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Capacity Bidding Program 2011 Impact Evaluation Jason Burwen, M.P.P. DRMEC Load Impact Workshop San Francisco, CA July 25, 2012. Presentation overview. Introduction to the Capacity Bidding Program Evaluation methodology and validation Ex post results Ex ante results. Capacity Bidding Program.

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  1. Capacity Bidding Program2011 Impact EvaluationJason Burwen, M.P.P.DRMEC Load Impact WorkshopSan Francisco, CAJuly 25, 2012

  2. Presentation overview Introduction to the Capacity Bidding Program Evaluation methodology and validation Ex post results Ex ante results

  3. Capacity Bidding Program • Statewide program • Capacity payments ($/kW) for nominated load reductions • Additional energy payments ($/kWh) for bundled customers • Customers nominate amounts to be made available monthly • Different product options • Notification: day-ahead (DA) or day-of (DO) • Event duration: 1-4 hours, 2-6 hours or 4-8 hours • All customers participated through aggregators in 2011

  4. CBP differs across the utilities (1) SCE tended to call events for a subset of nominated accounts, whereas PG&E and SDG&E tended to call events for all nominated accounts simultaneously.

  5. CBP differs across the utilities (1) 245 kW 229 kW IOU & Avg Size SCE tended to call events for a subset of nominated accounts, whereas PG&E and SDG&E tended to call events for all nominated accounts simultaneously. Industry mix and customer size differed between utilities.

  6. CBP differs across the utilities (1) 245 kW 229 kW IOU & Avg Size SCE tended to call events for a subset of nominated accounts, whereas PG&E and SDG&E tended to call events for all nominated accounts simultaneously. Industry mix and customer size differed between utilities. CBP product mix differed between utilities.

  7. CBP differs across the utilities (2) Thus, ex post results are not strictly comparable across the three utilities • Event hours and event duration vary by event and utility • Hours ranged from 1 PM to 7 PM • Durations ranged from 1 hour to 5 hours • Practices for event dispatch vary • System load patterns across utilities are not always coincident, particularly for Northern and Southern California • PG&E's system peaked on June 21 • SCE and SDG&E's peaked on September 7

  8. Several tests were conducted to ensure impacts were accurate • How well do the regressions predict out-of-sample on event-like days? • Do the regressions confound event conditions with other factors? • Do we get similar results if we change the regression model? • How accurate are the regressions across temperature conditions? • How accurate are the regressions for the largest customers, who are likely to account for most of the load reductions?

  9. Predictive accuracy during event-like days was high for all three utilities • The better the electricity use pattern during event conditions is explained, the less likely it is that other factors will be confounded with impacts • Steps in assessment • Identify event-like days based on highest system load • Exclude those days from regression models • Predict load for excluded event-like days • Assess how well predicted values match up with actual values

  10. The regressions are unlikely to confound other factors with impacts • We used a “false experiment” to assess the potential for confounding impacts • Identify event-like days • Introduce “fake event” variables • The impacts are in fact zero since no event was called. Does the regression get it right or does it pick up impacts when there are none?

  11. CBP ex post results: PG&E • Manufacturing customers accounted for 40% of day-ahead load impacts • Retail customers accounted for 42% of same-day load impacts • Central Valley customers accounted for 89% of day-ahead load impacts and 59% of same-day load impacts • The largest 1/5 of customers accounted for 62% of day-ahead load impacts and 48% of same-day load impacts

  12. PG&E’s CBP-DA load impact on August 26 was 17.9 MW, or 33.3%

  13. CBP ex post results: SCE • Retail customers accounted for 82% of day-ahead load impacts and 94% of same-day load impacts • Retail stores reduced loads by 30% on average for day-ahead customers and 20% on average for same-day customers • Los Angeles Basin customers accounted for 76% of day-ahead load impacts and 74% of same-day load impacts • The largest 1/5 of customers accounted for 49% of day-ahead load impacts and 31% of same-day load impacts

  14. SCE’s CBP-DA load impact on August 24 was 6.1 MW, or 31.4%

  15. CBP ex post results: SDG&E • Manufacturing customers accounted for 84% of day-ahead load impacts • Customers shed 90% of loads on average • Retail customers accounted for 67% of same-day load impacts • Customers shed 17% of loads on average • The largest 1/5 of customers accounted for 89% of day-ahead load impacts and 43% of same-day load impacts

  16. SDG&E’S CBP-DA load impact on August 26 was 10.1 MW, or 38.7%

  17. Ex ante impacts are based on historical data and performance Electricity use data for 2010 and 2011 Ex Ante Load Impact Estimates Adjustments 1) Weather Year (1-in-2 and 1-in-10) 2) Enrollment forecasts Weather data for 2010 and 2011 Individual customer regressions Program nominations for 2010 and 2011 Ex Post Load Impact Estimates Participant information (industry, region, etc.) Event information (date and time) for 2010 and 2011 Ex ante impacts reflect the load reduction capability under a standard set of weather conditions that align with those that drive system planning

  18. Enrollment assumptions • Growth forecasts vary by utility • PG&E: Forecast 20% growth during 2012-2013, 0% growth during 2013-2015, and then 1-2% growth • SCE: Forecast 3% growth during 2012-2013, 6% growth during 2013-2014, and then 0% growth • SDG&E: Forecast 10% growth during 2012-2013, 5% growth during 2013-2015, and then 0% growth • Growth is assumed to be proportional to 2011 industry mix and customer size

  19. Ex ante forecasts based on enrollment forecasts and historical performance 2010-2011 • Proportion of customers nominated for an event assumed to equal average proportion nominated 2010-2011 • Customers make load reductions to meet nomination targets that do not vary over time or in proportion to loads • Thus, results do not vary between 1-in-2 and 1-in-10 weather years • Ex ante estimates illustrate resources available when all nominated accounts are solicited for an event • Ex post estimates are based on actual events, which often did not solicit resources from every nominated account; therefore, ex ante resources will be larger than indicated by ex post results

  20. Ex ante forecast load impacts 2012-2022

  21. For any questions, feel free to contact Jason Burwen, M.P.P. Freeman, Sullivan & Co. 101 Montgomery Street 15th Floor, San Francisco, CA 94104 joshbode@fscgroup.com 415.777.0707

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