Resource Adequacy Demand Forecast Coincidence Adjustments

1. Resource Adequacy Demand Forecast Coincidence Adjustments R.11-10-023 2013 Resource Adequacy Workshop January 26, 2012 Lynn MarshallElectricity Supply Analysis Division Lynn.Marshall@energy.state.gov / 916-654-4767 California Energy Commission www.energy.ca.gov

2. RA Year Ahead Demand Forecast Process 1.Each Jurisdictional LSE submits a noncoincident monthly peak forecast, for each TAC area for the forthcoming calendar year. 2.CEC makes adjustments for reasonableness, demand-side impacts, and consistency (within 1%) with the CEC reference forecast for each TAC area. 3.CEC applies a TAC-area-specific monthly factor to adjust each LSE monthly peak forecast for coincidence with the CAISO system peak. Current RA rules that the same factor is applied to all jurisdictional LSE’s. California Energy Commission www.energy.ca.gov

3. Table 1: 2012 RA Coincidence Factors for CPUC-Jurisdictional LSEs California Energy Commission www.energy.ca.gov

4. Current Coincidence Rule “... PG&E recommends addition of a single adjustment factor for all LSEs. Thus, each LSE's forward procurement obligation would be its final, forecasted non-coincident load for a month, as determined by the CEC, reduced by a factor that reflects the average load diversity ... in that month. As PG&E notes, averaging is more stable and easier to calculate, monitor, and apply. We adopt the PG&E approach, and grant discretion to the CEC to determine the exact method by which the PG&E approach is implemented.” (CPUC Decision 05-10-042 October 27, 2005 page 35) 2011 RA Workshop The AREM proposal to use LSE-specific or sector-specific coincidence factors was considered. CEC Staff presented data on LSE and sector coincidence demonstrating the inaccuracies caused by the current rule.

5. CPUC Decision 11-06-022 June 23, 2011: “An average coincidence factor across all customer classes hides certain cost differences among classes and LSEs. In essence, this method serves as a cross subsidy from industrial and commercial customers to residential .” Finding of Fact 5, p. 63 “The average coincident factor method is also inconsistent with methods used to develop a bundled customer forecast in support of the Commission's long-term procurement process. In both RA and long-term procurement proceedings, the Commission has determined that the adopted CEC forecast is to serve as the reference case. The CEC also provides LSE-specific coincidence adjustments to each California LSE which is outside of the Commission's jurisdiction for LSEs' use in CAISO RA compliance filings. Adopting an LSE-specific methodology for RA would harmonize the long-term procurement process and RA procurement process, as well as improve cost allocation related to cost causation.” (p. 17) California Energy Commission www.energy.ca.gov

6. CEC Staff Demand Forecast Coincidence Methods • Accounting for LSE coincidence with TAC and CAISO peaks is part of CEC demand forecasting methods. Bundled and direct access are distinct, with ESPs modeled as a group.* • For RA, CEC staff estimate coincidence for the aggregate of IOU service area loads using historic hourly loads (the sum of bundled and direct access) • CEC staff prepare LSE-specific factors for each nonjurisdictional LSE in the CAISO using LSE hourly loads. CEC coincidence adjustments are required by the CAISO tariff. • The same method can be applied to jurisdictional LSEs. This would be address cost-shifting, and allow for consistency with LTTP assumptions. *See for example p.44 and p. 51 in http://www.energy.ca.gov/2009publications/CEC-200-2009-012/CEC-200-2009-012-CMF.PDF. California Energy Commission www.energy.ca.gov

7. CEC LSE Specific Coincidence Concept: expected load at the time of a 1-in-2 system peak Data: 1-3 years of hourly loads for CAISO and for each LSE Estimated hourly impacts of demand response events Weather data Hourly loads are adjusted for demand response events. Weekends and holidays, and days with atypical weather are excluded For most LSEs, the coincidence factor used is the median coincidence of the upper 0.5 % of system peak hours, ranked by the magnitude of the CAISO peak. Validation includes evaluation of consistency with forecast, comparison across years, comparison with average peak hour loads

8. Application to Year-Ahead RA Forecasts The historic load method is also valid for jurisdictional LSEs. Each LSE’s historic hourly loads within a TAC area reflect the factors that determine coincidence: customer characteristics and the geographic distribution of their load. • Most LSE’s load composition does not change significantly from year to year, so recent loads, taking into account temperature, usually provide the best estimate of expected coincidence patterns. • ESP forecasts are based heavily on current customers. • Direct access enrollment is capped, so there is limited opportunity for migration. California Energy Commission www.energy.ca.gov

9. Year-Ahead Implementation Issues • IOUs should provide CEC with demand response capacity (in MW) by program and LSE. This would improve allocation of DR event impacts to hourly loads. • An additional validation check can be implemented to correct for within-month load migration • For new LSE’s, composite ESP factors for each TAC area can be used. • It may occasionally occur that a small LSE forecasts a significant change from the previous year. CEC can use alternative methods to develop appropriate factors, such as are currently used for non-CPUC jurisdictional LSEs or the CEC’s own forecast. For example, development of factors for water pumping LSEs take into account variations in hydrologic conditions. California Energy Commission www.energy.ca.gov

10. Month-Ahead Coincidence Adjustments • Revised monthly forecasts are submitted 2 months before the compliance period. Month ahead forecasts may be revised for load migration only. • Over the course of a year, 1 to 2 percent of service area load (or less) migrates, but for smaller LSE’s the percentage change can be much larger. • Adjustments should be consistent within a TAC area, for a given customer • AREM proposes multiple LSE-type categories, but this would result in different factors for the same customer. • IOU load profiles could be used to adjust migration by class; trade-off of “accuracy” versus forecast preparation difficulty. Load profiles are based on samples of all customers in a class, not specifically direct access. Individual customers will deviate from average. California Energy Commission www.energy.ca.gov

11. Table 3: Illustrative Coincidence Factors for SCE Area California Energy Commission www.energy.ca.gov

12. Options for Month-Ahead Coincidence Adjustment • Data sources for Table 3: • SCE 2011 RA factors were estimated from the sum of SCE bundled and all ESP hourly loads. • Composite SCE ESP estimated from sum of SCE ESP hourly loads. • SCE load profile factors are estimated from SCE’s 2010 rate group load profiles developed for settlement purposes. Similar data are available for PG&E and SDG&E. (http://www.sce.com/AboutSCE/Regulatory/loadprofiles/default.htm) • Alternative 1: Use an ESP composite factor for all non-CCA migrating load. Have a separate factor for CCA load. • Alternative 2: Use class-specific groups by TAC area. Each LSE would need to report net load migration by group in their month-ahead load forecast. California Energy Commission www.energy.ca.gov