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2013 SDG&E Summer Saver Load Impact Evaluation Dr. Stephen George DRMEC Spring 2014 Load Impacts Evaluation Workshop San Francisco, California May 7, 2014.

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Presentation on theme: "2013 SDG&E Summer Saver Load Impact Evaluation Dr. Stephen George DRMEC Spring 2014 Load Impacts Evaluation Workshop San Francisco, California May 7, 2014."— Presentation transcript:

1 2013 SDG&E Summer Saver Load Impact Evaluation Dr. Stephen George DRMEC Spring 2014 Load Impacts Evaluation Workshop San Francisco, California May 7, 2014

2  Summer Saver program overview  Ex post methodology and results  Ex ante methodology and results  Relationship between ex post and ex ante 1 Agenda

3  The program is available to both residential and small commercial customers  Participants can select from multiple cycling options with corresponding variation in the annual incentive payment  Enrollment is expected to stay relatively constant in the near future  The number of enrolled customers increased by less than 3% from 2012 to SDG&E’s Summer Saver program is a CAC direct load control program Customer Type Cycling Option Enrolled Customers Enrolled Control Devices Enrolled Tons Commercial 30%1,4693,91115,148 50%3,4017,68429,863 Total4,87011,59545,011 Residential 50%12,15814,29050, %11,44414,10851,140 Total23,60228,398101,173 Grand Total28,47239,993146,184

4  Residential ex post impact estimates were developed using two randomly selected groups, with each group alternating as the treatment and control group during load control events  Total sample of 1,460, split roughly evenly into A and B groups – group sizes were limited due to desire to minimize M&E’s impact on DR capability  Impacts estimated as difference between A and B groups for each event, adjusted slightly for differences between the groups during hours leading up to the event period  Based on whole building interval data  Due to small commercial population and the desire to maximize the aggregate load impacts (e.g., not hold back a large control group), the commercial ex post impact estimates were developed using the difference in loads between cycled customers and a statistically matched control group from the non-participant population 3 The ex post evaluation methodology differs between the residential and commercial program segments

5 4 The randomly assigned residential A/B groups are well- matched on the 5 highest nonevent system load days in 2013

6 5 Loads for the statistically matched control group and the participant population of commercial customers are also similar on the five highest system load days in 2013

7 6 Ex post load impacts were based on a simple difference calculation with a small same-day adjustment Unadjusted Adjusted Residential 50% cycling average kW load impact per premise on 9/6/2013

8 7 Average 1-5 PM event load impact was 20.8 MW – two events covering later hours of the day delivered fewer MW

9 8 The same methodology was used to produce ex ante impacts for both residential and commercial participants Average Ex Post Load Impacts and Ex Ante Predictions from 2 to 4 PM for Residential 50% Cycling Participants

10 9 The largest ex ante aggregate impacts for Summer Saver are predicted to occur in September under both 1-in-2 and 1-in-10 year weather conditions

11 FactorAggregate Load Impact (MW) Explanation Ex post aggregate impact18.9*All sectors, all cycling options, 97% of participant population Scaled to 100% of Summer Saver population 19.43% difference when control group customers are not held back Ex post weather and event window, ex ante model 20.1In aggregate, model predicts well – some over and under for sub-segments Ex post weather, ex ante event window, ex ante model 20.0Most ex post events were from 1 to 5 PM which is similar to the RA window from 1 to 6 PM 1-in-2 year weather, typical event day 16.2**Mean17 for ex ante weather equal to 77°F compared with mean17 for 2013 events equal to 80.1°F 1-in-10 year weather, typical event day 19.4**Mean17 for ex ante weather equal to 79°F compared with mean17 for 2013 events equal to 80.1°F Relationship Between Ex Post and Ex Ante Estimates *This value is less than the ex post value in table on slide 7 because value on slide 7 is only for the four events with common hours from 1 to 5 PM whereas this is based on all 6 ex post 2013 events **Nexant has recommended that SDG&E re-examine their development of ex ante weather values prior to next year’s evaluation 10

12 For comments or questions, contact: Stephen George Senior Vice President, Utility Services or Candice Churchwell Senior Consultant Nexant, Inc. 101 Montgomery St., 15 th Floor San Francisco, CA


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