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PG&E’s 2013 SmartRate Program Evaluation

PG&E’s 2013 SmartRate Program Evaluation. Dr. Stephen George DRMEC Spring 2014 Load Impacts Evaluation Workshop San Francisco, California May 7, 2014. Agenda. Program overview Ex post methodology and results Ex post methodology Event day impacts Structural winner analysis

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PG&E’s 2013 SmartRate Program Evaluation

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  1. PG&E’s 2013 SmartRate 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 post methodology • Event day impacts • Structural winner analysis • Balanced payment plan analysis • Ex Ante methodology and results • Relationship between ex post and ex ante

  3. Overview of PG&E’s SmartRate Program • Voluntary, critical peak pricing (CPP) rate that is an overlay on residential customers’ existing rate schedules • High price period from 2 PM to 7 PM on up to 15 event days per summer • Peak price adder is $0.60/kWh • Credits apply to non-peak usage from June through September • Participation has changed dramatically over last two years • Grew by a factor of 6 (from 20,000 to 120,000) in two years • Targeted customers with high likelihood of owning and using a/c and having higher likelihood of reducing load (focus on non-CARE because they are more responsive, dually enrolled because have larger loads) • Enrollment increased by 50% from October 2012 to June 2013 but aggregate demand reduction increased 2.5 times, from 17.8 to 44.1 MW

  4. SmartRate Ex Post Methodology • Propensity score matching used to match all SmartRate customers to a comparable control group • Match based on geographic location, usage quartiles, and load shape variables • Control customers can be matched to more than one SMR customer • Smart Meter data from treatment and control groups used to calculate impacts on event days

  5. SmartRate Ex Post Methodology (cont’d) Control group was very similar to the SmartRate population not only based on usage, but also based on CARE status and geographical location SmartRate and Matched Control Group Usage on Hot, Non-event Days

  6. SmartRate 2013 Average Event Day • Average hourly impact of 0.38 kW across 8 event days in 2013 • This represents about 21% of whole-house load • 2012 average impact was 0.29 kW (30% less), which was 19% of whole house load

  7. SmartRate 2013 Ex Post Impacts

  8. SmartRate Impacts from 2008 - 2013 PG&E’s targeted marketing effort has led to substantial increases in average load impacts per customer for a given event day temperature • Higher a/c likelihood • Higher usage • Substantially lower share of total customers on CARE

  9. New Issues Were Investigated to Help Improve Program • Structural winner analysis • Do structural winners who are informed sign up at a higher rate than if they are not informed? • Do those who are informed respond differently from those who are not? • Balanced Payment Plan (BPP) analysis • Does the BPP mask the time-varying price signal and, therefore, lead to lower average impacts?

  10. Load Impacts for Structural Winners • Conducted a test in which roughly 64,000 structural winners were sent mailings encouraging them to sign up for SmartRate • A randomly selected treatment group of nearly 49,000 customers received marketing materials with a bill comparison which alerted customers that they were structural winners • The remaining 15,000 randomly selected control customers were also structural winners, but received marketing materials without a bill comparison • Structural winners provided with bill comparison information signed up at a slightly higher rate than those who did not receive the information – the small difference in load impacts was not statistically significant

  11. Customers on the Balanced Payment Plan • SmartRate customers on BPP actually provided larger absolute load reductions per customer than non-BPP customers, due to being larger users to start with • The percent impacts were the same between the two groups

  12. SmartRate 2013 Ex Ante Load Impacts • To produce ex ante impacts, ex post impacts needed to be developed that represent the current (future) SmartRate population • A two-stage matching process was used to find a suitable control group for the ex ante analysis • First, a group of customers in the program since 2012 that matches the population at the end of 2013 was found • Then, a control group was matched to this representative sample • A model of average hourly ex post impacts was estimated using this sample and control group

  13. Relationship Between Weather and Load Impacts • The model is based on the average temperature from midnight until 5 PM (mean17) on each event day • Ex ante weather conditions were used as input to estimate ex ante load impacts based on 1-in-2 and 1-in-10 weather year conditions

  14. Ex Ante Load Impact Estimates

  15. Relationship Between Ex Post and Ex Ante Estimates

  16. For comments or questions, contact:Stephen GeorgeSenior Vice President, Utility Servicessgeorge@nexant.comorAimee SavageProject Analystasavage@nexant.comNexant, Inc.101 Montgomery St., 15th FloorSan Francisco, CA 94104415-777-0707

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