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Comprehensive Austin Energy Load Forecast Analysis and Resource Demand Modeling

This document presents a detailed overview of Austin Energy's load forecasting process, including annual and monthly energy sales, net to system generation, and peak demand analysis. It explores statistical models segmented by residential, commercial, and industrial sectors, incorporating economic drivers and demand-side management trends. The impact of significant legislation on energy independence and economic recovery is evaluated, alongside the factors influencing peak demand predictions and resource peak capacity. Historical data and current economic conditions are integral to establishing future load requirements.

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Comprehensive Austin Energy Load Forecast Analysis and Resource Demand Modeling

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  1. Austin Energy Load Forecast Outputs • Annual and monthly energy sales • Net to System Generation • Annual, Monthly Peak Demand • Long Term Hourly Load forecast

  2. Austin Energy ProcessOverview • Sector models (Residential, Commercial, Industrial) • statistically adjusted end use modeling with economic drivers and demand side management trends • Historical KWh, meter reading schedules, net to system • Net To system is a function of current month sales and next month sales • Peak model is a function of Net to System, temperature and DSM Active Load Control deployment

  3. Risks: Impacts of New Law – 2007 Energy Independence & Security Act (EISA) & 2009 American Recovery and Reinvestment Act (ARRA) Magnitude and speed of economic recovery Impact of major industrial key accounts and their expansion plans Magnitude of New DSM Savings Goals in light of economic conditions Base Case Scenario Assumptions 3

  4. Peak Demand Model Specification Resource Peak Demand requirements (ResourcePeakMWy,m ): ResourcePeakMWy,m = PeakMWy,m - DLCy,m where: PeakMW y,m - Peak One Hour MW Demand Regression Model estimates before any DSM Direct Load Control (DLC) deployments in forecast year y, month m DLC y,m - DSM Active Load Control (ALC) Program deployments measured in MW in forecast year y, month m. Peak One Hour MW Demand Regression Model: PeakMWy,m = f ( ExtremeDayTempy,m , NTSy,m ) where: ExtremeDayTemp y,m- Peak Day Extreme Max/Min Temperature in forecast year y, month m NTS y,m- AE’s Net-to-System Generation or Net Energy for Load Requirements in forecast year y, month m

  5. Peak Demand Model Specification In regression model form, the Peak One Hour MW Demand model is specified by the following equation: where: NovHDD y,m , DecHDDy,m, JanHDDy,m ,FebHDDy,m ,MarHDDy,m- Peak Day Min. Dry Bulb Temperature Heating Degree-Days for the months of November, December, January, February, and March in year y, month m, based on 55 F heating breakpoint temperature. FebCDD y,m , MarCDDy,m, AprCDDy,m , …, ,OctCDDy,m- Peak Day Max. Dry Bulb Temperature Cooling Degree-Days from the months of February till October in year y, month m, based on 65 F cooling breakpoint temperature. TrendVar - Linear Time Trend variable

  6. One Hour Peak MW Model Performance Model Stats:

  7. Austin Energy Load Flow Inputs • System Peak = Substation Loads + Losses Substation Load Allocation Considerations: • Actual Metered Load History • Distribution Feeder Configuration/Switching History • Abnormal Load Levels for Industrial Customers • Adjust for High-Probability Spot Load Growth • Scale Trended Loads to Match Forecasted Peak

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