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PG&Es 2013 SmartAC Program Evaluation Dr. Stephen George DRMEC Spring 2014 Load Impacts Evaluation Workshop San Francisco, California May 7, 2014.

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Presentation on theme: "PG&Es 2013 SmartAC Program Evaluation Dr. Stephen George DRMEC Spring 2014 Load Impacts Evaluation Workshop San Francisco, California May 7, 2014."— Presentation transcript:

1 PG&Es 2013 SmartAC Program Evaluation Dr. Stephen George DRMEC Spring 2014 Load Impacts Evaluation Workshop San Francisco, California May 7, 2014

2 Agenda Program overview Ex post methodology and results Ex Ante methodology and results Relationship between ex post and ex ante Page 1

3 Overview of PG&Es SmartAC Program Page 2 AC load control through installation of control devices that limit the duty cycles of AC units Two types of devices: switches and PCTs Events called between May 1 st and October 31 st, up to 6 hours or less in each event, for a maximum of 100 hours per season: For testing purposes Based on an economic trigger For emergency or in anticipation of emergency conditions 151,000 residential accounts with 168,000 control devices Roughly 39,000 accounts and 43,000 devices dually enrolled in SmartRate – ex post impacts for this group are included in SmartRate evaluation but ex ante impacts on SmartAC only days included here 5,800 SMB accounts with 9,000 control devices Closed to new enrollment since 2011

4 Two test events were called in 2013 for residential SmartAC only accounts (none were called for SMB accounts) A series of one-hour test events on July 1, using different control and treatment groups for each hour, spanning the hours from 10 AM to 8 PM designed to assess how impacts differ for hours inside and outside the traditional resource adequacy (RA) window A one hour test event on September 9, from 2 to 3 PM Four events were called at the sub-LAP level in response to local emergency conditions East Bay on June 7 Los Padres on July 2 Geysers on July 3 North Coast on July 3 SmartAC ex post events in 2013 Page 3

5 Ex post impacts for test events were based on the difference in loads between large (~12,000), randomly chosen treatment & control groups that are near perfect clones Page 4 Loads for each randomly chosen group on July 11, 2012 event day

6 Hour Ending Treatment Group Reference Average Impact per Device Percent Aggregate Impact Average Temperature 1111.630.138%1.5587 1221.930.2111%2.4891 1332.250.3214%3.7694 1442.560.4016%4.7496 1552.840.5419%6.3499 1663.120.6120%7.2299 1773.340.7623%8.96100 1883.510.7923%9.35100 1993.550.7722%9.0397 2003.390.6920%8.0794 AverageN/A2.810.5218%6.1596 Load impacts during the July 1 st multi-hour test events varied significantly across hours in the day Average impacts leading up to RA window were significantly less than during RA window Reference load between 11 AM and noon is ½ what it is from 5 to 6 PM and load impact is 1/6 of what it is from 5 to 6 Average impacts in later evening hours were comparable to those during the RA window PG&E plans to conduct more tests spanning these hours in 2014 RA Window (1 to 6 PM) Page 5

7 The ex ante load impact requirements are to produce forecasts for specified weather conditions and event window 1-in-2 year weather conditions 1-in-10 year weather conditions The basic approach is to use ex post impacts as input to a model describing the relationship between impacts and temperature Model was estimated on pooled data from 2011, 2012 and 2013 Future enrollment predicted to increase from 151,000 to 58,000 in 2015 and then stay constant until 2024 Similar methods were used for both residential and SMB Ex post impacts for SMB were taken from 2011 events since no test events were called in 2012 or 2013 Enrollment forecasted to decline steadily from around 5,000 in 2014 to roughly 3,800 in 2024 Page 6 Ex Ante Methodology

8 Four steps : Create weighted average ex post estimates combining SmartAC only and dually enrolled ex post estimates for each LCA for 2011, 2012 and 2013 using 2013 participant weights Regress 4-5 PM ex post impacts at LCA-level on LCA-specific customer- weighted average temperature from midnight to 5 PM (mean17) – a single regression was run pooling data across LCAs as testing showed that the relationship between mean17 and load impacts was consistent across LCAs Use regression to predict impacts from 4-5 PM under ex ante conditions Predict impacts for other hours (1-4 PM, 5-6 PM) based on observed ratios between impact at 4-5 PM and impact at each other hour (ratios are LCA- specific and vary with temperature) Commercial model is essentially the same, but makes concessions for smaller sample sizes LCA-specific temperatures, but one model across all LCAs using 2011 data Page 7

9 Ex Ante Methodology Page 8 Average Event Impacts From 4-5 PM Versus Mean17 Across All LCAs (2011, 2012 and 2013) Comparing ex ante and ex post estimates by LCA shows how often ex ante impacts are outside the historical range for some LCAs

10 SmartAC Residential Ex Ante Impacts Greatest aggregate load impact from 1 to 6 PM under 1-2 year conditions – 93 MW – is on the July system peak day The highest hourly impact under 1-in-2 year weather conditions is 125 MW Under 1-in-10 year weather conditions, the average impact from 1 to 6 pm – 109 – is also predicted to occur on the July system peak day The maximum hourly impact under 1-in-10 year conditions is estimated to equal 133 MW Estimates are about 10% lower than last years estimates Increase of dually enrolled from 18% to 26% reduces average Did not gross up 1 st hour for switch ramp in Aggregate Impacts Based on 2014 Projected Enrollment of 156,800 Weather Year Day Type Mean Hourly Per Customer Impact (kW) Max. Hourly Per Customer Impact (kW) Aggregate Mean Hourly Impact (MW) Aggregate Max Hourly Impact (MW) 1-in-2 Typical Event Day0.480.587590 May Peak Day0.320.405061 June Peak Day0.400.496277 July Peak Day0.600.7093109 August Peak Day0.460.567287 September Peak Day0.470.567287 October Peak Day0.240.323849 1-in-10 Typical Event Day0.640.7498114 May Peak Day0.530.648398 June Peak Day0.590.7091108 July Peak Day0.760.86117133 August Peak Day0.670.77104119 September Peak Day0.530.648399 October Peak Day0.460.567187 Page 9

11 FactorAggregate Load Impact (MW) Explanation Ex post aggregate impact6.810% of SmartAC only population Scaled to full SmartAC population 81.7Combines SmartAC and dually enrolled for 100% of participant population Ex post weather, event conditions and enrollment, ex ante model 77.9Ex post is for 2012/2013 combined. Ex ante regression based on 2011, 2012 & 2013 data. 3 year model under predicts compared with two-year model below mean17 of 83°F. Ex post mean17 = 77°F. Ex post weather and enrollment, ex ante event window, ex ante model 67.7Ex ante event window from 1 to 6 PM reduces load impact by about 13%. Most ex post events were for much shorter window during hottest time of day. Ex post weather, ex ante window and enrollment (2014) 70.32013 residential enrollment was almost 4% less than 2014 projected enrollment 1-in-2 year weather74.6Mean17 for ex post events = 77.4; mean17 for 1-in-2 year weather = 78.5 1-in-10 year weather98.5Mean17 for ex post events = 77.4; mean17 for 1-in-10 year weather = 83.6 Relationship Between Ex Post and Ex Ante Estimates Page 10

12 For comments or questions, contact: Stephen George Senior Vice President, Utility Services or Christine Hartmann Project Analyst Nexant, Inc. 101 Montgomery St., 15 th Floor San Francisco, CA 94104 415-777-0707

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