification more difficult on feeders with smaller number of
customers.
CAIDI: CAIDI is calculated as SAIDI divided by SAIFI, therefore, if SAIFI decreases faster than SAIDI then
CAIDI will increase. This is counterintuitive, since a reduction in SAIFI and SAIDI represents an
improvement in reliability, while an increase in CAIDI would be interpreted by some as a decline in
performance. For instance, if SAIDI and SAIFI for a system decrease by 10% and 20%, respectively,
then CAIDI will increase by 12.5%. This occurs often when system enhancements, such as reclosers,
are installed to reduce the number of customers interrupted by a specific event. By avoiding an
outage to the customers that may have been quickly restored, SAIFI and SAIDI are reduced, but CAIDI
increases for the resultant outage because the lower number of customers are still impacted by the
total outage duration.
MAIFIE: Like SAIFI and SAIDI, MAIFIE tends to drive investments to feeders with greater number of
customers. Most importantly, it discourages the utilization of some of the most effective solutions for

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

reliability improvement, such as automatic reclosing, fuse saving overcurrent protection philosophy,
and distribution automation, since they increase the occurrence frequency of momentary
interruptions, although they are very effective to minimize the effect of sustained interruptions and
improve SAIFI and SAIDI. In summary, MAIFIE will likely increase when implementing these types of
solutions, although reliability will improve.
CEMIn: CEMIn can only be used in conjunction with other indices, since it addresses the occurrence
frequency of interruptions only, and does not account for interruption duration. Therefore, it will
assign zero value to reliability improvement solutions that only reduce the duration of interruptions,
such as deployment of Faulted Circuit Indicators (FCI). CEMIn will also assign zero value to solutions
that reduce the occurrence frequency of interruptions beyond the CEMIn threshold. For instance, if a
utility uses CEMI3 and reduces the frequency of interruptions experienced by a group of customers
from ten to five, CEMI3 will assign zero benefit to this solution, because it still exceeds the CEMI3
threshold of 3 or more interruptions, despite the fact that reliability has undoubtedly improved.

Reliability indices such as SAIFI, SAIDI, and CAIDI have the advantage of being relatively easy to calculate,
however, one of their key limitations is that they estimate the average performance of distribution
systems, i.e., the same average index is assumed to represent the reliability performance for all the
customers served by the system. This means that average reliability indices do not consider the spatial
distribution of reliability and the topological features of the distribution grid, i.e., the fact that customers
located close to substations are expected to have better reliability than those located at feeder ends. For
this reason, reliability indices should be used in conjunction with historical outage and operational data
to identify poor-performing areas within feeders (e.g., by pinpointing most frequently operated devices)
and properly allocate investments and reliability improvement solutions.
As utilities implement grid modernization programs, deploy AMI, and transition towards the utilization of
computational models to analyze distribution reliability, it is expected that the automated calculation of
customer-level reliability indices becomes a reality, and the high granularity spatial evaluation of
distribution reliability becomes a standard practice. Therefore, while these capabilities become available
to utilities, it is advisable to use a combination of reliability indices, e.g., SAIFI, SAIDI, MAIFIE and CEMIn to
attain a more comprehensive depiction of feeder performance.
Here it is important to recognize the fact that MAIFIE is in general more difficult to track than more
traditional indices such as SAIFI, SAIDI and CAIDI. Partly because of the lack of the monitoring
infrastructure (e.g., communications systems, SCADA4, ADMS5, AMI, etc.) to accurately collect the data
required for its calculation. For instance, a utility may not monitor all its reclosing and switching devices
(circuit breakers, reclosers, and distribution automation switches) and therefore, may not be able to
collect all the device operation data needed to accurately calculate momentary interruptions and MAIFIE.
However, as utilities transition into a modernized grid, such monitoring infrastructure will gradually
become available. Therefore, it is a good practice to collect this type of data for those devices that are
already being monitored, and estimate base MAIFIE indices that can serve as a starting point to evaluate
the historical evolution of momentary interruptions. This estimation will become more accurate as more
devices are monitored and more data is collected6.

4

Supervisory Control and Data Acquisition (SCADA)
Advanced Distribution Management System (ADMS)
6
This is analogous to the increased accuracy in the calculation of traditional indices (SAIFI, SAIDI) attained through the deployment of AMI.
5

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Reliability reporting is sometimes used to compare performance among utilities, here it is important to
consider discrepancies regarding reliability analysis assumptions and practices, and differences in intrinsic
features of each utility’s service territory and distribution system that affect reliability performance.
Examples of differences in reliability assumptions include major event exclusion methodology used in
analysis, momentary interruption threshold, consideration of planned interruptions, etc. Examples of
important geographic and distribution system features to take into account include lightning flash density,
precipitation, temperature, percentage of overhead and underground lines, customer density, etc. It is
important to take these factors into account when benchmarking performance to ensure conclusions are
relevant and applicable to the reality of the utility under analysis.
It is also important to recognize that reliability indices are the outcome of a combination of numerous
internal and external distribution system variables. Due to the unpredictability of these variables (e.g.,
future weather patterns), it is not possible to forecast the exact value of distribution reliablity indices for
upcoming years. Instead, techniques used by utilities aim at estimating trends, ranges of likely values, or
expected values of reliability indices. In the specific case of reliability improvement programs, it is
expected that reliability indices will show an improving (decreasing) trend for upcoming years after
implementation begins. However, these metrics may exhibit noticeable variations in consecutive years.
For instance, reliability indices may improve significantly in a year and then only moderately (or even
worsen) in the the next one, because part of the improvement is due to the implementation of the
reliability program, and the rest is the effect of the inherent randomness of reliability variables. When
that randomness leads to “good years” (e.g., years with fewer storm days and/or cool summers) the
benefits of improvement programs are likely to become more evident than during “bad years”. For this
reason, a recommended practice is to analyze the values of reliability metrics over multiyear periods (e.g.,
3 to 5 years), rather than over consecutive years. This approach allows to capture the expected mid/longterm improvement trends (instead of focusing on potential consecutive year variations) and to a certain
extent account for the effect of randonmness.

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3

PROJECT APPROACH AND METHOD

3.1

Scope of Work

The contracted scope of work for the reliability assessment included the following:
A comprehensive benchmarked assessment of the current ENO reliability plan, including programs,
processes, practices and implementation.
a) Comparison utilities should have similar features (e.g., urban area, older system, and
legacy design issues) and be high performing in reliability or have made large
improvements in their reliability.
b) The review should primarily focus on conventional components of reliability, not grid
modification, automation or advanced metering.
c) Sharing of the firm’s perspectives, based on the firm’s research and experience, regarding
the potential reliability benefits associated with “Grid Mod” and AMI implementation for
a typical utility.
d) Include an assessment of the expected reliability improvement if the plan is implemented
as designed, e.g., validating Entergy’s planned targets (total CI reduction, etc.)
Quanta was also asked for input and recommendations on other administrative items as part of the work
scope, however, those items were beyond the primary area of review of Entergy’s reliability improvement
plans and practices.

3.2

Approach and Methodology

3.2.1 Project Team
Quanta Technology’s approach to any professional engagement begins with ensuring that the appropriate
consulting personnel are assigned to the project. In this engagement the core team consisted of three
consultants with over 100 years combined experience in the electric power industry as engineers,
operational managers, executives and regulators. Additional Quanta personnel performed business
oversight to the project as well as contributing to technical scope where appropriate. Short resume’s of
the team members are included in Appendix A. The team experience includes engagement with utility
companies, industry associations, government organizations, regulatory bodies and industrial customers.
The geographic scope of engagements includes the US, Canada, Latin America, the Caribbean, Asia, and
Europe. The resulting experience and competency of Quanta Technology in distribution operations and
reliability is extensive.
An additional qualification of the Quanta team is the current industry participation in the Power & Energy
Society (PES) of the Institute of Electric and Electronic Engineers (IEEE). Quanta employees participate in
IEEE in various working groups, subcommittees, committees, and executive leadership. Specific to this
engagement, it is noted that Dr. Julio Romero Agüero, who serves as the technical lead for the ENO
project, is currently the Chair of the IEEE Distribution Subcommittee. Within the PES committee structure
the Distribution Reliability Working Group operates as a subset of the Distribution Subcommittee. Dr.
Romero Agüero, through his consulting work as well as his IEEE activity, is highly knowledgeable of

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distribution planning and reliability practices and standards and is widely known for his publications on
these topics, as well as the impacts of distributed energy resources on grid operations.

3.2.2 Project Approach & Methodology
At its core, this engagement is considered to be an assessment of practices. It is more qualitative than
quantitative in approach and method, as required by both the scope and the compressed schedule. The
work scope focused on evaluating ENO’s ongoing reliability improvement actions and providing industry
comparisons for reliability programs and improvement. From those comparisons, recommendations for
additional efforts can be formulated, as identified.
The basic approach to this engagement follows the flow diagram depicted in Figure 2.

Scope Definition

Data
Requirements

Initial Data
Reviews

Subject Matter
Interviews &
Industry Survey

Preliminary
Analysis &
Information Gap
Assessment

Additional
Information
Gathering &
Discernment

Analytics &
Evaluation

Conclusions &
Recommendations

Figure 2 - Project Approach

3.2.3 Data Requests
Prior to initial meetings with ENO personnel, Quanta Technology submitted a comprehensive data request
for system information such as:













Service territory area
System reliability indices (SAIDI, SAIFI, CAIDI, etc.) without and with MED
Number of substations
OMS (Y/N)
Number of feeders
Number of customers
Peak demand
Installed capacity
Primary distribution voltage (e.g., 12.47 kV, etc.)
Miles of overhead and underground lines
Percentage of urban, suburban and rural distribution
Reliability indices (SAIDI, SAIFI, CAIDI, etc.) without and with MED

The data request also asked for available information on current reliability improvement plans and
reliability practices. The data request included such items as:




Overall Report/Summary/Strategy
Reliability data by feeder
Reliability analysis tool and methodology

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









Benefit-cost and prioritization methodology for selection of reliability improvement projects
Proposed project management metrics and project status report to evaluate implementation of
reliability plan
Proposed metrics to assess effectiveness of reliability plan
Outage cause classification methodology
Description of Outage Management System (OMS) capabilities
Description of outage data collection process (explain what parts of the process are manual,
automated, semi-automated, and describe potential sources of error/inaccuracies)
Reliability indices of interest (SAIDI, SAIFI, CAIDI, MAIFI, CEMI, etc.)
Definition of sustained/momentary interruptions
Definition of Major Event Days

Upon receipt of information from ENO, Quanta was able to develop an initial understanding of ENO
operations, reliability practices, and programs that are underway for reliability improvement. This initial
understanding was then enhanced through on-site meetings and interviews with ENO subject matter
experts in operations, reliability assessment, organizational performance analysis, asset management,
and other content areas related to the project scope. These meetings were an opportunity for open
discussion about ongoing programs and to clarify Quanta’s understanding of organization, operations,
and performance. As part of the on-site work, the Quanta team also had the opportunity to see areas of
the ENO system that have been upgraded or refurbished as part of reliability programs as well as areas
yet to be addressed. All of the described activities informed Quanta’s understanding of the current
practices at ENO to address reliability performance, as well as the infrastructure challenges faced by the
company in its efforts.

3.2.4 Benchmarking
A major element of this engagement was to benchmark ENO performance against other companies in the
industry that have high performance in reliability and to, where possible, understand what efforts had
been made by those leaders to achieve their current levels of performance. The methods used to
determine benchmark candidates are explained in detail in Chapter 5 and will not be repeated here.
The benchmark process was used to identify leading practices employed in the industry to achieve,
monitor, report and maintain high reliability performance. As with all benchmarking programs, results are
varied and unique to the company’s providing the information. What works in one location or to address
one company’s specific problem cannot always be applied by other companies. However, the information
obtained through benchmark surveys does allow the requesting company, ENO in this case, to understand
how others approach their problems and to take from that understanding anything that can be applied to
ENO’s specific situation. Those findings and comparisons are discussed in the later chapters of this report.

3.2.5 Analysis, Evaluation, and Conclusions
Applying all the information gathered through the processes described, Quanta then began a process of
analytics and evaluation to compare ENO’s reliability improvement practices to the industry. The
comparative information comes from the benchmark information, publicly available reliability data,
Quanta’s experience in working with other clients, and the personal experience of the project team
members, all of whom have direct experience in utility operations and management. The resulting
assessment constitutes the body of this report.

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4

DISTRIBUTION RELIABILITY AT ENTERGY NEW ORLEANS

4.1

Current State

The reliability performance at Entergy New Orleans, as measured by standard industry indices, has
declined in recent years and has been a cause for concern within ENO and the community. In 2018, ENO
is experiencing improvement in SAIFI, the primary reliability measure used by the company. The
improvement is largely due to the ongoing distribution reliability efforts the company has undertaken for
several years. Comparison of CI by major outage cause codes in the first six months of 2017 and 2018
shows:






Animal
Conductor
Equipment
Vegetation
Weather

-27.4%
-39.4%
-40.4%
-12.4%
-76.2%

The majority of the improvement can be directly related to improvement efforts by ENO, however, more
random events that affect reliability, such as weather, also contribute to the overall improvement.
ENO’s SAIFI performance for 2016, 2017 and through October 21, 2018 is shown in Table 2. The table
provides the overall customer view SAIFI as well as the contribution to SAIFI by distribution operations
and transmission operations.
Table 2 - ENO SAIFI as of October 21 Each Year

ENO System
Distribution View
Transmission View
Customer View

2016
1.3947
0.1526
1.5473

2017
1.3809
0.2082
1.5892

2018
0.9921
0.3936
1.3857

The results show that 28.5% of the 2018 customer view SAIFI is due to transmission and substation
outages. Individual transmission outages, which include substation outages, while less frequent than
distribution outages on any utility system, have a greater impact on reliability indices due to the numbers
of customer affected and, in many cases, the extended duration of the outage.
Distribution (DLIN) performance metrics as presented in June 2018 testimony filed with the City of New
Orleans are shown as Table 3. Prior to 2016 the company’s reliability indices were considerably better as
compared to the industry. A notable decline occurred in 2016-17 which now is showing evidence of having
been arrested and reversed.
Table 3 – ENO Year End DLIN Reliability Indices

Source: Entergy New Orleans Testimony, T. Patella, June 2018

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As shown in Table 1 in the Executive Summary, distribution (DLIN) customers interrupted (CI) is down
from 2017. Correspondingly, SAIFI is currently tracking at ENO target levels for the year and significantly
improved from the same time in 2017.
Figure 3 provides a 2017 versus 2018 SAIFI comparison through October 21. As shown, 2018 has had a
consistent performance in reliability while in 2017 there was a significant SAIFI increase in the month of
June. Data review shows that 25% of the total CI attributed to weather in 2017 occurred in the month of
June. The significant 2017 increase demonstrates the challenge of maintaining reliability performance
throughout the year and that it can change significantly due to a small number of events.

Figure 3 - ENO 2018 Distribution SAIFI Performance - As of 10/21/2018
Source: Entergy

Distribution reliability performance improvement is a high priority at ENO and has been a priority for
several years, as indicated by the various programs and initiatives that have been instituted and are
discussed in this report.
Consistent improvement in system wide distribution reliability performance is seldom a quick fix. Many
variables, controllable and uncontrollable, contribute to reliability performance. In this evaluation,
Quanta Technology has reviewed years of performance metrics, interviewed many stakeholders and
participants in the ENO efforts, performed a field visit to view the physical infrastructure of the company,
all to gain an understanding of the efforts made to improve performance and the challenges faced in those
efforts.
The following sections include: Quanta’s observations and findings regarding the challenges faced by ENO
in improving distribution reliability, both general and specific; information on the current efforts to
address the challenges and improve overall performance; and an assessment of the effectiveness of the
ongoing efforts.

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4.1.1 Legacy Distribution Design
The electric distribution grid in New Orleans is made up of aged facilities built to a design standard from
many decades ago, as shown in Figure 4. Areas of the system date to the 1920s and while age does not
equate to a lack of functionality, there are certain aspects of the legacy system that do not contribute
positively to reliability performance. Much of the fundamental design of the ENO distribution system is
based on industry standards developed in the mid-twentieth century. Distribution system design in that
era was focused on power delivery and not reliability. The following excerpt from the book Aging Power
Delivery Infrastructures (second edition 2013) makes the point:
“Any reasonable layout style, conductor size set, switching zone, and conductor sizing rule base will result
in a distribution system that does its basic job as interpreted from the traditional perspective: route power
from substations to customers without violation of voltage, loading, or power factor standards. However,
this interpretation of the distribution system’s job is obsolete and incomplete. It looks at the distribution
function only from the perspective of moving power and neglects its role in and capability for
improvement of customer-service reliability. Very few primary distribution systems are planned and
engineered to make maximum use of the distribution system as a resource for customer reliability."7
The legacy design standards and criteria were developed to distribute power in an economical manner
that did not violate voltage or loading standards, as detailed in the preceding excerpt. The emphasis was
on economics and operating criteria. This resulted in a system built with multiple circuits (primary feeder,
laterals, open-wire secondary) on common structures. This made maximum use of fewer distribution
transformers and more poles, crossarms and conductors, which was the most cost effective construction
at the time. The primary benefit was a level of redundancy in service to customers that resulted in high
availability. The downside, being experienced in recent years, is a higher level of collateral damage when
a single component such as a crossarm fails and impacts more circuitry than would be in place on a single
pole under today’s standards. The age of the design, as well as the age of the infrastructure, have
compounding effects. ENO is challenged with a legacy design that has many more components than a
modern design would use, the age of the components, and the urban environment that makes
infrastructure upgrades more complex and therefore more expensive.
Quanta Technology performed a field patrol of the ENO system in September 2018, which highlighted
areas of the legacy design in operation. Multiple circuits on a single structure were observed, including
the combination of feeder circuits (large conductor) and a lateral (small conductor) beneath it. The more
conductors that are in operation for the same number of customers results in increased exposure and
ultimately a higher level of outages. This field patrol confirmed the issues related to the legacy design of
the system. An example is shown as Figure 4.
Grid modernization projects throughout the industry today include a large component of infrastructure
upgrades and application of “advanced distribution planning” principles. The planning and design
concepts being used in grid modernization efforts are based on using the full capability of a distribution
system to provide availability, which was the primary objective of legacy designs, and reliability, which is
the focus of most utility customers today.

7

H. Lee Willis and R.R. Schreiber, Aging Power Delivery Infrastructures, Second Edition, CRC Press

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Figure 4 – Example of Deteriorated Legacy Construction8

As with any older, urban utility system, there are areas of the distribution system in need of upgrade.
Entergy’s reliability improvement programs, discussed later, are aimed at addressing many of the issues
created by the legacy design and aged infrastructure. The efforts include upgrading system components
to the company’s current design standards as individual reliability issues are addressed. Entergy’s current
standards include improvements such as:





Increased pole loading criteria – Entergy designs many poles to the NESC extreme wind
criteria which exceeds the minimum requirements for the service territory.
Crossarm material – Entergy is currently installing fiberglass crossarms as replacements for
failed or aged wood crossarms. Fiberglass arms have higher strength and resiliency and
require less maintenance.
BIL standards – Standard design ensures that appropriate BIL values are met in new
construction.

These items as well as other design and infrastructure improvements are addressed in more detail in
Section 4.2.

8

Source: Field inspection conducted by Quanta Technology as part of this project

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Implementing new design standards is the right approach and has already had positive effects in arresting
the trend of declining reliability. Comprehensive infrastructure upgrades must continue to affect
continued improvement, which can be further enhanced with automation technology.

4.1.2 Infrastructure
Similar to many urban utility companies in the United States, ENO has a high population of aged
equipment. In the utility industry, age does not mean that equipment is no longer functioning as designed.
In fact, some older equipment designs and manufacture are much more robust than new equipment.
Older facilities do, however, represent increased risk of failure simply due to approaching end of normal
service life.
Entergy’s current asset information does not allow for an in-depth analysis of age of a class of equipment
or other analysis often done in review of equipment failure situations. Some data on the distribution line
transformer and wood pole population at ENO was obtained and is basis for discussion of those asset
classes.
4.1.2.1 Distribution Overhead Transformers
Table 4 shows the age distribution of ENO’s service transformer population.
Table 4 –Distribution Transformers

Source: Entergy data

Between 2013 and the present, 2,531 transformers (8% of population) have been removed. In service
failures accounted for 1,261 of the units removed (4%) and all others (1,270 or 4%) were removed as no
longer in service (idle account) or changed/upgraded for additional load. The 5 year failure rate of 4% or
average annual rate of 0.8% is within industry norms for this class of equipment. Table 5 provides an
industry reference for the failure rate.

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Table 5 – Transformer Failure Rates

Failure Rates
Median Failure (50%) 95% Failure
Distribution Transformers
0.3
1.8
Transformer Type

ENO Actual
0.8

Source: EPRI, Estimating Reliability of Critical Distribution System Components, Technical Report 1001703, 2003

The age distribution of the population shows that 34% of the in-service units were installed before 1980
indicating a minimum number of years in service for this portion of the population of 38 years. Expected
life of distribution service transformers has been found in some studies to range from 40 to 50 years with
many examples of even greater service life. As with all assets, the actual service life depends upon many
variables including load, geographic location, and environmental factors, to name a few. Failure rate
estimates vary widely as a result.
4.1.2.2 Distribution Wood Poles
Other equipment classes at ENO where some data has been obtained is poles and crossarms. Age data is
not readily available, but a reasonable estimation of the total population and number of failures is shown
as Table 6.
Table 6 – ENO Equipment Failures 2013-17
Equipment Class
Poles
Crossarms
Source: Entergy

Approximate
Number in
Service
90,000
500,000

Failures

5 yr Failure
Rate

Annual
Failure Rate

166
731

0.2%
0.1%

0.04%
0.03%

Wood pole service life varies widely in the industry based on geographic location, type of wood and
original treatment, and the ongoing inspection and treatment regimen, if any. Geographic factors
represent risks as indicated in Figure 5 – Decay Hazard Zones, a map of the wood deterioration zones of
the US. The ENO service territory experiences high humidity and high rainfall, as well as having a high
water table. Risk to the condition of wood poles is highest in this region of the country. Nonetheless, wood
poles have been shown to have usable service life well in excess of fifty years in many companies and as
high as 90 years in some. The average age of the ENO wood pole population is not known, but is expected
to be at and beyond the 30-40 year service life that is considered a reasonable minimum for wood poles.

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Figure 5 – Decay Hazard Zones9

The ENO failure rate, as shown in Table 6, is again within industry expectations despite the high hazard
area. A 2013 survey by Oregon State University of 86 utility companies reported a wood pole replacement
rate of 0.56%.11 This rate included all poles replaced for any reason, not only failures. That study estimated
that approximately one-half to two-thirds of the poles replaced were not restorable, which would equate
to an approximate “failure” rate in the range of .28% to .38%. The ENO wood pole failure rate is
approximately 0.04%.
Failure data on crossarms alone has not been obtained, however, it is reasonable to expect similar rates,
or lower rates, than poles.
ENO has in place a wood pole inspection program that is consistent with practices in the industry. The
cycle time for ENO pole inspections is twelve years, which results in 8.5% of the population being
inspected each year. As part of the ongoing reliability improvement efforts, however, ENO in 2017
accelerated pole inspections to cover approximately 50% of the pole population. The inspection process
used was an intrusive inspection which included excavating the pole base to examine condition below
ground line. This inspection resulted in approximately 800 identified for pole replacement and 3000 poles
identified for reinforcement. This intense effort to assess pole condition provides ENO confidence that
the condition of the pole population is known and that pole failure hazard is reduced.

9

Estimated Service Life of Wood Poles, North American Wood Pole Council, Technical Bulletin 16-U-101, February
2016

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Two things are noteworthy from the data presented on transformers and poles:



Failure rates by equipment class are within the industry norms
The number of crossarms per pole (average of 5+) is very high, a function of the legacy design

These equipment examples are provided to demonstrate that while failures are occurring in these classes
of equipment, the rates of failure are not beyond what should be expected for the specific equipment
classes.
In general, ENO is challenged with an aging infrastructure in an urban environment that experiences
deterioration risk from environmental factors as well as age. Failure rates do not seem excessive at this
time but the legacy design features of the system along with high customer density and a congested urban
environment do, in many cases, contribute to higher CI and CMI.

4.1.3 Metrics and Outage Data
As discussed in Chapter 2 – Distribution Reliability Tutorial, commonly used interruption indices in the
industry are SAIDI, SAIFI, CAIDI, CEMI, and ASAI. It was also noted that CI (customers interrupted) and CMI
(customer minutes of interruption) are often used as proxies for SAIFI and SAIDI, respectively. Internally,
ENO focuses on CI and CMI as the primary operating metrics to track reliability. This practice gives more
emphasis to the “customer experience” than SAIFI and SAIDI which are often regarded as external
reporting metrics.
ENO’s focus on CI and CMI is evident in their internal reporting and practices. The ongoing reliability
improvement programs are built around impact to CI and CMI and employees are well-versed in the status
of CI and CMI against targets. As previously shown in Table 3, SAIFI and SAIDI and their respective proxies
CI and CMI have trended upward from 2013 through 2017. Table 2 and Figure 3 however, indicate
improvement due to many of the initiatives that have been implemented by ENO over several years.
4.1.3.1 2013-2017 CI and CMI Increase Evaluation
In 2013 ENO was a solid 2nd quartile performer in both SAIFI (CI) and SAIDI (CMI). The upward trend since
that year has been reviewed above. For distribution line (DLIN) outages, the 2013 to 2017 increases in
customers interrupted (CI) and customer minutes interrupted (CMI) are shown as Figure 6.

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Figure 6 – CI and CMI Trend

As indicated, 2017 CI was 65% higher than 2013. In addition, 2017 CMI was 112% higher than 2013. The
CMI increase is primarily due to the CI increase of 65%. The remaining reason for the CMI increase is due
to an increased average minutes for restoration. The average duration has increased by about 25 minutes,
or 28%. This figure also provides the 2016-2018 year-to-date performance as of October 21, for both CI
and CMI. As shown here and previously stated, 2018 performance year-to-date has improved.
For distribution line (DLIN) outages, the 2013 to 2017 proportional increases in customers interrupted (CI)
and customer minutes interrupted (CMI) are shown by reported cause in Figure 7 and Figure 8. Analysis
of ENO outage records indicates that 64% of the CI increase between 2013 and 2017 is due to three cause
codes: equipment (41%), conductor (12%), and vegetation (11%). The same cause codes contributed 61%
of the CMI increase between 2013 and 2017: equipment (42%), conductor (12%), and vegetation (7%).
Within the Equipment cause, the two highest increases were recorded as crossarm and pole related
outages. Pole and crossarm outages explain to a large degree any increase in restoration times. Once
again, legacy design features contribute to the time required to replace poles and arms. It is not
uncommon to find that multiple poles may need to be changed due to one pole failure. Consistent with
one of ENO’s ongoing reliability improvement programs, ENO attempts where possible, to rework
associated circuits to achieve a less complex and congested circuit layout.

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Figure 7 – Proportional DLIN CI Increases

Figure 8 – Proportional DLIN CMI Increases

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While “Equipment Failure” was found to be the highest summary category used to code outage events, it
is somewhat misleading. A deeper look into the outage records indicates many events being coded as
equipment failure when the root cause was something external to the infrastructure, such as weather or
tree interference. For example, the outage code for a blown distribution line fuse is summarized under
equipment failure. A blown fuse, however, is not a failure of equipment; it is in fact the proper operation
of the equipment. In examining outage reports, it is found that many blown fuse outages are due to an
identified root cause that should be captured under the appropriate category for that cause, e.g.
vegetation, weather. ENO is working as part of their reliability awareness efforts to ensure that proper
outage codes are used. Historical data, however, does not reflect that more recent emphasis.
4.1.3.2 Current Reliability Data- Outages and Events
ENO has previously provided the City with outage and reliability data including number and causes of
outages, duration, dates, times, etc. Quanta has worked with the same raw data during this review. One
observation on the collection of outage data that has been made has to do with how outage events are
counted during a step restoration process.
Currently, when an outage occurs, ENO tracks the data on a per restoration step basis. For example, when
a tree contact results in a substation breaker operation, that outage may be restored in a series of steps
to minimize the duration of the outage to as many customers as possible. The restoration steps include
field switching to restore power to areas that can be fed from sources not affected by the initial outage
cause. In order to accurately track CI and CMI each step in that restoration process is counted as a separate
outage event. Entergy’s current outage management systems do not have the capability to ultimately
aggregate the multiple steps into a single outage event. For example, if the breaker outage resulted in 3
restorations steps, the records will list them separately and will indicate 3 outages instead of 1, as well as
3 tree events instead of 1. This results in a higher number of outage events than what actually occurred.
This process does not impact the accuracy of the CI and CMI data.
The extent of the impact of this practice is not known and is not anticipated to be highly significant,
however, it does artificially inflate the number of outage events that have occurred. The impact of the
practice is on the ability to develop accurate failure rate data that is based on actual number of outage
events, such as outages per line mile. To the extent the practice exists in the industry, it is commonly being
addressed as utility companies upgrade outage management systems to have capability to aggregate the
multiple events. Entergy is implementing a new ADMS which should have the capability needed to address
the issue.

4.2

Primary Reliability Improvement Programs and Initiatives

As has been reported in testimony by Entergy personnel, there are a number of ongoing reliability
improvement programs and initiatives within ENO. Ongoing maintenance and inspection programs such
as vegetation management, pole inspection, and cable replacement continue. Initiatives such as FOCUS,
BACKBONE, and R1 have been implemented to bring additional emphasis to improving distribution
reliability through design, construction, maintenance, and cultural change within the organization.

4.2.1 FOCUS Program
ENO currently has multiple efforts underway to improve the safety, reliability and resiliency of their
distribution system. The FOCUS program is one of their major improvement efforts. This program is based

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on reviewing previous outages and addressing the root cause. In this regard, the program is considered
to be a reactive practice as it addresses previously occurring issues. Quanta has reviewed the processes
around the program and feels that it is effective in properly addressing the outage cause issues identified
in Figure 7 and Figure 8. A brief review of the program follows.
Process Flow Diagram
The FOCUS flow diagram shown in Figure 9 indicates the overall process.

Figure 9 - FOCUS Life Cycle Process

Project Identification
FOCUS project identification begins with the Algorithm method described in Figure 10. This shows that
projects implemented into the program are based on historical CI performance, which is a reasonable
approach. Once identified, the projects are added to the FOCUS list.

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Figure 10 - FOCUS Algorithm Method

Inspections
Once identified and included in the FOCUS list, that circuit segment gets a visual detailed inspection.
The inspection personnel are ready and equipped to immediately resolve problems that may soon cause
a reliability problem.
The inspections take into consideration multiple problem categories including:










Bad poles
Bad crossarms
Bad crossarm braces
Bad connections
Damaged Insulators
Loose guys
Bad anchors
Damaged arrestors
Vegetation problems

Additionally, based on established ENO criteria, potential infrastructure changes are identified including:



Install, relocate, remove lightning arrestors
Relocate fused switch

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





Remove and install grounds
Install fuses on laterals
Install animal guard
Resolve slack and/or damaged conductors
Improve Basic Insulation Level (BIL)

ENO has placed emphasis on the BIL ratings and upgrades of the distribution system to enhance its
resiliency against lightning events.
Design and Construction
Following the field inspection, the results are reviewed. If no conflicts exists, the project is then designed
and sent to construction.
Performance Tracking
Once implemented in the field, the performance of the circuit segment is monitored and compared to
past performance. The expectation is that CI will be improved by 70% based on historical evaluation. The
70% improvement value is common to all Entergy operating companies.
The FOCUS program uses an approach that is common in the industry for addressing reliability
performance. Other companies may refer to the effort as a “worst performing feeder” program or a
program to address CEMI (customers experiencing multiple interruptions). The FOCUS program includes
elements of both approaches and addresses sections of circuits that have experienced high CI and CMI.
As the name implies, FOCUS is focused on addressing known areas of reliability problems with priority.

4.2.2 BACKBONE Program
The BACKBONE program has a similar approach to the FOCUS project with a key difference. While FOCUS
is considered reactive based on reliability performance, BACKBONE is proactive. Inspections are based
primarily on time elapsed since the feeder was last inspected. The BACKBONE program inspection is
focused on the line segment from the substation breaker to the first sectionalizing device as shown in
Figure 11.

Figure 11 – BACKBONE Inspections

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This inspection is comprehensive and includes detailed visual inspection and infrared or thermographic
inspection to identify potential equipment issues. Using this approach, the BACKBONE program supports
the avoidance of entire circuit outages, which normally have the highest CI impact and has been effective
in identifying issues that were addressed and outages avoided. The following Figure 12 is taken from an
internal ENO report and provides a status of projects, as of 10/25/18, that are being worked in 2018 under
the FOCUS and BACKBONE initiatives. The avoided customer interruptions is based on estimates
developed as part of each individual project scope.

Figure 12 – Reliability Projects

4.3

Other Efforts to Improve Reliability

4.3.1 R1 Program
The purpose of the R1 Program is to improve reliability every time a crew performs work on the primary
system. The applied phrase to provide guidance is “Build it Right the First Time”. Besides the work already
being done, reliability is further improved by taking corrective action on identified issues that would likely
result in an outage. Key R1 efforts include ensuring the structure meets the enhanced BIL ratings, all visibly
damaged equipment is resolved, ensuring structure spacing is maximized, and resolving vegetation issues,
either by them or by a follow-up crew depending on the extent of trimming required.
The list of items to be addressed in each of the areas mentioned (BIL, structure, vegetation, visible
damage) is comprehensive and effectively ensures that the infrastructure under repair is brought up to
current standards as opposed to being repaired to the original standard to which it was designed. This is
in effect a piece by piece inspection and rebuild of the infrastructure. It is effective, but it is also time
consuming, which affects CI and CMI where outages are required to perform the work.

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4.3.2 Pole Program
With the aging legacy infrastructure, the condition of poles and its equipment is important for reliability.
ENO has the Pole Program, which is a cyclical proactive inspection, treatment, and preventative
maintenance program.
The Pole Program requires a visual inspection of the pole and its installed infrastructure, such as crossarms
and insulators. To validate the condition of the pole, an excavation is performed where possible. The
program’s excavations have been more successful than prior methods at identifying poles in need of
follow-up. The process includes pole loading analysis to ensure a pole has the required strength based on
pole condition, equipment on the pole, and desired level of wind resiliency. Depending on the overall
results, the pole may be determined to be in satisfactory condition, may need treatment to maintain its
health, may require re-enforcement, or may need to be replaced. As noted earlier, the standard pole
inspection cycle for ENO is twelve years or 8.5% of the population per year. Recent inspections have been
accelerated as part of the ongoing reliability emphasis. Pole inspection cycles within the industry vary,
typically from eight to twelve years with some as high as fifteen and some companies having no formal
program at all.
The National Electric Safety Code (NESC) provides utilities infrastructure installation requirements. Per
NESC Rule 250B, 60 foot poles in ENO territory are required to withstand 60 MPH winds. Most distribution
poles are less than 60 feet, in height but for enhanced pole strength, ENO is instead using a wind speed
rating of 110 MPH for both pole replacements and new poles, resulting in higher class poles where
physically feasible and cost effective, or where considered necessary due to criticality of the circuit or a
specific customer. Based on engineering analysis, ENO is now installing Class 1 poles, where feasible, in
areas where under NESC requirements Class 3 poles would suffice. Class 1 poles can be rated anywhere
between 20% and 50% stronger than Class 3 poles depending upon the specific application.

4.3.3 Infrared Inspections
ENO is utilizing infrared inspections to identify potential failures on the distribution system. Infrared
cameras detect elevated temperatures which likely indicates an imminent failure. The infrared findings
are prioritized based on the severity of the elevated temperature along with other observations on that
specific equipment. The infrared inspections are used for all applicable equipment including connectors,
switches, connections where the overhead transitions to underground, and jumpers.

4.3.4 Underground Cable Renewal Program
The Underground Cable Renewal Program improves reliability by identifying cables that do not meet
performance criteria. Those identified are targeted for replacement. This includes replacing full
underground cable segments as opposed to repair of failed segments by splicing.
Aging underground cable is and has been an issue throughout the industry for a number of years.
Proactive replacement of full cable segments or half loops, as done at ENO, is practiced by many in the
industry, as splicing cable faults has been proven to be a short-term fix in many cases.

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4.3.5 Vegetation Management Program
The ENO Vegetation Management Program is both proactive and reactive. On the proactive side, it has a
cyclical approach based on trim cycles that are evaluated annually based on multiple factors including
growth rates, vegetation type, and density. It is also evaluated using the impact on reliability.
Currently, the average cycle is 1.4 years which is considerably shorter than industry averages for trim
cycles. The short cycle time is driven by the limited trim clearances allowed within the City. The majority
of the trees trimmed are between the street and sidewalk and are City owned trees. With City owned
trees, ENO is allowed to trim a maximum of 4 feet from primary conductors, with no allowance for
secondary conductors, street light wires, and down guys.
Entergy’s internal vegetation clearance standards are shown as Table 7. Clearances of 6-10 feet are
expected depending on tree species to maintain safety and reliability. The City’s requirement does not
allow ENO to trim to the standard used in the rest of the Entergy system.
Table 7 – Entergy Vegetation Clearance Specifications

On the reactive side, vegetation clearing is performed based on reports from ENO personnel or the public.

4.3.6 Basic Insulation Level (BIL) Enhancements
Lightning is one of the biggest causes for outages on the ENO distribution system. To improve the
performance during these weather events, ENO has upgraded its standards related to the Basic Insulation
Level (BIL). The BIL has been enhanced by modifying the insulator placements and pole framing
configurations. The BIL enhancement is accomplished by increasing the air and wood path distance to a
grounded conductor. Guidance under the R1 program is to ensure the structure BIL is 300kV or higher.
To further increase the BIL when a shield wire is present, Hendrix cable is utilized.

4.3.7 Sectionalizing Program
The Sectionalizing Program strives to mitigate the impact of outages by reducing the number of customers
impacted and reducing the outage duration time. This is performed by the installation of automatic
isolating devices, such as reclosers. These devices quickly sectionalize the circuits into smaller segments
with fewer customers. By immediately isolating the faulted section, the remaining section can potentially
be more quickly restored.
For 2018, ENO plans to install an additional 30 reclosers on its circuits. The installations are selected and
implemented based on the expected impact to both SAIFI and SAIDI.

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4.3.8 Storm Hardening
The primary goal of the Storm Hardening program is to improve the resiliency of the circuits serving Critical
Customers. Critical Customer examples include public safety and health facilities, civil defense facilities,
and facilities important to ENO’s restoration process. Other criteria used for project selection include the
overall number of customers served as well as findings from other reliability programs inspections. The
primary method used in the storm hardening program is increased pole strength as previously discussed.
Storm hardening efforts throughout the industry are primarily focused on pole integrity through strength
rating, pole condition management, and vegetation management. Methods used in the industry to
increase pole strength include using larger wood poles, using poles of other materials (concrete, steel),
and shortening span lengths, as examples. ENO is primarily using larger wood poles in their strengthening
efforts.
In the case of ENO, storm hardening has also included an element of circuit reconfiguration. This approach
is based in both storm resiliency and everyday reliability improvement. As discussed, legacy design issues
are a contributor to reliability performance at ENO and circuit reconfiguration is an important element to
addressing the legacy issues. This effort will improve operational reliability as well as enhance the system
resiliency to severe weather as redundant facilities are removed and a more efficient design is
implemented.

4.3.9 Internal Program
The purpose of the Internal Program is to address findings that are not compliant with the National Electric
Safety Code and Entergy Service Standards. Internal Program projects are normally identified by internal
parties such as the Region Manager, Operations Manager, Line Supervisors, and Design Managers.
Projects identified in this program often deal with updating legacy design issues.

4.3.10 Cross-Company Reliability Improvement Collaboration
Reliability improvement at ENO is clearly a high priority with multiple efforts in progress. As part of
these efforts different groups meet to coordinate and communicate new projects, schedule, status, and
issues to resolve. Examples of those groups are Network Reliability, ENO Reliability Docket, and the
Reliability Steering Committee. Each of these currently operating internal groups has a specific area of
focus as part of the reliability improvement efforts. Additionally, enhanced reliability reporting tools
have been developed and implemented to effectively and timely communicate reliability performance.
ENO has instituted a dedicated reliability crew. The crew is composed of the reliability service men (RSM)
previously assigned to the individual networks. In that capacity their role was to identify emergent or high
priority work that was expected to have an immediate impact on reliability. However, as individuals, the
impact they could have was limited. By combining the RSMs into a crew covering ENO, they can now
resolve emergent high priority issues and perform proactive inspections in addition to the already existing
programs.
The Entergy “Reliability Champion Guidebook” is also an example of the extensive collaboration. Some
of the key content in the Guidebook includes:
 Key Behaviors of a Reliability Champion- Includes holding regular face-to-face meetings to
review topics such as recent outages, training, and performance reports
 Metrics Definitions – Includes SAIFI, SAIDI, CI, CMI, Average Outage Duration

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







Metrics Calculations- How CI, CMI, and duration are determined
Asset Management Programs- Includes FOCUS, BACKBONE, and R1-Build it Right the First Time
R1 Key Actions- Includes detailed listing of R1 actions
Reliability Meetings- Includes required and optional attendees as well as recommended agenda
Reliability Reports- Explains the reports contents and the value
Data Scrubbing- Explains how to validate that the acquired outage data is correct
Causal Codes- Explains how to select the right code and the value

This is regarded by Quanta Technology as an excellent program to engage all employees in the subject
and emphasis of achieving and maintaining system reliability. The program has been effective in
identifying situations that could cause outages and in working to avoid those outcomes.

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5

INDUSTRY PERFORMANCE AND TRENDS

Quanta Technology conducted a review and benchmark of the distribution reliability practices of ENO and
selected electric utilities. The benchmark consisted of four activities: 1) a review of publicly available data
(from industry and regulatory reports and publications) regarding distribution reliability performance and
trends of US electric utilities, 2) the selection of utilities with good distribution reliability performance and
distribution systems with similar characteristics as ENO, 3) review and application of prior Quanta
Technology industry surveys and data collection, and 4) the comparison of ENO’s distribution reliability
practices versus those of the selected utilities via a survey questionnaire and other applicable information.
The objective of the benchmark was to identify potential gaps, areas for improvement, and lessons
learned that could be used by ENO as a reference to improve its distribution reliability practices. This
section describes the methodology used to conduct the review and benchmark, and the main findings and
conclusions from these tasks.

5.1

Reliability reports and metrics

Distribution reliability is a topic of great interest in the industry, therefore, there are various organizations
that periodically collect, analyze, and publish important reliability reports that have become key
references for the utility industry. The most well-known documents are the annual reports and
benchmarks published by the IEEE Distribution Reliability Working Group10, the Energy Information
Agency (EIA)11, the American Public Power Agency (APPA)12, and the National Rural Electric Cooperative
Agency (NRECA)13. These documents provide important insights regarding reliability performance for
Investor-Owned Utilities (IOU), public power utilities, and rural cooperatives. Table 8 shows an example
of a summary benchmark of distribution reliability indices using 2015 EIA’s data; the results are
categorized based on utility ownership (IOU, public power, cooperative, and all utilities) and type of
reliability standard being used (IEEE 1366-2012 or other Results include average SAIDI and SAIFI values
with and without MED.

10

http://grouper.ieee.org/groups/td/dist/sd/doc/Benchmarking-Results-2017.pdf
https://www.eia.gov/electricity/data/eia861/
12 https://www.publicpower.org/reliability-tracking/distribution-system-reliability-and-operations-survey-and-report
13 http://grouper.ieee.org/groups/td/dist/sd/doc/2019-07-19%20NRECA%202016%20Distribution%20Reliability%20Study%20Results%20%20Tony%20Thomas.pdf
11

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Table 8 - Summary of Distribution Reliability Indices based on EIA Data (2015). 14

Figure 13 (pp. 33-34) was extracted from the 2018 IEEE distribution reliability benchmark (which includes
2017 results for 93 utilities). The plots show the sorted SAIFI and SAIDI values for participant utilities
without MED. Utilities are identified by a confidential code number unique to each participant and have
been grouped in quartiles, which is a terminology typically used in the industry to describe performance.
Each quartile represents 25% of the total sample. High performing utilities (e.g., those with SAIFI and SAIDI
values under 1.03 int/cust-yr and 78 min/cust-yr) are typically referred to as being “1st quartile”, i.e., the
top 25% of the survey data set. Similarly, 2nd quartile performance represents the subsequent 25% of the
sample (between 25% and 50% of the total data set), and so on. Bar colors indicate the region of the US
and Canada where participant utilities are located: Northeast (orange), Mid-atlantic (yellow), Southeast
(green), Midwest (light blue), South (purple), Northwest (red) and Southwest (dark blue).

14

http://grouper.ieee.org/groups/td/dist/sd/doc/2017-07-19%20APPA%20Reliability%20Update%20-%20Hoffman.pdf

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Figure 13 – SAIDI and SAIFI Quartiles from 2018 IEEE Annual Benchmark (without MED).

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Figure 14 and Figure 15 show the historical evolution of the boundaries between quartiles for indices
calculated without and with MED, respectively. Figure 14 plots show the boundaries between quartiles
for the SAIFI and SAIDI values of the annual IEEE benchmarks from 2005 to 2017. Results show that the
boundary between 1st and 2nd quartiles (without MED) has oscillated between 78 and 109 min/cust-yr
for SAIDI, and 0.82 and 1.11 int/cust-yr for SAIFI. Figure 15 plots include MED and show that the boundary
between 1st and 2nd quartile has oscillated between 107 and 202 min/cust-yr for SAIDI, and 0.9 and 1.33
int/cust-yr for SAIFI.
Table 3 in Section 4.1 shows ENO’s historical reliability performance. Those results show that ENO’s
reliability performance for 2017 is close to the borderline between 3rd and 4th quartile in the IEEE
benchmark.

Figure 14 – Historical SAIDI and SAIFI Quartiles from 2018 IEEE Annual Benchmark (without MED).

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Figure 15 – Historical SAIDI and SAIFI Quartiles from 2018 IEEE Annual Benchmark (with MED).

Finally, Figure 16 shows the sorted SAIFI and SAIDI values and respective sample quartiles for participant
utilities included in the annual NRECA reliability survey of 2016. Results include SAIDI and SAIFI values for
all participant utilities (excluding MED), each bar represents a utility (no utility names are included).
Utilities are grouped by quartiles, each quartile represents 25% of the sample of participant utilities, 1st
quartile represents top reliability performance and 4th quartile represents poorest performance from the
sample. The corresponding table with each plot shows boundary values between quartiles, minimum and
maximum reported values, and average value of the sample.

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Figure 16 – Historical SAIDI and SAIFI Quartiles from 2016 NRECA Annual Benchmark (without MED).

5.2

Benchmark of distribution reliability practices

In order to evaluate ENO’s distribution reliability program versus leading industry practices, Quanta
Technology conducted a thorough review of the proposed components of the program, and a benchmark
of ENO’s distribution reliability practices versus those of utilities with similar features as ENO and top
reliability performance. The objective of benchmarking versus high performing utilities was to identify
potential gaps, areas for improvement and lessons learned that could be considered by ENO in its
distribution reliability program. It is worth noting that ENO’s distribution system and service territory

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features are unique, consequently, it is difficult to identify utility peers to make a one-to-one comparison.
Therefore, the aim of this task is to identify utilities that are sufficiently similar, so their distribution
reliability practices, success stories, and lessons learned can be relevant for ENO. This section describes
the methodology used to conduct the benchmark. The studies described in this section were made
through a combination of quantitative and qualitative analyses, heuristics, and Quanta Technology’s
expertise. All the data used in the process was obtained from publicly available sources, such as industry
and Public Utility Commission (PUC) reports, websites, etc.
The distribution reliability benchmark data from surveys such as those conducted by IEEE, APPA and
NRECA are provided by utilities using automated online software tools and kept confidential. Distribution
reliability data is publicly available in some PUC websites, since some states require utilities to make
annual filings of distribution performance. The EIA annually collects and makes distribution reliability data
available via their website, specifically as part of its Form EIA-861 report. This data set is very
comprehensive and includes information from IOU and public power utilities (including ENO), as well as
from rural cooperatives.
Quanta Technology used the 2016 reliability data from the EIA report to identify and preselect a subset of
28 utilities15, based on high reliability performance16 (e.g., Consolidated Edison, Madison Gas and Electric),
geographic similarities to ENO’s service territory (e.g., utilities located in the Gulf of Mexico area, such as
Mississippi Power, Alabama Power, and Gulf Power), and knowledge of the implementation of important
initiatives pertaining to reliability practices (e.g., storm hardening work at Florida Power and Light,
distribution automation deployment at ComEd). The objective of this preselection was to reduce the
search space to be considered in the benchmark to a reasonable amount, given the level of effort required
to collect data, and the fact that high performing utilities were the main target of the benchmark.
Then, a prioritization approach was used to rank the preselected utilities. The approach consisted of
calculating a total similarity metric for each utility, based on the following variables:






Reliability indices (SAIFI and SAIDI)
Customer density (customers/square-mile)
Average electricity price ($/kWh)
Grid design (percentage of overhead and underground lines)
Weather (average temperature, lightning flash density, precipitation, and relative humidity)

The variables customer density, average electricity price, grid design, and weather were selected due to
their evident impact on reliability performance. For instance, areas with higher customer density tend to
have better reliability performance, since cost-effectiveness analyses of proposed investments tend to
favor projects located in those areas. There is also a correlation between electricity price and reliability,
higher electricity prices are generally a reflection not only of generation costs, but also of investments
levels in transmission and distribution infrastructure, which are recovered through rates. Similarly, areas
with high percentage of underground grid tend to have better reliability performance due to the reduced
overhead exposure and vulnerability of distribution lines and equipment, i.e., underground distribution is
15

The majority of utilities (76%) included in this list calculate their reliability indices as per IEEE Std. 1366-2012, the remaining utilities use other
methodologies (e.g., defined by their respective PUC)
16
Defined for the objective of this benchmark as SAIFI and/or SAIDI within 1st and lower 2nd quartile performance of 2017 IEEE Benchmark

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intrinsically more reliable (lower failure rates) than overhead distribution, this is also shown graphically in
Figure 17.

Figure 17 – SAIDI and SAIFI values for overhead (OH) and underground (UG) transmission and distribution
systems.17

Finally, weather parameters such as temperature, lightning flash density, precipitation and relative
humidity have a direct effect on various aspects of distribution reliability performance18. Figure 18 shows
examples of the reliability performance of the subset of 28 preselected utilities and ENO, as a function of
17

Out of Sight, Out of Mind 2012, An Updated Study on the Undergrounding of Overhead Power Lines, Edison Electric Institute (EEI), Jan. 2013
http://www.eei.org/issuesandpolicy/electricreliability/undergrounding/Documents/UndergroundReport.pdf
18
For instance, among several relevant findings, the 2015 report “Assessing Changes in the Reliability of the U.S. Electric Power System” by
Lawrence Berkeley National Laboratory found that (if major events are not included) a 10% increase in the number of customers per line mile is
correlated with a 4% decrease in SAIFI. The report also found that (if major events are included): a) a 10% increase in the percentage share of
underground line miles is correlated with a 14% decrease in SAIDI, b) a 10% increase in annual lightning strikes is correlated with a 2% increase
in SAIFI, and c) a 10% increase in annual precipitation—above the long‐term (generally, 13‐year) average—is correlated with a 10% increase in
SAIDI https://emp.lbl.gov/sites/all/files/lbnl-188741.pdf

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customer density, percentage of underground distribution and average electricity price. The results show
that although ENO’s customer density is higher than that of most peer utilities, its distribution reliability
indices are also higher. This is influenced by the fact that the percentage of ENO’s distribution grid that is
underground is smaller than that of the majority of peer utilities. These features, combined with the
unique weather patterns and physical vulnerabilities of the area, make reliability improvement at ENO a
challenging and complex task.
Reliability vs. UG (%)

200

200

180

180

160

160

140

140

SAIDI (min/cust-yr)

SAIDI (min/cust-yr)

Reliability vs. Customer Density

120
100
80

120
100
80

60

60

40

40

20

20

0

0
0.0

0.5

1.0

1.5

2.0

0.0

0.5

1.0

SAIFI (int/cust-yr)

1.5

2.0

SAIFI (int/cust-yr)

Reliability vs. Price
200
180
160

SAIDI (min/cust-yr)

140
120

100
80
60
40
20
0
0.0

0.5

1.0

1.5

2.0

SAIFI (int/cust-yr)

Figure 18 – Benchmark Comparisons.
(Reliability (SAIDI and SAIFI) versus customer density (customers/square-mile), percentage of underground
distribution, and average electricity price ($/kWh) for the subset of 28 preselected utilities. Bubble size shows the
variable of interest (customer density, percentage of underground distribution, and average electricity price). ENO
is shown in red. Results were calculated using 2016 data compiled by the U.S. Energy Information Agency (EIA). The
results show that although ENO’s customer density is higher than that of most peer utilities, its distribution reliability

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indices are also higher. This is influenced by the fact that the percentage of ENO’s distribution grid that is
underground is smaller than that of the majority of peer utilities.)

The total similarity metric evaluates how similar ENO and each utility are. The smaller the total metric for
a specific utility is, the more similar ENO and that utility are. The total metric was calculated as the
weighted sum of five metrics, as follows19:
 The individual metrics for the variables customer density and average electricity price were calculated
as the absolute value of the difference between the respective normalized values of ENO and those
of each utility
 The individual metric for the variable grid design was calculated based on the sub-variables
percentage of overhead and underground construction. The grid design metric was calculated as the
Euclidean distance between the respective normalized values of ENO and those of each utility.
 The individual metric for the variable weather was calculated based on the sub-variables average
temperature, lightning flash density, precipitation, and humidity. The weather metric was calculated
as the Euclidean distance between the respective normalized values of ENO and those of each utility.
 The individual metric for reliability indices (SAIFI and SAIDI) was calculated as the inverse of the
Euclidean distance between the respective normalized values of ENO and those of each utility. That
way utilities with high reliability performance were assigned a small value and utilities with poor
performance were assigned a large value.
(The similarity metric formulae are included as Appendix C).
Finally, utilities were prioritized based on their total similarity metric from minimum to maximum. The
top 20 utilities with smallest total similarity metrics, which are shown in Table 9, were selected to
participate in the benchmarking of distribution reliability practices. Quanta Technology invited contacts
at those utilities to participate in the benchmark by answering a confidential online questionnaire. A copy
of the questionnaire is included in Appendix D of this report. Five utilities agreed to participate and
completed the online questionnaire. Quanta Technology complemented the results from the survey by
reviewing and extracting relevant data from publicly available sources, specifically, from reliability reports
published by the PUCs of Florida20, Illinois21, Pennsylvania22, and Texas23.

19

The respective formulae and data are included in Appendix B of this report
http://www.psc.state.fl.us/ElectricNaturalGas/ElectricDistributionReliability
21 https://www.icc.illinois.gov/Electricity/utilityreporting/ElectricReliability.aspx
22 http://www.puc.state.pa.us/consumer_info/electricity/reliability.aspx
23
https://www.puc.texas.gov/industry/electric/reports/sqr/default.aspx
20

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Table 9 - Prioritized list of utilities to benchmark based on results of similarity analysis
Rank
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28

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Utility
Mississippi Power
Gulf Power
South Carolina Electric & Gas Company
Orlando Utilities Commission
PPL Electric Utilities
Florida Power & Light Co
CPS Energy
Austin Energy
Public Service Electricity & Gas
Alabama Power
Commonwealth Edison
Duke Energy Florida
Atlantic City Electric
Georgia Power
Tampa Electric
JEA
CenterPoint Energy
Virginia Electric & Power (Dominion Energy)
Madison Gas & Electric
NSTAR Electric Company
Delmarva Power
Hawaiian Electric
Baltimore Gas & Electric
Potomac Electric Power
AEP Texas Central Company
San Diego Gas & Electric
Cleco Power
Consolidated Edison

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5.3

Peer comparisons

5.3.1 Metrics
The results from the benchmark show that peer utilities use SAIDI, SAIFI, CAIDI, CEMI, MAIFI and MAIFIE
for evaluation of reliability performance (other indices used by peer utilities include L-bar and CELID24).
Some of these indices are used for internal control and others for regulatory reporting. SAIFI and SAIDI
are the most widely used reliability indices in the industry, as shown in Figure 19. For instance, these
indices are being used for regulatory reporting by ENO, Austin Energy, CenterPoint, Duke Energy Florida,
Florida Power and Light25, Gulf Power, Tampa Electric, and Pennsylvania Power and Light, among others.
Another popular combination is SAIFI and CAIDI, which is being used for regulatory reporting by
Commonwealth Edison and Consolidated Edison, among others. However, as discussed in previous
sections, CAIDI can provide misleading results and MAIFI is difficult to track accurately (since it counts
every momentary interruption, rather than momentary interruption events, which are tracked by MAIFIE).

Figure 19 – Survey of Reliability Metrics used for Regulatory and Internal Reporting.
(Numbers inside each bar show the number of participant utilities that selected each option. Results show that SAIFI,
SAIDI and CAIDI remain the most common indices for regulatory reporting and that CEMI n and MAIFI are becoming

24

As per IEEE 1366-2012 Customers Experiencing Long Interruption Durations (CELID) indicates the ratio of individual customers that experience
interruptions with durations longer than or equal to a given time. That time is either the duration of a single interruption (s) or the total amount
of time (t) that a customer has been interrupted during the reporting period.
25
Florida Power and Light tracks CEMI-3, CEMI-5, CEMI-8, CEMM-35, CEMM-50 and CME (“Customer Momentary Experience”)

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popular for internal reporting. Results show that out of the less traditional indices, MAIFI is the most common one
for regulatory reporting. 26)

CEMIn and MAIFIE are becoming increasingly popular reliability indices. The utilization of this set of indices
is expected to provide a more complete assessment of distribution reliability for reporting purposes and
also for evaluation and allocation of investments (e.g., benefit-cost analysis). For instance, as shown in
Figure 20, CEMI5 and MAIFIE are being used for external and regulatory reporting by JEA27, Duke Energy
Florida, Florida Power and Light, Gulf Power, and Tampa Electric28. CEMIn (or jurisdiction-specific indices
that are similar to CEMIn) are also reported by other utilities not included in Table 9, for instance, the IOUs
in California (Southern California Edison, Pacific Gas and Electric and San Diego Gas and Electric)29,
Potomac Electric Company (Pepco), Avista, and IOUs in Illinois, Delaware, Maryland, New Jersey,
Connecticut and North Dakota30. Similarly, in Pennsylvania, electric distribution companies are required
to report MAIFI data provided the equipment capability is available to obtain relevant data (momentary
interruption threshold used by utilities in Pennsylvania is 5 minutes). Furthermore, PPL Electric has a
program to address CEMI, under this program all customers have their interruption count monitored on
a rolling 12-month basis and appropriate remediation strategies are developed31.

26

S. Martino, 2017 EPRI General Reliability Survey, Preliminary Results, 2017 IEEE PES General Meeting, Jul. 2017, Chicago, IL
http://grouper.ieee.org/groups/td/dist/sd/doc/2017-07-18%20EPRI-IEEE%20Survey%20Results%20-%20Sal%20Martino.pdf
27 https://www.jea.com/about/electric_systems/reliability/cemi-5/
28 Review of Florida’s Investor-Owned Electric Utilities 2016 Service Reliability Reports, Florida Public Service Commission, Division of
Engineering, Nov. 2017 http://www.psc.state.fl.us/ElectricNaturalGas/ElectricDistributionReliability
29
M. Kurtovich, M. Zafar, California Electric Reliability Investor-Owned Utilities Performance Review 2006-2015, CPUC, May 2016
http://www.cpuc.ca.gov/uploadedfiles/cpuc_public_website/content/about_us/organization/divisions/policy_and_planning/ppd_work/ppd_w
ork_products_(2014_forward)/ppd%20reliability%20review.pdf
30 Customer Specific Reliability Metrics: A Jurisdictional Survey https://www.oeb.ca/oeb/_Documents/EB-20100249/OEB_Customer_Specific_Reliability_Metrics_Report.pdf
31
Electric Service Reliability in Pennsylvania, Pennsylvania Public Utilities Commission, 2016

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Figure 20 – Adjusted CEMI5 and MAIFIE reported by IOUs in Florida

Figure 20 presents information from Duke Energy Florida, Florida Power and Light, Gulf Power and Tampa
Electric. Results show the percentage of total number of customers that experienced more than 5

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interruptions per year, and the annual frequency of momentary interruption events experienced by an
average customer in the service territory of these utilities.
Reliability indices and MAIFIE in particular are certainly influenced by the choice of sustained and
momentary interruption threshold. The shorter the threshold the greater the share of interruptions that
are considered as sustained and counted by the traditional indices (SAIFI and SAIDI). Figure 21 shows the
results of a survey conducted by Lawrence Berkeley National Laboratory (LBNL) among 123 utilities
regarding their practices for defining sustained interruptions. The results show that 5 minutes, which is
the threshold recommended by the IEEE 1366-2012 Std. is the most common value used by this utility
sample, and is the value that ENO is currently using as threshold for sustained/momentary interruptions32.
ENO uses the IEEE 2.5 Beta method for identification of MED. As shown in Figure 22, this is an industry
leading practice that has been adopted by many PUCs and utilities. As discussed in previous sections, some
utilities use exclusion methods defined by their state PUCs.

Figure 21 – Utility Practices for Defining Sustained Interruptions. 3

32

Surveys conducted by the APPA among public power utilities show an increase from 38% to 48% from 2013 to 2015 in the number of utilities
that use 5 min. as the threshold for momentary and sustained interruptions

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Figure 22 – Major Event Identification and Exclusion Methodology. 30

5.3.2 Reliability programs
Effective reliability programs strive to improve reliability by achieving the following:




Fewer outage events
Fewer customers interrupted per outage
Shorter outage duration.

To maximize the value achieved, analytics should be performed. The net result would be a benefit/cost
(B/C) metric. With this metric, proposed projects can then be prioritized to achieve the highest value for
the same investment.
Customer minutes interrupted (CMI) is often used for benefits estimate since it includes the three
categories shown above, while customers interrupted (CI) does not include duration. If SAIFI is the driver,
then customers interrupted (CI) is instead used.
To estimate the benefit, the basis is the current reliability performance using either historical average CMI
or CI. Another effective approach is to use failure rates for the infrastructure. Infrastructure failure rates
can be grouped in multiple ways depending on the available data. A simple common approach is to
estimate failure rate on a per mile basis, such as outages per mile-year. Estimated outage durations is also
necessary. Once the base case is established, then the improvement can be estimated based on the work
performed, which should improve the failure rates and/or the outage duration. The difference between

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the before and after cases establishes the expected reliability benefit. The methodology to perform this
analysis varies across the industry.
Not all utilities have a formal budgeted and scheduled roadmap for achieving significant reliability
improvements. If a utility is already in one of the top two industry quartiles, a formal roadmap may not
be necessary.
Conventional reliability improvement programs are very consistent across the industry in the approach
used and the methods applied to address reliability issues. Data from two independent surveys confirm
this point.
Table 10 presents information from the American Public Power Association’s survey of distribution system
reliability and operations practices among 112 of its members. The numbers associated with each item
are the number of respondents (out of 112) that use that method of addressing reliability.
Table 11 presents findings from a Quanta Technology survey of 12 utility companies regarding distribution
reliability practices and spending. The items in this table are ranked by total spending for each initiative
among the 12 utilities surveyed. This survey is dated (2008) but correlates highly with APPA 2015 data
confirming the point that conventional distribution reliability practices have changed little. Grid
technology and automation implementation offer additional tools to address reliability but the traditional
methods (vegetation management, animal guards, inspection and maintenance, etc.) still apply.

Table 10 – Reliability Improvement Initiatives 33

Table 11 – Reliability Improvement Survey of 12 Utilities 34

33

American Public Power Association (APPA), Evaluation of Data Submitted in APPA’s 2015 Distribution System
Reliability and Operations Survey
34
Quanta Technology Survey of Utility Methods and Spending on Distribution Reliability

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Vegetation management
Overhead to underground conversion
Proactive cable replacement
Pole inspection and maintenance
Worst circuit improvement
Distribution hardening
Non-automated line devices
New automated line devices
Visual feeder inspections (no infrared)
Small wire replacement
Faulted Circuit Indicators
Visual/Infrared feeder inspections
Improved lightning protection
Animal guards
Cable rejuvenation

The application of this information for this report is to compare these common and proven reliability
improvement methods to actions currently being taken by ENO. ENO is currently using the outage
prevention programs and assessments listed in Table 10 and Table 11. The level to which each program is
used varies among all utilities depending upon the specific needs of that company’s system. The one area
where ENO is less active is overhead to underground conversions.
Underground conversion in the New Orleans area has been reviewed and discussed often as a method to
prevent outages during major storm events. A number of prior industry studies have shown the difficulty
in showing economic justification for major underground conversion in any environment. One of the most
notable of those studies is the EEI report “Out of Sight, Out of Mind”19 previously referenced in this report
(Figure 17, Section 5.2). That 2013 report was an update of previous versions of the same study. The EEI
report presented average cost data for underground conversion which is shown as Figure 23.

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Figure 23 – Cost per Mile: Converting Overhead to Underground Distribution 19

In 2013 as part of an engagement to review system performance and emergency response to Hurricane
Isaac, Quanta Technology developed OH to UG cost estimates for Orleans Parish. Those estimates are
shown as Table 12. The estimated costs at that time were considerably higher than the averages shown
by the EEI study and were based on a system model specific to the New Orleans area that considered all
the variables associated with conversion to UG in the City. Without doubt, UG conversion within the City
of New Orleans would be extremely expensive due to congested infrastructure, lack of space for a utility
corridor for underground vaults and other facilities, and difficulty of construction in the urban
environment.
Table 12 – Estimated Costs to Convert OH to UG in New Orleans 35

35

Quanta Technology, “Reliability of the Electric System in Orleans Parish” 2013.

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As an approach to improving and maintaining reliability, there are many more cost effective methods
which ENO is currently pursuing. However, a practice of targeted undergrounding for new facilities and
perhaps some existing facilities should be included as part of an overall infrastructure management
strategy. This has been exhibited in ENO’s approach to storm hardening where targeted undergrounding
and alternative resiliency/reliability improvement measures have been considered and compared.
Considering the high cost of underground conversion in the ENO territory, it is highly likely that more
improvements can be achieved on the overhead system for the same level of expenditure.
Other industry practices from the five utilities benchmarking assessment conducted with this project are
as follows:














Less than half the utilities had regulatory performance metrics with economic impacts. ENO
reports its results, but currently they do not have an economic impact.
Majority of utilities have multi-year reliability performance targets, including ENO.
Majority of utilities, including ENO, use technology to enhance outage data gathering
Most common outage causes listed were equipment failure, vegetation, animals, and weather
related, similar to ENO.
More than half the utilities use grid analytics to determine failure rates and performance. ENO
has analytics for determining performance but does not have the data for failure rates analysis.
Three utilities provided outages per mile per year: One utility provided 0.4 outages per mile per
year combined for overhead and underground; another provided 1.32 for overhead, and 0.59 for
underground; and the third provided 0.72 for overhead, and 0.25 for underground. ENO does not
have the data for that failure rate.
Reliability improvement is largely proactive but also poor performing circuits are identified for
improvement. ENO is both proactive and reactive.
Majority of the utilities prioritize projects based on either CI improvement/cost or CMI
improvement/cost. ENO prioritizes primarily based on estimated CI improvement.
For the benefit/cost analysis, different methods are used including software tool and internal
spreadsheet based models. ENO uses CI algorithm for prioritizing improvement efforts.
Common efforts to improve reliability include: modern reclosers, fusing, FLISR, circuit reconfiguration, aging infrastructure replacement, vegetation clearance, hardware inspection and
replacement. This includes ENO.
Different departments are involved in project identification and implementation. Project results
are tracked using reliability indices. This also applies to ENO.
Majority of the utilities do not have a full distribution system inspection and maintenance
program. This also applies to ENO.

The compressed schedule of this project limited the time available for responses to the benchmark survey,
however, the items listed above are consistent with surveys conducted by other organizations, Quanta
Technology’s experience in the industry, and the current survey. Overall, ENO’s distribution reliability
practices are similar to the other utilities included in the survey. The primary difference is related to the
failure rate analysis which is not possible at this time, but is expected to be available in the future as new
systems are implemented.

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5.3.3 Asset management
Management of utility assets and in particular aging assets is an important element of overall distribution
system operations and reliability. There are many methods by which the management of utility assets can
be achieved ranging from internally developed spreadsheets and analytics to commercially available
systems designed specifically for use in the utility industry. In the end, the method or system used for
asset management is simply a tool; the important element is the philosophy and practices of the asset
owners.
It is common in today’s electric utility industry for companies to use asset management software as a
system of record for all assets. A basic asset registry that includes nameplate data from equipment is
fundamental to the asset management system. The systems are also used as the repository for all
inspection and maintenance records for individual assets. This information gives the users the capability
to perform analytics on classes of asset by various attributes: age, manufacturer, voltage, location, etc.
This information is used in overall asset management for purposes of determining operational risk in the
event of multiple failures of a particular class of asset, developing a manageable asset replacement
strategy, and ensuring inspection and maintenance requirements are met, to name a few examples.
Asset management programs and systems are becoming the norm as more utilities attempt to move from
time-based maintenance programs to reliability-centered or condition-based practices. These practices
provide for maintenance to be performed based on data analytics and condition monitoring capabilities.
As opposed to a time-based maintenance schedule, use of resources is optimized to perform inspections
and maintenance when data indicates that apparatus or equipment are approaching higher risk of
malfunction or failure. This of course requires a comprehensive asset information capability to track
performance of a class of assets and develop failure statistics to drive maintenance.
A true condition-based maintenance program also includes a significant capability in real-time, monitoring
of equipment status, which is often included, where feasible, as part of an overall grid modernization
program. In reality, continuous monitoring of the condition of distribution assets is not economically
justifiable due to the volume of devices, relatively low cost of devices or apparatus, and the overall design
and operations philosophy of distribution systems, which limit the number of customers exposed to
individual line device outages. Implementation of AMI, however, does provide the capability for
distribution transformer loading to be monitored to determine if there are overloaded units in service.
Benchmarking responses confirm what Quanta has found to be typical in the industry today:






Distribution asset philosophy remains primarily “run to failure” with trends and initiatives moving
toward preventive and/or condition-based maintenance. Run to failure, while most cost effective
in distribution asset management, is becoming a less desirable approach as companies work
toward improving the “customer experience” by minimizing unplanned outages.
Preventive and condition-based practices are often most common in evaluating underground
cable condition and implementing proactive cable replacement measures. Partial discharge and
other diagnostic testing of cables are the common condition assessment methods.
Most utilities have in recent years implemented a resiliency or weather hardening initiative. The
scope of the projects varies widely based on the specific risks being mitigated (wind, ice, floods,

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

etc.) and the design criteria being used by the utility. In all cases, however, the utility considers a
weather hardening program as part of an overall reliability improvement strategy. While weather
hardening programs are primarily intended to increase mechanical strength of the infrastructure
to withstand extreme conditions, the result is also a benefit to everyday reliability. Some of the
methods used in weather hardening programs include increasing pole size/class, changing pole
materials (steel, concrete), replacing small gauge conductor, and targeted conversion of overhead
conductor to underground.
Asset management databases are used in determining failure rates of asset classes and also to
perform predictive analytics for reliability improvement and system resiliency.

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5.4

Grid Modernization – Industry Trends and Activities

Electric power systems around the world are undergoing an unprecedented transformation. In the US,
this evolution has been clustered and described under various terms, including smart grid, grid of the
future, grid modernization, and utility of the future. Despite slight differences among these terms, all of
them recognize that the status quo is no longer able to fulfill the changing needs and growing expectations
of end users, while providing electric utilities and other industries with the opportunity to thrive in a
dynamic and modern market; therefore, they have encouraged the introduction of new paradigms. The
terms “smart grid,” “grid of the future,” and “grid modernization” emphasize the need to build an
intelligent grid that can be monitored and controlled in real-time to allow for providing a reliable, safe,
and secure service and empower customers to actively participate and benefit from greater and more
diverse market opportunities and services.
Building this intelligent grid is a monumental task (particularly on the distribution and grid-edge36,37 sides,
which are vast and heterogeneous) that has led to the emergence of new concepts, technologies, and
paradigms. Examples of this include debates regarding future grid architecture (a distributed, hybrid, or
centralized grid); advances in grid modeling, simulation, and analysis; the introduction of the microgrid
concept as an alternative to enhance resiliency and facilitate the DER integration; and the convergence of
information and operations technologies (IT/OT).
The idea of the utility of the future, on the other hand, has a broader connotation and encompasses the
need for all aspects pertaining to the utility industry to evolve and adapt to this new and dynamic
customer-centric reality. This includes business and engineering processes, regulation, policies, rate
design, asset ownership, service diversification, and relationships with customers. Although grid
technology related aspects are challenging and complex, changes and solutions in this area are at a more
advanced stage than those needed to address emerging regulatory, policy, and business problems and
needs (some of which are being triggered or enabled by technology developments). In summary,
addressing the business, legal, regulatory, and policy side of the utility of the future is an area where
significant work is required38.
An important point to emphasize is that the pace of the transition toward a modernized grid, particularly
on the distribution side, is a function of the existing and expected system conditions and trends of every
utility system and market, e.g., existing and forecasted DER penetration levels. For instance, utilities
operating in states such as Louisiana39 (where DER adoption is rapidly becoming a reality) must continue
this evolution toward a modernized distribution grid at a faster pace than utilities operating in emerging
DER markets. Otherwise, DER proliferation will lead not only to significant operations, planning and
engineering challenges and inefficiencies, but also will prevent utilities (and ultimately customers and
society in general) to attain the potential benefits derived from the adoption of these technologies.
Furthermore, since even larger-scale adoption of DER is inevitable, given the imminent (or existing)
achievement of grid parity by Photovoltaic Distributed Generation (PV-DG) in US markets, additions in
36

The interface between the customer and the electric power supply https://www.tdworld.com/grid-opt-smart-grid/grid-s-edge
The varying hardware, software and business innovations that are increasingly enabling smart, connected infrastructure to be installed at or
near the “edge” of the electric power grid https://www.ase.org/blog/so-what-exactly-grid-edge-thing-anyway
38 J. Romero Agüero; A. Khodaei; R. Masiello, The Utility and Grid of the Future: Challenges, Needs, and Trends, IEEE Power and Energy Magazine,
Sep/Oct 2016, pp. 29-37 http://ieeexplore.ieee.org.prox.lib.ncsu.edu/document/7549242/
39 For instance, Louisiana moved from the 45th position in 2017 to the 35th position in 2018 in the Solar Energy Industry Association (SEIA)’s state
ranking of installed capacity of solar generation (13,740 installations and 307 MW of additional capacity expected to be added over the next 5
years) https://www.seia.org/state-solar-policy/louisiana-solar
37

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grid modernization infrastructures and systems should largely be considered “required” rather than
“optional” investments to enable the normal operation of modern and future distribution systems. It is
worth noting that utilities operating in states with incipient penetration levels of DER, recognize the
imminence and urgency of preparing for the transition to this new paradigm, and are actively working on
modernizing their distribution grids and overall practices so that they are suitable for operation in this
new reality40,41.
The industry is currently involved in numerous grid modernization proceedings, for instance, Figure 24
shows the results from the latest grid modernization state index published by GridWise Alliance42, based
on a wide range of grid modernization policies, practices and investments. The index is calculated based
on a wide range of grid modernization policies, practices and investments. The top 10 states shown have
the greatest number of grid modernization initiatives in place.

Figure 24 – Grid Modernization Index by State42

40

Illinois Moves Up to #2 in the Nation for Electrical Grid Modernization https://www.comed.com/News/Pages/NewsReleases/2016_01_25.aspx
J. Romero Aguero et al., Modernizing the Grid: Challenges and Opportunities for a Sustainable Future, IEEE Power and Energy Magazine, Vol.
15, No. 3, pp. 74-83
42
Grid Modernization Index 4, GridWise Alliance, Nov. 2017
41

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Similarly Figure 25, Figure 26, and Table 13 show a recent summary of heterogeneous actions related to
grid modernization being implemented by US states, such as studies, business models and rate reforms,
deployments, etc.43

Figure 25 – Total Number of Grid Modernization Actions by Quarter.36

43

50 States of Grid Modernization, Q2 2018 Quarterly Report, NC Clean Energy Technology Center, Aug 2018 https://nccleantech.ncsu.edu/wpcontent/uploads/Q2-18-GridMod-Exec-Final-1.pdf

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Figure 26 – Legislative and Regulatory Grid Modernization (Q2 2018). 36

Table 13 - Summary of Grid Modernization Actions (Q2 2018).36

5.4.1 Grid Modernization – Reliability Benefits
Quanta Technology conducted a review of grid modernization and smart grid programs implemented or
proposed by selected utilities. The review included an assessment of key initiatives included within each
program (with focus on those that are relevant for distribution reliability), and their estimated benefits in
terms of distribution reliability improvement. There is a variety of technologies and concepts that are key
elements of grid modernization that are discussed in more detail in Appendix E of this report. Specific
technologies of interest for this section includes deployment of distribution automation solutions,
specifically, smart reclosers and switches operating in FLISR schemes, such as the ones proposed by ENOin
its grid modernization program. This section discusses the findings and conclusions of that review. This
task consisted of an assessment of publicly available documentation from selected utilities, industry

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reports, and Quanta Technology’s expertise on the subject. Some of the reviewed programs were
implemented or proposed by utilities included in the selected list for benchmark shown in Table 9.
Grid modernization and smart grid programs include a variety of technologies to address specific needs
of utilities. Some of these technologies have a direct impact on improving distribution reliability,
specifically decreasing SAIDI and SAIFI through the reduction of CMI and CI. Examples of solutions and
technologies included in grid modernization programs are shown in Figure 27, the technologies that have
direct impact on distribution reliability are highlighted under the category “Automated Outage
Management and Service Restoration” and “Grid Visibility and Diagnostics” and include FLISR, AMI and
ADMS, which are technologies that ENO has also planned to deploy.

Figure 27 – Smarter Energy Infrastructure Features & Grid Modernization Technologies. 44

44

J. Blansfield, A. Cooper, Grid Modernization Technologies: Key Drivers of a Smarter Energy Future, The Edison Foundation, Institute for
Electric Innovation, May 2017
http://www.edisonfoundation.net/iei/publications/Documents/Final_Grid%20Modernization%20Technologies_IEI%20White%20Paper.pdf

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Figure 27 shows that grid modernization technologies have significant benefits in terms of improving grid
visibility, diagnostics, outage management and restoration, which directly impact distribution reliability
Figure 28 provides a detailed breakdown of devices and systems typically included as part of FLISR
deployment and their impacts on distribution reliability. The figure demonstrates that most Distribution
Automation (DA) technologies have significant benefits in terms of reliability improvement

Figure 28 – Devices and Systems that Support Distribution Automation Applications.47

Figure 29 offers a conceptual description of FLISR operation, and Figure 30 shows the distribution
reliability benefits (reduction in interruption duration) that can be achieved from centralized45 FLISR
implementation as a function of the selected operation mode (manual, semi-automated and fully
automated).

45

Controlled by an Advanced Distribution Management System (ADMS)

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Figure 29 – Conceptual Example of FLISR Operation.

Figure 29 shows how after a fault occurs (A), FLISR helps automatically locate (B) and isolate (C) faulted
sections of a feeder (which allows to restore service to customers located upstream from the fault), and
then restore service to customers located downstream from the fault by automatically transferring those
sections to a neighbor feeder (D)47

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Figure 30 – Comparison of Fault Recovery Timelines for Different Operation Modes in FLISR Implementation.

Figure 30 conceptually shows the total fault location, isolation and restoration time for: manual outage
management and restoration, and for three operation modes of FLISR (for various participation levels of
the distribution system operator)46
It is worth noting that distribution automation and particularly FLISR are now established technologies
that have been deployed by numerous utilities and there is an abundant body of work and documented
experiences regarding their expected benefits from a reliability improvement perspective. For instance,
the US Department of Energy (DOE) published a report in 2016 that summarizes the results from the
implementation of distribution automation projects included in the Smart Grid Investment Grant (SGIG)
program47. This report summarizes the distribution reliability benefits (improvements in SAIFI and SAIDI)
achieved by 62 utilities48 due to the deployment of a variety of technologies, including smart reclosers and
switches as part of distribution automation schemes, such as FLISR.
Figure 31 describes the nearly 82,000 DA field devices installed under the SGIG, and Figure 32 shows a
summary of the main findings and results from the program. The results show that the deployment of
FLISR led to up to 45% reduction in the number of CI and up to 51% reduction in CMI.

46

http://www.energypa.org/assets/files/2017/March%20Event/Presentation%20Papers/Smart%20Grid%20Automation%20&%20Centralized%20
FISR%20-Colby.pdf
47 Distribution Automation - Results from the Smart Grid Investment Grant Program, US DOE, Sep. 2016
https://www.smartgrid.gov/document/SGIG_Results_for_Distribution_Automation_2016.html
48 Distribution Automation investments in the electric distribution system totaled about $2.19 billion—including Recovery Act funds from DOE
and cost share from the utilities—accounting for 27 percent of the total SGIG investment

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Figure 31 – DA Asset Deployments by Participating Utilities Under SGIG Projects.47

Additionally, Figure 33 shows the CMI reduction from projects implemented by 15 utilities that
participated in the SGIG program, although the period of data collection is not identical for all utilities, the
results show significant benefits in terms of reliability improvement.
Reliability can be further improved when combined with refurbishment of foundational infrastructure and
enhanced maintenance practices (e.g., replacement of aging assets, weather hardening, vegetation
management, etc.) and deployment of additional intelligent systems (e.g., AMI, ADMS, etc.). It is worth
noting that the specific improvement is a function of a variety of variables, such as system configuration,
overhead and underground construction ratio, customer density, asset condition, etc. The overall results
from the DA SGIG program are consistent with the expected distribution reliability improvements
estimated by ENO due to the deployment of its grid modernization program49.

49

Direct Testimony of Erica H. Zimmerer on behalf of Entergy New Orleans, LLC., Application of Entergy New Orleans, LLC for a Change in
Electric and Gas Rates Pursuant to Council Resolutions R-15-194 and R-17-504 and for Related Relief, July 2018

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Figure 32 – Reliability and Outage Management Results from DA Investments from the SGIG program.47

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Figure 33 – CMI Avoided by DA Operations.47

The results from the SGIG program are also consistent with others reported in the literature, such as those
published by Commonwealth Edison (ComEd) as part of the benefits of its Energy Infrastructure
Modernization Act (EIMA). ComEd reported that as of March 2017, grid modernization investments
(including the deployment of 3,100 smart switches operating in distribution automation schemes, along
with significant investments in foundational infrastructure50) led to 7.6 million avoided customer
interruptions since 2012, with outages reduced by 44% and duration of outages reduced by 48%51. The
implementation of ComEd’s grid modernization program has provided important benefits in terms of
overall system reliability and customer satisfaction, for instance, Figure 34 and Figure 35 show the
evolution of ComEd’s system SAIFI and customer reliability complaints in the last five years, during the
same period its grid modernization program was implemented. The results show a reduction of about
39% in SAIFI and a reduction of about 80% in the number of annual customer reliability complaints (with
respect to the average value from the 2007-2011 period, respectively).

50

A. Dahwan, Overview of Energy Infrastructure Modernization Act (EIMA), Jun. 2014 https://www.eesi.org/files/Anil-Dhawan-061814original.pdf
51 Delivering on Smart Grid, Five-Year Capstone Report
https://www.comed.com/SiteCollectionDocuments/AboutUs/ComEdProgressReport2017.pdf

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Figure 34 – ComEd System SAIFI improvement since the beginning of the implementation of grid modernization
program47

Chart 5: ComEd System - Customers' Reliability Complaints

Complaints

Total Complaints

2,670

1,416

1,113

987

595

575

512

2007-2011 Avg

2012

2013

2014

2015

2016

2017

Figure 35 – ComEd System Customers Reliability Complaints decrease since implementation of grid
modernization program 47

Additional benefits associated with this type of grid modernization initiatives include operations and
maintenance efficiency and savings, and improved customer satisfaction and public awareness. An
important benefit to consider is customer savings in terms of avoided interruption costs, for instance,
Dominion Energy has estimated $2.0B in savings for customers over a 20 year period from reliability
improvement (SAIDI)52 due to the implementation of the Grid Transformation and Security Act of 2018
(GTSA)53.

52

Savings estimated using the Interruption Cost Estimate (ICE) Calculator developed by Lawrence Berkeley National Laboratory (LBNL)
https://eaei.lbl.gov/tool/interruption-cost-estimate-calculator
53
https://www.dominionenergy.com/library/domcom/media/about-us/electric-projects/grid-transformation/gtsa-072418.pdf?la=en

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6

ENO RELIABILITY PERFORMANCE IMPROVEMENT

The primary causes for CI increases since 2013 are related to infrastructure issues such as crossarms,
conductors, and poles. The reliability programs currently underway are effectively addressing these issues
on a per project basis.

6.1

Current Reliability Program Results to Date

DLIN SAIFI has increased from 1.04 in 2013 to 1.584 in 2017. This resulted in enhancing current activities
as well as implementing new ones to improve reliability. Overall there has been significant expenditures
since 2016 to improve reliability. Table 14 shows expenditures for FOCUS, BACKBONE, Reliability Blitz,
and Storm Hardening, 2016 through mid-year 2018.
Table 14 - 2016 thru Mid-2018 Reliability Expenditures

Grand Total

2016
$ 13,834,476

2017
$ 23,025,913

2018
$ 15,604,026

Grand Total
$ 52,464,415

Source: Entergy New Orleans Testimony, T. Patella, June 2018

Post construction evaluations have indicated a significant decrease in CI for sections improved with the
current efforts such as FOCUS and BACKBONE. The current efforts under those programs appropriately
address areas of known problems and areas with potential for highest CI and CMI impacts. Once the
immediate areas of concern are addressed focus should turn to more proactive inspection and repair. This
effort will take time.
Based on SAIFI thru September 2018, additional improvements, and on-going aging, ENO has developed
a 2018 year-end projected SAIFI of 1.65.

6.2

Potential Impact of Current Initiatives

With the multiple efforts underway to improve its distribution reliability, ENO has developed an
appropriate methodology to estimate the SAIFI reduction by year taking into consideration the capital and
O&M costs. A key component is a “dollars per CI reduction” metric which was developed using recent
experiences. The method has been used to develop a separate metric for its FOCUS, BACKBONE, and DA
(Distribution Automation) programs. With the different cost/benefit metrics, a projected SAIFI
improvement can be estimated for each of the programs and then combined for an overall expected result
on an annual basis with an established budget.
Also, since aging infrastructure has been an on-going issue and will continue indefinitely, it is also taken
into consideration for the estimate of its future SAIFI. The values of the metrics used in developing
estimates (dollars per CI and infrastructure rate of deterioration) have not been evaluated as part of this
report. Nevertheless, the overall approach at estimating the future SAIFI based on level of costs and aging
is appropriate.

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6.3

Potential Improvement Efforts

As is normally the situation in most reliability improvement efforts, enhancements to the process are an
on-going opportunity. Quanta Technology has identified multiple recommendations that can help
expedite achieving the desired reliability improvement.

6.3.1 Metrics
ENO is currently keeping track of SAIFI and SAIDI for internal and external reporting purposes. ENO’s
reliability indices for 2017 are close to the borderline between 3rd and 4th quartile of industry performance,
as defined by the 2017 IEEE Distribution Reliability Benchmark. The calculation of reliability indices is
largely performed according to IEEE Std. 1366-2012. Benefit-cost analyses and prioritization of reliability
improvement projects are performed based on CI reduction. This approach is adequate, and can be
enhanced to address the specific aspects associated with average reliability indices discussed in section
2.3. This section provides recommendations in that regard.
Recommendation
1. It is recommended that ENO consider using SAIDI, along with SAIFI, as part of the metrics used in the
benefit-cost analysis for evaluation and prioritization of reliability improvement projects.
Consideration of MAIFIE and CEMIn is also recommended to the extent these indices can be applied
with the currently available data gathering technology.
a. It is recommended that ENO consider SAIDI (along with CMI and $/CMI metrics54) to account
for projects that improve reliability by reducing interruption duration (such as deployment of
Fault Circuit Indicators (FCI) and sensors, manual switches, etc.), but that do not reduce
interruption frequency. Using both indices would allow ENO to have a more complete
perspective of the benefits derived from the implementation of its proposed reliability
improvement projects, and to use this information to prioritize selection and deployment.
b. It is recommended that ENO consider using MAIFIE to evaluate the effect of momentary
interruptions, and the balance between sustained and momentary interruptions when
identifying and evaluating distribution reliability improvement projects. The calculation of
MAIFIE will become particularly important as more distribution automation (FLISR) schemes
are implemented in ENO’s system. As discussed in section 2.4, the accurate calculation of
MAIFIE requires monitoring infrastructure that may not be fully available to ENO at present,
however, a base estimation using existing monitoring infrastructure can help obtain
preliminary reference values to assess the historical evolution of this index. This estimation
can be progressively refined as the required monitoring infrastructure becomes available.
c. It is recommended that ENO consider using CEMIn (e.g., CEMI5) to help identify worstperforming customer pockets within feeders, regardless of customer density. This would help
ENO identify and target needed investments to poor performing areas within feeders with
54

Other utilities have used CMI reduction and $/CMI to evaluate intelligent infrastructure investments, for instance Southern
California Edison (SCE) used this metric in its 2018 Rate Case, specifically SCE used $2.32/CMI to assess the cost savings
associated with CMI avoided
http://www3.sce.com/sscc/law/dis/dbattach5e.nsf/0/F3C79866183D17BB8825814100830540/$FILE/SCE18V10.pdf

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average SAIFI and SAIDI indices. The limitations of effective use of CEMIn were noted in earlier
discussion and should be acknowledged if used. It is worth noting that ENO is already
following a similar approach as part of its existing FOCUS project. Using CEMIn will facilitate
benchmarking with peer utilities for performance assessment purposes, and serve as a
starting point for the potential calculation of greater granularity indices (e.g., at customer
level), once the required infrastructure is available (e.g., AMI).
2. It is recommended that ENO consider accelerating the implementation of a data analytics program,
to the extent possible within regulatory requirements. An analytics program will provide the required
data for the implementation of advanced distribution planning applications.
The utilization of additional reliability indices for benefit-cost analysis and prioritization is a more
complex activity that involves simulations using specialized software, such as the reliability
assessment module in Synergi. ENO is already working on developing guidelines for advanced
distribution planning applications, which includes the utilization of this type of software solutions. It
is recommended to accelerate the adoption of this advanced distribution planning process (as it
pertains to distribution reliability assessment using Synergi) to evaluate the implementation of
conventional (e.g., asset replacement) and advanced solutions (e.g., FLISR schemes) for distribution
reliability improvement. Currently, ENO is using a combination of spreadsheet-based and modelbased approaches to identify and prioritize investments. The utilization of spreadsheet-based
approaches is still common in the industry, however, more utilities are transitioning toward the
utilization of model-based approaches, including predictive reliability55. This is considered an industry
leading practice that ENO is recommended to continue adopting.
3. It is recommended to consider estimated customer benefits due to outage cost reduction. As
discussed in section 5.4.1, other utilities have included this type of analysis (e.g., using the ICE tool) in
the benefit-cost evaluation and prioritization of distribution reliability improvement
projects/programs, particularly for those that require large investments. ENO has explored this
concept as well. The inclusion of customer and societal benefits have proven to be valuable to justify
this type of investments.

6.3.2 Reliability Programs
6.3.2.1

Outage Data and Benefit/Cost Prioritization

ENO has an effective approach at selecting areas for improvement. There are opportunities for enhancing
the overall prioritization process. As described earlier, the current method of collecting outage data only
provides the impact beyond a device involved in the restoration process. It does not sum the overall CI
and CMI for individual outages nor does it indicate it is from a single cause. While the issues of data
collection are secondary to addressing infrastructure issues, accuracy of data ultimately is needed for
measuring performance improvement and ultimately for predictive reliability analytics.

55

J. Romero Aguero, L. Xu, Predictive Distribution Reliability Practice Survey Results, 2014 IEEE PES JTCM, Jan. 2014, New Orleans, LA,
http://grouper.ieee.org/groups/td/dist/sd/doc/2014-01%20Predictive%20Distribution%20Reliability%20Practice%20Survey%20Results.pdf

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Recommendations
1. The process for recording outage events needs to be modified to aggregate the multiple restoration
events into a single outage. Although this is being pursued as part of the ENO Grid Mod/ADMS project
it should be evaluated for a change in the near future. This will reduce the number of outages
reported, will provide the ability for establishing failure rates, and will ensure that when ADMS is
implemented that process will be aligned properly.
2. Currently ENO is reporting outage count based on the number of events which includes scheduled
outages. With a count in excess of 2000 that number appears excessive for a utility the size of ENO.
The industry norm is to not include scheduled outages, thus ENO should consider excluding those (or
reporting scheduled outages separately) when the overall outage count is provided externally.
Improvement opportunities in data collection and management are discussed in more detail:
Outage Count for Failure Rate Data
The actual outage count will be less than the quantity identified by summing the multiple restoration
steps. Without detail review of thousands of events, an actual outage count may not be possible at this
time. Without knowing the actual number of outages, the preferred method of benefit/cost analysis is
not doable.
Effective predictive analytics is dependent on “before and after” failure rate values on either a per mile
basis or based on specific equipment. The before failure rates are determined by quantifying the number
of failures for different types or categories of infrastructure and comparing it to the quantity of that type
of infrastructure. For example, if a utility has 2000 miles of overhead primary lines and it has on the
average 200 outages per year on the overhead system, the average failure rate would be 0.1 outages per
mile per year. Similarly, failure rates for different types of equipment can be evaluated. If a system is rebuilt, then a revised failure rate would need to be determined to predict the enhanced performance.
Also, if devices such as reclosers are applied, predictive analysis can provide estimated benefit. As a
simplified example, if a feeder had 10 miles of overhead line (0.1 outages/mile-year) with 2000 customers
evenly distributed, the before analysis would indicate an average of 1 outage per year for the 2000
customers. If a recloser is to be installed on the circuit, then the predicted CI and CMI improvement can
be determined based on where it is installed.
Reported Outage Count
Without an actual outage count, ENO has been reporting the number of events which is higher than the
actual outage count. In addition, scheduled outages are included in that count. Instead of 2800+ outage
events reported, the actual number of forced outages is less, although the number has not been
determined.
6.3.2.2

Prioritization of Enhancement Projects

The FOCUS and BACKBONE program are an effective approach at identifying and resolving infrastructure
issues. It will take time to make a significant impact on reliability. To maximize the benefits sooner, an

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enhancement of the prioritization method needs to be considered. More effective prioritizing can result
in having more improvement sooner for the same level of effort and costs.
Currently, FOCUS identifies projects based on the CI beyond devices, such as breakers, reclosers, switches,
and fuses. Since the outage tracking process does not capture the entire outage impact, this is a
reasonable approach. When a project is added to the FOCUS list it is generally prioritized based on
projected CI reduction, as opposed to a detailed benefit/cost calculation. The projects that do get
implemented are typically based on the level of funding provided to those types of projects.
Recommendations
1. With Current Outage Data- Before both design and construction, some level of prioritization should
be pursued. Currently a 70% CI improvement is estimated. Since that value is based on overall
Entergy, a value for ENO should be pursued. Once the inspection has been performed and expected
enhancements identified, a ballpark cost should be developed for a benefit/cost (B/C) metric. With
that metric, it can be determined if the project is reasonable to be designed. Once designed and a
more accurate estimate is determined, then the benefit/cost can also be re-done to ensure the
highest B/C value projects move forward.
2. With Aggregated Outage Data- Once the multiple outage events can be aggregated, analysis can be
performed to determine infrastructure failure rates. Including a before and after. These results would
provide an enhanced B/C analysis.
6.3.2.3 Outage Duration
ENO has not pursued reduction of outage duration at a significant level due to potential impacts on crew
safety. Field crew safety does supersede outage duration impact. Crews must be allowed to perform the
work safely at the proper pace. Nevertheless, there are efforts that can be pursued to reduce the average
outage duration without impacting crew safety.
Recommendation
1. Outage durations should be evaluated for potential enhancements. With the increase in SAIFI, SAIDI
has increased by a larger proportion indicating that average outage durations have also increased. A
large proportion of the SAIDI impact during an outage often occurs before the crew is on site for
repairs. The average duration for the customers impacted can be reduced via sectionalizing devices
that expedite partial restorations, as well as outage response from the time the outage began until
repairs have been made.
6.3.2.4 Feeder Sectionalizing Plan
ENO’s distribution system has 195 radial feeders and 155 reclosers, ENO is already deploying smart
reclosers in new locations and replacing existing reclosers with newer microprocessor-based technology.
There is an opportunity to further increase the number of smart reclosers and distribution automation
schemes (e.g., FLISR) in the system to reduce the number of customers by switching/protection zone. As
discussed in section 5.4.1, this is a very effective practice for distribution reliability improvement.

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Recommendation
1. It is recommended that ENO evaluate the additional implementation of distribution automation
schemes (FLISR) to complement ENO’s grid modernization program and reduce the system average
amount of customers within each switching/protection zone to 500 customers. This is an industry
leading practice that is gradually being adopted by other utilities.
For example, a feeder that provides service to 2,000 customers and has a normally open-tie to a
neighbor feeder would be allocated 3.5 reclosers (a recloser in the normally-open tie would be shared
with its neighbor feeder). This type of initiative could be implemented proactively by ENO to
complement its proposed deployment. As discussed in the examples provided in the report, the
distribution reliability improvements derived FLISR implementation can be significant (e.g., about 45%
reduction in CI and 51% reduction in CMI based on results reported by the Smart Grid Investment
Grant program).
2. It is recommended that ENO explore the implementation of advanced reclosing solutions that are
available in modern microprocessor-based reclosers (e.g., single-phase reclosing/tripping and
lockout).
As discussed in Appendix E, this is a technology that has proven to be very effective when
implemented in suitable locations (e.g., instead of fusing in three-phase laterals serving only singlephase loads). Similarly, the utilization of advanced reclosing solutions will also give ENO the flexibility
to use fuse saving and fuse clearing overcurrent protection philosophies in a customized manner,
depending on the application. In general, fuse saving provides greater reliability improvement than
fuse clearing, since it allows temporary faults to self-extinguish, and events that otherwise would be
sustained interruptions become momentary interruptions. For this reason, it remains the
recommended practice to minimize the effect of temporary faults (which represent the majority of
events in distribution systems). In the last decades, the utilization of fuse saving has experienced a
decrease56, largely due to the growing interest in the industry in limiting the effect of momentary
interruptions. Modern remotely-controlled microprocessor-based reclosers, ADMS and advanced
FLISR schemes, allow utilities to customize the overcurrent protection philosophy used by each
device. When this is combined with single-phase tripping and distribution automation applications, it
provides utilities with a tremendous variety of options to improve distribution reliability by minimizing
sustained interruptions, while monitoring and controlling the effect of these advanced reclosing and
distribution automation schemes on momentary interruptions.
3. It is recommended that ENO consider accelerating, to the extent possible within regulatory
requirements, the implementation of its grid modernization, AMI and ADMS programs, which will
provide some of the foundational and intelligent infrastructure and systems (e.g., FLISR schemes)

56

R. McCarthy, R. O’Leary, D. Staszesky, A New Fuse Saving Philosophy, DistribuTECH 2008 https://www.sandc.com/globalassets/sacelectric/documents/sharepoint/documents---all-documents/technical-paper-766-t84.pdf

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needed to improve distribution reliability, including the ability to automate outage data collection
and analysis.
6.3.2.5 Corrective Maintenance Program
The FOCUS and BACKBONE are effective projects to pursue upgrades of the distribution system. However,
their implementation will take time and may not address significant potential safety or reliability events
on sections of circuits not being pursued.
Recommendation
1. ENO should pursue a corrective maintenance program that is based on a 100% inspection of the entire
distribution system within an identified cycle, such as every 5-8 years. This would be similar to an
expansion of the BACKBONE program in that the effort is to identify and fix specific problems and not
perform an extensive rebuild. For example, if a broken crossarm or excessive leaning pole is identified,
that needs to be fixed soon. As part of this effort, an overall standard practice should be developed
specifying the requirements. Elements of a system inspection currently exist in the reliability programs
currently underway at ENO. Full distribution inspection programs are not common practice in the
industry, however, the current efforts by ENO offer a good start toward such an effort.
6.3.2.6 Vegetation Clearance Requirement
The 2013 versus 2017 CI and CMI comparison identified vegetation as one of the leading contributors to
the overall increase. Infrastructure rebuild is not immune to tree contacts and will still result in outages
and infrastructure damage depending on different circumstances.
Recommendation
1. An overall evaluation of the current ENO vegetation program should be performed to review current
trim cycles, clearance requirements, trimming obstacles, and the different types of vegetation
outages. ENO currently operates with highly restrictive vegetation practices within the City and
deeper evaluation of the impact of those restrictions is warranted. That information can then be used
to determine the need for improvements in the program and whether regulatory support will be
required.
6.3.2.7 Transmission Reliability Evaluation and Improvement Plan
Earlier in the report, the 2018 SAIFI results thru 10/21/2018 showed that transmission performance
contributed to 28.5% of the customer view SAIFI. Although a comparison of transmission versus
distribution reliability was not part of this overall evaluation, Quanta Technology’s perspective is that the
transmission impact is disproportionally high for 2018.
Recommendation
1. An evaluation of the transmission reliability should be performed combined with a plan to improve
the transmission reliability.

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6.3.2.8 Internal Audit Program
There is no information indicating established procedures are not being followed within ENO. It is
ultimately in the best interest of the utility as well as the departments responsible for various programs
to have a regular review of processes and procedures to ensure compliance with internal standards and
expectations.
Recommendation
1. An Internal Audit Program should be pursued to ensure current and new processes are effectively
pursued and implemented. The level of an internal audit can vary, but should ensure that committed
requirements are being followed. As a first step, requirements should be documented. Examples of
validation audits are:
a.
b.
c.
d.
e.

Outage data
Prioritization process
Corrective maintenance program
Tree trim clearance
Pole inspections

6.3.3 Asset Management
Improvement in overall asset management capability is an identified need within Entergy at the corporate
level. Currently at Entergy, asset management is located organizationally as a corporate services function
supporting all Entergy operating companies. In order to implement a comprehensive asset management
capability, Entergy is in process of implementing an enterprise asset management solution. The company
has selected the IBM Maximo software product, which is a well-known and respected asset and
maintenance management system. Maximo has been used in the utility industry for many years as a
computerized maintenance management system (CMMS). The software has been continually enhanced
with capabilities that now constitute a fully functional enterprise asset management system.
Implementation of Maximo will give Entergy the ability to track all classes of assets, including basic
nameplate information, maintenance history, outage and failure records. This database will then be
available for detailed analytics that will provide the company with ability to perform failure analytics,
predictive analytics, condition-based maintenance scheduling, and other analysis that ultimately makes
the company more efficient operationally and provides information to support optimal reliability
improvement efforts.
As part of the overall systems improvement capability, Entergy will also implement new work
management and GIS platforms, all integrated with the asset management system. Once implemented
and fully functional, the asset and work management capabilities, coupled with the new ADMS and AMI
systems as they are currently specified and anticipated, will give Entergy an industry leading capability in
operations and asset management.

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7

CONCLUSIONS

Quanta Technology conducted a review of ENO’s distribution reliability program and a comparison of its
distribution reliability practices versus industry leading practices and those of a group of high performing
utility peers57. ENO serves a high customer density and compact service territory that is very vulnerable
to weather events58 with a distribution system that consists of mostly overhead legacy design and
construction, and a customer base with one of the highest poverty rates in US metro areas59.
ENO’s distribution reliability performance has declined in the last five years, and its 2017 key distribution
reliability indices (SAIFI and SAIDI) were close to the borderline between 3rd and 4th quartile of the 2017
IEEE Annual Distribution Reliability Benchmark. ENO has increased its reliability spending in the last three
years and has planned further investments in key infrastructure, technologies and systems to stop and
reverse this trend and improve reliability performance. These activities are starting to yield positive results
and have helped ENO improve its reliability performance during 2018. Year-to-date key indices and overall
distribution (DLIN) outage data show a reduction in the number of customer interruptions and customer
minutes of interruption. Transmission outages in 2018 have, however, increased and mask the DLIN
improvements when looking at the customer view reliability indices.
The results of Quanta Technology’s assessment indicate that ENO’s distribution reliability program
includes adequate components to continue addressing these pressing needs. If investments in distribution
reliability continue as planned it would be expected that ENO’s distribution reliability indices improve to
2nd quartile performance over the next few years. These improvements would not be immediate, since
some investments are needed to prevent further decline of reliability indices (slow down the trend) and
others are intended to improve performance (reverse the trend), and most importantly, because of the
legacy construction and design of ENO’s distribution grid coupled with aging infrastructure. The current
practices will affect incremental improvement in system reliability. Implementation of distribution
automation technology coupled with the ongoing conventional reliability efforts offers more immediate
improvement.
There are important opportunities to improve the program that are described in greater detail in the
recommendations section. The following are examples and highlights from that section:
1. Metrics: along with more detailed recommendations, it is recommended that ENO consider additional
reliability indices (SAIDI, CEMIn and MAIFIE), along with SAIFI, to identify and prioritize the
implementation of distribution reliability improvement projects, and that eventually adopts these
indices to evaluate and keep track of performance. A recommended practice is to analyze the values
of reliability metrics over multiyear periods (e.g., 3 to 5 years), rather than over consecutive years.
This approach allows to capture the expected mid/long-term improvement trends (instead of focusing
on potential consecutive year variations) and to a certain extent account for the effect of

57

Given the uniqueness of ENO’s distribution system a one-to-one comparison is not possible, utilities peers in this comparison are sufficiently
similar to ENO on various important features of its service territory and distribution system, but not all.
58 New Orleans is the wettest city in the US https://www.iweathernet.com/educational/u-s-cities-with-the-most-extreme-weather and has a tree
cover of over 30% (D.J. Nowak, E.J Greenfield, Tree and Impervious Cover Change in U.S. Cities, Urban Forestry & Urban Greening, Volume 11,
Issue 1, 2012, Pages 21-30, https://www.nrs.fs.fed.us/pubs/40114)
59
http://www.labudget.org/lbp/2018/09/new-orleans-poverty-rate-tops-among-u-s-metros/

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randonmness. This is the tactic that ENO is recommended to use to monitor and evaluate the
effectiveness of its reliability program.
2. Processes: it is recommended that ENO enhance the data analytics process associated with counting
outage events, this will provide ENO with more accurate failure rate information that can be used in
cost-benefit and prioritization analyses. Moreover, there is an opportunity to implement an internal
audit program to verify and ensure compliance of distribution reliability practices.
3. Infrastructure: it is recommended that ENO consider the further deployment of distribution
automation schemes (e.g., FLISR) to reduce customer counts within each switching/protection zone.
4. Inspections: it is recommended that ENO implement an inspection program that covers 100% of the
distribution system and conduct an overall evaluation of the existing vegetation program to review
trim cycles, clearance requirements, etc.
In general, there is a valuable opportunity to accelerate performance improvement by expediting the
implementation of the components of the reliability program discussed in this report, along with the grid
modernization AMI and ADMS programs proposed by ENO. As these investments are subject to regulatory
requirements, expediting the programs may not be feasible but it should be acknowledged that
implementation of these technologies are a key element to long-term reliability improvement.
Coordination of the reliability improvement initiative with grid modernization components needs to occur
at the project management and implementation levels. The proposed reliability improvement programs
are intended to address foundational infrastructure needs. These investments, combined with
deployment of intelligent infrastructure and processes enhancements that are part of ENO’s grid
modernization, AMI and ADMS programs, are expected to improve ENO’s distribution reliability
performance. It is worth noting that there is a synergistic relationship between these different types of
investments and practices. For instance, foundational infrastructure (e.g., condition-based replacement
of assets, such as transformers and distribution lines) enables the deployment of intelligent assets (e.g.,
distribution automation and control)60, and the latter provides greater visibility and data that allows to
accurately and efficiently monitor asset performance to evaluate condition via data/grid analytics
solutions. Therefore, both types of investments need to be considered in a comprehensive plan to
improve and maintain distribution reliability.

60

An industry leading example is ComEd’s EIMA program, which considers deployment of both foundational and intelligent infrastructure ($1.3B
in each one) https://www.eesi.org/files/Anil-Dhawan-061814-original.pdf and has delivered significant distribution reliability improvements over
the 2012-2017 period

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APPENDIX A: PROJECT TEAM

Vic Romero
Vic Romero, Executive Advisor, Distribution and Asset Operations, has over 40
years of utility experience with a broad range of knowledge including
operations, engineering, and grid modernization. He has led efforts to initiate
and implement grid modernization projects, enhance reliability, and improve
business processes. Examples include leading the implementation of ADMS,
Borrego Springs Microgrid, reliability strategy, and storm processes.
Executive Advisor
Business Strategy &
Services Management

Areas of Expertise
 Cross functional knowledge of utility processes
 Benefits of Grid Modernization
 Distribution Reliability Analytics
Experience & Background








Years of experience in the electric power industry ............................................................1977–Present
Executive Advisor- Quanta Technology LLC .......................................................................2017–Present
Director, Technology Solutions & Reliability, San Diego Gas & Electric (SDG&E) ................. 2014–2016
Director, Asset Management & Smart Grid Projects, SDG&E ............................................... 2011–2014
Director, Transmission & Substation Construction & Maintenance, SDG&E ........................ 2005–2011
Manager of Operating District, SDG&E ................................................................................. 2002–2005
Electric Distribution Standards Manager, SDG&E ................................................................. 1999–2002

Accomplishments & Industry Recognition
 Registered Professional Engineer, State of California
 A- General Engineering Contractor holder for SDG&E 2009-2016
 2016 Recipient of SDG&E and PA Consulting's "Reliability One Outstanding Contribution to Reliability
Award"
Education
 BS, Electrical Engineering, California Polytechnic State University, 1977
Vic can be contacted at VRomero@Quanta-Technology.com

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Julio Romero Agüero, PhD, MBA
Julio Romero Agüero, PhD, MBA, EXECUTIVE ADVISOR, Vice President, Strategy
and Business Innovation, has been with Quanta Technology since 2007. Julio
provides leadership to Quanta Technology in the areas of technology and
business strategy, grid modernization, distribution systems analysis, planning
and engineering, distributed energy resources, and emerging technologies. He
has assisted electric utilities and regulatory boards in the U.S., Canada, Latin
America, the Caribbean, and Asia.
Vice President
Strategy &
Business Innovation

Areas of Expertise





Grid modernization
Distributed energy resources integration and Smart Grid
Distribution system analysis, planning and engineering
Emerging technologies and technology strategy

Experience & Background










Years of experience in the electric power industry ............................................................1995–Present
Vice President, Strategy and Business Innovation, Quanta Technology ...........................2007–Present
Commissioner, National Energy Commission of Honduras .................................................... 2006–2007
Consultant, National Energy Commission of Honduras ......................................................... 2005–2006
Adjunct Professor, National University of Honduras ............................................................. 2005–2007
PhD Researcher, National University of San Juan - Argentina ............................................... 2000–2005
Operations Manager, National Electric Utility of Honduras................................................... 1999–2000
Advisor, National Electric Utility of Honduras ........................................................................ 1998–1999
Regional Manager, National Electric Utility of Honduras....................................................... 1996–1998

Accomplishments & Industry Recognition







IEEE Senior Member
Chair of IEEE Distribution Subcommittee
Member of Advisory Committee of DistribuTECH
Past Chair of IEEE Working Group on Distributed Resources Integration
Former Editor of IEEE Transactions on Power Delivery
Former Editor of IEEE Transactions on Smart Grid

Education
 PhD, Electrical Engineering, National University of San Juan, Argentina (UNSJ), 2005
 MBA, North Carolina State University (NCSU), 2013
 BSc, Electrical Engineering, National Autonomous University of Honduras (UNAH), 1996
Julio can be contacted at Julio@Quanta-Technology.com

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Bill Snyder
Bill Snyder, EXECUTIVE ADVISOR, Senior Vice President, Distribution and Asset
Operations, has a broad background in utility operations, management, and
change initiatives resulting from over 39 years of experience in the electric
utility industry. He has successfully led consulting engagements to review and
evaluate operational processes and standards, conducted evaluations of asset
condition and value, and led major process change identification and
implementation programs in the engineering and operations functions. At
Quanta Technology, Bill’s consulting engagements range from day-to-day
system operations management, including emergency restoration planning
and management, to strategic planning and implementation of major process
and systems initiatives to improve workforce efficiency and service delivery.

Sr. Vice President
Distribution and Asset
Operations

Areas of Expertise





Field operations planning and management
Storm plan development and restoration management
Asset condition assessment and management
Major business initiative planning, implementation and change management

Experience & Background








Years of experience in the electric power industry ............................................................1979–Present
Quanta Technology, electric power industry consultant ...................................................2007–Present
Sr. Principal Consultant, KEMA T&D Consulting ..................................................................... 2004–2007
Manager, Service Marketing, Eaton Electrical ....................................................................... 2003–2004
Director, Wind Power Services, ABB Power T&D ................................................................... 2001–2003
Consultant, Distribution Solutions, ABB Power T&D .............................................................. 2000–2001
Director/Manager/Engineer (various positions), Progress Energy ........................................ 1979–2000

Accomplishments & Industry Recognition
 Member – IEEE, Power & Energy Society and Industrial Applications Society
Education
 MBA, Babcock Graduate School of Business, Wake Forest University
 BS, Engineering Operations, NC State University

Bill can be contacted at BSnyder@Quanta-Technology.com

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Carl L. Wilkins, PE
Carl L Wilkins, PE, Vice President, Distribution & Asset Operations, has
extensive experience in the electric utility industry serving in a variety of
managerial, research, and consultative roles working with electric & gas utility
companies. Combining experience in engineering, sales, marketing, and
project management with applied technical skills, he brings a wide array of
talents to the Asset Operations team. Carl managed several projects related to
T&D storm hardening assessments and emergency preparedness plans for
large electric utilities. Carl has led benchmarking studies on transmission
maintenance and construction practices. His team performs grid integration
studies for wind and solar PV projects and distribution planning studies.

Vice President
Distribution & Asset
Operations

Carl served as the Director of Utility Services at Advanced Energy where he managed senior
management relationships and the delivery of energy efficiency and renewable energy services to
electric utilities. He has worked with utilities in areas of smart grid, plug-in hybrid electric vehicles,
policies to address climate change and environmental sustainability.
Carl was the chief architect in the design, coordination, and development of North Carolina's green
power program, NC GreenPower, the first state-wide green power program where all electric utilities
in the state agreed to collaborate and use one marketing campaign.
Areas of Expertise
 Distributed Generation and Distributed Energy Resources
 Distribution Grid Impact Studies
 Asset Operations
Experience & Background





Years of experience in the electric power industry ............................................................1976–Present
Vice President, Distribution & Asset Operations, Quanta Technology ..............................2008–Present
Director-Utility Services, Advanced Energy Corporation ....................................................... 2000–2008
Manager - Large Commercial Accounts, Duke Energy Progress ............................................ 1976–1999

Accomplishments & Industry Recognition





North Carolina Energy Policy Council (2013)
NC Governor's Energy Advisory Team (2013)
NC Governor's Scientific Advisory Panel on Offshore Energy & Energy Infrastructure (2011)
Bureau of Ocean Energy Management, Regulation and Enforcement (BOEMRE) NC Task Force on
Offshore Energy

Education
 BS, Electrical Engineering, NC State University
 Executive Education, Harvard Business School
Carl can be contacted at CWilkins@Quanta-Technology.com

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APPENDIX B: RELIABILITY INDICES
Distribution Reliability Indices
The following are the definition of the most commonly used distribution reliability indices, as per IEEE
Std. 1366-2012
System Average Interruption Frequency Index (SAIFI)
𝑆𝐴𝐼𝐹𝐼 =

𝑇𝑜𝑡𝑎𝑙 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝐶𝑢𝑠𝑡𝑜𝑚𝑒𝑟 𝐼𝑛𝑡𝑒𝑟𝑟𝑢𝑝𝑡𝑖𝑜𝑛𝑠
𝑇𝑜𝑡𝑎𝑙 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝐶𝑢𝑠𝑡𝑜𝑚𝑒𝑟𝑠 𝑆𝑒𝑟𝑣𝑒𝑑

System Average Interruption Duration Index (SAIDI)
𝑆𝐴𝐼𝐷𝐼 =

𝐶𝑢𝑠𝑡𝑜𝑚𝑒𝑟 𝐼𝑛𝑡𝑒𝑟𝑟𝑢𝑝𝑡𝑖𝑜𝑛 𝐷𝑢𝑟𝑎𝑡𝑖𝑜𝑛𝑠
𝑇𝑜𝑡𝑎𝑙 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝐶𝑢𝑠𝑡𝑜𝑚𝑒𝑟𝑠 𝑆𝑒𝑟𝑣𝑒𝑑

Customer Average Interruption Duration Index (CAIDI)
𝐶𝐴𝐼𝐷𝐼 =

𝐶𝑢𝑠𝑡𝑜𝑚𝑒𝑟 𝐼𝑛𝑡𝑒𝑟𝑟𝑢𝑝𝑡𝑖𝑜𝑛 𝐷𝑢𝑟𝑎𝑡𝑖𝑜𝑛𝑠
𝑆𝐴𝐼𝐷𝐼
=
𝑇𝑜𝑡𝑎𝑙 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝐶𝑢𝑠𝑡𝑜𝑚𝑒𝑟𝑠 𝐼𝑛𝑡𝑒𝑟𝑟𝑢𝑝𝑡𝑖𝑜𝑛𝑠 𝑆𝐴𝐼𝐹𝐼

Average System Availability Index
𝐴𝑆𝐴𝐼 =

𝐶𝑢𝑠𝑡𝑜𝑚𝑒𝑟 𝐻𝑜𝑢𝑟𝑠 𝑆𝑒𝑟𝑣𝑖𝑐𝑒 𝐴𝑣𝑎𝑖𝑙𝑎𝑏𝑖𝑙𝑖𝑡𝑦 8760 − 𝑆𝐴𝐼𝐷𝐼
=
𝐶𝑢𝑠𝑡𝑜𝑚𝑒𝑟 𝐻𝑜𝑢𝑟𝑠 𝑆𝑒𝑟𝑣𝑖𝑐𝑒 𝐷𝑒𝑚𝑎𝑛𝑑
8760

Customers Experiencing Multiple Interruptions
𝐶𝐸𝑀𝐼𝑛 =

𝐶𝑢𝑠𝑡𝑜𝑚𝑒𝑟𝑠 𝐸𝑥𝑝𝑒𝑟𝑖𝑒𝑛𝑐𝑖𝑛𝑔 𝑀𝑜𝑟𝑒 𝑡ℎ𝑎𝑛 𝑛 𝑇𝑜𝑡𝑎𝑙 𝐼𝑛𝑡𝑒𝑟𝑟𝑢𝑝𝑡𝑖𝑜𝑛𝑠
𝑇𝑜𝑡𝑎𝑙 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝐶𝑢𝑠𝑡𝑜𝑚𝑒𝑟𝑠 𝑆𝑒𝑟𝑣𝑒𝑑

Momentary Average Interruption Frequency Index
𝑀𝐴𝐼𝐹𝐼 =

𝑇𝑜𝑡𝑎𝑙 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝐶𝑢𝑠𝑡𝑜𝑚𝑒𝑟 𝑀𝑜𝑚𝑒𝑛𝑡𝑎𝑟𝑦 𝐼𝑛𝑡𝑒𝑟𝑟𝑢𝑝𝑡𝑖𝑜𝑛𝑠
𝑇𝑜𝑡𝑎𝑙 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝐶𝑢𝑠𝑡𝑜𝑚𝑒𝑟𝑠 𝑆𝑒𝑟𝑣𝑒𝑑

Momentary Average Interruption Event Frequency Index
𝑀𝐴𝐼𝐹𝐼𝐸 =

𝑇𝑜𝑡𝑎𝑙 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝐶𝑢𝑠𝑡𝑜𝑚𝑒𝑟 𝑀𝑜𝑚𝑒𝑛𝑡𝑎𝑟𝑦 𝐼𝑛𝑡𝑒𝑟𝑟𝑢𝑝𝑡𝑖𝑜𝑛 𝐸𝑣𝑒𝑛𝑡𝑠
𝑇𝑜𝑡𝑎𝑙 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝐶𝑢𝑠𝑡𝑜𝑚𝑒𝑟𝑠 𝑆𝑒𝑟𝑣𝑒𝑑

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APPENDIX C: SIMILARITY METRIC
𝑔

𝑆𝑖 = 𝑤 𝑑 𝑆𝑖𝑑 + 𝑤 𝑐 𝑆𝑖𝑐 + 𝑤 𝑟 𝑆𝑖𝑟 + 𝑤 𝑔 𝑆𝑖 + 𝑤 𝑤 𝑆𝑖𝑤
𝑆𝑖 = 𝑆𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦 𝑚𝑒𝑡𝑟𝑖𝑐 𝑏𝑒𝑡𝑤𝑒𝑒𝑛 𝑈𝑡𝑖𝑙𝑖𝑡𝑦 𝑖 𝑎𝑛𝑑 𝐸𝑁𝑂
𝑆𝑖𝑑 = 𝐶𝑢𝑠𝑡𝑜𝑚𝑒𝑟 𝑑𝑒𝑛𝑠𝑖𝑡𝑦 𝑠𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦
𝑆𝑖𝑐 = 𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦 𝑝𝑟𝑖𝑐𝑒 𝑠𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦
𝑆𝑖𝑟 = 𝑅𝑒𝑙𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝑠𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦
𝑔

𝑆𝑖 = 𝐺𝑟𝑖𝑑 𝑠𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦
𝑆𝑖𝑤 = 𝑊𝑒𝑎𝑡ℎ𝑒𝑟 𝑠𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦

𝑤𝑑 + 𝑤𝑐 + 𝑤𝑟 + 𝑤𝑔 + 𝑤𝑤 = 1
𝑤 𝑑 = 𝐺𝑟𝑖𝑑 𝑠𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦 𝑤𝑒𝑖𝑔ℎ𝑡
𝑤 𝑑 = 𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦 𝑝𝑟𝑖𝑐𝑒 𝑤𝑒𝑖𝑔ℎ𝑡
𝑤 𝑟 = 𝑅𝑒𝑙𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝑤𝑒𝑖𝑔ℎ𝑡
𝑤 𝑔 = 𝐺𝑟𝑖𝑑 𝑤𝑒𝑖𝑔ℎ𝑡
𝑤 𝑤 = 𝑊𝑒𝑎𝑡ℎ𝑒𝑟 𝑤𝑒𝑖𝑔ℎ𝑡

𝑆𝑖𝑑 = 𝑎𝑏𝑠 1 −

𝑑𝑖
𝑑𝐸𝑁𝑂

𝑑𝑖 = 𝐶𝑢𝑠𝑡𝑜𝑚𝑒𝑟 𝑑𝑒𝑛𝑠𝑖𝑡𝑦 𝑜𝑓 𝑢𝑡𝑖𝑙𝑖𝑡𝑦 𝑖
𝑑𝐸𝑁𝑂 = 𝐶𝑢𝑠𝑡𝑜𝑚𝑒𝑟 𝑑𝑒𝑛𝑠𝑖𝑡𝑦 𝑜𝑓 𝐸𝑁𝑂

𝑆𝑖𝑐 = 𝑎𝑏𝑠 1 −

𝑐𝑖
𝑐𝐸𝑁𝑂

𝑐𝑖 = 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑒𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦 𝑝𝑟𝑖𝑐𝑒 $ 𝑝𝑒𝑟 𝑘𝑊ℎ 𝑜𝑓 𝑢𝑡𝑖𝑙𝑖𝑡𝑦 𝑖
𝑐𝐸𝑁𝑂 = 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑒𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦 𝑝𝑟𝑖𝑐𝑒 $ 𝑝𝑒𝑟 𝑘𝑊ℎ 𝑜𝑓 𝐸𝑁𝑂

𝑆𝑖𝑟 = 1

1−

𝑆𝐴𝐼𝐹𝐼𝑖
𝑆𝐴𝐼𝐹𝐼𝐸𝑁𝑂

2

+ 1−

𝑆𝐴𝐼𝐷𝐼𝑖
𝑆𝐴𝐼𝐷𝐼𝐸𝑁𝑂

2

𝑆𝐴𝐼𝐹𝐼𝑖 = 𝑆𝐴𝐼𝐹𝐼 𝑜𝑓 𝑢𝑡𝑖𝑙𝑖𝑡𝑦 𝑖
𝑆𝐴𝐼𝐹𝐼𝐸𝑁𝑂 = 𝑆𝐴𝐼𝐹𝐼 𝑜𝑓 𝐸𝑁𝑂
𝑆𝐴𝐼𝐷𝐼𝑖 = 𝑆𝐴𝐼𝐷𝐼 𝑜𝑓 𝑢𝑡𝑖𝑙𝑖𝑡𝑦 𝑖
𝑆𝐴𝐼𝐷𝐼𝐸𝑁𝑂 = 𝑆𝐴𝐼𝐷𝐼 𝑜𝑓 𝐸𝑁𝑂
𝑔

𝑆𝑖 =

1−

𝑂𝐻𝑖
𝑂𝐻𝐸𝑁𝑂

2

+ 1−

𝑈𝐺𝑖
𝑈𝐺𝐸𝑁𝑂

2

𝑂𝐻𝑖 + 𝑈𝐺𝑖 = 1
𝑂𝐻𝐸𝑁𝑂 + 𝑈𝐺𝐸𝑁𝑂 = 1
𝑂𝐻𝑖 = 𝑃𝑒𝑟𝑐𝑒𝑛𝑡𝑎𝑔𝑒 𝑜𝑓 𝑜𝑣𝑒𝑟ℎ𝑒𝑎𝑑 𝑔𝑟𝑖𝑑 𝑜𝑓 𝑢𝑡𝑖𝑙𝑖𝑡𝑦 𝑖

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𝑆𝑖𝑤

=

1−

𝐿𝑖
𝐿𝐸𝑁𝑂

2

+ 1−

𝑃𝑖
𝑃𝐸𝑁𝑂

2

+ 1−

𝑇𝑖𝑙

𝑙
𝑇𝐸𝑁𝑂

2

+ 1−

𝑇𝑖ℎ

ℎ
𝑇𝐸𝑁𝑂

2

𝐻𝑖
+ 1−
𝐻𝐸𝑁𝑂

2

𝐿𝑖 = 𝐿𝑖𝑔ℎ𝑡𝑛𝑖𝑛𝑔 𝑓𝑙𝑎𝑠ℎ 𝑑𝑒𝑛𝑠𝑖𝑡𝑦 𝑜𝑓 𝑢𝑡𝑖𝑙𝑖𝑡𝑦 𝑖
𝐿𝐸𝑁𝑂 = 𝐿𝑖𝑔ℎ𝑡𝑛𝑖𝑛𝑔 𝑓𝑙𝑎𝑠ℎ 𝑑𝑒𝑛𝑠𝑖𝑡𝑦 𝑜𝑓 𝐸𝑁𝑂
𝑃𝑖 = 𝑃𝑟𝑒𝑐𝑖𝑝𝑖𝑡𝑎𝑡𝑖𝑜𝑛 𝑜𝑓 𝑢𝑡𝑖𝑙𝑖𝑡𝑦 𝑖
𝑃𝐸𝑁𝑂 = 𝑃𝑟𝑒𝑐𝑖𝑝𝑖𝑡𝑎𝑡𝑖𝑜𝑛 𝑜𝑓 𝐸𝑁𝑂
𝑇𝑖𝑙 = 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑎𝑛𝑛𝑢𝑎𝑙 𝑡𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒 𝑙𝑜𝑤 𝑜𝑓 𝑢𝑡𝑖𝑙𝑖𝑡𝑦 𝑖
𝑙
𝑇𝐸𝑁𝑂
= 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑎𝑛𝑛𝑢𝑎𝑙 𝑡𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒 𝑙𝑜𝑤 𝑜𝑓 𝐸𝑁𝑂

𝑇𝑖ℎ = 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑎𝑛𝑛𝑢𝑎𝑙 𝑡𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒 ℎ𝑖𝑔ℎ 𝑜𝑓 𝑢𝑡𝑖𝑙𝑖𝑡𝑦 𝑖
ℎ
𝑇𝐸𝑁𝑂
= 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑎𝑛𝑛𝑢𝑎𝑙 𝑡𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒 ℎ𝑖𝑔ℎ 𝑜𝑓 𝐸𝑁𝑂

𝐻𝑖 = 𝑅𝑒𝑙𝑎𝑡𝑖𝑣𝑒 ℎ𝑢𝑚𝑖𝑑𝑖𝑡𝑦 𝑜𝑓 𝑢𝑡𝑖𝑙𝑖𝑡𝑦 𝑖
𝐻𝐸𝑁𝑂 = 𝑅𝑒𝑙𝑎𝑡𝑖𝑣𝑒 ℎ𝑢𝑚𝑖𝑑𝑖𝑡𝑦 𝑜𝑓 𝐸𝑁𝑂

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APPENDIX D: INDUSTRY SURVEY
Following is the questionnaire that was developed and used as an industry survey. These questions were
put into an online survey tool and distributed to companies that agreed to participate in the survey. A
sample page from the online survey tool is also shown.

Distribution Reliability Practices Questionnaire
A. Reliability Metrics and Targets
1. What metrics are used by your company (e.g., SAIDI, SAIFI, CAIDI, CEMI, other, etc.) to track and
assess distribution reliability performance?
2. What definition is used by your company to calculate distribution reliability indices (e.g., IEEE
1366, internal, defined by regulator, other)?
3. What type of service interruptions are included in the calculation of your company’s distribution
reliability metrics (unscheduled, planned, generation, transmission, substations,
primary/medium-voltage distribution, secondary/low-voltage distribution, etc.)?
4. Does your company keep track of momentary interruptions? If so, what index is used (MAIFI,
MAIFIE, other)?
5. What is the threshold for momentary and sustained interruptions (e.g., 1 min., 3 min., 5 min.,
etc.)?
6. What major event exclusion methodology does your company use (e.g., IEEE 2.5 Beta method,
internal, defined by regulator, other)?
7. If applicable, which distribution reliability metrics are only used for internal control by the
company, and which metrics are requested by the regulator for official reporting purposes?
8. Does the regulator request your company to comply with a maximum limit for specific reliability
indices (e.g., SAIDI and/or SAIFI lower than a specific predefined value)?
9. If applicable, are there any financial (or other) penalties for noncompliance with maximum
distribution reliability limits?
10. Does your company have short and long term reliability improvement targets (e.g., reduce SAIDI
and/or SAIFI to a specific value by a specific date, etc.)?
11. If applicable, how are these targets calculated (e.g., defined by the company, set by the regulator,
other)?
B. Grid Analytics
12. How is interruption data collected (e.g., automatically via ADMS, OMS, CIS and AMI, manually,
semi-automatically, etc.)
13. What type of interruption data is collected (status of individual protection/switching devices,
individual customers affected by each interruption, etc.)?
14. How accurate is the collected interruption data used for reliability analysis?

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15.
16.
17.
18.
19.

How accurate are the associated information systems used in reliability analysis (GIS, CIS, etc.)?
How many interruption root causes are used by your company?
Are root causes divided by sub-categories? If so, how many?
What are the most common root-causes in your service territory?
Are grid analytics used to calculate failure rates by root-cause and component, worst performing
areas within feeders, etc.?
20. Are predictive grid analytics used by your company for reliability analysis? If so, how?
21. How many primary distribution forced outages per mile per year do you have for the overhead
and underground system?
C. Reliability Improvement
22. What is your company’s approach to reliability improvement/maintenance (e.g., proactive as part
of an annual process, reactive in response to regulator request, etc.)?
23. Does your company identify worst performing feeders? If so, what criteria and approach is used
to select these feeders?
24. Is distribution reliability assessment part of your annual planning process, or is it a separate
process?
25. How are reliability improvement projects selected (e.g., worst performing feeders, proposed by
distribution planners, etc.)?
26. How are reliability improvement projects evaluated and prioritized by your company (e.g., using
benefit-cost ratio, etc.)?
27. Does your company use a benefit-cost metric to evaluate reliability improvement projects? If so,
what metric is used ($/CMI reduction, $/CI reduction, other)?
28. Does your company use a reliability model to conduct benefit-cost analyses?
29. What type of benefits are considered in the estimation to justify improvement projects (benefits
for utility, benefits for customers, societal benefits, all of them, etc.)?
30. How are benefits estimated (e.g., using computational models, using planning guidelines, etc.)?
D. Reliability Programs
31. Do you have an official multi-year reliability program to improve SAIDI & SAIFI to a specific value?
If yes, what is the target percentage improvement and when is it expected to be achieved?
32. What type of initiatives for distribution reliability improvement have been implemented by your
company in the last 5 years (e.g., deployment of microprocessor-based reclosers, deployment of
additional switchgear (e.g., disconnect switches, etc.), fusing of laterals, deployment of
distribution automation schemes, circuit reconfiguration, component (pole, crossarm,
underground cable) inspection and replacement, vegetation management, weather hardening,
aging infrastructure replacement, etc.)?
33. What is the scope of these initiatives/program (targeted reliability improvement of selected
feeders, targeted reliability improvement of selected areas of the system, system-wide
improvement, maintaining – rather than improving – system reliability, etc.)?

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E. Program Implementation
34. What organization(s) is responsible for distribution reliability data collection and analysis,
including project selection, justification and prioritization (e.g., dedicated reliability organization,
distribution operations, distribution engineering, distribution planning, etc.)?
35. If more than one organization is responsible for these activities, where do the responsibilities of
each organization begin and end?
36. What organization(s) is responsible for the implementation of reliability improvement projects
and programs (e.g., distribution planning, distribution engineering, etc.)?
37. Is there a follow up process to assess the effectiveness of reliability improvement projects and
programs? If so, what metrics are evaluated?
38. What type of reports are prepared to assess project/program effectiveness?
F. Grid Modernization
39.
40.
41.
42.

Has your company implemented a grid modernization program in the last 5 years?
What are the components of the grid modernization program?
What are the objectives of the grid modernization program?
Is distribution reliability improvement a specific objective of the grid modernization program? If
so, what is the expected improvement from the program?
43. Is grid modernization part of an overall reliability program?
G. Aging infrastructure
44. Has your company implemented an aging infrastructure program in the last 5 years?
45. What are the components of the aging infrastructure program (e.g., underground cable
replacement, pole replacement, etc.)?
46. How are asset replacements selected and prioritized?
47. Is distribution reliability improvement a specific objective of the program? If so, what is the
expected improvement from the program?
48. Is the aging infrastructure program part of an overall reliability program?
H. Vegetation Management
49. What is your company’s vegetation management cycle (3 yrs., 5 yrs., other)?
50. What is the scope of the vegetation management program (feeder trunks only, trunks and
laterals, primary only, primary and secondary, hazard vegetation only, hazard and non-hazard
vegetation, etc.)?
51. What are the regulatory vegetation clearance requirements for the primary distribution system?
Does your company have regulatory authorization to trim to that clearance?
52. Is distribution reliability improvement a specific objective of the program? If so, what is the
expected improvement from the program?

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53. Is vegetation management part of an overall reliability program?
I.

Weather Hardening
54. Has your company implemented a weather hardening program in the last 5 years?
55. What are the components of the weather hardening program (e.g., overhead to underground
conversion, reinforcement of critical overhead structures, deployment of tree wire or aerial
bundled cable (Hendrix), etc.)?
56. How are the hardening areas selected and prioritized?
57. Is distribution reliability improvement a specific objective of the program? If so, what is the
expected improvement from the program?
58. Is weather hardening part of an overall reliability program?

J.

Asset Management
55. What is your asset management strategy for distribution assets (e.g., run-to-failure, preventive,
condition-based, other, etc.)?
56. Has your company implemented a new program in the last 5 years as part of your asset
management strategy?
57. What are the components of the program (e.g., real-time monitoring of critical equipment,
deployment of data analytics solution, deployment of asset management information system,
etc.)?
58. Is distribution reliability improvement a specific objective of the asset management program? If
so, what is the expected improvement from the program?
59. Is asset management part of an overall reliability program?
60. Does your company calculate and keep track of equipment historical failure rates of distribution
assets?
61. Does your company use predictive methods to develop expected failure rates of distribution
assets?
62. Do you have an inspection and maintenance program for the entire distribution system (not solely
specific equipment)? If yes, what are the inspection cycle requirements?
63. Is the inspection and maintenance program required by regulations?

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TECHNOLOGY

 

ENTERGY NEW ORLEANS RELIABILITY ASSESSMENT

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APPENDIX E: GRID MODERNIZATION TECHNOLOGIES
There are a number of technologies and concepts that are key elements of grid modernization, examples
include:


Smart reclosers and switches
Recloser technology has progressed significantly in the last two decades with the introduction of
advanced microprocessor-based controllers with capabilities comparable to those of modern relays.
This includes the ability to:
o Store and use multiple protection settings, which allows ensuring coordination for different
system configurations and loading conditions, and increasing the number of reclosers that can
be installed and coordinated along a distribution circuit. The latter effectively reduces the
number of customers affected by faults and improves reliability.
o Reclose and open individual phases (single-phase tripping) of distribution lines to minimize the
number of customers affected by faults and service interruptions. Figure 36 shows a comparison
of the reliability improvements achieved by single-phase tripping versus conventional threephase tripping for a set of 20 circuits for two different types of overcurrent protection
philosophies, the results show that single-phase tripping attains greater reliability benefits61.
o Monitor, record and provide valuable operational data to distribution operations centers that
can be processed by modern ADMS to increase visibility over the distribution grid.
o Reduce the amount of energy associated with its reclosing operations and reduce the impact on
valuable utility assets such as substation transformers and underground cable62.
o Operate in sophisticated automation schemes (such as Fault Location, Isolation and Service
Restoration (FLISR)) in coordination with other advanced devices (relays, switches, breakers, and
sensors). Utilities that have successfully deployed FLISR have multiple sectionalizing points,
between three to four locations per circuit to minimize the impacts of outages to customers,
and multiple ties to neighbor circuits.
 Figure 37 shows an example of this type of configuration being used by Commonwealth
Edison (ComEd) of Chicago. ComEd has installed hundreds of distribution automation
devices since 201263,64,65.
 Another example of an advanced distribution automation loop scheme that uses
multiple reclosers and high speed peer-to-peer communications is shown in Figure 38,
this scheme was implemented by Public Service Electric & Gas (PSE&G) Company and
allows further minimization of reliability impacts from distribution faults. This

61

J. Romero Aguero, J. Wang, J. Burke, Improving the reliability of power distribution systems through single-phase tripping, 2010 IEEE PES T&D
Conference and Exposition, May 2010 http://ieeexplore.ieee.org/document/5484372/
62 http://www.sandc.com/products/switching-overhead-distribution/intellirupter-pulsecloser/
63 https://www.comed.com/News/Pages/NewsReleases/2016_06_07.aspx
64
ComEd has installed more than 3,000 distribution automation reclosers on the 12-kV system and 1,000 switches on the 34-kV system since the
mid-1990s.
65 According to its 2015 Annual Progress Report, “smart switch” distribution automation investments resulted in 1.5 million avoided customer
interruptions in 2015, bringing the total to 4.8 million avoided customer interruptions since 2012, with an associated $1.1 billion in societal
savings, $976 million of that from distribution automation alonehttps://www.comed.com/SiteCollectionDocuments/AboutUs/Progress-ReportFinal.pdf

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technology allows a significant reduction (between 77% and 97%) of momentary
interruptions with respect to traditional loop scheme applications.
The development of lower cost intelligent reclosers and switches that can isolate faults in smaller
sections are needed to support increased flexibility and improve reliability with both traditional and
distributed grids. In the future, remote reclosers and switches will be more intelligent and adaptive
to distribution system conditions in conjunction with adaptive protection. This is considered a best
practice and is capable of operating in a high DER penetration environment.
Despite the notable progress, more work is still needed by the industry to enable widespread adoption
and utilization of this technology and the aforementioned applications. This requires not only the
further evolution of distribution equipment (reclosers, switches, etc.) but also, and most importantly,
the modernization of distribution operations practices to allow distribution automation technologies
in general to assist system operators in outage management and restoration activities, especially
during multiple contingency conditions such as those typical of major weather events, as well as
during normal operation to increase grid efficiency. This is paramount, given the increasing complexity
of the distribution grid and the multiple objectives and variables that need to be taken into account,
particularly when other goals such as ensuring operation within allowable limits (voltage, power
factors, losses, equipment ratings, etc.) are considered.


Fusing
Traditionally lateral fusing has been an economical way to prevent faults on lateral circuits from
causing larger outages on the circuit. However, this also acts to prolong outages on the lateral. Fuses
are also exposed to backfeed which under high penetration of DER could exceed ratings. Therefore,
it is evident that fuses, which lack adaptability and control capabilities, are not compatible with
modern distribution systems, which are intrinsically complex and dynamic. As penetration levels of
DER increase, the benefits of using more advanced devices (e.g., single-phase reclosers) for lateral
fusing is expected to increase, particularly considering the decreasing prices of these modern
technologies. Given the pervasive use of lateral fusing in existing distribution systems, detailed
benefit-cost evaluations will be required to estimate reliability and operational improvements and
overall feasibility of using advanced devices instead of conventional fuses.

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Percentage of faults that are single-phase
Figure 36 – Reliability benefits (SAIDI reduction) from single-phase tripping

Significant SAIDI reduction is achievable from single-phase tripping versus conventional three-phase
tripping for fuse clearing (above) and fuse saving (below) overcurrent protection philosophies. Results
show that single-phase tripping achieves a greater reduction in SAIDI (i.e., a greater reliability

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improvement) than three-phase tripping. Improvement grows as the percentage of single-phase faults in
the system increases61

Figure 37 – ComEd loop distribution automation schemes using a combination of modern reclosing technologies 66,67

Figure 38 – PSE&G advanced loop scheme using multiple reclosers and high speed peer-to-peer communication68



Distribution Automation

66

J. Gates, ComEd Advances System Reliability, T&D World Magazine, September 2013 http://tdworld.com/distribution/comed-advancessystem-reliability
67
M. Mondello, A. Dhawan, R. Gupta, ComEd Rolls Out Modern Infrastructure, August, 2014, T&D World Magazine
http://tdworld.com/distribution/comed-rolls-out-modern-infrastructure
68 R. Wernsing, J. Hubertus, M. Duffy, G. Hataway, D. Conner, E. Nelson, Advanced Loop Scheme: Improving Reliability Through Better Operational
Methods, IEEE Industry Applications Magazine, Vol. 18, Issue 2, pp. 14 - 22 http://ieeexplore.ieee.org/document/6112292/

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On the Distribution Automation side utilities are actively pursuing the implementation of FLISR and
Volt-VAR Optimization (VVO) applications that allow improving the reliability and efficiency of the
distribution grid. In the specific case of FLISR this implies deploying advanced reclosers and/or
switches that allow to automatically locate and isolate faults and restore service via reconfiguration
and execution of load transfers to neighbor feeders. This requires not only the ability to monitor and
control the grid in real-time to ensure that reconfigurations do not violate equipment and line ratings
and voltage limits, but also adjustment of settings of respective protection and voltage
regulation/control equipment. A conceptual description of the benefits associated with a FLISR
implementation are shown in Figure 39. Accomplishing this objectives requires the utilization of
intelligent control algorithms. Leading practices for implementation of FLISR in urban circuits of large
metropolitan areas aim at dividing distribution circuits in at least four sectionalizing or protection
zones that allow splitting load and customers into manageable circuit components that can be isolated
and/or transferred to neighbor feeders to minimize impacts on system reliability, e.g., approximately
500 customers per sectionalizing/protection zone69. This requires having at least two normally open
ties per distribution circuit that can serve as backup sources during contingencies. It is expected that
in the future such backup sources could include DER operating in islanded mode as part of microgrid
implementations. There are numerous examples of successful implementations of distribution
automation schemes using reclosers and switches. Table 15 shows a summary of the reliability
benefits (measured via SAIFI, SAIDI and MAIFI) achieved by 42 projects (Table 16) implemented via
the US Department of Energy (DOE) Smart Grid Investment Grant (SGIG) that included the deployment
of automated feeder switching on 1,250 distribution feeders70. Here negative values represent
reductions in reliability indices attained by the projects (with respect to base case), i.e., reliability
improvements. Table 16 shows a list of utilities that participated in this program. The results show the
tremendous potential and benefits of distribution automation to improve reliability performance. It
is worth noting that FLISR applications is one of the key components of ENO’s grid modernization
program.

69

K. Tweed, New Switches Cut 80,000 Outages for ComEd, Greentech Media, http://www.greentechmedia.com/articles/read/new-switches-cut80000-outages-for-comed
70 Reliability Improvements from the Application of Distribution Automation Technologies – Initial Results, US Department of Energy, December
2012 https://www.smartgrid.gov/files/Distribution_Reliability_Report_-_Final.pdf

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Figure 39 – Conceptual description of the reliability benefits associated with a FLISR implementation.

The first diagram conceptually shows the amount of time required to locate and isolate faults and restore
service (without and with FLISR). The set of three plots show how FLISR restores service to most customers
supplied by feeder B by automatically transferring loads to neighbor feeders A and C71,72

Table 15 – Summary of Changes in Distribution Reliability
Reliability Indices
SAIFI
SAIDI
MAIFI

Range of Improvement % Change
-13% to -40%
-2% to -43%
-28%

Range of Baselines
0.8 to 1.07
67 to 107
9.0

71

D.M Staszesky, D. Craig, C. Befus, Advanced Feeder Automation is Here, IEEE Power and Energy Magazine, Sep/Oct 2005, pp. 56-63
http://ieeexplore.ieee.org/document/1507027/
72 J. Romero Agüero, Applying Self-Healing Schemes to Modern Power Distribution Systems, 2012 IEEE Power and Energy Society General Meeting
http://ieeexplore.ieee.org.prox.lib.ncsu.edu/document/6344960/

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Table 16 – Utilities Deploying Distribution Reliability Devices and Systems as Part of SGIG



Relays and enhanced system protection
Digital relays are a proven technology today and are already widely deployed (although many utilities
still have a large number of electromechanical relays). Relay settings are typically adjusted only by the
utility protection department and are rarely modified remotely. Protection settings which are
adaptive to system conditions, e.g. DER production or various system configurations (including
microgrids) are rarely used in the industry. DERs with inverter technology create various operating
scenarios which are not presently addressed by existing protection schemes. Circuit power flows and
fault current levels will change based on DER size, output, and location on the circuit. Protection
systems should automatically adapt to DER production and circuit conditions. New substation
automation applications and new methodologies in protection engineering are needed to address
these challenges. It is technically possible to set relays remotely or even to program adaptive settings

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from the ADMS. In the future, the capabilities of digital relays to support adaptive protection settings
(which may be determined at the substation or system level via new applications) will be needed to
support protection under high penetration levels of DER and resolve issues such as insufficient fault
current, island operation, etc. Additionally, these types of protection schemes can also help with
existing safety concerns around increasing the sensitivity to hazardous fault conditions and protection
coordination during temporary configurations (e.g., during outage management and service
restoration activities). Protection applications such as use of negative sequence values, Direct Transfer
Trip (DTT) that depend on sensors and measurements on the circuit instead of at the circuit breaker,
as well as use of Synchronized Phasor Measurements, are expected to become more common.


Advanced sensors
Advanced sensors provide cost-effective monitoring of key electric variables, including bi-directional
power flows, voltages, currents, equipment and DER status, etc., as well as fault information to circuit
breakers and other protection devices. Distribution systems are increasingly becoming more complex
and difficult to predict, due to the growing variability of DER outputs and loads, and the deployment
of distribution automation schemes with the ability to automatically reconfigure the grid to minimize
the impact of faults on service reliability. Therefore, there is an increasing need for advanced sensors
with higher resolution and time-synchronization capabilities to accurately capture distribution system
dynamics. The data provided by these devices will also help detect fault currents at a remote location
or high impedance conditions not sufficient to trip normal protection technologies. Implementation
of synchrophasor technology in distribution systems and applications based on such may be desirable
(as a means to address operational and power quality issues derived from DER variability, for
example)73. As the cost of these technologies drops over time, and the bandwidth of communications
systems used to monitor and control distribution grids increases, synchrophasor technology are
expected to become a useful tool at the distribution level, particularly in non-radial distribution
configurations.



Advanced distribution reliability planning
Modern distribution reliability planning plays a vital role in accomplishing quality of service objectives.
For instance, Figure 40 and Figure 41 show an example of the estimated reliability improvement due
to the synergistic implementation of a portfolio of projects that include conventional solutions, such
as weather hardening, and advanced technologies, including distribution automation and reclosing
equipment. This level of reliability improvement cannot be attained by each technology when
deployed individually, instead, their combined implementation allows taking advantage of their
synergies.

73

Here it is worth noting that the ability of synchrophasors technology to provide high resolution time-synchronized measurement data is
extremely valuable under high penetration levels of DER to estimate the state of the distribution system

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Substation

Substation

Figure 40 – Expected reliability improvements due to implementation of portfolio of projects

The reliability of the distribution grid represented in Figure 40 is improved through multiple projects
including conventional and advanced technology solutions, including distribution automation and
advanced sensors. The graphics spatial distribution of SAIDI74 before (left) and after (right) implementing
project portfolio. Red and orange lines have worst reliability performance than green and blue lines.

Figure 41 – Project prioritization

74

System Average Interruption Duration Index (SAIDI) is defined as the average duration of interruptions for customers served during a
specified time period and is typically measured in min/cust-yr.

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The Figure 41 curve demonstrates project prioritization and expected overall reduction of SAIDI versus
portfolio cost for distribution grid of Figure 40. Individual projects have been prioritized based on their
expected benefit-cost ratio.
Modern distribution reliability planning techniques, such as the utilization of predictive reliability models
using software solutions (Synergi, CYME, etc.), allow estimating benefits and prioritizing and deploying
solutions in a cost-effective manner. These technique rely on the utilization of historical reliability and
outage data to estimate the existing reliability of the distribution grid and then simulate the
implementation of a portfolio of projects for distribution reliability improvement. The simulations
consider important system operation constraints, such as equipment ratings and reserve capacity of
distribution feeders. Results from the simulations are used to prioritize projects and develop a costeffective portfolio of solutions to achieve specific reliability improvement targets, such as the one shown
in Figure 41. It is worth noting that this type of techniques is being considered as part of ENO’s advanced
distribution planning initiative.

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