2013 Load Impact Evaluation Capacity Bidding Program (CBP)

1. 2013 Load Impact Evaluation Capacity Bidding Program (CBP) Steve Braithwait, Dan Hansen, and Dave Armstrong Christensen Associates Energy Consulting DRMEC Spring Workshop May 7, 2014

2. Statewide CBP Programs • Features of programs • Methods and validation • Ex-post load impacts (2013) • Load impacts by program, product & event • Ex-anteload impacts

3. CBP Features • Capacity ($/kW) payments for nominated load • Energy ($/kWh) payments to bundled customers • Monthly load reduction nominations (MW) • Product-type options • Day-ahead (DA) or Day-of (DO) notice • Event windows (1-4; 2-6; or 4-8) • 4 to 6 aggregators at each utility (a few are individual customers)

4. CBP Events - 2013

5. Nominated Customer Accounts; by Utility, Year, and DA & DO Notice

6. Nominated Customers, by Industry Type

7. Ex-Post Regression Model (Individual Customer Level) • Dependent variable = kWh/hour • Independent variables: • To estimate hourly event-day load impacts -- • Indicator variables for each hour of every event day • To control for weather conditions -- • CDH65_3MA and 24MA • To establish typical hourly load profile -- • Separate hourly indicator variables for Monday, Tuesday - Thursday, and Friday • To control for typical load level -- • Day-of-week indicator variables • Month-of-year indicator variables

8. Ex-Post Regression Model (2) • Independent variables (continued): • Event-hour indicators for events of other DR programs in which the customer is enrolled • Summer pricing season differences • Summer defined according to tariff season definitions • Separate summer load level and hourly load profile • Day-of, morning-load adjustment to improve accuracy • Average hourly load from hour-ending 1 through 10

9. Model Validation • Estimate models with event-like non-event days withheld from the sample (one at a time) and examine performance of model predictions on those days (MAPE, MPE, R Sqr) • Model variations included 18 different combinations of weather variables

10. Model Validation (2) • Also estimate “synthetic” event-day models • Test significance of coefficients on variables for event-like non-event days • Coefficients that are not statistically significant indicate that models do not falsely estimate load impacts on non-event days • Examine sensitivity of estimated hourly load impacts on actual event days across 18 alternative specifications • Compare predicted to actual loads on event-like days

11. Model Validation (3) • Findings from model validation: • Synthetic event tests do not find significant “false” load impact estimates • Little sensitivity of estimated load impacts across the tested specifications • Models predict well on event-like days

12. Sensitivity of Load Impact Estimates to Weather Variable Specification (PG&E)

13. Actual and Predicted Loads – Average Event-Like Non-Event (SCE)

14. CBP Ex-Post Load Impacts (2011 – 13)Typical Event – Average Event-Hour (MW)

15. Ex-AnteLoad Impact Simulation Process Estimate Customer-level Regression Coefficients 1-in-2 and 1-in-10 Weather Conditions Simulate Hourly Reference Loads Calculate % Load Impacts (Based on 3 Years of Ex-Post LI) Enrollment Forecasts Ex-AnteLoad Impacts per Customer Aggregate Load Impacts

16. Comparison of Previous Ex-Ante to Current Ex-Post and Ex-Ante [CBP]

17. Key Factors in Ex-Ante Changes • PG&E believes that aggregators with both CBP and AMP contracts have focused on achieving AMP commitments, leading to lower CBP load impacts • SCE anticipates movement of AMP-DA contract to CBP-DA, and shifting less responsive customer accounts from AMP-DO to CBP-DO • SDG&E results varied somewhat due to • unexpected changes in customer nominations, • changes in performance of a few large customers, and • changes in the mix of % load impacts in previous 3 years

18. Questions? • Contact – Steve Braithwait or Dan Hansen, Christensen Associates Energy ConsultingMadison, Wisconsin • Steve@CAEnergy.com • Danh@CAEnergy.com • 608-231-2266