VELCO Long-Term Demand Forecast Kick-off Meeting June 7, 2010 Eric Fox

1. VELCO Long-Term Demand Forecast Kick-off MeetingJune 7, 2010Eric Fox

2. 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

3. 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

4. VELCO Daily Peak Demand (MW)

5. VELCO Monthly Peak (MW)

6. 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)

7. 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

8. Statistically Adjusted End-Use (SAE) Framework Heating Saturation Resistance Heat Pump Heating Efficiency Thermal Efficiency Home Size Income Household Size Price AC Saturation Central Room AC AC Efficiency Thermal Efficiency Home Size Income Household Size Price Saturation Levels Water Heat Appliances Lighting Densities Plug Loads Appliance Efficiency Income Household Size Price Heating Degree Days Billing Days Cooling Degree Days XOther XCool XHeat

9. 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

10. Efficiency Program Impacts Cooling Efficiency Program Marginal Efficiency No DSM Efficiency Path

11. Adjusted End-Use Indices (kWh per Cust)

12. Statistically Adjusted End-use Modeling (cont.) Estimate monthly average use regression models:

13. XCool

14. XHeat

15. XOther

16. Residential Average Use Forecast

17. End-Use Energy Forecast

18. Last Year’s Approach Combine end-use energy with end-use shapes Residential Base Use Cooling

19. Peak-Day System Hourly Load Profile (MW) Aggregate Class Load Forecasts to System Load Forecast And Find Annual System Peak System Residential Commercial Lighting Industrial

20. 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

21. Simulation Results from Sales Models Cooling Residential Small C&I Large C&I Municipal Heating Other

22. 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, … Total Monthly Energy Total Monthly Energy – Normal Weather -- All Classes Total Monthly Energy (GWh)

23. Heating Variable Construction Sum monthly heating values from the sales model. Interact heat index values with peak day temperatures and prior day temperatures. Use splines if needed. • Annual Heating Transforms • Monthly Heating Transforms

24. Cooling Variable Construction Sum monthly heating values from the sales model. Interact cool index values with peak day temperatures and prior day temperatures. Use splines if needed. • Annual Cooling Transforms • Monthly Cooling Transforms

25. Residential Monthly Usage Profiles Lighting Loads are larger in winter due to increased hours of darkness. Water Heating loads are lower in summer due to warmer inlet water temperatures Heating and Cooling Refrigerator and Freezer loads are larger in summer due to warmer ambient conditions inside the home.

26. Residential Hourly Usage Profiles Lighting Loads are larger in winter due to increased hours of darkness. Water Heating loads are lower in summer due to warmer inlet water temperatures Refrigerator and Freezer loads are larger in summer due to warmer ambient conditions inside the home.

27. Base Use Variable Construction Sum monthly energy values from the sales model. Interact other annual usage with peak monthly peak fractions by class and end use. • Annual Other Transforms • Monthly Other Transforms

28. Example of Transformations – Res Lighting 343 MW 248 MW Res Light CP 31 MW 42 MW

29. Estimate Peak Model

30. 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

31. 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

32. 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

33. EE&F Forecast Guideline Discussion • 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

34. 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