Storm Restoration Community Insights Conference August 20-22, 2014 Vail, CO 2014 Electric T&D Benchmarking

Storm Restoration

Community Insights Conference

August 20-22, 2014

Vail, CO

2014 Electric T&D Benchmarking

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Agenda – Storm Restoration

◼ Overview Industry Perspective (SCQA) 1QC Community Key Success Factors Background on our Benchmarking Efforts

◼ Profiles & Trends

◼ 2013 Benchmarking Results Key Measures Correlation Analysis Findings

◼ Practice Information Highlights

◼ Discuss “The Future of Storm Restoration Benchmarking”

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Overview

Storm Restoration

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Storm Process Model

Daily system Activities:• Design

• Construction

• Operations

• Maintenance

Eventfollow-up:• Rate

treatment

• System changes

• Process Changes

We developed this high-level process model in 2012 to help organize our review of company storm restoration practices

Situation

• Many large storm events have been experienced in recent years across the U.S. and Canada

• Electric utility response to these events has often been criticized by customers, media, regulators and public officials

• Climatologists are predicting more violent storms in the future due to global warming

Complication

• North American utilities provide excellent reliability most of time; as a result, customers are not accustomed to dealing with long outages

• Proposed programs to “harden” the electric T&D system and make it more resilient during storm events are expensive and will take many years to complete

Question

• What can utilities do in the short term to improve stakeholder satisfaction with storm restoration performance?

• What are the key

things that must be done well in order to succeed?

Answer

• Develop a robust storm restoration plan that is capable of handling the “worst case” scenario

• Use process metrics to help manage the execution of the plan

• Share information about the plan and its execution with all stakeholders

Where Are We: 1QC Industry Perspective for Storm Restoration

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Ideas for Storm Process Metrics *

Short Term Preparations1. Make all staffing and work force notifications to

ensure minimum staffing levels as defined by the pre-event classification

2. Complete initial public awareness announcements

3. Complete life support equip. customer notification process

4. Complete Critical Customer notification process

5. Complete pre-event mutual aid conference calls

…. etc…

Restoration 6. Mobilize line crews, tree crews, wire-guards and

other field support roles consistent with the event classification in each geographic area

7. Mobilize office and control center staff consistent with the event classification in each geographic area

8. Initiate wire-down assessment and make-safe process , including hourly management reporting of volumes reported ,number dispatched, made-safe, etc.

9. Complete preparations for the arrival of off-system crews ……. etc….

.

Damage Assessment1. Mobilize required field resources consistent with the

event classification in each geographic area

2. Complete and report results of high level damage assessment of targeted areas within 12 hours after the storm has passed

3. Develop and publish schedule and personnel assignments for detailed assessments in heavily damaged areas within 4 hours after the high-level assessment is complete … etc….

External Communications1. Develop and communicate initial global ERT estimate

within 4 hours after high level damage assessment is complete; update on a 24 hour cycle thereafter

2. Develop and communicate initial regional and community ERT estimates no later than 24 hours after the storm passes; update on a 24 hour cycle thereafter

3. Issue Public Information updates in accordance with the Emergency Plan

4. Schedule and complete municipal conference calls in accordance with the Emergency Plan

etc.

* Many of these ideas came from Stephen Prall, Section Manager at Orange and Rockland Utilities, in response to a question posted in LinkedIn

1QC Community Key Success FactorsStorm Restoration (Focusing on Electric T&D Organization Responsibilities)

Keep current on vegetation management and critical equipment maintenance to reduce the impact of storm events

Document the storm restoration plan, organizational structure and roles & responsibilities; conduct training and drills to solidify and test the plan

Use planning models to predict storm damage and labor resource needs; acquire and position needed off-system resources as early as possible

Execute a comprehensive and efficient physical damage assessment process; effectively use the information to create and adjust resource plans

Ensure that field labor resources are efficiently deployed and closely track their work progress

Provide sufficient material deliveries and other logistical support (lodging, food, fuel, crew guides, system maps, etc.) to enable an efficient restoration effort

Follow a well thought-out process to create, update and communicate Estimated Restoration Times (ERTs) at a sufficiently granular level to meet stakeholder needs

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Lead

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Inte

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Background on our Benchmarking Efforts for Storms

◼ The Storm Restoration section of the T&D survey was initiated in 2012 in conjunction with a Discussion Topic that reviewed storm restoration practices. The data collected in 2012 covered storm experience from 2007 to 2011, including detailed hourly restoration data on a small sample of the larger storms that our community experienced over that timeframe.

◼ An analysis of the large storm data collected in 2012 was presented in an article published in the on-line edition of Public Utilities Fortnightly (“SPARK”).

◼ Last year, we collected detailed data on additional larger storms that occurred from 2007 to 2012 and did a more complete analysis which was presented at the 2013 T&D Insights Conference

◼ Including the data collected this year, we now have a database covering a total of 47 large storm events (storms interrupting 10% or more of a company’s total distribution customers) that we can use to calculate quartile values on key performance measures and produce correlation graphs that provide insights on storm restoration performance

◼ This year we added 25 practice questions to the survey to enhance our understanding of the current storm restoration processes of the companies in our community

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Storm Classifications

◼ We have found that it is helpful to group storms based on size. ◼ Our reports and analyses use the following size classifications:

Significant Storms (>1-10% of customers interrupted) are relatively frequent, predictable and process driven. The utility’s normal staffing and systems are geared to handle these events.

Major Storms (>10-20% of customers interrupted) are infrequent and trigger a major emergency response from the utility, including the acquisition and deployment of mutual assistance resources from neighboring utilities.

Catastrophic Storms (>20% of customers interrupted) are more rare but extremely destructive, often resulting in major damage to transmission lines and substations as well as to distribution facilities. These storms strain the utility’s communications and logistics processes and often impact large areas of the country, resulting in constraints and competition for mutual assistance crews and other resources.

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Completeness of Our Storm Restoration Data

Impact of IEEE Std 1366 “Major Event Days (MED)” on total 2013 SAIDI

DR Report page 8 (question DR 30) RP Report Page 2 (question RP5)

# of Reported Significant, Major and Catastrophic Storms in 2013

A comparison of these two charts reveals that Companies 25, 28 and 34 very likely had Major and/or Catastrophic storms in 2013 that they did not report in the Storm Restoration section of the survey and Companies 33 and 40 also very likely had Significant or larger storms that they did not report

N = 15 N = 8

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Profiles and Trends

Storm Restoration

Storm Activity Profiles

2013 YE 2012 YE 2007-2011

Min Mean Max# of Bars


Mean# of Bars

Number of Storm Events Per Company Per Year

Significant Storms 3 6.6 11 8 1 8.4 15 11 8.0 15

Major Storms 0 0.5 2 8 0 0.7 3 12 0.5 17

Catastrophic Storms 0 0.1 1 8 0 0.3 2 12 0.2 17

Average Storm CAIDI Per Event (minutes)

Significant Storms 99 163 223 8* 135 185 325 11* 162 15*

Major Storms 104 424 751 3* 104 219 348 4* 444 9*

Catastrophic Storms 465 465 465 1* 450 1467 1883 3* 1248 8*

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* # of reporting companies that experienced storms in these size ranges

The average reported 2013 storm counts per responding company were lower than the averages reported over the prior six years. There is no discernable trend in the average storm CAIDI values.

Storm Severity Profiles – Major and Significant Storms (Combined)

2013 YE 2012 YE 2007-2011



Mean# of Bars

Customer Impact

% Out at Peak (Simultaneous) 4.8% 6.6% 8.1% 5* 1.0% 17.1% 34.4% 5* 14.4% 32*

% Interrupted (Cumulative) 8.7% 15.5% 27.0% 5* 11.3% 33.5% 61.5% 5* 29.3% 33*

Hours to Restore

After Outage Peak 23 73.8 130 5* 29 124.0 215 5* 134.2 31*

After Storm Start 29 79.8 143 5* 31 134.0 231 5* 149.1 31*

Other Measures of Severity

% of total poles replaced .0095% .0185% .0500% 5* .0033% .0871% .1938% 4* .1387% 17*

Cost per customer restored $3.16 $43.70 $86.83 5* $32.88 $90.29 $128.16 4* $60.09 20*

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* # of storm events for which the listed data was provided

The various measures that we calculate on storm severity indicate that the group of major and catastrophic storms reported by our responding companies in 2013 were less severe than those reported over the prior six years

Storm Staffing Profiles – Major and Significant Storms (Combined)

2013 YE 2012 YE 2007-2011



Mean# of Bars

Line Staffing

Per 1,000 customers out at peak 6.7 8.2 11.3 5* 5.0 7.5 10.0 4* 7.5 23*

Per 1,000 customers restored 1.8 3.9 5.1 5* 2.7 3.6 5.7 4* 3.3 22*

Total Field Staffing (Line, Tree-trimming and Other Field Staffing)

Per 1,000 customers out at peak 8.1 11.9 20.8 5* 7.3 13.9 22.5 4* 11.0 23*

Per 1,000 customers restored 3.0 5.2 6.3 5* 4.0 6.9 13.2 4* 5.0 22*

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* # of storm events for which the listed data was provided

The average line staffing deployments on the reported 2013 major and catastrophic storms were a little higher than prior years, while average total field staffing was less than in 2012 but greater than the 2007-2011 average

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Number of Storm Events By Size Classification – 2013

Report page 2

Min 3

Mean 7.2

Max 12

Storm experience varied widely across the community in 2013

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Average Storm CAIDI – 2013

Report page 3

Avg. for Significant Storms

163

Avg. for Major Storms 424

Avg. for Catastrophic Storms

435

As expected, the overall averages for Storm CAIDI increased with storm size. There were some rather wide variations in the average CAIDI values for 2013 Major Storms

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Major and Catastrophic Storms – Percent of Poles Replaced

Report page 8

2013 2007 - 2012

# Bars 4 19

Min 0.0095% 0.0033%

Mean 0.0185% 0.1368%

Max 0.0500% 0.9471%

Pole damage for four of the five 2013 storms was very uniform. Overall, the 2013 pole damage statistics fall at low end of the very wide range experienced in 2007 to 2012

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Major and Catastrophic Storms - Peak Line Staffing Per 1,000 Customers Out at Peak

Calculation Used:Storm 1: RP30.1A/RP20f.1A, RP30.2A/RP20f.1AStorm 2: RP30.1B/RP20f.1B, RP30.2B/RP20f.1B Report part 2, page 2

2013 2007 - 2012

# Bars 5 27

Min 6.7 2.3

Mean 8.2 7.5

Max 11.3 33.5

Peak line staffing levels for the five 2013 storms were fairly uniform when normalized by the number of customers out at peak. The average for the 2013 storms was about 10% higher than the average for the 2007 to 2012 storms

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Major and Catastrophic Storms - Peak Total Field Staffing Per 1,000 Customers Out at Peak

Calculation Used:Storm 1: RP30.1A/RP20f.1A, RP30.2A/RP20f.1A, RP30.3A/RP20f.1A, RP30.4A/RP20f.1A, RP30.5A/RP20f.1A, RP30.6A/RP20f.1AStorm 2: RP30.1B/RP20f.1B, RP30.2B/RP20f.1B,RP30.3B/RP20f.1B, RP30.4B/RP20f.1B, RP30.5B/RP20f.1B, RP30.6B/RP20f.1B

Report part 2, page 3

2013 2007 - 2012

# Bars 5 27

Min 8.1 5.0

Mean 11.9 12.2

Max 20.8 40.3

Peak total field staffing per 1,000 customers out at peak for the 2013 storms ranged more widely, but all values fell within the range experienced on storms in 2007 to 2012. The average was slightly lower in 2013

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Major and Catastrophic Storms – Restoration Rates(Customers Restored Per Hour Per Line Employee)

Report page 9

2013 2007 - 2012

# Bars 5 25

Min 1.87 0.64

Mean 6.10 2.84

Max 19.14 10.65

Restoration rates were fairly uniform for four of the five 2013 storms. The highest 2013 value (Company 38) was nearly double the highest value reported by any company on any storm over the prior six years.

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Major and Catastrophic Storms – Total Restoration Cost per Customer Restored (Capital + O&M)

Report page 10

2013 2007 - 2012

# Bars 5 24

Min $3.16 $3.59

Mean $43.70 $65.12

Max $86.83 $131.61

The cost per customer restored varied widely on the 2013 storms. The lowest 2013 value (Company 38) is slightly lower than the minimum value that was reported over the prior six years. The average for all 2013 storms is 33% lower than the 2007-2012 average

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Restoration Curves for Major Storms (>10 to 20% of Customers Interrupted)

Source: Question RP35

One 2013 major storm was excluded from this analysis due to data irregularities. Performance on the other three 2013 major storms ranged from Q1 to Q4

10 of 22 total analyzed 2007-2013 major storms were fully restored within 3 days20 of 22 were fully restored within 5 days

Major Restoration % of Peak Still Out (2007 to 2013 Events)Milestones AVG Quartile 1 Quartile 2 Quartile 324 hours (1 Day) After Peak 18.7% 7.0% 18.3% 28.2%48 Hours (2 Days) After Peak 8.5% 1.3% 5.3% 11.2%72 Hours (3 Days) After Peak 1.6% 0.0% 0.3% 2.5%96 Hours (4 Days) After Peak 0.2% 0.0% 0.0% 0.1%120 hours (5 Days) After Peak) 0.1% 0.0% 0.0% 0.0%

Excludes Company 38 Feb. 2013 Wind Storm

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Restoration Curves for Catastrophic Storms (>20% of Customers Interrupted)


The single 2013 catastrophic storm tracked the median (Q2) line

10 of 18 total 2007-2013 catastrophic storms were fully restored within 6 days15 of 18 were fully restored within 9 days

Major Restoration % of Peak Still Out (2007 to 2013 Events)Milestones AVG Quartile 1 Quartile 2 Quartile 324 hours (1 Day) After Peak 50.3% 34.6% 44.0% 69.4%48 Hours (2 Days) After Peak 24.9% 8.3% 18.1% 40.0%72 Hours (3 Days) After Peak 16.0% 1.8% 8.3% 24.1%96 Hours (4 Days) After Peak 10.5% 0.0% 1.6% 13.4%120 Hours (5 Days) After Peak 6.9% 0.0% 0.3% 7.0%144 Hours (6 Days) After Peak 5.5% 0.0% 0.0% 2.5%168 hours (7 days) After Peak 4.1% 0.0% 0.0% 0.5%

Restoration Rates for Major Storms(Customers Restored Per Hour Per Peak Line Employee)

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Source: Questions RP55 and RP30

One 2013 major storm was excluded from this analysis due to data irregularities. The rates for the other three 2013 major storms tracked in the Q2 to Q3 range by late in the second day of restorationExcludes Company

38 Feb. 2013 Wind Storm

Time After Peak Cust Restored Per Hour Per Peak Line FTEOutage Hour AVG Quartile 1 Quartile 2 Quartile 3Through First Day 7.8 9.3 7.3 5.4Through Second Day 4.3 5.1 3.9 3.5Through Third Day 3.3 3.8 2.9 2.6From Beginning to End 4.0 3.6 2.4 2.0

Restoration Rates for Catastrophic Storms(Customers Restored Per Hour Per Peak Line Employee)

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Time After Peak Cust. Restored Per Hour Per Peak Line FTEOutage Hour AVG Quartile 1 Quartile 2 Quartile 3Through First Day 7.8 8.5 6.5 3.2Through Second Day 5.5 6.6 4.8 2.8Through Third Day 4.0 4.7 3.6 2.3Through Fourth Day 2.7 3.3 2.9 2.0Through Fifth Day 2.5 2.9 2.5 2.4From Beginning to End 2.6 2.8 2.2 1.4

The single 2013 catastrophic storm tracked close to the median (Q2) line throughout its restoration period

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Cost Per Customer Restored – Major and Catastrophic Storms

One of the 2013 major storms was excluded from this analysis due to data irregularities. The other three 2013 major storms and the single 2013 catastrophic storm had cost per customer restored values that fall in the Q2 to low Q3 ranges of the charts for their respective storm size group

Excludes Company 38 Feb. 2013 Wind Storm


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Correlation Analysis Findings

Storm Restoration

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Correlation Analysis

◼ More than 30 different correlation analyses were performed on the 2007 to 2013 storm data in an attempt to identify factors that explain the wide variations in performance that we have seen on our key storm restoration performance benchmark measures:

• Average Storm CAIDI• Hours to Restore• Restoration Rates• Cost Per Customer Restored

◼ We found that many of the most interesting and useful correlations were non-linear

◼ The more interesting and useful correlation graphs are provided in Appendix A

◼ The key findings from our analysis are summarized on the next two pages

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Correlation Analysis – Key Findings

1. The variance in the 2013 average Storm CAIDI values for Significant Storms are correlated somewhat to company distribution field staffing levels -- higher company staffing enables lower (better) average Storm CAIDI Companies can consider the correlation graph on page 52 of Appendix A when

determining what minimum staffing levels they should maintain to provide an “adequate” response to Significant Storms under worst-case scenarios when contractor and/or other off-site resources may not be available

2. The variance in the Hours to Restore results for 2017 to 2013 Major and Catastrophic Storms are strongly correlated to both the % of Customers Out at Peak and the % of Poles Replaced: During storm events companies can use the correlation graphs on pages 54 and 55

of Appendix A to “vector in” on a reasonable Hours to Restore goal based on the recorded customer outage peak and an initial high-level damage assessment that focuses on pole damage

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Correlation Analysis – Key Findings (Continued)

3. Restoration Rates (Customers Restored Per Hour Per Assigned Line Employee) for the 2007 to 2013 Major and Catastrophic Storms are strongly correlated to the % of Poles Replaced: During storm events, companies can use detailed pole damage assessment data

and the correlation graphs on pages 58 and 59 of Appendix A to determine the line staffing needed to achieve their established Hours to Restore goals

4. The Cost Per Customer Restored results for 2017 to 2013 Major and Catastrophic Storms are also strongly correlated to the% of

Poles Replaced : After storm events, companies can use actual customer restoration and pole

replacement data and the correlation graphs on pages 60 and 61 of Appendix A to predict and evaluate the total restoration costs for the event

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Storm Restoration Practice Information Highlights

(Data Collected in the Distribution Reliability Section of Questionnaire)

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Storm Process Model

Emergency Preparedness

Emergency Plan

Emergency Organization

Weather Tracking

Communications/Alert System

Prediction Models

Planning Criteria

Resource Planning/

Recruitment

Pre-Event External Communications

Storm Restoration - Execution

Short-Term Preparations

Damage Assessment Restoration Wrap-Up and

Demobilization

Management, Support, & Logistics

Communications – with customers, outside stakeholders

• Mobilization/Deployment

• Field Restoration

• Progress Tracking

• Ramp-down, cleanup

• Post-storm critique, follow-up

• Command Center Operations

• Logistics• IT and Telecom. Systems

• Safety Management

• Status reporting, conference calls

• Customer Communications

• EMA and Other Agencies

This year we added 25 practice questions to our survey to enhance our understanding of how companies accomplish key parts of this process

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Practice Highlights

Most Important Improvement Initiatives – Page DR85◼ When asked what were their most important current initiatives to improve storm

response, 5 of the 10 responding companies cited specific system or process changes that they were implementing. The responses of the other 5 companies focused on improved relationships with mutual assistance groups (2 companies), improving training and documentation of their ICS organization and protocols (2 companies), and improving employee call-out response (1 company)

Emergency Plan – pages DR86 and DR87◼ 8 of 11 responding companies made significant changes to their storm plans in

2013, focusing on organization (3 companies), process changes (3 companies), resource plans (2 companies) and crew tracking systems (2 companies) [some companies had multiple responses]

◼ 8 of 11 responding companies conducted storm plan exercises or drills in 2013. The objectives and scope of the drills varied

Emergency Organization– page DR88◼ 11 of 12 responding companies now use the U.S. Department of Homeland

Security’s Incident Command System (ICS) organization model during large storm events

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Practice Highlights (Continued)

Weather Tracking -- pages DR89 and DR90◼ The 13 responding companies’ most frequently cited primary source of

weather forecast information was contracted weather services (9 companies use), followed by T.V./radio station meteorologists (4 companies), company meteorologists (3 companies), direct communication with the National Weather Service (3 companies) and online doppler radar websites (1 company). [most companies had multiple responses]

Prediction Models -- pages DR92 and DR93◼ Only 4 of the 13 responding companies have developed statistical models

to aid them in planning for anticipated storm events and/or in managing their response to storm events after they occur: 2 of the 4 company models use weather information and other data to

predict customer outages. Only 1 of these two models attempt to predict pole damage

The other 2 models use actual customer outage data to predict labor resource requirements and global ERTs. The responses indicate that neither of these 2 models explicitly incorporates pole damage information/estimates into the forecasts of labor resource requirements and global ERTs.

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◼ Resource Planning and Recruitment -- pages DR94 and DR95◼ All 12 responding companies belong to at least one utility mutual assistance group. 7

of the 12 belong to multiple mutual assistance groups ◼ 4 of 12 responding companies reported that they have pre-established contracts with

one or more off-system contactors that they may need to use during large events, while 2 said they do not [responses from the other 6 companies were vague].

Damage Assessment – pages DR104 and DR105◼ 3 of 12 responding companies always deploy field damage assessors during any

significant storm, 2 deploy based on storm size criteria, 2 deploy based on other criteria and 3 leave the decision up to field operations management with no stated criteria [responses from the other 2 companies were vague]

◼ All 11 responding companies indicated that damage assessment data is collected manually in the field on paper forms and/or maps. One of these companies is currently investigating the use of tablet computers for this part of the process.

◼ 6 of 11 responding companies enter the damage assessment data into some type of automated system (e.g., OMS, SharePoint, in-house web application). [The other 5 either do not have or simply did not describe any form of automated support for tabulating and communicating the collected damage assessment information]

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Managing and Supporting Off-System Crews -- pages DR96, DR99 and DR101◼ The 12 responding companies’ most commonly used tool for tracking off-system crews

(acquisition, travel, area assignments, work hours and eating/sleeping arrangements, etc.) was Excel spreadsheets (4 companies use) followed by specialized in-house databases (3 companies) and company Work Management Systems (3 companies). 2 companies reported that they do not have any formal tracking system

◼ All 12 of the responding companies provide safety and system orientation training to off-system crews before they are put to work. 5 companies noted that crews are then assigned to “crew guides” (various titles) who assist the crew in obtaining needed material and equipment. Other items that companies said they provide to crews when they arrive are a mutual assistance guide booklet (1 company), pre-assembled storm material kits (1 company), maps (1 company) and construction standards drawings (1 company)

◼ The types of work that are typically assigned to off-system crews varies : Off-system crews are most commonly assigned OMS tickets for primary work

(11 companies) and for transformer and secondary work (10 companies) 2 of 13 responding companies only assign large “patrol/inspect/repair” projects to

off-system crews, while 6 of 12 do not assign that type of work to off-system crews Only 4 of 13 responding companies assign transmission system repairs to off-

system crews

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Managing and Supporting Off-System Crews (Continued) -- page DR103◼ 5 of 10 responding companies use their crew guides to track and report the

work completed and customers restored by off-system crews. 2 companies have the off-system crews call into an office to report completions and 1 uses mobile data terminals for off-system crew reporting [The responses from the 2 other companies were vague]

Work Dispatching – page DR97◼ 4 of 13 responding companies dispatch all storm restoration work through

individual outage events identified and tracked in their OMS ◼ 9 companies dispatch work in very heavily damaged areas using a

“patrol/inspect/repair” strategy. Under this strategy, crews working under one overall leader are assigned a large area, defined by the electrical boundaries of a distribution substation or individual primary circuit, and told to handle the repair and restoration of everything in that area autonomously and at their own discretion.

◼ At least 5 of the 9 companies that use a patrol/inspect/repair dispatching strategy reported that they also dispatch work through their OMS during most large storm events (e.g., for work in less heavily damaged areas).

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Wire-Down Response – page DR98◼ The 13 companies that responded to the text question on this topic

provided a great deal of detail on their processes. In their comments, 6 of the 13 companies mentioned their use of a Wire-Down Guard role during large storm events and 3 companies reported that fire and/or police agencies are provided a direct line to their DOC to report wire-down locations and receive updates, rather than handling those calls through their customer care call centers.

Resources Used for Residential Service Reconnections – page DR106◼ 8 of the 11 responding companies use company personnel from non-line

work groups (e.g., meter servicers, substation electricians and power plant electricians) to perform residential service reconnections during large storm events. 3 of these 8 may also use commercial electricians for this work if they experience an especially high volume of individual service outages

◼ The other 3 responding companies do not use any supplemental labor resources for this work – all reconnection work is assigned to line personnel (line crews, troubleshooters or service crews)

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Logistics – Crew Staging Sites and Base Camps – page DR100◼ 5 of 12 responding companies have agreements in place with landowners for access

to potential crew staging site or base camp locations◼ 4 companies have contracts for logistical support at staging and/or base camp sites◼ 3 companies indicated that they do not have any formal contracts or other pre-

established external arrangements for staging sites or base camps

ERT Estimation and Communication -- pages DR107 through DR109◼ Only 4 of 10 responding companies described the use of a formal analytical process

to develop global ERT estimates for specific communities/areas of their system during large storm events. [The other 6 either do not have or simply did not describe their analytical process for producing ERT estimates]

◼ 5 of 10 reporting companies indicated that they communicate their global community/area ERT estimates through their OMS and/or external outage websites as well as through their call centers, media outlets and personal contact with large customers and government officials

◼ Only 4 of 12 responding companies provide individual, customer-specific ERTs during their larger storm events, At 3 of these companies, initial customer-specific ERTs are assigned based on pre-set default values and then updated manually as needed. The other company bases its initial individual customer ERTs on an overall restoration strategy that it develops after analyzing the total scope of damage

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Emergency Government Agency Communications – page DR111◼ 9 of 12 responding companies have people assigned as emergency

government agency liaisons during large storm events. These liaisons will report to the county or state/province EOC if requested

◼ 1 company provides a seat in its own EOC for a representative from their state emergency management department

◼ 1 company routes all emergency government communication through its Public Affairs Department

◼ [The response from the 1 remaining company did not clarify their responsibilities and protocols in this area]

Regulatory Communications – page DR112◼ 3 of 11 responding companies have specific people assigned to function as

the single point of contact with state PUCs during large storm events ◼ 5 companies route and receive all regulatory communication through a

specific company department such as Corporate Communications or Regulatory Affairs

◼ [The responses from the 3 remaining companies did not clarify their responsibilities and protocols in this area]

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Communications With Elected Officials – page DR113◼ 3 of 11 responding companies have specific people assigned to function

as the single point of contact with individual elected officials during large storm events

◼ 5 companies route and receive all communication with elected officials through a company department such as Corporate Communications, Government Relations or Public Affairs.

◼ [The responses from the 3 remaining companies did not clarify their responsibilities and protocols in this area]

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Storm Restoration Practice Conclusions

◼ Storm restoration practices vary widely across our T&D community in the following areas: Use of prediction models Pre-established arrangements with off-system contractors Damage assessment criteria Automation of the damage assessment process Automation of off-system crew tracking (re: acquisition, travel, area assignments,

hours worked, food & lodging arrangements, etc.) Types of work assigned to off-system crews How the work performed by off-system crews is tracked and reported Method of work dispatch for heavily damaged areas (i.e., do companies ever

dispatch under a “patrol/inspect/repair” strategy) Use of wire-down guards and direct telephone lines with fire and police in the wire

down response process Resources used for residential service reconnections ERT estimation and communication processes for both global and individual

customer ERTs Processes for communicating with governmental stakeholders (EMAs, regulators

and elected officials)◼ A program to develop standardized practices and related support systems may

be beneficial to the community (and to the entire industry)

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The Future of Storm Restoration Benchmarking

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Current Situation

◼ The number of companies responding to the Storm Restoration (RP) section of the survey has been declining:

• 14 companies responded in 2012 • 12 companies responded in 2013• 8 companies responded in 2014

◼ Through this section of the survey we have collected some useful detailed data on a total of 47 large storm events that occurred between 2007 and 2013 (storms affecting >10% of total utility customers).

◼ Some of the responses that we received on those 47 large storms were incomplete. As a result, we have fewer than 47 data points for graphs and correlations that use the following data elements:

• # of customers out at peak (simultaneous) 42 data points• # of customers interrupted (cumulative) 42 “ “• Hours to restore (including curves) 41 “ “• Field labor assigned to the restoration effort 32 “ “• Total restoration cost 29 “ “• Number of poles replaced 26 “ “

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Concerns

◼ There appears to be waning interest in this topic after companies reacted initially to a spike of very large storms that affected North America in 2011-2012

◼ We aren’t growing our large storm database as quickly as we would like: • Ideally we should have a database of 80-100 events with complete

data to be sure that our conclusions based on statistical correlations are valid.

• We added only 5 events in 2013 and only 4 with complete data• At the rate that we are going, it will take us an additional 10 to 15

years to get there!◼ While we collected some interesting practice information this year

through a new set of questions, we are not able to draw any conclusions yet about the relationship of practices to performance because we only have 4 good large storm performance data points to work with in our 2013 dataset.

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Future Vision/Opportunities

◼ With a larger and more geographically disbursed database of storm data, we could perform a more refined analysis, segmenting based on storm weather types (e.g., the types used in Robert Broadwater’s analysis summarized in Appendix B of this presentation)

• Today we just group and analyze based on “storm size” (% of customers out)

◼ By cross referencing and correlating company storm practice information to their performance results on various types of storm events over several years, we may be able to gain some insights on what are the most effective practices for the types of large storms that are most likely to impact different regions of North America.

◼ If we are successful on the above expanded benchmarking efforts, industry organizations such as EEI, EPRI and the IEEE might eventually join/help us advance this work into a set of measurement standards and best practice guidelines to benefit the entire electric utility industry.

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Possible Next Steps (Alternatives for 2015 Program)

◼ Continue as we are for now, but make a more concerted effort to get responses from many more panel members on both the storm performance data and practice questions (How?)

◼ Remove Storm Restoration from the regular T&D benchmarking program and start a separate Storm Restoration Benchmarking Program that would be marketed to interested companies within and outside of the current T&D panel (How?)

◼ Leave Storm Restoration in the regular T&D benchmarking program and also start a separate, parallel Storm Restoration Benchmarking program that would be marketed only to companies outside of the current T&D panel (How?)

What are your thoughts on this?

Thank you for your Input and Participation!

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First Quartile Consulting is a utility-focused consultancy providing a full range of consulting services including continuous process improvement, change management, benchmarking and more. You can count on a proven process that assesses and optimizes your resources, processes, leadership management and technology to align your business needs with your customer’s needs.

Visit us at www.1stquartileconsulting.com | Follow our updates on LinkedIn

About 1QC

Satellite Offices

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Dave [email protected]

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http://www.1stquartileconsulting.com/

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Appendix ACorrelation Analysis Graphs and Commentary

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2013 Average Storm CAIDI versus Customer Density and Percent of Circuit Miles That are Underground

Source: Questions RP15, ST5, ST30 and ST35

The correlations for Major Storm CAIDI are very strong but this is based on just three data points

The correlations of Significant Storm CAIDI to these two demographic measures are extremely weak

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2013 Average Storm CAIDI versus Distribution Field C&M Employees (Direct) Per 100K Customers and Per 1K Square Miles

Source: Questions RP15, SO15, ST5, ST30

The correlations for Major Storm CAIDI are very strong but are based on just three data points

The correlations of Significant Storm CAIDI to these two staffing measures are somewhat stronger and trend in the expected direction. However, they are biased by one somewhat extreme data point in the lower right (hidden under the Major Storm data point)

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2013 Average Storm CAIDI versus Percent of Customers on Circuits with Remote Switching Capability

Source: Questions RP15 and ST115

The correlations for Major Storm CAIDI are strong and the data trends in the expected direction. However, the analysis is based on just three data points for substation breaker SCADA and two for Line Switch SCADA

The correlations of Significant Storm CAIDI to these two DA penetration measures are very weak and trend in an unexpected direction

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Hours to Complete Restoration versus Peak Customer Outages For Major and Catastrophic Storms


The five 2013 storms fall above, below and on the trend line

The data for 2007 to 2013 shows a pretty strong correlation between restoration times and the peak percentage of utility customers that were out simultaneously during each major and catastrophic storm event

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Hours to Complete Restoration versus Pole Damage For Major and Catastrophic Storms


The five 2013 storms fall above, below and on the trend line

The data for 2007 to 2013 storms also shows a pretty strong correlation between restoration times and the relative amount of pole damage

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Peak Customer Outages versus Pole Damage For Major and Catastrophic Storms

Source: Questions RP20, ST5 and ST45

Pole damage and peak customer outages are strongly correlated• The variances above and below the trend line may be due to differences in storm weather

differences (e.g., wind versus lightning), system demographics, including % underground, and/or whether storm damage was focused in more urban or rural areas

• All five of the 2013 storms fall below the trend line (three of their data points overlap on this graph)

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% Customers Interrupted and Restored (Cumulative) versus % Customers Out at Peak -- Major and Catastrophic Storms


• This analysis excludes one 2013 storm due to data irregularities and three storms from prior years that had large secondary peaks

• During events, the formula could be used to predict total customer interruptions as soon as an outage peak has been reached

• Two of the four analyzed 2013 storms plot below the trend line, one plots on the trend line and one plots significantly above the trend line

This correlation is strong but is biased by one data point with extremely high values on each scale which is not shown

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Approx. Line Hours Expended versus Customers Restored -- Major and Catastrophic Storms


• Variances above and below the trend line are partially explained by the relative mount of pole damage (see page 59 for a predictive chart that factors in both customers interrupted and pole damage)

• One 2013 storm was excluded from this analysis due to data irregularities. Three of the four analyzed 2013 storms plot below the trend line and one plots essentially on the trend line

This correlation is very strong, but is biased by one data point with extremely high values on each scale which is not shown

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Restoration Rates versus Pole DamageMajor and Catastrophic Storms

Source: Questions RP20, RP30 and ST45

Three of the four analyzed 2013 storms plot below the trend line and one plots just above the trend line. One 2013 storm was excluded from this analysis due to data irregularities

Differences in the relative amount of pole damage partially explain the variation in customer restoration rates

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Total Restoration Cost versus Customers Restored -- Major and Catastrophic Storms


• Variances above and below the trend line are partially explained by the relative amounts of pole damage (see page 61 for a predictive chart that factors in both customers interrupted and pole damage)

• One 2013 storm was excluded from this analysis due to data irregularities. All four of the analyzed 2013 storms plot very close to and below the trend line

The correlation between total restoration cost and the number of customers interrupted and restored is very strong

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Restoration Costs Per Customer versus Pole Damage -- Major and Catastrophic Storms


One 2013 storm was excluded from the analysis due to data irregularities. Of the four analyzed 2013 storms, one plots far above the trend line, one plots slightly above the trend line, one plots essentially on the trend line and one plots far below the trend line

The variation in restoration costs per customer is partially explained by the relative amount of pole damage

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Storm Restoration Correlation Analysis Conclusions

The variance in the 2013 average Storm CAIDI values can be partially explained by differences in company field staffing levels. Correlations of Storm CAIDI to other demographic and DA-penetration measures are very weak◼ Correlations for 2013 Significant Storm CAIDI range from weak to

extremely weak◼ We do not have sufficient data points to draw any conclusions about Major

Storm CAIDI◼ Also, regarding the impact of DA, one piece of external research indicates

that adding automated/remote switching would not improve average Storm CAIDI results to any major degree (see Appendix B)

Through our analyses, we have determined quartiles of performance and identified primary factors that explain differences in company performance on the three key restoration measures that we have been tracking for large storm events (greater than 10% of customers out): ◼ “Hours to Restore x%” [our Restoration Curves]◼ “Restoration Rates” [Customers Restored Per Hour Per Assigned Line

Employee] ◼ “Total Restoration Cost Per Customer Restored”

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Storm Restoration Correlations Analysis Conclusions (Continued)

Our correlations show that the amount of pole damage has a large impact on all three of the large storm restoration performance measures◼ Efforts to predict or estimate pole damage in the very early phases of the

storm response process should be beneficial to utilities in their goal setting and resource planning

Companies can use the large storm measures and the various graphs in this presentation to evaluate their performance on past storms and as a planning tool for future events

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Appendix BExternal Research

Automation Impact on Storm Response

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External Research -- Automation Impact on Storm Response

Modeling performed by Dr. Robert Broadwater of the Electric Distribution Design (EDD) consulting firm and summarized in a December 3, 2013 presentation:

Compared automated versus manual switching models for a portion of Orange & Rockland Utilities (ORU)’s electric distribution system

Used a Storm Outage Prediction model to predict the number of outage events (various types of equipment failures) that would occur under the following types of storms for every hour of each storm

H = High Temperatures and Low or Moderate Wind Speeds (Heat Events)

HS = High Temperatures and High Wind Speeds

M = Moderate Temperatures and Low or Moderate Wind Speeds

MS = Moderate Temperatures and High Wind Speeds

L = Below Freezing Temperatures and Low or Moderate Wind Speeds

LS = Below Freezing Temperatures and High Wind Speeds

Ran Monte Carlo simulations to develop estimated Storm SAIDI results for both the manual and automated switching models

http://www.bnl.gov/wius2013/talks/pdf/RBroadwater.pdf

http://www.bnl.gov/wius2013/talks/pdf/RBroadwater.pdf

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Storm Analysis Results – Dr. Robert Broadwater, PhD

1. The predicted Storm SAIDI improvements attributable to automation were very modest (see below)

2. Considering that the predicted SAIDI improvements were partially driven by predicted SAIFI reductions as a result of FLISR functionality, the storm CAIDI results are likely less impressive and perhaps even negative on some storm types (First Quartile’s opinion)

Documents

Storm Restoration Community Insights Conference August 20-22, 2014 Vail, CO 2014 Electric T&D Benchmarking