Load Impact Evaluation: SDG&E PTR Pilot

1. Load Impact Evaluation:SDG&E PTR Pilot Steve Braithwait and Dan Hansen Christensen Associates Energy Consulting DRMEC Spring Workshop July 26, 2012

2. Peak Time Rebate Pilot • Key features of the PTR pilot & evaluation challenges • Participant & Control group samples • Ex-post load impacts (2011) • Methods • Load impacts • Ex-ante load impacts • Approach • Results

3. Key Features of PTR Pilot • SDG&E randomly selected and assigned a representative sample of customers to the pilot • Simulates automatic PTR enrollment in 2012 • Differs from most other PTR pilots, which have solicited volunteers • 100 of 3,000 sample also Summer Saver (SS) • Smart metering allowed easy selection & use of control group • Participants were eligible for bill credits for event-period usage reductions relative to customer reference load ($0.75/kWh, or $1.25 for SS participants) • Customers notified a day ahead by automated phone message (options to receive electronic notification)

4. Key Features of PTR Pilot (2) • Events turned out to have unusual properties, which made evaluation challenging: • 5 events, called on the only hot days (no comparable non-event days) • First event was on Sunday (non-comparable) • Second of two consecutive September events interrupted by system-wide outage • Last two events were in mid-October (customers’ loads lower than in mid-summer)

5. 2011 PTR Pilot Events

6. Key Findings (1) • Average participant reduced usage by 1 to 5 percent (relative to control group) • Usage reduction on most “typical” event (Sept. 7) was 4.5%, or 0.06 kWh/hr • 63% of participants reported they were aware of their participation and of being notified of events

7. Key Findings (2) • Analysis of customer-level data identified 11% of participants who were significant “responders” (41% usage reduction) • Comparison of regression-based and CRL-based usage reductions indicated moderate correlation, and suggests that CRL-based estimates under-state reductions relative to regression

8. Characteristics of PTR Participants

9. Observed Loads on Sept. 7 EventOverall Participant and Control Groups Overall 5.6% usagereduction relative tonon-event load difference

10. Study Methodology • Evaluation used regression analysis applied to Smart Meter data • Data averaged across 12 sub-sets of customers by size (3) and location (4 climate zones) • Analysis conducted on differences in hourly load between participants and control groups

11. Ex-Post Regression Model • Dependent variable = kWhC – kWhT • Independent variables: • To estimate hourly event-day load impacts -- • Indicator variables for each hour of every event day • To control for weather conditions -- • CDH, lag CDH, CDD, lag CDD • To establish typical hourly load profile -- • Separate hourly indicator variables for each weekday-type

12. Overall Estimated Load Impacts

13. Observed Loads of Average “Responder,” Participant & Control

14. Observed Loads of Average Control & Average “Control Responder”

15. Relationship Between Regression-Based & CRL-Based Usage Reductions

16. Ex Ante Load Impact Simulation Process Customer-level Regression Model Coefficients 1-in-2 and 1-in-10 Weather Conditions Simulate Hourly Reference Loads Percentage Load Impacts Enrollment Forecasts Ex Ante Load Impacts

17. Basis for Ex-Ante Load Impacts • Percentage load impacts are based on September 7th load impacts • Other event days had unusual features • % LI adjusted for: • Differences in ex post and ex ante weather conditions and time of year using SPP estimates • Differences in historical versus expected awareness rates (63% historically versus 50% expected)

18. Enrollment Forecast • Enrollment forecast is simply SDG&E’s expected number of residential customers • SDG&E assumes that 50 percent of customers will be “aware” of the program • We incorporate the awareness rate when developing percentage load impacts

19. Enrollment Forecast (2)

20. Ex Ante Load ImpactsAugust 2014 1-in-2 Peak Day

21. Ex Ante Load ImpactsMonthly 2014 1-in-2 Peak Days

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