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Residential Customer Response to Real-Time Pricing: The Anaheim Critical-Peak Pricing Experiment Frank A. Wolak Department of Economics Stanford University.

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Presentation on theme: "Residential Customer Response to Real-Time Pricing: The Anaheim Critical-Peak Pricing Experiment Frank A. Wolak Department of Economics Stanford University."— Presentation transcript:

1 Residential Customer Response to Real-Time Pricing: The Anaheim Critical-Peak Pricing Experiment Frank A. Wolak Department of Economics Stanford University Stanford, CA 94305-6072 wolak@zia.stanford.edu http://www.stanford.edu/~wolak

2 Outline of Talk Description of experiment design Assessing validity of experimental design Measurement framework employed –Treatment effect of CPP event –Sensitivity of estimation results to assumptions What does treatment effect measure? –Reference level inflation Setting reference level for rebates for budget balance CPP with rebate as transition to default real-time pricing of electricity to residential consumers

3 Anaheim CPP Experiment During the summer of 2005, the City of Anaheim Public Utilities (APU) ran a Critical Peak Pricing (CPP) experiment During late 2004, a random sample of APU residential customers were selected to participate in experiment Customers in this sample were randomly assigned to the control and treatment groups –Control customers were not notified of this selection but simply had interval meters installed at their dwelling –Treatment group customers first received a letter notifying them that they had been selected to participate in CPP program and were asked to return a reply form with their phone number and/or e-mail address Follow-up phone calls to sign-up those that did not respond to mailing Follow-up mailing to recruit those who could not be contacted by phone Final result--Very little attrition from randomly selected treatment group Process ultimately resulted in 52 control customers and 71 treatment customers, or 123 total customers

4 Anaheim CPP Experiment All customers (treatment and control) paid a fixed retail price of 6.75 cents/kWh up to their monthly baseline of 240 KWh –Monthly consumption beyond 240 KWh baseline charged at 11.02 cents/kWh Customers in treatment group were subject to a maximum of 12 CPP days for experiment period –Day-ahead notification of CCP days via telephone or e-mail (depending on customer’s choice on reply card)

5 Anaheim CPP Experiment CCPs days are required to be on weekdays that are not holidays Consumption below reference level during peak period (noon to 6 pm) of CPP days eligible for refund of 35 cents/KWh –Consumers receive a rebate if their average consumption during peak periods of CPP days is less than their reference peak period consumption Rebate on day d = max(0,(q(ref) – q(peak,d)))*p(rebate) –Rebate implies that customers guaranteed not to pay more than they would have under control tariff Reference peak period consumption is customer’s “typical” peak period consumption –Defined as average peak period consumption during three highest non-CPP days (excluding weekends and holidays during experiment) All CPP-eligible days that were not CPP-days during experiment

6 Dataset Used in Analysis Daily Peak and Off-peak period consumption for 123 locations Peak period—noon to 6 pm Peak(i,d) = Peak period consumption for location i on day d –Off-period—all other hours of the day OffPeak(i,d) = Off-Peak period consumption for location i on day d Temp(d) = Maximum daily temperature at Fullerton Airport for day d Day(d) = Indicator for whether day d=1,…,136 (all days during sample period) LOC(i) = Indicator for location i, i=1,…,123 Treat(i) = Indicator for whether location i is in treatment group CCP(d) = Indicator for whether day d is a critical peak day

7 Pre-Treatment Period Comparison Meters installed for all customers in experiment before June 1, 2005 start date of experiment Consumption recorded at 15-minute intervals throughout the day for customers in both groups Comparison of pre-treatment 15-minute means to assess randomness of selection of customers into experiment and their assignment to treatment and control groups Treatment Control

8 Pre-Treatment Period Comparison For virtually all 15-minute periods, 95 percent confidence interval on mean difference in pre-experiment period consumption by treatment and controls groups contains zero Conclusion—No evidence of non-random selection into experiment or subsequently into treatment versus control groups

9 Measuring Price Response Two models estimated for peak period Average peak period treatment effect –ln(Peak(i,d)) = αCCP(d)*Treat(i) + λ d + δ i + ε id –δ i = location-specific fixed effect (controls for persistent differences in consumption across locations) –λ d = day-specific fixed effect (controls for persistent differences in consumption across days in sample) · ε id = observable mean zero stochastic disturbance Temperature dependent peak period treatment effect –ln(Peak(i,d)) = αCCP(d)*Treat(i) + γCPP(d)*Treat(i)*TEMP(d) + ν d + μ i + η id   i = location specific fixed-effect (controls for persistent differences in consumption across locations) –ν d = day-specific fixed effect (controls for persistent differences in consumption across days in sample) –η id = observable mean zero stochastic disturbance

10 Measuring Price Response Two models estimated for off-peak period Average off-peak period treatment effect –ln(Off-Peak(i,d)) = αCCP(d)*Treat(i) + λ d + δ i + ε id –δ i = location-specific fixed effect (controls for persistent differences in consumption across locations) –λ d = day-specific fixed effect (controls for persistent differences in consumption across days in sample) · ε id = observable mean zero stochastic disturbance Temperature dependent peak period treatment effect –ln(Off-Peak(i,d)) = αCCP(d)*Treat(i) + γCPP(d)*Treat(i)*TEMP(d) + ν d + μ i + η id   i = location specific fixed-effect (controls for persistent differences in consumption across locations) –ν d = day-specific fixed effect (controls for persistent differences in consumption across days in sample) –η id = observable mean zero stochastic disturbance

11 Estimation Results

12 *Arrellano (1987) covariance matrix used, **Estimates computed using Cochrane-Orcutt procedure assuming AR(2) errors. All regressions include 135 day-of-sample fixed effects

13 Temperature Dependent Treatment Effects

14 Dynamics of Price Response Examine if substitution across days occurred as a result of CCP days Include lagged value of CPP(d)*Treat(i) –ln(Peak(i,d)) = α 1 CCP(d)*Treat(i) + α 1 CCP(d-1)*Treat(i) + λ d + δ i + ε id –δ i = location-specific fixed effect (controls for persistent differences in consumption across locations) –λ d = day-specific fixed effect (controls for persistent differences in consumption across days in sample) · ε id = observable mean zero stochastic disturbance Include lagged value of CPP(d)*Treat(i) –ln(Off-Peak(i,d)) = αCCP(d)*Treat(i) + γCPP(d-1)*Treat(i) + ν d + μ i + η id   i = location specific fixed-effect (controls for persistent differences in consumption across locations) –ν d = day-specific fixed effect (controls for persistent differences in consumption across days in sample) –η id = observable mean zero stochastic disturbance Same regression with lead value of CPP(d)*Treat(i)

15 Estimation Results No evidence of lagged or lead effects (model with CPP(d+1)*Treat(i) instead of CPP(d-1)*Treat(i)) of CCP events for treatment group *Arrellano (1987) covariance matrix used.

16 Summary of Results Load-reduction effect--Peak period consumption of treated group approximately 13% lower than consumption of control group during CCP days –Controlling for all fixed differences across locations, and fixed differences across days Load-reduction effect—Evidence of larger consumption reduction in higher temperature days –Five degree temperature increase implies 4 percentage point increase in the consumption reduction of treated group versus control group Little evidence of load shifting to off-peak periods –No statistically significant difference in treatment versus control group mean consumption during off-peak periods on CPP days –No statistically significant difference in treatment versus control group mean consumption during peak and off-peak periods in day before or day after CPP day

17 Customer-Level Heterogeneity in Treatment Group

18 Customer-Level Heterogeneity in Control Group

19 Total Expenditure Below Baseline of 240 KWh—Treatment Group

20 Total Expenditure Below Baseline of 240 KWh—Control Customers

21 Total Expenditure Above Baseline of 240 KWh—Control Customers

22

23 Rebates Received All customers in treatment group benefited from program Some benefited enormously--One customer was paid rebates equal to 40% of its bill over the experiment period Why did some customers benefit so much more than others? Potential for reference level inflation—Increase consumption during non- CPP day peak periods that are eligible to be CPP days to increase reference level Reference level set too high so that rebates would be paid even it customer did not respond to CPP day Answer first question by comparing mean consumption of treatment and control groups during peak periods on non-CPP days that are eligible to be CPP days and therefore enter into reference level calculation Answer second question by comparing rebates that control group (which had no incentive to increase reference level or reduce consumption during CPP days) would have received to those received by treatment group

24 Total Rebates Received for 12 CPP Days—Treatment Customers

25 Total Rebates for 12 CPP Days that Would Been Received by Control Customers

26 Total Rebates Received Divided by Total Bill—Treatment Customers

27 Total Rebates that Would Have Been Paid Divided by Total Bill (Control Customers)

28 Reference Level Inflation Treatment customers can influence reference level relative to which refunds are issued by how they consume during CPP-eligible days that are not CPP days Question—What impact did process used to set reference level have on magnitude of treatment effect? Compare mean consumption of treatment and control groups during days used to determine a customer’s reference peak period consumption relative to which rebates were computed

29 Reference Level Inflation Treatment customers have 7 percent higher consumption than control in non-CPP days that are eligible to be CPP days Approximately half of estimated treatment effect of CPP event due to inflation in reference level by treatment group Treatment customers have 14 percent higher consumption than control group during non-CPP days that are eligible to be CPP days Major challenge to formulating CPP pricing program with rebate is how to set reference level Second challenge--Paying for consumption reductions relative to reference level that are far greater than actual consumption reductions due to a CCP event

30 Total KWh Reduction Predicted by Treatment Effect for 12 CPP Days (Treatment Customers)

31 Total KWh Paid 35 cents/KWh Rebate Over All 12 CPP Days (Treatment Customers)

32 KWh Reduction Predicted by Treatment Effect for 12 CPP Days (Control Customers)

33 KWh that Would Have Been Paid 35 cents/KWh Rebate over All 12 CPP Days (Control Customers)

34 Setting Reference Level Pay treatment customers for ~7 times more KWh than treatment effect says they reduced consumption in CPP peak period Would have paid control customers for ~6 times more KWh than their predicted decrease during CPP period Setting reference level too high can make it very hard for CPP with rebate to satisfy cost/benefit test Savings from wholesale energy purchase costs greater than total rebates paid plus other costs program

35 Optimal Reference Level If goal of CPP pricing is to create predictable and substantial load reduction on day-ahead basis, some reference level inflation may be optimal Customers consume more in other peak periods to be able to predictably reduce their consumption by a substantial amount during CPP periods Predictable and sizeable load reduction on a day-ahead basis allows retailers to discipline unilateral exercise of market power by suppliers in short- term energy and ancillary services markets Predictable and sizeable load reduction on a day-ahead basis can significantly enhance system reliability Trade-off between process used to set reference level and magnitude of rebate paid during peak hours of CPP day Large rebate payment with low reference level may cause customers to give up on reducing demand during certain CPP periods, which reduces predictability and size of response Estimating dynamic model of electricity consumption outlined in paper can provide useful input to answering these questions

36 Conclusions Load reduction due to CPP event confined to load period in which event occurs Load reduction of close to 13% confined to peak periods on CPP days Evidence of larger percentage load reductions in peak periods on higher temperature CPP days Strong evidence of reference price inflation by treatment customers Strong evidence that reference level set too high for many customers Neither of the above two results imply that CPP with rebate cannot provide predictable and substantial load reduction that satisfies cost/benefit test Some reference level inflation and some payment for load reductions beyond those induced by CPP event may be optimal CPP with rebate consistent with provisions of AB1X which requires no customer pay more than fixed rate customers Can help to demonstrate economic and reliability value of real-time pricing to load while still being consistent with AB1X

37 Questions/Comments For more information: http://wolak.stanford.edu/~wolak

38 Limited Benefits of Restructuring in US Without Involving Demand US has privately-owned, profit-maximizing firms facing cost-of-service price regulation or incentive regulation plan –Detailed prudence review of investment –Hard to argue there are large deviations from minimum cost production –Vertically integrated ownership and centralized dispatch should be able to improve on bid-based dispatch on true production cost basis

39 Markets use prices to allocate scarce resources Competitive market should be able to get by with lower level of capacity and serve same customers –This implies lower capacity costs for market at large –If dispatch costs are close to the same, then average price in competitive market should be less than average price in regulated market A necessary condition for this to occur is a sufficient number of price-responsive consumers

40 Optimal Capacity Choice Under Regulation versus Competition K reg >> K comp

41 Example--US Airline Industry Load Factors = (Seats Filled)/(Seats Total), –In regulated regime highest load factors approximately 55% in 1976 –Currently Load Factors are close to 75% This increased capacity utilization rate allows real average fare per passenger-mile to be significantly less than under regulated regime Regime works because of large number of sophisticated price-responsive consumers.

42 Even Residential Consumers Can Respond Weekly Consumption Monday to Sunday

43 Even Residential Consumers Can Respond Weekly Consumption Monday to Sunday

44 Even Residential Consumers Can Respond Weekly Consumption Monday to Sunday

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