CEC Load Forecast Adjustment ProcessMarch 12-13 2019 CPUC Resource Adequacy Workshop

Lynn MarshallLynn.Marshall@energy.ca.govEnergy Assessments DivisionCalifornia Energy Commission

Forecast Adjustment Overview1. Develop Reference forecasts for service area and direct access, and now DA and CCAs.2. Estimate and apply coincidence factor to LSE forecasts.3. Develop reference estimate or forecast for each LSE based on available data.4. Evaluate LSE forecasts for plausibility adjustment. 5. Apply adjustments for demand side modifiers.6. Apply the pro-rata adjustment to bring the total of the forecasts to within 1% of the CEC service area forecast.7. Evaluate the reasonableness of the pro-rata adjustment and total forecast for each LSE and service area. 8. If step 7 indicates pro-rata adjustment is too large, repeat step 5-8 as needed.

Summary of 2019 Results (MW)Element Service AreaJan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

SCE 12,257 12,043 11,661 11,573 13,303 15,254 18,406 21,818 16,702 13,171 12,094 12,938 SDG&E 3,016 2,962 2,847 2,843 2,969 3,099 3,335 3,783 3,938 3,347 3,113 3,204 PG&E 12,570 12,086 12,310 12,452 13,922 16,220 16,825 16,499 15,939 13,283 12,185 12,687 Total 27,843 27,090 26,818 26,868 30,194 34,573 38,566 42,100 36,578 29,801 27,391 28,828

SCE (511) (713) (543) (1,101) (1,173) (976) (1,298) (1,769) (850) (1,004) (1,130) (640) SDG&E (213) (323) (305) (277) (166) (449) (240) (363) (252) (301) (335) (223) PG&E (847) (752) (2,030) (1,990) (1,131) (1,459) (642) (1,597) (703) (506) (803) (441)

SCE 55 36 732 1,354 696 2,068 1,105 (1,203) 3,054 2,991 130 (442) SDG&E 43 23 (5) (66) 238 257 387 309 30 541 41 10 PG&E (202) (27) (908) 222 869 1,559 1,114 308 1,701 430 (34) 84

SCE (226) (289) (314) (375) (481) (537) (528) (537) (525) (533) (438) (394) SDG&E (98) (91) (94) (106) (112) (127) (137) (136) (128) (123) (102) (95) PG&E (616) (571) (632) (667) (910) (995) (1,034) (1,080) (1,012) (911) (623) (646)

SCE 244 439 895 1,781 1,606 1,914 982 1,418 1,494 1,876 1,474 620 SDG&E 127 151 270 364 243 305 189 393 155 246 230 72 PG&E 1,057 1,099 2,000 2,641 2,188 2,559 1,589 2,873 1,794 1,371 1,471 1,037

SCE 11,819 11,516 12,431 13,232 13,950 17,724 18,668 19,726 19,874 16,502 12,130 12,081 SDG&E 2,875 2,721 2,712 2,759 3,172 3,086 3,535 3,986 3,743 3,710 2,947 2,967 PG&E 11,961 11,834 10,740 12,659 14,938 17,884 17,852 17,002 17,719 13,667 12,196 12,721 Total 26,655 26,072 25,883 28,649 32,060 38,694 40,054 40,714 41,336 33,878 27,272 27,768

Pro rata adjustment

Final Load Forecast

Coincidence Adjustment

Plausibility Adjustment

EE/DG/DR Adjustment

Submitted LSE Forecast

• 2020 Forecast will be based on CED 2018 Update Mid-Mid TAC Area Monthly Coincident Peaks• Energy efficiency and other load modifiers adopted as part of the

forecast.• Compared to RA 2019 final year-ahead forecasts, coincident peaks

in the adopted forecast are generally higher in winter months, and lower in summer months.

• Relatively high forecasts used for 2019 for some months contributed to unallocated load in 2019.

• Minimal load growth is forecasted for 2020-2022.

CEC Reference Forecasts

Forecast Comparison: RA 2019 v. CEC 2018 Update Mid-Mid Case

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CAISO

Coinc

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(MW) CEC 2018 Update

RA 2019Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec MW Difference 1,237 1,201 599 76 17 (2,022) (11) (372) (791) (1,358) 354 1,059

• The conceptual basis is expected LSE demand at the time of a 1-in-2- system peak in each month. One to three years of historical hourly loads, for both the CAISO and each LSE are used.

• Median of top 5 peak hours is a good starting point, but:• Load diversity declines as demand rises, so if sample doesn’t include

sufficient number of relatively high load days, diversity will be overestimated, leading to increased unallocated load.

• Intra-month load migration can skew statistics• As LSEs add communities and classes, load profiles may change• Overall and many individual coincidence adjustments were notably

larger in 2019 than previous years.• Solutions:

• Exclude mild years or days from statistics data set, for all LSEs• Supplemental load data for migrating customers• Develop composite bundled/CCA factors for validation/comparison

Coincidence Adjustments

Example: PG&E TAC Area

• Adopted CEC forecast for April 2020 is 32,399 MW:

• CEC staff will evaluate 2017-2019 loads, temperatures, timing of peak, and resulting coincidence factors for consistency with 1-in-2 forecasted conditions.

• Additional analysis for CCAs and IOUs to account for migrating load• Develop representative factors for aggregate of CCAs & IOUs

• Decision rules will be applied consistently across LSEs.• Specific methodology and results will be presented at June DAWG.

Coincidence Analysis for 2020

• Staff uses various data to construct a reference estimate (of current loads for ESPs, or forecast for CCAs) for each LSE by service area:• Staff considers historic loads, the most recent month-ahead load

forecasts and DASR activity• A 5% percent deviation is a flag for additional review.• Adjustments should be internally consistent (i.e., logical pattern across

months.• 2019 adjustments did include a pro-rata adjustment in the plausibility

adjustment, which is not the standard methodology.• IOU comparison addresses service area differences• New for 2020: Pairwise CCA versus IOU comparison

• Additional documentation requested to support evaluation• CEC staff will first seek to resolve discrepancies via LSE forecast

revision• Final discrepancies allocated between CCA and IOU

LSE-Specific (“Plausibility”) Adjustments

• Supplemental historic loads requested with the year-ahead forecast submittal:• CCAs who are expanding their service area or customer class reach

should provide 12 months of aggregated recorded hourly loads for the targeted communities or sectors which are in the LSE’s forecast but not reflected in the previous year’s historic hourly loads.

• IOUs should provide a monthly peak forecast of departing load disaggregated by CCA.

• Both IOUs and CCAs should provide their assumed transition schedule of number of accounts by CCA, month, and class.

CEC Data Request Changes

Forecast Documentation to be submitted with the year-ahead forecast: • initial and steady state participation rate/opt-out assumptions, by

customer class and phase or community• Weather normalization methods• Specific economic or demographic factors affecting the forecast• Energy efficiency programs, behind-the-meter resources, EV

growth, or any other programs or technologies incorporated in the demand forecast

CEC Data Request Documentation

• Unallocated load is allocated based on an LSE’s adjusted forecast share of total service area adjusted forecasts.

• With appropriate adjustments in prior steps, the total amount of pro-rata load should be relatively small.

• Large amount of unallocated load indicate needs for review of earlier steps.

• Validity checks for 2020 will also include:• Aggregate of CCA load consistent with staff reference estimate• Aggregate of Direct Access load consistent with staff reference

estimate based on recent loads and current DA cap.

Pro-Rata Adjustment

• CEC Demand Analysis Working Group (DAWG) is forum for technical discussion of demand forecasting issues• Anyone may participate

• June workshop will include• Review of results for 2020 RA aggregated adjusted forecasts

• Coincidence methods• Plausibility results (CCA v. ESPs)• Demand side load modifiers

• Discussion and comparison of TAC-area 2019 IEPR hourly model results, to be used for 2021+

• Tentative date of June 12th• See http://dawg.energy.ca.gov/ for email list signup

2020 Adjustment Review Workshop