load impact evaluation of sdg&e’s · september 2014 3 features of sdg&e ptr in 2012...
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Load Impact Evaluation of SDG&E’s Default Residential PTR Program –Effects of Estimation with Control
Group
Steve Braithwait
Christensen Associates Energy Consulting
2014 IEPEC - Berlin
September 2014
September 2014 1
September 2014 2
Overview
Evaluating demand response (DR) programs differs from EE evaluation Limited numbers of events (e.g., 6 – 10)
Allows treatment-only analysis using hourly data on non-event days to estimate counterfactual reference loads for measuring load reductions
Can also use treatment/control group approach– Experimental if part of design
– Quasi-experimental matching if after the fact
September 2014 3
Features of SDG&E PTR in 2012
SDG&E automatically enrolled all eligible residential customers (1.2 million) in peak-time rebate (PTR)
Bill credits for reducing usage below baseline levelduring 11 a.m. to 6 p.m. on event days
Customers encouraged to sign up to receive event notification, or Alerts (email/text)
Seven events called, including two Saturdays
September 2014 4
Target Population and Analysis Samples
Population
Analysis
Samples
Summer Saver (excluded) 23,998 -
SDEC (excluding SS) 4,633 4,631
Opt-in Alert 41,243 13,745
Remaining Population 1,154,144 29,692
Total (Excluding SS) 1,200,020 48,068
PTR Subgroup
September 2014 5
Original Study Methodology
Designed & selected stratified random samples from: 41,000 Opt-in alert and 1.1 million non-alert population
Customer-level regression analysis to hourly Smart Meter data (i.e., treatment-only approach): 14,000 Opt-in Alert; 30,000 non-alert population
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Key Findings from Original Study
Opt-in Alert customers reduced usage by small but statistically significant amounts (5%)
No significant load impacts for non-notified(non-Alert) customers
Estimated load impacts for Opt-in Alert varied considerably across events
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Estimated % Load Impacts, by Event: Opt-in Alert – Coastal & Inland
Objectives of Study Update
SDG&E interested in testing whether a treatment and control group approach would improve impact estimation
Leveraged on two factors: Finding of no response for non-alert customers
Availability of an existing sample from the non-alert population
September 2014 8
Approach in Study Update
Select a matched control group from non-alert sample Match each Opt-in Alert customer to most-similar non-
alert customer
Based on ZIP code and previous-year usage patterns
Apply fixed-effects regression to treatment and control group customers Daily observations on event-window usage
Difference-in-differences approach: [Control – Treatment usage on event days] – [Control – Treatment usage on non-event days]
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Estimated % Load Impacts for AverageEvent, by Analysis Approach
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Estimated % Load Impacts by Eventby Climate Zone & Approach
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Difference Between Control and Treatment Loads – August 14 Event
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Conclusions
Treatment/control approach produced larger & more consistent load impact estimates Generic finding, or due to weather issues in SD?
Source of control group for default PTR?
As of 2014, CA utilities restricting bill credits to customers who request event notification Largely due to baseline inaccuracy
Implies future source of control group customers
September 2014 14
Questions?
Contact – Steve Braithwait, Christensen Associates Energy ConsultingMadison, Wisconsin [email protected] 608-231-2266