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© 2007, Itron Inc. VELCO Long-Term Demand Forecast Kick-off Meeting June 7, 2010 Eric Fox
© 2007, Itron Inc.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
© 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 3
© 2007, Itron Inc. VELCO Daily Peak Demand (MW) 4
© 2007, Itron Inc. VELCO Monthly Peak (MW) 5
© 2007, Itron Inc. 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) 6
© 2007, Itron Inc. 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 7
© 2007, Itron Inc. 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 Heating Degree Days Cooling Degree Days Billing Days XCool XHeat XOther
© 2007, Itron Inc. 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 9
© 2007, Itron Inc. Efficiency Program Impacts Cooling Efficiency Program No DSM Efficiency Path Marginal Efficiency Marginal Efficiency
© 2007, Itron Inc. Adjusted End-Use Indices (kWh per Cust)
© 2007, Itron Inc.12 Statistically Adjusted End-use Modeling (cont.) Estimate monthly average use regression models:
© 2007, Itron Inc.13 XCool
© 2007, Itron Inc.14 XHeat
© 2007, Itron Inc.15 XOther
© 2007, Itron Inc. Residential Average Use Forecast 16
© 2007, Itron Inc. End-Use Energy Forecast 17
© 2007, Itron Inc. Last Year’s Approach Residential Cooling Base Use Combine end-use energy with end-use shapes Combine end-use energy with end-use shapes
© 2007, Itron Inc. Peak-Day System Hourly Load Profile (MW) Aggregate Class Load Forecasts to System Load Forecast And Find Annual System Peak Aggregate Class Load Forecasts to System Load Forecast And Find Annual System Peak System Residential Commercial Industrial Lighting
© 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 20
© 2007, Itron Inc. Simulation Results from Sales Models Residential Small C&I Large C&I Municipal 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, … Total Monthly Energy (GWh) 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.
© 2007, Itron Inc. 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.
© 2007, Itron Inc. 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. Interact other annual usage with peak monthly peak fractions by class and end use.
© 2007, Itron Inc. Example of Transformations – Res Lighting Res Light CP 343 MW 42 MW 248 MW 31 MW
© 2007, Itron Inc. Estimate Peak Model Regression Statistics Iterations1 Adjusted Observations114 Deg. of Freedom for Error103 R-Squared0.91 Adjusted R-Squared0.902 AIC11.576 BIC11.84 Std. Error of Regression311.69 Mean Abs. Dev. (MAD)236.96 Mean Abs. % Err. (MAPE)3.76% Durbin-Watson Statistic1.427 VariableCoefficientStdErrT-Stat BaseVar1.0120.09510.627 CoolVar151.8525.129.774 CoolVar_May-51.7679.89-5.235 CoolVar_Oct38.60617.1592.25 HeatVar7.7193.1162.477 MA_OtherLoad1.5580.1758.901 Sep01-1125.709316.035-3.562 Apr02-1518.308314.963-4.821 Oct02-1364.632315.374-4.327 Apr05-1201.245314.945-3.814 Jun06-995.097316.178-3.147
© 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 30
© 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 31
© 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 32
© 2007, Itron Inc. 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 33 EE&F Forecast Guideline Discussion
© 2007, Itron Inc. 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
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