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Resurrecting ALoad Research Program

Susan RomerNSTAR

2006 AEIC Load Research ConferenceSeptember 2006

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NSTAR Territory

NSTAR Electric

NSTAR Gas

Combined Gas & Electric

•Massachusetts’ Largest Investor-owned Electric And Gas Utility

•Transmitting & Delivering Electricity & Natural Gas For More Than 100 Years

•Serve @1.4 M Residential & Business Customers In Over 100 MA Communities 

•@ 3,200 employees 

Electric Gas

Residential 970,000 160,000

Business 225,000 20,000

Total 1,195,000 180,000

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NSTAR - Electric

• Three Electric Operating Companies– Boston Edison Company (BECO)– Commonwealth Electric (COMM)– Cambridge Electric (CAMB)

• 4,959 MW – Peak Load (08/02/06-4 PM)• 23,236 GWh – 2005 Annual Energy• 17% Share of ISO-New England Load

• Load And Customers Served by 13 Competitive Suppliers as of July 2006– 47% of load

• (Industrial – 77%; Commercial – 28%; Residential 16%)– 18% of customers

• (Industrial – 59%; Commercial – 26%; Residential 17%)

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Where’s Load Research

1993-1998Homeless

1993Dismantled

1999Sales??

Load Research

1980-1993Rates

PURPA Baby

2005Load Services

2000Energy Supply

Finance & Accounting > Energy Supply > Load Services > Load Research – 1 Analyst > Load Settlement & Load Response – 3 Engineers

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Detour Impact

Loss of employees with knowledge

No senior management sponsor

No field commitment “Those Meters Are Only Used For Research”

Data not available when needed

Load data gateway shifted to others

Load Research

1992 - 10 Year Rate Freeze1993 - MA Electric Deregulation1999 - Merger2000> - Management Changes

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Regulatory Requirements

• Continuous Load Research

• Meet 95/10 Sample Results Accuracy Levels

• Most Northeast Utilities Perform Load Research At 95/5 Accuracy Levels

• Many Northeast Utilities Use Same Month Profiles For Settlement Month

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Opportunities

• 2004 Rate Case and No Load Research Data

• Awareness of ISO Settlement Dispute B/W Two Other Parties

• Understanding of How Interchange Point Malfunction Can Impact the Calculation of System Loads

• 2005 Energy Policy Act DST Change

Senior ManagementSponsorship

Fear ofFinancial Exposure

=

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Load Research Supports• Estimation of Competitive Supplier Loads and ICAP Values

• Development of Rates Charged to Customers

• Unbilled Energy Sales And Unbilled Revenues Balances

• Billed/Unbilled Sales & Substation Forecasts

• Accrual Of Purchased Power Expense As Compared to Actual

• Development of Rates Charged to Customers

• Local Network Service Billing for Wholesale Customers

• Regional Network Transmission Billing for ISO

• Development of Engineering Loss Studies

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Load Settlement & Load Research

•Determine suppliers’ load obligations from load research for the purposes of ISO-New England energy market settlement

•A contributing factor to unaccounted for energy (UFE)

Metering Error – Meter FailuresLoad Profiling Error** – Sample EstimatesAccounting Error – Billing EstimatesDistribution Loss Estimation Error – Incorrect Loss FactorsTheftUn-metered Energy – Company Use

•Revenue Annually @$2 Billion

ProfiledLoads55%

TOULoads45%

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Load Research ProgramAssessment Observations

• Age of the Samples - Over 9 Years Old

• Significant Data Loss – 47% for BECO

• Sample Sizes All Less than 30 Per Stratum

• Sample Bias Caused By Installation Procedures– Over 50% Indoor Meters– “Don’t Really Need THOSE Meters”

• Relative Precisions Averaged from 20-30%

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Sample Augmentation Plan

We Need To Consider Options To Improve The Sample Results Until The New Samples Are Installed.

Techniques

• Ratio Estimation– Takes Advantage Of The Correlation Of The Variable Of Interest With

Another Variable To Increase Precision

• Post Stratification– Can Be Used Within A Sample Study, But Can Also Be Utilized To Post

Stratify Transferred Data From Another Utility With Similar Population Characteristics

• Load Data Transfer– A Transfer of Load Data From One Distribution Company To Another

Distribution Company For Certain Samples. Can Utilize Post Stratification To Fit The Transferred Data To The Sample

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Preliminary Test Results

• 2 Primary Goals– To Improve the Current Sample Results in the Interim

– To Use Ratio Estimation as an Expansion Methodology

• Tested 2 Samples for July 2005– Commonwealth Residential R-1

– Commonwealth Small General Service G-1

• Looked At Relative Precisions– Ratio Estimation Versus Mean-Per-Unit

– Ratio Estimation Versus Ratio Estimation Post-Stratified

• Did Not Look At Load Data Transfer, Yet

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Residential R-1 Relative Precisions MPU Vs. Ratio Estimation

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

1 25 49 73 97 121 145 169 193 217 241 265 289 313 337 361 385 409 433 457 481 505 529 553 577 601 625 649 673 697 721

Pre

cisi

on

%

MPU Ratio

Improved Average Relative Precision From 16.33 to 13.10

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Small GS G-1 Relative Precisions MPU vs. Ratio Estimation

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

1 25 49 73 97 121 145 169 193 217 241 265 289 313 337 361 385 409 433 457 481 505 529 553 577 601 625 649 673 697 721

Pre

cisi

on

%

MPU Ratio

Improved Average Relative Precision From 25.72 to 11.17

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Post StratificationAssignment Distribution

Strata OriginalAssignment

Post Stratified

Assignment

1 20 21

2 25 23

3 25 30

4 25 21

Strata OriginalAssignment

Post Stratified

Assignment

1 28 36

2 24 21

3 27 26

4 28 24

ResidentialR-1

Small General ServiceG-1

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Residential R-1 Relative PrecisionsRatio Estimation Post-Stratified

Ratio Estimation vs. Ratio - Post Stratified Commonwealth Rate Residential General Service R1

July 2005

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

1 25 49 73 97 121 145 169 193 217 241 265 289 313 337 361 385 409 433 457 481 505 529 553 577 601 625 649 673 697 721

Pre

cisi

on

%

Ratio Ratio-Post Stratified

Improved Average Relative Precision From 13.10 to 10.97

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Small GS G-1 Relative Precisions Ratio Estimation Post Stratified

Ratio Estimation vs. Ratio - Post Stratified Commonwealth Small General Service G1

July 2005

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

1 25 49 73 97 121 145 169 193 217 241 265 289 313 337 361 385 409 433 457 481 505 529 553 577 601 625 649 673 697 721

Pre

cisi

on

%

Ratio Ratio-Post Stratified

Improved Average Relative Precision From 11.17 to 9.83

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Relative Precision Ranges

Residential Small

General Service

Mean Per Unit 10.4 - 34.3 17.9 - 38.3

Ratio 07.9 – 29.2 05.9 – 30.8

Ratio Post Stratified 06.5 – 20.2 06.5 – 25.0

Reduction in Variability in the Precision Values

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New Sample DesignsObjectives• Achieve Design Accuracy Of +/- 5% At The 95% Confidence Level For All Hours Of The Year

• Stay Within Budget Constraints

• Use Stratifying Variables That Will Achieve The Targeted Accuracies For All Hours Of The Year

• Utilize A 100% (Certainty) Stratum In The Design Process When Feasible

• Have Longevity Of At Least Five Years

• Represent Target Populations Optimally

• Have A Minimum Of 30 Sample Points In Each Stratum

• Minimize Sample Bias In The Installation Procedures

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Current and Proposed Program

• Do We Need A Separate Sample For R-1 Water Heating (WH)? No

• Do We Need A Separate Samples For R-2? Yes

• Do We Need Separate Samples For BECO’s G-2 Commercial And G-2 Industrial? No

• Proposed• 13 Sampled Rates

– @2002 Sample Meters• 11 100% Monitored Rates (TOU)

– @4400 Meters• @225 Interchange Meters

Current Samples

  BECO COMM CAMB TOTAL

R-1 109 100 140 349

R-1-WH 102     102

R-2 99     99

R-3 138 90 64 292

G-0       0

G-1 115 120 124 359

G-2       0

G-2C 90     90

G-2I 90     90

743 310 328 1381

New Samples

  BECO COMM CAMB TOTAL

R-1 220 176 160 556

R-2 82 88 80 250

R-3 161 165 165 491

G-1 200 165 198 563

G-2 160  160

823 594 603 2020

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Program Objectives

• Have a quality, compliant, and maintainable load research program

• Lower risks of disputes in regulatory proceedings and in ISO settlements

• Minimize loss of data

• Improve the predictability of costs of operations, maintenance, & future capital expenditures

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Load Research Roadmap

Re-Engineer To MonthlyProduction

Redesign and Install

New Samples

Build Sample Tracking

System

Load Research

Recast Analysis

Use Same Month Profiles For Settlement

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Next Steps

• Sharing Performance Metrics – Between Metering, Meter Data Management, & Load Services– Sample Installation Rate; Data Retrieval Rate; Interval Meter Check

• Installing The New Samples – 2006 and 2007• Build a Sample Tracking System • Improve Data Validation Procedures• Re-Engineer Load Profile Production to Monthly For Settlement• Implement A Process Of Load Study Cycling And Replacement• Cross Training

– Cloning the Old Fashion Way

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Now, Who’s Driving the Bus?

Load Research

Data Heaven