Resource Adequacy Forecast Adjustment(s) Allocation Methodology

Miguel Cerrutti

Demand Analysis Office Energy Assessments Division

R.14-10-010 WorkshopCalifornia Public Utility Commission

San Francisco, February 9, 2015

The challenges

Year-ahead load forecast adjustments

Coincident factor (CF) adopted CF adjustment methodology

Weather normalization (WN) and short-term load forecasting (STLF)

Improvements

Outline

Arrive at LSE-specific final year-ahead load forecasts for RA compliance

Assign a value for each LSE’s contribution to CAISO peak loads

Forecast weather normalized short-term peak loads for IEPR (summer) and RA (monthly)

Ensure a transparent and repeatable process with well-supported and consistent key assumptions with RA and CEC

Challenges

LSEs file historical load data

LSEs file year-ahead

load forecast

LSEs receive initial year-ahead allocations

Final date to file year-ahead load forecast

changes

Year-ahead compliance filings

due

March 20th

April 24th July 31st

September 18th

October 30st August 19th

LSEs receive final year-ahead allocations

Year-ahead load forecast time line

Coincident adjustment – LSE-specific peak load contribution at time of CAISO’s monthly peak load

Plausibility adjustment – reconcile aggregate LSEs monthly peak load forecasts against CEC’s monthly WN STLF for IOU service areas

Prorated adjustments to LSEs forecasts to account for demand side energy savings paid for through distribution charges

Pro rata adjustment to match CEC forecast within 1%

Year-ahead forecast adjustments

D.12-06-025 Coincident Factor O.P. 4“The resource adequacy program shall be modified so that the coincidence adjustment factor uses a load service entity-specific coincidence adjustment factor for annual resource adequacy requirements, and an energy service provider-composite coincidence factor for monthly resource adequacy requirements, as follows:

*Annual Resource Adequacy Requirements – The California Energy Commission will calculate a Load Serving Entity-specific coincidence adjustment factor using Load Serving Entity hourly loads; and

*Monthly Resource Adequacy Requirements – The California Energy Commission will calculate an

Electric Service Provider-composite coincidence factor, which would be applied to each Electric Service Provider’s migrating load for the month; migrating load for community choice aggregators would be treated separately.”

Coincident factor (CF) adjustment - CPUC adopted

CAISO’s EMS hourly load data (across 1-3 years)

five highest monthly CAISO system peak hours

LSE hourly load data (across 1 – 3 years)

monthly non-coincident peaks

Average hourly peak loads

Weather data

Weather normalized daily LSE and system peaks

Coincident factor (CF) – the data

LSEs coincident peaks associated with the monthly five highest CAISO system peak hours

Monthly CF as a median over the ratios of the five LSE’s coincident peaks to its non-coincident peak

Include peak producing days – typical weather

Monthly CF to develop LSEs peak forecasts coincident with the CAISO system peak hours

Coincident factor (CF) - the process

LSE’s with stable load shapes and/or correlated with system loads one year of current load data

LSEs with unstable load shapes and/or not correlated with system loadsat least three previous years of data average hourly peak loads

LSEs with slightly higher load responses to more than normal weather patternsWN CF - daily time-series regressive model to normalize daily LSE and CAISO system peaks

Coincident factor (CF) - the process … continuation

Review and validity assessment

Small sample problemsno days closer to one-in-two conditions

Over time inconsistent loads so unstable coincidence patterns – meaningless statistics

Monthly load migration

CF for aggregate of ESPs

Coincident factor (CF) - the process … continuation

Coincident factor (CF) - the process … continuation

LSE Moy CF3CP

CF5CP

CFAvg

WN CF CP / WN CP

NCP / WN NCP

LSE1 10 .528 .937 .841

LSE2 11 .920 .868 .789

LSE3 8 .605 .718 .802

LSE4 6 .720 .719 .842

LSE5 12 .674 .674 .859

ESP 7 .695 .695 .937

All ESP 7 .923 .897 .884

LSE8 7 .836 .897 .853 1.166 1.143

LSE9 8 .895 .845 .904 1.277 1.265

LSE10 6 .636 .789 .916 1.438 .998

LSE11 8 .978 .914 .782 .842 1.052

LSE12 3 .612 .765 .978 1.341 .839

Better information with well reasoned-analysis suggests a more appropriate LSEs CF

Accurate CF improves cost allocation

Provides a realistic (as possible) LSE-specific CF without unfairly impacting the CFs of other LSEs

Once a CF is assigned, it is considered fixed and is not changed

CF is only corrected if it is found to be in error due to data filing or calculation errors

Coincident factor (CF) - benefits

WN STLF is used to reconcile the aggregate LSEs year-ahead forecasts in each IOU area for RA compliance (plausibility adjustment)

Inputs to WN STLFmost current IEPR (e.g., for 2016 RA, 2014 IEPR update)four years of CAISO hourly EMS datahourly demand response impacts30 years weather conditions

Weather normalized (WN) short-term load forecasting (STLF)

First time-series regressive modeling prior three years selecting functional form and explanatory effects using sample analysis (current year)

Second time-series regressive modeling last three years estimating peak load sensitivities to selected effects

Monte Carlo probabilistic simulationpeak load sensitivities and 30 years weatherone-in-two WN STLF for IEPR and one-in-ten (extreme weather) for CAISO’s LCR

Weather normalized (WN) short-term load forecasting (STLF) – the process

Improving allocation of DR events and non-

events to hourly loads, LSE’s year-ahead forecasts, and CEC’s forecasts

unclear whether or not DR impacts are embedded in LSE’s historic hourly loads and year-ahead forecastsLSEs need to provide additional information about the extent and type of DR embedded in the hourly and forecast data

For transparency, there will be an attempt to post the monthly five highest CAISO system coincident peak load hours

Improvements