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EILS Third Baseline Analysis

EILS Third Baseline Analysis. DSWG Meeting March 27, 2009. Overview. Background Information Analysis Methodology Baseline Ranking Summary Statistics Conclusions. Background Information. Proposed baseline concepts analyzed but not adopted

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EILS Third Baseline Analysis

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  1. EILS Third Baseline Analysis DSWG Meeting March 27, 2009

  2. Overview • Background Information • Analysis Methodology • Baseline Ranking Summary Statistics • Conclusions

  3. Background Information • Proposed baseline concepts analyzed but not adopted • Baseline kW = average Load for the hour before notification • High X of Y (3 of 10) • Average of last X (1, 5, 10) days of same day-type • Average of last X (7, 10) days of same day-type, excluding highest and lowest days

  4. Background Information (continued) • Day of Adjustment concepts analyzed but not adopted • Adjustment Type • Additive • Adjustment amount = (actual kW – base kW) / n where n = number of adjustment intervals • Adjusted base kW = adjustment amount + base kW • kWh values determined over selected adjustment intervals • Adjustment Intervals • Two Before • Eight intervals beginning two hours before the event start time • No time gap between adjustment intervals and event • Four Before • Eight intervals beginning four hours before the event start time • Two hour time gap between adjustment intervals and event

  5. Background Information (continued) • EILS sub-team identified two additional baseline methodologies to support a “drop-by” curtailment • Mid 8 of 10 (Olympic) with Day of Adjustment • Matching Day Pair with Day of Adjustment • Ercot analysis goal was to identify which of the two additional baselines should be offered.

  6. Regression Model(default) • Model development continues unchanged from beginning of EILS • Added baseline day-of adjustment: • Scalar • Adjustment factor = actual kWh / base kWh • Adjusted base kW = adjustment factor × base kW • kWh values determined over selected adjustment intervals • Three Before • Eight intervals beginning three hours before the event start time • One hour time gap between adjustment intervals and event

  7. Mid 8 of 10(Olympic) • Find 10 most recent days with the same day-type as the event day • Compute kWh for each of the 10 days • Eliminate the days with the highest and lowest kWh • Day Types • Weekday • Weekend/Holiday • Baseline is the average of the 8 days together by interval • Baseline day-of adjustment: • Scalar • Adjustment factor = actual kWh / base kWh • Adjusted base kW = adjustment factor × base kW • kWh values determined over selected adjustment intervals • Three Before • Eight intervals beginning three hours before the event start time • One hour time gap between adjustment intervals and event

  8. Matching Day Pair Baseline(Déjà VuMethod) • Find 10 closest matching day-pairs -- match intervals for the entire day before and the day of the event up to 1 hour before start of event • Match By Day Pair Types (based on event day) • Weekday • Weekend/Holiday • Baseline is the average of the 10 day-pairs together by interval • Baseline day-of adjustment: • Scalar • Adjustment factor = actual kWh / base kWh • Adjusted base kW = adjustment factor × base kW • kWh values determined over selected adjustment intervals • Three Before • Eight intervals beginning three hours before the event start time • One hour time gap between adjustment intervals and event

  9. Analysis Methodology Identified two groups of ESIIDs selected from previous contract cycles • Alternate Baseline ESIIDs • Eliminated very large “Batch Processing” Loads • 137 ESIIDs used in the analysis • Default Baseline ESIIDs • 67 ESIIDs used in the analysis • Extracted interval data for January 1, 2006 thru February 13, 2009 • Simulated 2 hour EILS events for every possible baseline hour for each ESIID • Determined unadjusted baseline kW for the 3 “drop-by” baselines • Applied day-of adjustment factor to create an adjusted baseline kW • Calculated the difference between actual and baseline Load for all intervals in the event • Computed summary statistics to compare the baselines at the ESIID level • R-Square • Mean absolute percent difference • Mean percent difference • 95th percentile overprovision (kW) • 95th percentile overprovision as a percent of mean kW • Established thresholds for drop-by baseline consideration: • Mean percent difference (absolute value < 5%) - Unbiased • 95th percentile overprovision as a percent of mean kW (<20%) • Ranked baselines for each percentage statistic and determined the best baseline(s) for each ESIID based on overall accuracy

  10. Baseline Ranking Summary Statistics D = Default M = Matching Day Pair O = Mid 8 of 10 • Table above shows counts of ESIIDs meeting the threshold requirements and with enough data to determine all 3 “drop-by” baselines • For many ESIIDs baseline assignments tend to be Contract Period Specific • An ESIID could be “M” in OctJan and “O” in FebMay • Many ESIIDs currently on “D” would have the option any of the 3 drop-by baselines • About one-half of ESIIDs previously assigned to the Alternate baseline could now be assigned to a drop-by baseline • About one-fourth of ESIIDs previously assigned to the Alternate baseline would have the option any of the 3 drop-by baselines

  11. Baseline Ranking Summary Statistics D = Default M = Matching Day Pair O = Mid 8 of 10 • Tables above are counts of ESIIDs meeting the threshold requirements and with enough data to determine all 3 “drop-by” baselines • Baseline assignments are Time Period as well as Contract Period Specific • ESIID baseline assignment for a Contract Period would be dependent on which Time Periods are being offered • If an ESIID bids into less then the 4 Time Periods a drop-by assignment becomes more likely

  12. Conclusions • “Matching Day Pair” and “Mid 8 of 10” baselines should be made available for EILS • More ESIIDs would have the option of a drop-by baseline • Baseline shadowing is more manageable • Many ESIIDs could be offered more than one drop-by baseline option • ERCOT will provide comparative statistics to assist QSE with the baseline selection • If no “Drop-by” baseline performs adequately then “Alternate” baseline would be assigned • QSE could opt for “Alternate” baseline • Overprovision levels have to be carefully considered for any drop-by baseline by each QSE • ERCOT will provide 95th percentile overprovision levels to facilitate QSE decision • Performance and availability for an aggregation can be computed even with a mix of drop-by baselines assignments • For initial roll-out, the sub-team has recommended assigning the same baseline to all ESIIDs in an aggregation • Drop-by baseline assignments can be contingent on the specific set of Time Periods being offered • ERCOT’s baseline assignment could change between Resource Identification and Bid Submission if the Time Periods are modified • ERCOT’s baseline review service needs to modified to consider all 3 drop-by baselines • The service may not be available prior to the next bidding cycle • ERCOT is continuing to analyze the performance of the baseline algorithms

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