Transcript Slide 1
Dynamic Pricing - Potential and Issues
Joe Wharton and Ahmad Faruqui Kansas Corporation Commission Workshop on Energy Efficiency March 25, 2008
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?
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Dynamic pricing can lower system peak demand by 5 percent, considerably below the economic and technical potential Estimates of Total Potential Peak Demand Reduction
60% 52% 50% 40% 30% 20% 10% 0% 5% 15%
Market Projection Economic Potential
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Technical Potential
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 6.0
5.0
4.0
3.0
2.0
1.0
0.0
Annual Value of a 5% Reduction in Peak Demand
Avoided Energy Cost
5.5
0.7
2.0
Avoided Costs
Avoided Capacity Cost
Wholesale Price Reduction NPV of Avoided Costs = $31 billion
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There is a range of pricing options – from static (fully hedged) to dynamic
Reward (Discount from Flat Rate) 10%
Risk Averse Customers PTR?
5% TOD 0% 0 Seasonal Rate Inverted Tier Rate Flat Rate
Risk Seeking Customers
CPP-F CPP-V VPP RTP 0.5
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1 Risk (Variance in Price)
What peak demand reductions come from dynamic pricing - results from pricing pilots
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Across the TOU pilots, there is solid evidence of demand response 35% 30% 25% 20% 15% 10% 5% 0% Ontario- 1 Ontario- 2 Percentage Reduction Estimates from Reviewed TOU Pilot Programs SPP TOU PSEG Pilot Program
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PSEG ADRS- 04 ADRS- 05 TOU w/ Tech Gulf Power-1
Dynamic pricing gives rise to greater peak reductions Percentage Reduction Estimates from Reviewed CPP/PTR Pilot Programs 60% 50% 40% 30% 20% 10% 0% P1 O nt ario - CP O nt ario - CP P2 SPP Aus tra lia CPP Id aho Amere n 0 4 Amere n 0 5 PSE G Aneheim nt ario - 1 O nt ario - 2 PTR Pilot Program
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SPP - A SPP - C Amere n 0 4 Amere n 0 5 ADRS 0 4 ADRS 0 5 CPP w/ Tech G PSE G ulf P ower -2
The Peak Time Rebate (PTR) rate has achieved demand response in two pilots Comparison of Peak Time Rebate (PTR) Program Tariffs and Resulting Impacts 0.50
0.40
0.30
0.20
0.10
0.00
-0.10
-0.20
-0.30
Ontario
Existing Peak Off-Peak PTR
Anaheim
Mid-Peak Load Impact
Pilot Program
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Different Critical Peak Pricing (CPP) tariffs induce different load impacts during “event days” Comparison of Critical Peak Pricing (CPP) Program Tariffs and Resulting Impacts 0.80
0.70
0.60
0.50
0.40
0.30
0.20
0.10
0.00
PSE&G Ontario -0.10
-0.20
-0.30
-0.40
-0.50
Note: PSE&G load impact on CPP days is not provided in the reviewed study. The load impact is calculated using the reported kWh reductions and an estimate of consumption during peak on CPP days.
AmerenUE
Existing Peak
Pilot Program
Off-Peak CPP
SPP
Mid-Peak Load Impact
Idaho
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Enabling technologies magnify demand response Role of Technology on Pilot Program Impacts 20% 15% 10% 5% 40% 35% 30% 25% 0% PSE&G (TOU) PSE&G (CPP) SPP (CPP)
Note: PSE&G load impacts on CPP days are not provided in the reviewed study. The load impacts are calculated using the reported kWh reductions and an estimate of consumption during peak on CPP days.
Pilot Program
11 No Technology Technology
AmerenUE 2004 (CPP) AmerenUE 2005 (CPP)
Mass Market customers’ response varies by enabling technologies and the customers’ end uses
Peak Demand Reduction by Customer Type 0% -5% -10% -15% -20% -25% -30% -35% 0.00
Non-CAC Average CAC CAC w/ Technology 0.20
0.40
0.60
Critical Peak Rate ($/kWh) 0.80
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1.00
Applying these relationships, one expects to find customer responses will vary by region Demand Response Comparison Across Regions
0% -5% -10% -15% -20% -25% 0.00
Hawaii Pacific Northwest Baltimore California - Zone 4 0.20
0.40
0.60
0.80
Critical Peak Rate ($/kWh)
1.00
13 1.20
1.40
But there is equity issue: could Bills rise for 50% of the customers choosing dynamic pricing
Distribution of Bill Impacts 20% 15% 10% 5% 0% 0% -5% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% -10% -15% Customers with Flatter Consumption Percentile of Customer Base
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Customers with Peakier Consumption
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 15
By adjusting for conservative risk premium, dynamic pricing rates become attractive for 70% of customers
Distribution of Bill Impacts
20% 15% 10% 5% 0% -5% 0% -10% -15% -20% -25% Revenue neutral Credit for hedging cost premium 10% 20% 30% 40% 50% 60% 70% 80% 90% Customers with Flatter Consumption Customers with Peakier Consumption 100%
Percentile of Customer Base
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Also factoring in the demand response expands the appeal to 90% Distribution of Bill Impacts
20% 15% 10% 5% 0% -5% 0% -10% -15% -20% -25% Revenue Neutral Credit for Hedging Cost Premium Demand Response Plus Credit for Hedging Cost Premium 10% 20% 30% 40% Customers with Flatter Consumption 50% 60% 70% 80% 90% Customers with Peakier Consumption 100%
Percentile of Customer Base
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Conclusion: the way forward should involve a careful look at the range of dynamic pricing options
Reward (Discount from Flat Rate) 10%
Risk Averse Customers PTR?
5% TOD 0% 0 Seasonal Rate Inverted Tier Rate Flat Rate
Risk Seeking Customers
CPP-F CPP-V VPP RTP 0.5
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1 Risk (Variance in Price)
Footnotes
See A. Faruqui and L. Wood,
Quantifying the Benefits of Dynamic Pricing in the Mass Market
, for EEI, Jan 2008. 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 2005.
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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