© 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
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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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Simulation Results from Sales Models
mmomo
mmhmh
mmcmcm
eTrendcXOtherb
HDDTrendcXHeatb
CDDTrendcXCoolbaSales
Residential
Small C&ILarge C&IMunicipal
Cooling
Heating
Other
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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
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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
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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
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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
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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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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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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