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Dynamic Pricing - Potential and Issues Joe Wharton and Ahmad Faruqui Kansas Corporation Commission Workshop on Energy Efficiency March 25, 2008.

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Presentation on theme: "Dynamic Pricing - Potential and Issues Joe Wharton and Ahmad Faruqui Kansas Corporation Commission Workshop on Energy Efficiency March 25, 2008."— Presentation transcript:

1 Dynamic Pricing - Potential and Issues Joe Wharton and Ahmad Faruqui Kansas Corporation Commission Workshop on Energy Efficiency March 25, 2008

2 2 Policy of Dynamic Pricing raises important questions 1.What is the potential impact of dynamic pricing on peak demand? 2.What is the value of this demand response (DR)? 3.How much does customer price responsiveness vary by customer and region? 4.How can rate design make dynamic pricing more attractive to customers?

3 3 Dynamic pricing can lower system peak demand by 5 percent, considerably below the economic and technical potential

4 4 A 5 percent reduction in US peak demand could be worth $31 billion over a 20-year period, just on avoided costs Assumptions 5% demand reduction in 757 GW $52/kW-year capacity price 20 year horizon 15% discount rate 2% peak growth rate Avoided cost of energy is 36% of avoided cost of capacity* Value of wholesale price reduction is 278% of avoided cost of capacity* * Derived from a study on the value of DR in PJM: The Brattle Group, 2007, Quantifying Demand Response Benefits in PJM, Prepared for PJM and MADRI NPV of Avoided Costs = $31 billion

5 5 There is a range of pricing options – from static (fully hedged) to dynamic

6 6 What peak demand reductions come from dynamic pricing - results from pricing pilots

7 7 Across the TOU pilots, there is solid evidence of demand response

8 8 Dynamic pricing gives rise to greater peak reductions

9 9 The Peak Time Rebate (PTR) rate has achieved demand response in two pilots

10 10 Different Critical Peak Pricing (CPP) tariffs induce different load impacts during event days

11 11 Enabling technologies magnify demand response

12 12 Mass Market customers response varies by enabling technologies and the customers end uses

13 13 Applying these relationships, one expects to find customer responses will vary by region

14 14 But there is equity issue: could Bills rise for 50% of the customers choosing dynamic pricing

15 15 A discount could be build-in for the insurance or risk premium incorporated in flat or hedged rates Empirically, this insurance premium is estimated to range from 3 to 13 percent for different types of time- varying rates Illinois used a value of 10 percent in its RTP pilot for residential customers Monte Carlo simulations with a standard financial equation suggest a mean value of 11 percent A conservative estimate is 3 percent

16 16 By adjusting for conservative risk premium, dynamic pricing rates become attractive for 70% of customers

17 17 Also factoring in the demand response expands the appeal to 90%

18 18 Conclusion: the way forward should involve a careful look at the range of dynamic pricing options

19 19 Footnotes See A. Faruqui and L. Wood, Quantifying the Benefits of Dynamic Pricing in the Mass Market, for EEI, Jan Note: Percentage reduction in load is defined relative to the different bases in different pilots. Following notes are intended to clarify these different definitions. TOU impacts are defined relative to the usage during peak hours unless otherwise noted. CPP impacts are defined relative to the usage during peak hours on CPP days unless otherwise noted. Ontario- 1 refers to the percentage impacts during the critical hours that represent only 3-4 hours of the entire peak period on a CPP day. Ontario- 2 refers to the percentage impacts of the programs during the entire peak period on a CPP day TOU impact from the SPP study uses the CPP-F treatment effect for normal weekdays PSEG program impacts represented in the TOU section are the % impacts during peak period on non-CPP days. PSEG program impacts represented in the CPP section are derived using the reported kWh reductions and the estimated consumption during the peak period on CPP days ADRS- 04 and ADRS- 05 refer to the 2004 and 2005 impacts. ADRS impacts on non-event days are represented in the TOU with Tech section CPP impact for Idaho is derived from the information provided in the study. Average of kW consumption per hour during the CPP hours (for all 10 event days) is approximately 2.5 kW for a control group customer. This value is 1.3 kW for a treatment group customer. Percentage impact from the CPP treatment is calculated as 48%. Gulf Power-1 refers to the impact during peak hours on non-CPP days while Gulf Power-2 refers to the impact during CPP hours on CPP days. Ameren-04 and Ameren-05 refer to the impacts respectively from the summers of 2004 and SPP- A refers to the impacts from the CPP-V program on Track A customers. Two-thirds of Track A customers had some form of enabling technologies. SPP-C refers to the impacts from the CPP-V program on Track C customers. All Track C customers had smart thermostats.


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