© 2007, Itron Inc.

VELCO Long-Term Demand Forecast Kick-off Meeting

June 7, 2010

Eric Fox

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Agenda

Discuss proposed framework for developing the long-term VELCO system and zonal demand forecasts

Review ISO-NE forecasting approach

Discuss issues related to Energy Efficiency and Forecasting (EE&F) Forecast Guidelines> Economic and weather data> Incorporating the impact of state efficiency activity> Incorporating the impact of interruptible load and demand response programs

Project schedule

© 2007, Itron Inc.

VELCO System and Zonal Demand Forecasts

Develop twenty-year demand forecasts that captures:> population trends, economic conditions, price

> peak day weather conditions

> end-use saturation and efficiency trends• Standards, impact of federal tax credit programs, price induced

efficiency gains• State and utility efficiency programs• Interruptible load and demand control programs

Team effort – > program efficiency savings integration

> implementing forecast within forecast committee guidelines

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VELCO Daily Peak Demand (MW)

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VELCO Monthly Peak (MW)

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Approaches for Forecasting Demand

Generalized econometric model> Approach used by New England ISO

• Demand = f(Energy, trends, peak day weather)– Energy = g(real income, price, monthly weather)

Hourly build-up approach> Approach used last year

• Forecast class and end-use sales (SAE specification)• Combine end-use sales with end-use load profiles• Aggregate to system peak

SAE peak model> Proposed approach

• Forecast class and end-use sales (SAE specification)• Demand = f(End-use coincident load, peak-day weather)

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Step 1: Estimate SAE Energy Models

Build monthly revenue class sales models> Construct SAE models for the residential and small

commercial customer classes base on actual sales data

> Estimate generalized econometric models for the large/commercial and industrial classes

• Supplement with specific customer estimates where available (such as IBM)

> Potentially estimate state level and utility service area models for GMP, Central Vermont, and BED

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Statistically Adjusted End-Use (SAE) Framework

AC Saturation Central Room AC

AC EfficiencyThermal EfficiencyHome SizeIncomeHousehold SizePrice

Heating Saturation Resistance Heat Pump

Heating EfficiencyThermal EfficiencyHome SizeIncomeHousehold SizePrice

Saturation Levels Water Heat Appliances Lighting Densities Plug Loads

Appliance EfficiencyIncomeHousehold SizePrice

Heating Degree Days

CoolingDegree Days

Billing Days

XCool XHeat XOther

mmomhmcm eXOtherbXHeatbXCoolbaSales

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Step 2: Develop End-Use Saturation and Efficiency Trends

Use AEO 2010 New England Census Region forecast as a starting points

Adjust end-use saturation and structural data to reflect Vermont> KEMA appliance saturation survey

> BED survey work

> Efficiency Vermont market analysis Modify historical and forecasted efficiency trends to

reflect the impact of state and utility specific efficiency programs

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Efficiency Program Impacts

Cooling Efficiency Program

No DSM Efficiency

Path

No DSM Efficiency

Path

Marginal EfficiencyMarginal Efficiency

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Adjusted End-Use Indices (kWh per Cust)

0

500

1,000

1,500

2,000

2,500

2000 2003 2006 2009 2012 2015 2018 2021 2024 2027 2030

EFurn

SecHt

CAC

RAC

EWHeat

ECook

Ref1

Ref2

Frz

Dish

CWash

EDry

TV

Light

Misc

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Statistically Adjusted End-use Modeling (cont.)

Estimate monthly average use regression models:

tt3t2t10t XOtherbXCoolbXHeatbbAvgUse

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XCool

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XHeat

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XOther

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Residential Average Use Forecast

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End-Use Energy Forecast

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Last Year’s Approach

ResidentialResidential

CoolingCooling

Base UseBase Use

Combine end-use energy with end-use shapes

Combine end-use energy with end-use shapes

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Peak-Day System Hourly Load Profile (MW)

Aggregate Class Load Forecasts to System Load Forecast

AndFind Annual System Peak

Aggregate Class Load Forecasts to System Load Forecast

AndFind Annual System Peak

SystemSystem

ResidentialResidentialCommercialCommercial

IndustrialIndustrial LightingLighting

© 2007, Itron Inc.

Step 3: Estimate SAE Peak Demand Model

Derive end-use coincident peak load estimates from the SAE sales models

• weight class estimates to reflect zonal area customer mix

Construct peak-day weather variables• 50% and 90% probability weather

Combine end-use energy stock estimates and peak-day weather into monthly SAE peak-day variables

Estimate system and zonal peak demand models Develop seasonal peak demand forecasts for 50% and

95% probability weather Adjust for interruptible load and demand response

program impacts

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© 2007, Itron Inc.

Simulation Results from Sales Models

mmomo

mmhmh

mmcmcm

eTrendcXOtherb

HDDTrendcXHeatb

CDDTrendcXCoolbaSales

Residential

Small C&ILarge C&IMunicipal

Cooling

Heating

Other

© 2007, Itron Inc.

Simulation Results from Sales Models

Sum of End-Use Energy> Normal heating for Res, SGS, LGS, …> Normal cooling for Res, SGS, LGS, …> Other loads for Res, SGS, LGS, …

To

tal M

on

thly

En

erg

y (G

Wh

) Total Monthly Energy – Normal Weather -- All Classes

Total Monthly Energy

© 2007, Itron Inc.

Heating Variable Construction

Annual Heating Transforms

Monthly Heating Transforms

Sum monthly heating values from the sales model.

Interact heat index values with peak day temperatures and prior day temperatures. Use splines if needed.

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Cooling Variable Construction

Annual Cooling Transforms

Monthly Cooling Transforms

Sum monthly heating values from the sales model.

Interact cool index values with peak day temperatures and prior day temperatures. Use splines if needed.

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Residential Monthly Usage Profiles

Water Heating loads are lower in summer due to warmer inlet water temperatures

Lighting Loads are larger in winter due to increased hours of darkness.

Refrigerator and Freezer loads are larger in summer due to warmer ambient conditions inside the home.

Heating and Cooling

© 2007, Itron Inc.

Residential Hourly Usage Profiles

Water Heating loads are lower in summer due to warmer inlet water temperatures

Lighting Loads are larger in winter due to increased hours of darkness.

Refrigerator and Freezer loads are larger in summer due to warmer ambient conditions inside the home.

© 2007, Itron Inc.

Base Use Variable Construction

Annual Other Transforms

Monthly Other Transforms

Sum monthly energy values from the sales model.

use

uu,y

use,yyuse,m PeakFrac

EnergySAE

EnergySAEBase_sReCP

Interact other annual usage with peak monthly peak fractions by class and end use.

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Example of Transformations – Res Lighting

use,m

uu,y

use,yyuse,m PeakFrac

EnergySAE

EnergySAEBase_sReCP

yBase_sRe

u

u,y

use,y

EnergySAE

EnergySAE

use,mPeakFrac

Res Light CP343 MW

42 MW

248 MW

31 MW

© 2007, Itron Inc.

Estimate Peak ModelRegression Statistics

Iterations 1

Adjusted Observations 114

Deg. of Freedom for Error 103

R-Squared 0.91

Adjusted R-Squared 0.902

AIC 11.576

BIC 11.84

Std. Error of Regression 311.69

Mean Abs. Dev. (MAD) 236.96

Mean Abs. % Err. (MAPE) 3.76%Durbin-Watson Statistic 1.427

Variable Coefficient StdErr T-Stat

BaseVar 1.012 0.095 10.627

CoolVar 151.852 5.1 29.774

CoolVar_May -51.767 9.89 -5.235

CoolVar_Oct 38.606 17.159 2.25

HeatVar 7.719 3.116 2.477

MA_OtherLoad 1.558 0.175 8.901

Sep01 -1125.709 316.035 -3.562

Apr02 -1518.308 314.963 -4.821

Oct02 -1364.632 315.374 -4.327

Apr05 -1201.245 314.945 -3.814

Jun06 -995.097 316.178 -3.147

© 2007, Itron Inc.

ISO New England Energy Requirement Forecast

Uses a generalized econometric modeling framework Forecasts total system energy by state/region

> Annual model. Log/log specification. Forecast drivers include:

• Prior year energy• Real personal income• Real price• HDD and CDD

> Historical sales adjusted for past utility program efficiency savings

> Exogenous adjustment for future efficiency savings• Federal efficiency standards after 2013 (residential lighting)• Passive efficiency savings as bid into the market

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© 2007, Itron Inc.

ISO New England Peak Demand Forecast

Forecasts system peak by state/region> Daily demand model by month. Linear specification.

Forecast drivers include:• Energy requirement forecast• Peak-day weighted THI• Trend interactive with peak-day THI

> Historical peaks adjusted for load interruptions

> Exogenous adjustment for future demand impacts• Passive efficiency savings as bid into the capacity market

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© 2007, Itron Inc.

ISO Forecast Methodology

Relatively simple model specifications> Annual energy vs. monthly sales

> Aggregate system level vs. revenue class

> Peak demand is primarily driven by the energy forecast

Easier to model data series that have been adjusted for prior efficiency savings> No explicit end-use information incorporated in the model

But significantly less information than that embedded in the SAE framework

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Economic Data> Forecast Vintage

> State vs. Regional Definition Weather Data

> Weather station

> Weather variables Modeling Approach End-Use Efficiency and Saturation Trends Incorporating the Impact Energy Efficiency Program Other Issues

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EE&F Forecast Guideline Discussion

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Proposed Project Schedule June

> Complete forecast database July

> Develop end-use efficiency and saturation data August

> Estimate preliminary system peak forecast

> Present preliminary results September

> Develop zonal demand forecasts

> Deliver preliminary forecast report October

> Deliver final forecasts and report

> Present final forecast 34