1. 2013 Load Impact Evaluation of Southern California Edison’s Peak Time Rebate Program Josh Schellenberg DRMEC Spring 2014 Load Impact Evaluation Workshop May 7, 2014 San Francisco, CA

2.  Program Overview  Evaluation Objectives  Ex Post Methodology  Ex Post Results  Ex Ante Methodology  Ex Ante Results 1 Presentation Overview

3. Program Overview 2

4.  Known to customers as the “Save Power Day” program  Encourages residential customers to reduce load by responding to the availability of a bill credit during PTR event periods  Bill credit is calculated based on 2 to 6 PM load reduction below customer- specific reference level  Most customers earn a rebate of $0.75 per kWh reduced  Customers with approved enabling technology, such as programmable communicating thermostats (PCT), are eligible to earn an additional $0.50 per kWh reduced, for a total incentive of $1.25 per kWh  Customers signed up for notification receive email, text message and/or phone alerts that PTR credits are in effect from 2 to 6 PM the following day  Five PTR events in 2013, which occurred between July 2 and Sept. 9 3 Overview of SCE Peak Time Rebate (PTR) Program

5.  In 2012 and 2013, PTR was the default rate option for residential customers with a smart meter (nearly everyone was eligible for a rebate)  Additionally, SCE encouraged residential customers to directly enroll in notifications of PTR events by email, text message and/or phone  My Account customers were defaulted onto email PTR notifications  In 2013, SCE did not require residential customers to directly request notification in order to be eligible for rebates  PTR changes planned for 2014 will require that residential customers directly enroll in notifications of PTR events in order to be eligible for rebates 4 Brief History of PTR Program Eligibility

6.  As of March 2013, there were 205,890 active opt-in alert PTR participants  55% of participants enrolled in 2011 or 2012 and 45% enrolled in 2013 or early 2014  Newer enrollees are more likely to receive notification of PTR events through text message, which may improve awareness of PTR events  By comparison, there were approximately 600,000 default alert customers (My Account) 5 Opt-in PTR Enrollment by Customer Category (March 2013)

7. Evaluation Objectives 6

8.  Although nearly all of SCE residential customers were automatically enrolled in PTR, the scope of this evaluation is restricted to PTR customers that received notification of 2013 PTR events or customers that had enabling technology that was activated during the 2013 PTR events  Therefore, the 2013 SCE PTR load impact evaluation focuses on the following three customer segments:  Opt-in alert PTR customers (opt-in): Customers that voluntarily enrolled in PTR event notification by phone, text message and/or email (approximately 206,000 customers)  Default alert PTR customers (default): My Account customers that were defaulted onto PTR event notification through email (approximately 600,000 customers)  Third party PCT customers (PCT): Customers that have a PCT and participated in the third party PCT study, which enabled demand response during 2013 PTR events (approximately 2,800 customers) 7 Scope of Evaluation

9.  For each of the three participant groups, the primary objectives of the load impact evaluation are to:  Estimate hourly ex post load reductions on the five 2013 PTR event days (aggregate and per-customer level)  Estimate ex post load reductions for each SCE local capacity area (LCA) and for areas affected by the SONGS closure (South of Lugo and Southern Orange County)  For opt-in PTR customers specifically, two additional key objectives are to:  Forecast 2014–2024 PTR hourly ex ante load impacts for a 1-in-2 and 1-in-10 weather year by month (aggregate and per-customer level)  Estimate ex ante load reductions for each LCA and for areas affected by the SONGS closure  Default PTR customers are not included in the ex ante analysis because they will no longer receive rebates 8 Evaluation Objectives

10. Ex Post Methodology 9

11.  Reference loads for the PTR impact estimates were calculated using a matched control group drawn from the non-alert population  Non-alert customers have not provided load impacts in the past, so they serve as good candidates for the control group  Control group was selected using a propensity score match to find non- alert customers who had similar load shapes to the opt-in PTR, default PTR and PCT customers (within the same weather station area)  Each customer in the treatment population was matched with a customer in the non-alert population with the closest propensity score  Optimized sample design that included all PCT customers, 46.7% of opt-in PTR customers and 1.6% of default PTR customers  Default customers will no longer receive rebates in the future, and are therefore not included in the ex ante analysis, so Nexant and SCE decided that it would be preferable to develop a relatively small default PTR sample in order to maximize the sample size and precision for opt-in PTR customers 10 Overview of Control Group Methodology

12. 11 There were many non-event weekdays with similar load as event days, which is ideal for developing a matched control group PTR event days are marked by squares

13. Difference-in-differences was used to calculate impacts, but the magnitude of the adjustment was small 12 Report includes many validations to show that the model produced accurate estimates

14. Ex Post Results 13

15.  Default PTR impacts were not statistically significant  Opt-in and default PTR percent impacts were similar to the 2012 impacts  Note: For each event day, if a PTR participant was also activated for SDP – SCE’s AC cycling program – that participant was removed from the ex post load impact estimates for PTR in order to avoid overestimating PTR impacts  Similarly, if a PCT study vendor did not activate certain PCTs for a given event day, that participant was also removed from the ex post load impact estimates for that day 14 2013 Average Event Ex Post Load Impact Estimates (2-6 PM) by Participant Group Participant Group Number of Customers Avg. Reference Load (kW) Avg. Load w/ DR (kW) Avg. Load Impact (kW) % Load Impact Aggregate Load Impact (MW) Heat Buildup (Avg. °F, 12 AM to 5 PM) Opt-in PTR157,7371.911.830.084.0%11.979.0 Default PTR587,6422.052.020.031.5%17.979.1 PCT Study1,3442.501.760.7529.8%1.078.7

16. 15 Aggregate Load Reductions (MW) by Event Day and Group

17.  PCTs delivered substantial pre- cooling before the event period  In the hour before the event, the load impact was negative 26%, which means that there was a large increase in cooling load before the event began (intended to mitigate discomfort during the event period)  Similarly, the load impact was negative 16% during the first hour after the event to make up for the increase in temperature during the event period  Overall energy use throughout the day was 3.2% lower 16 Aggregate Third Party PCT Study Reference Load and Load w/ DR for Average 2013 Event

18. Ex Ante Methodology 17

19.  At a high level, the modeling steps consist of the following: 1.Ex post estimates were developed for opt-in PTR customers by weather station 2.Ex ante regression model was developed to explain average ex post impacts from 2 to 6 PM as a function of temperatures that day 3.Ex ante impact estimates were converted to hourly impacts from 2 PM to midnight (including the post-event period) using a scaling factor based on the average ratio between impacts at different hours (based usage pattern for average 2013 event) 4.Finally, hourly whole-house reference loads were predicted for each set of ex ante weather conditions based on loads observed in 2013, using a simple regression model that related hourly usage to temperature, time of year and day of week for each customer segment  Each step was performed separately for opt-in PTR-only customers and for customers dually-enrolled in SDP  Enrollment forecast: SCE expects that the opt-in PTR program will grow to around 209,000 customers during summer 2014, decrease to around 202,000 at the end of 2014 and then remain constant throughout the remainder of the ex ante forecast period 18 Overview of Ex Ante Methodology

20. 19 Ex Post Impacts versus Mean17 for Opt-in PTR-only Customers

21. 20 Ex Post Impacts versus Mean17 for Dually-enrolled Customers

22. Ex Ante Results 21

23. 22 2015–2024 Opt-in PTR Ex Ante Load Impact (MW) Estimates by Month and Customer Type  Once enrollment reaches a steady state in the 2015–2024 time period, the program is expected to be capable of delivering up to 15.6 MW, which occurs during the August monthly peak under 1-in-10 weather conditions  From May through October under both 1-in- 2 and 1-in-10 weather conditions, dually- enrolled customers account for around 40% of aggregate load impacts (18% of customers)

24.  Average event temperatures in 2013 are most similar to the temperatures for the Sept. peak day under 1 ‑ in-2 weather conditions – for this day, the average 2-6 PM impacts are very similar 23 Comparison of Ex Post and Ex Ante Estimates of Opt-in PTR Average 2 to 6 PM Load Impact (kW)

25. For comments or questions, contact: Josh Schellenberg Managing Consultant Jschellenberg@nexant.com Jschellenberg@nexant.com Nexant, Inc. 101 Montgomery St., 15 th Floor San Francisco, CA 94104 415-777-0707