Developing Load Reduction Estimates Caused by Interrupting and/or Curtailing Large Customers By Carl L. Raish 2000 AEIC Load Research Conference

July 31, AEIC Load Research Conference 2 Tampa Electric’s Interruptible Rate Class 32 Customers, 72 Accounts 1,631.5 GWH for Class in GWH per Account 7.6 MW Average Non-coincident Peak MW 1999 Class Peak MW at 1999 Winter Peak 60.8 MW at 1999 Summer Peak

July 31, AEIC Load Research Conference 3 As a result of statewide generation shortages in 1999 the number of interruptions was at a record level

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July 31, AEIC Load Research Conference 8 Reduction Estimates at Individual Account Level Need to estimate amount of load interrupted in MW and MWH during 1998 and 1999 Use account 15-minute data for the year (100% load research sample in place) For each interruption, select demands for the day prior to and the day of interruption

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July 31, AEIC Load Research Conference 10 Reduction Estimates at Individual Account Level Notification is typically sent out 2 hours before actual interruption Find 10 closest matching day-pairs (without interruptions) -- match demands for the entire day before and the day of interruption up to 3 hours before start of interruption Average the 10 day-pairs together by interval

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July 31, AEIC Load Research Conference 12 Reduction Estimates at Individual Account Level Run linear regression on intervals prior to interruption Model actual demand as a function of average demand If R-square >.5 and there are no outliers, then use the regression estimate. Otherwise, use the 10-day average demands

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July 31, AEIC Load Research Conference 14 Reduction Estimates at Individual Account Level Run linear regression on intervals prior to interruption Model actual demand as a function of average demand Apply model to the average demands for the rest of the day to predict what the demand levels would have been without an interruption

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July 31, AEIC Load Research Conference 16 Reduction Estimates at Individual Account Level Interruption / curtailment starts when the percentage difference between the actual demand and the predicted demand is negative and its absolute value is greater than all differences prior to the interruption Interruption / curtailment ends when residual goes positive after the interruption end time or the residual percentage is 2/3 of the maximum

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July 31, AEIC Load Research Conference 18 Reduction Estimates at Individual Account Level Interruption / curtailment amount is the difference between the actual and predicted demands

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July 31, AEIC Load Research Conference 22 Reduction Estimates at IS Class Level Sum individual account reduction amounts on an interval-by-interval basis to obtain class totals

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July 31, AEIC Load Research Conference 24 Apply the same method and compare results on some days in 1999 without interruptions

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July 31, AEIC Load Research Conference 31 Dealing with interruptions / curtailments on consecutive days One occurrence in 1998: June 22 and 23 Four occurrences in 1999: April 5 and 6, April 23 and 24, July 29, 30 and 31

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July 31, AEIC Load Research Conference 39 Dealing with voluntary curtailments occurring more than three hours before the start of the actual interruption Three occurrences: June 19, 1998; April 26, 1999; July 30, 1999

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July 31, AEIC Load Research Conference 43 Shortest Interruption April 3, : :05

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July 31, AEIC Load Research Conference 45 Longest Interruption April 24, : :04

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July 31, AEIC Load Research Conference 47 Winter Morning Interruption January 6, : :14

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July 31, AEIC Load Research Conference 52 Follow-up Analysis Analyze the impact on Individual Customer and IS Class load as a result of third party purchases Focus on impacts associated with new notification system during May 2000 Customer notification includes hourly forecasts of: –Probability of Interruption –Probability of third party purchase –Third party purchase price levels –Duration of purchase