1. Resource Adequacy Forecast Adjustment(s) Allocation Methodology
Miguel Cerrutti
Demand Analysis Office
Energy Assessments Division
R Workshop
California Public Utility Commission
San Francisco, February 9, 2015

2. Year-ahead load forecast adjustments Coincident factor (CF)
Outline
The challenges
Year-ahead load forecast adjustments
Coincident factor (CF)
adopted CF adjustment methodology
Weather normalization (WN) and
short-term load forecasting (STLF)
Improvements

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

4. Year-ahead load forecast time line
LSEs file historical load data
Year-ahead compliance filings due
Final date to file year-ahead load forecast changes
LSEs file year-ahead load forecast
LSEs receive final year-ahead allocations
LSEs receive initial year-ahead allocations
October 30st
March 20th
August 19th
July 31st
April 24th
September 18th

5. Pro rata adjustment to match CEC forecast within 1%
Year-ahead forecast adjustments
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%

6. D.12-06-025 Coincident Factor O.P. 4
Coincident factor (CF) adjustment - CPUC adopted
D 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.”

7. CAISO’s EMS hourly load data (across 1-3 years)
Coincident factor (CF) – the data
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

8. Include peak producing days – typical weather
Coincident factor (CF) - the process
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

9. LSE’s with stable load shapes and/or correlated with system loads
Coincident factor (CF) - the process … continuation
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 loads
at least three previous years of data
average hourly peak loads
LSEs with slightly higher load responses to more than normal weather patterns
WN CF - daily time-series regressive model to normalize daily LSE and CAISO system peaks

10. Review and validity assessment Small sample problems
Coincident factor (CF) - the process … continuation
Review and validity assessment
Small sample problems
no 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

11. Coincident factor (CF) - the process … continuation
LSE
Moy CF
3CP
5CP
Avg
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
.859
ESP 7
.695
All ESP
.923
.897
.884
LSE8
.836
.853
1.166
1.143
LSE9
.895
.845
.904
1.277
1.265
LSE10
.636
.916
1.438
.998
LSE11
.978
.914
.782
1.052
LSE12 3
.612
.765
1.341
.839

12. Accurate CF improves cost allocation
Coincident factor (CF) - benefits
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

13. most current IEPR (e.g., for 2016 RA, 2014 IEPR update)
Weather normalized (WN)
short-term load forecasting (STLF)
WN STLF is used to reconcile the aggregate LSEs year-ahead forecasts in each IOU area for RA compliance (plausibility adjustment)
Inputs to WN STLF
most current IEPR (e.g., for 2016 RA, 2014 IEPR update)
four years of CAISO hourly EMS data
hourly demand response impacts
30 years weather conditions

14. First time-series regressive modeling prior three years
Weather normalized (WN)
short-term load forecasting (STLF) – the process
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 simulation
peak load sensitivities and 30 years weather
one-in-two WN STLF for IEPR and one-in-ten (extreme weather) for CAISO’s LCR

15. Improving allocation of DR events and non-
Improvements
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 forecasts
LSEs 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