Download
1 / 52
520 likes | 621 Views
Developing Load Reduction Estimates Caused by Interrupting and/or Curtailing Large Customers. By Carl L. Raish 2000 AEIC Load Research Conference. Tampa Electric’s Interruptible Rate Class. 32 Customers, 72 Accounts 1,631.5 GWH for Class in 1999 22.8 GWH per Account
E N D
Developing Load Reduction Estimates Caused by Interrupting and/or Curtailing Large Customers By Carl L. Raish 2000 AEIC Load Research Conference
Tampa Electric’s Interruptible Rate Class • 32 Customers, 72 Accounts • 1,631.5 GWH for Class in 1999 • 22.8 GWH per Account • 7.6 MW Average Non-coincident Peak • 285.9 MW 1999 Class Peak • 174.3 MW at 1999 Winter Peak • 60.8 MW at 1999 Summer Peak 2000 AEIC Load Research Conference
As a result of statewide generation shortages in 1999 the number of interruptions was at a record level 2000 AEIC Load Research Conference
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 2000 AEIC Load Research Conference
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 2000 AEIC Load Research Conference
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 2000 AEIC Load Research Conference
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 2000 AEIC Load Research Conference
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 2000 AEIC Load Research Conference
Reduction Estimates at Individual Account Level • Interruption / curtailment amount is the difference between the actual and predicted demands 2000 AEIC Load Research Conference
Reduction Estimates at IS Class Level • Sum individual account reduction amounts on an interval-by-interval basis to obtain class totals 2000 AEIC Load Research Conference
Apply the same method and compare results on some days in 1999 without interruptions 2000 AEIC Load Research Conference
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 2000 AEIC Load Research Conference
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 2000 AEIC Load Research Conference
Shortest Interruption • April 3, 1998 12:52 - 13:05 2000 AEIC Load Research Conference
Longest Interruption • April 24, 1999 12:30 - 20:04 2000 AEIC Load Research Conference
Winter Morning Interruption • January 6, 1999 06:37 - 08:14 2000 AEIC Load Research Conference
