Develop Long-Term Peak Demand Forecasts. Eric Fox 2010 AEIC Load Research Conference Sandestin, Florida

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1 Using Electric / Gas Load / Water Research Data to Develop Long-Term Peak Demand Forecasts Eric Fox 2010 AEIC Load Research Conference Sandestin, Florida August 15-18, 2010

2 Overview Every three years, the Vermont Electric Company (VELCo) is required to develop a long-term transmission capacity plan, including a long-term demand forecast for the system and twelve delivery points. The challenge is to develop a demand forecast that incorporates differences in customer class sales growth resulting from new efficiency standards, efficiency tax credits, and aggressive state-wide efficiency program activity. This presentation evaluates two demand forecasting approaches for achieving this goal.

3 Vermont Load Characteristics Relatively flat system load profile Large base load relative to heating and cooling loads Recently shifted from winter to summer peaking Peak demand has been growing faster than energy Strong growth in residential room air conditioning saturation Declining saturation of electric space heating Large statewide efficiency program that has been focused on lighting 80% of residential program savings from lighting 60% of commercial program savings from lighting

4 VELCO Daily Peak Demand (MW) MW Winter Spring Summer Fall Average Daily Temperature

5 VELCO Winter and Summer Month Peaks (MW) MW Winter Summer Average Daily Temperature

6 Typical Peak Demand Model Specification

7 Forecasting Peak Demand Standard approach Build a daily or monthly peak forecast regression model with peakday weather, day of the week, season, and an economic driver (such as GDP, Population). Or drive monthly or annual peak based on energy forecast. Problem Approach works well for near-term demand forecast, but over the longer timeframe does not adequately capture the impact of changes in class and end-use load diversity on peak hour demand. Solution First capture the impact of end-use saturation and efficiency trends in the class energy forecast and use resulting class level and enduse forecasts to drive peak demand.

8 Statistically Adjusted End-Use (SAE) Framework AC Saturation Central Room AC AC Efficiency Thermal Efficiency Home Size Income Household Size Price Heating Saturation Resistance Heat Pump Heating Efficiency Thermal Efficiency Home Size Income Household Size Price Saturation Levels Water Heat Appliances Lighting Densities Plug Loads Appliance Efficiency Income Household Size Price Cooling Degree Days Heating Degree Days Billing Days XCool XHeat XOther Sales = a + b XCool + b XHeat + b XOther + m c m h m o m e m

9 Changes in Residential Equipment Efficiency Central Air Conditioner Efficiency (SEER) Heat Pump Heating Efficiency (HSPF) Refrigerator Efficiency (Cubic Feet/KWh/Day) Electric Water Heater Efficiency (Energy Factor) Electric Cloths Dryer Efficiency (Days/KWh) Lighting Efficiency (Lumens/Watt)

10 Example of SAE Inputs (KWh/Household)

11 End-Use Variable - Cooling XCool y, m = CoolIndex y CoolUse y, m CoolIndex y Type Sat y Type = StructuralIndex y Weight y Type Type Sat 05 Eff Eff Type y Type 05 CoolUse y,m CDD = CDD y,m 05 HHSize HHSize y,m Income Income y,m Price Price y,m

12 Residential XCool Variable

13 Residential XHeat Variable

14 Residential XOther Variable

15 VELCO Class Energy Forecast (MWh) Residential Commercial Industrial

16 VELCO Monthly Energy Projections (MWh) Long-Term Energy Forecast Reflects End-Use Saturation and Efficiency Trends as well as Price and Economic Projections

17 Going from Energy to Peak Approach 1: Build-up hourly load system forecast from forecasted hourly customer class loads (The HELM approach) Estimate customer class hourly load profiles from load research data Hourly profiles that capture the impact of weather conditions and calendar events day of the week, season, holidays Combine with monthly class energy forecast that reflects changing end-use energy composition with class load profiles Aggregate monthly class profiles and scale to actual system load Find monthly peaks from build-up load profile forecast

18 Class Hourly Load Forecasts (MW) July 2012 Residential Commercial Industrial

19 System Load Forecast Summer Peak 2012 System Load Forecast 2012 Summer Peak Demand 964 MW July 18, 4:00 PM

20 Problem with the Build-up Model Model was generating winter annual peaks even though the recent trend has shifted to summer peaks Hourly load models tend to under-predict peak demand Not adequately capturing the end-use impacts on demand Spreading cooling and lighting load across too many hours Summer Peak 994 MW Winter Peak 1,030 MW

21 SAE Peak Model Approach 2: Explicitly capture end-use impacts on monthly peak demand (SAE Peak Model) Estimate monthly peak regression model that incorporates end-use load forecasts and peak-day weather conditions Model variable construction Estimate historical and projected heating, cooling, and other use energy requirements from the class sales models Use the heating and cooling energy estimates (weathernormalized and forecast) to construct peak-day heating and cooling variables Combine other use sales with end-use load profile data to build a monthly base-use load variable

22 Calculate heating, cooling, and other use energy (MWh for normal weather conditions) Sales m = a b b b c h o XCool XHeat m m XOther m + c + c c h + c Trend o Trend m Trend m m CDD HDD + e m m m Cooling Residential Small C&I Large C&I Municipal Heating Other

23 Construct Peak-Day Heating Variable Heat Index: Sum monthly heating values from the sales model. 1. Heat t = ResHeat t + ComHeat t 2. Heat_Idx t = Heat t / Heat 05 Heat Model Variable: HeatVar m = PkDayHDD m * Heat_Idx t Interact Heat Index with peak day HDD.

24 Construct Peak-Day Cooling Variable Cool Index: Sum monthly cooling values from the sales model. 1. Cool t = ResCool t + ComCool t 2. Cool_Idx t = Cool t / Cool 05 Cool Model Variable: CoolVar m = PkDayTHI m * Cool_Idx t Interact Cool Index with peak day CDD.

25 Construct Base Use Load Variable Start with typical end-use load 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.

26 Find End-Use Coincident Peak Loads 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.

27 Calculate End-Use Coincident Peak Variables Estimate peak fractions PeakFrac m, use = CPk m, use Energya, use Apply to end-use energy estimates EnergySAE Re scp Other PeakFrac m, use = Re s _ m, use = Com _ y y y, use EnergySAEy, u u EnergySAE ComCP Other PeakFrac y, use EnergySAEy, u u use use

28 Example of Transformations Res Lighting EnergySAE u y,use EnergySAE y,u PeakFrac m,use Re s _ Other y EnergySAE y, use CP m, use = Re s _ Othery PeakFracm, use EnergySAE y, u u Res Light CP 343 MW 248 MW 42 MW 31 MW

29 Add Class Coincident Peak Load Estimates BaseVar m = ResCP m + COMCP m + IndCP m +OtherCP m

30 Estimate Peak Model

31 SAE Peak Demand Model Forecast Summer Peak Winter Peak 2012 Summer Peak: 1,042 MW Winter Peak: 1,002 MW

32 Forecasted Annual Growth Summer peaks increase faster than energy and winter peaks Winter Peak Summer Peak Energy New lighting standards have a visible impact on 2013 energy and winter peak demand growth with a relatively small impact on summer peak.

33 Summary Both the hourly load build-up approach and the SAE peak model provide a framework for capturing the impact of class load diversity, and end-use saturation & efficiency trends on system peak demand The load build-up model will do a slightly better job of capturing the impact of load diversity over time The SAE model will do a better job capturing the impact of change in end-use loads Good load research data is required to support both modeling approaches.

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