Using Residential Customer Response to Manage Hydro Risk Frank A. Wolak Department of Economics Stanford University Stanford, CA 94305-6072 wolak@zia.stanford.edu http://www.stanford.edu/~wolak
Goals of Talk • Demand can and must participate in wholesale electricity market to manage hydro risk – Higher wholesale prices do not cause more rain so demand must be reduced when there is less water available – Real-time pricing of electricity makes this possible without declaring crisis or curtailing firm load • Critical peak pricing (CPP) with a rebate is a form of real-time pricing that is popular with consumers – May become default residential tariff in California • Critical peak pricing schemes can be implemented in hydro-dominated systems such as New Zealand
Outline of Talk • Real time pricing versus time-of-use pricing • Rationale for and description of Anaheim experiment • Critical peak pricing (CPP) with a rebate experimental design • Assessing validity of experimental design • Measuring treatment effect of CPP event • Applying results to hydro-dominated system such as New Zealand
Real-time vs. Time-of-use pricing • Real-time pricing (RTP) – Retail prices that vary with real-time system conditions – In fossil-based system this requires hourly meters to implement • Must measure consumption within hour – In hydro-dominated system real-time pricing possible with monthly meter reading • Time-of-use pricing (TOU) – Retail prices that vary with time of day, regardless of system conditions • Low price from midnight to 12 pm and 6 pm to midnight • High price from noon to 6 pm – Does not require hourly meter • Only meter that records monthly consumption in two time periods
Real-time vs. Time-of-use pricing • Real-time pricing – Customers have incentive to reduce demand during periods with high wholesale prices and stressed system conditions • Reduces wholesale price volatility and increases system reliability • Limits ability of suppliers to exercise unilateral market power – Retailers with RTP customers can use them to exercise monopsony power • Time-of-use pricing – Customers have no incentive to reduce demand during periods with high wholesale prices and stressed system conditions • Similar incentive to single fixed price tariff – Two fixed prices all days as opposed to one fixed price all days – Inelastic hourly demand for electricity with respect to hourly wholesale price
Anaheim Critical Peak Pricing Experiment • Why is a Anaheim critical peak pricing experiment necessary? – Interval metering must be installed to implement real-time pricing tariffs • A major source of benefits of interval metering is ability to implement real- time pricing tariffs – Savings on meter reading costs because meters are remotely readable – Operational benefits because can see exactly where power outages are in real time • Symmetric treatment of load and generation for just large customers is very difficult for regulator to implement politically – All customers with peak demands above 200 kW have had interval meters (paid for by California taxpayers) since summer of 2002 – Default price for these customers is still fixed retail price • Results of experiment can be used to quantify benefits of implementing universal interval metering – Results can also be used to estimate dynamic model of intertemporal behavior to compare treatment effect of CPP day to own-price demand elasticity • Wolak (2007) “What Does a Treatment Effect Measure: Evidence from Anaheim Critical Peak Pricing Experiment”
AMI Communication Networks Local Area Networks Wide Area Networks Consumer Telephone Telephone Internet Internet Utility User Local power lines Wireless Wireless Network Network Data Center Wireless Distribution lines
Hourly Consumption of a Northern California Residential Consumer Weekly Consumption Monday to Sunday
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
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 – This is tariff paid by all APU customers • 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)
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 – Recruitment letter emphasized “You can’t lose”
Anaheim CPP Experiment • Rebate mechanism addresses customer concern that real-time pricing “charges high prices at time when electricity is really needed” – CPP-R rewards customers that reduce when others need electricity • Reference peak period consumption is customer’s “typical” peak period consumption – Defined as average peak period consumption of that customer during three highest consumption non-CPP days that are eligible to be CCP days during entire experiment period
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
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 consum t i on 0. 26 0. 25 0. 24 0. 23 0. 22 0. 21 0. 2 0. 19 0. 18 0. 17 0. 16 0. 15 0. 14 Treatment 0. 13 0. 12 0. 11 Control 0. 1 0. 09 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 t i m e r ed=cont r ol gr oup bl ue=t r eat m ent gr oup
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 Difference in Pre-Experiment Period Treatment and Control Means 0.2 0.1 0 1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96 -0.1 -0.2 15-Minute Intervals Difference in Means Upper 95% Confidence Bound Lower 95% Confidence Bound
Measuring Price Response • Two models estimated for peak period • Average peak period treatment effect – ln(Peak(i,d)) = α CCP(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)) = α CCP(d)*Treat(i) + γ CPP(d)*Treat(i)*TEMP(d) + ν d + μ i + η id μ 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
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