dynamic pricing & customer behavior...2010/03/08 · dynamic pricing programs they also indicate...
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DYNAMIC PRICING & CUSTOMER BEHAVIOR
Ahmad Faruqui, Ph. D.Fourth Annual Electricity Conference
Carnegie Mellon UniversityMarch 9, 2010
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2Carnegie Mellon University
The potential impact of dynamic pricing
The FERC projects that 20% of US peak demand could be offset by demand response programs if dynamic pricing programs are universally deployed to all electric customers in the United States
This will require the universal deployment of smart meters; at this time, five percent of the meters are smart, up from one percent just two years ago; in the next five years, about 50 million of the 145 million meters are expected to become smart
And it will require a major change in the way Americans think about their electric service
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The FERC Assessment
650
700
750
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850
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950
1,000
2009 2011 2013 2015 2017 2019
GW
38 GW,4% of
82 GW,9% of
138 GW,14% of
188 GW,20% of
BAU 1.7% AAGR
Expanded BAU
1.3% AAGR
FullParticipation 0.0% AAGR
AchievableParticipation 0.6% AAGR
No DR (NERC) 1.7% AAGR
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The Top 10 states
Achievable Potential Peak Reduction from Pricing with Tech:Top 10 States
0%
2%
4%
6%
8%
10%
12%
14%
16%
18%
AZ NV GA FL NC MD TN ID SC TX
Peak
Red
uctio
n
Pricing Participants With Enabling Technology
Pricing Participants Without Enabling Technology
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What do we know about customer behavior?
Over the past decade, several pilots have been carried out within the US, Canada, the European Union and Australia
These pilots have featured 70 tests of dynamic pricing some of which can be called experiments, others can be called quasi experiments and the remainders are simply technology demonstrations
While there is much variation in the quality of results from the 70 tests, they have yielded valuable insights about customer response to dynamic pricing
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A bird’s eye view of the 70 tests
0%
10%
20%
30%
40%
50%
60%1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70
Pricing Pilot
% R
educ
tion
in P
eak
Load
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The picture improves if results are sorted by pilot
0%
10%
20%
30%
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50%
60%1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70
Pricing Pilot
% R
educ
tion
in P
eak
Load
Colorado Ontario, Canada
New Jersey
Maryland Calif. Calif.ADRS
Miss. OP GP Others
Notes: (1) OP refers to Olympic Peninsula Pilot. (2) GP refers to Gulf Power Pilot. (3) Others include Anaheim, ESPP, Australia, GPU, Idaho and PSE pilots.
Connecticut DC
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It also improves if the results are sorted by rate
0%
10%
20%
30%
40%
50%
60%1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70
Rate Design Tested
% R
educ
tion
in P
eak
Loa
d
Time-of-use(TOU)
Critical peak pricing(CPP)
Peak time rebate(PTR)
RTP
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And it improves further with technology
0%
10%
20%
30%
40%
50%
60%1 2 3 4 5 6 7 8 9 10 1 1 12 13 14 15 1 6 17 18 1 9 20 21 22 23 24 2 5 26 27 2 8 29 30 31 32 3 3 34 35 3 6 37 38 39 40 41 4 2 43 44 4 5 46 47 48 49 5 0 51 52 5 3 54 55 5 6 57 58 5 9 60 61 6 2 63 64 65 66 6 7 68 69 7 0
Pricing Pilot
% R
educ
tion
in P
eak
Load
TOU TOU w/ Tech
PTR CPP CPP w/ Tech
RTPRTPw/
TechPTR w/
Tech
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10Carnegie Mellon University
The newest results come from the Northeast
0%
5%
10%
15%
20%
25%
30%
35%
40%1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 1 9 20 21 22 23 24 25 2 6 27
Pricing Pilot
% R
educ
tion
in P
eak
Load
CT DC MD
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There is much unexplained variation
This can be probed further by using a common modeling framework, such as that provided by the widely-used Price Impact Simulation Model (PRISM)
The architecture of PRISM revolves around two fundamental equations, one of which models changes in load shapes that are induced by rate design and one of which models changes in energyconsumption that are induced by changes in rate level
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The Zen of PRISMetrics
DynamicRate
WeatherData
LoadShape
CACSaturation
PRISM
Customer-Level
Demand Response
Customer Participation
Forecast
System-wide Peak
Reduction
Avoided Capacity
Avoided Energy
Market Price
Mitigation
AdditionalBenefits
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For a given elasticity of substitution, demand response rises with the peak-to-off peak price ratio
Peak Reduction with Different CPP Peak/Off Peak Price Ratios
0%
5%
10%
15%
20%
25%
30%
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Peak/Off Peak Price Ratio
Peak
Red
uctio
n
ResidentialSmall General ServiceMedium General Service
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Demand response varies with elasticity
Peak Reduction with Different Elasticities(Residential Customers on CPP Rate)
0%
5%
10%
15%
20%
25%
30%
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Peak/Off Peak Price Ratio
Peak
Red
uctio
n
Elasticity = -0.13Elasticity = -0.122
Elasticity = -0.104Elasticity = -0.097Elasticity = -0.091
Elasticity = -0.073
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What we know quite well
Customers respond to price by lowering usage during expensive periods
Customer response rises with prices but at a diminishing rate
Customer response gets a boost with enabling technologies
Customer response gets a boost with hotter temperatures
Customer response persists across two or three days that are called in sequence
Customer response persists across two or three days
Customer response is generally higher for customers who have college education, higher than average incomes and live in single family homes
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16Carnegie Mellon University
What we know imperfectly
Customers respond equally to peak time rebates and critical peakpricing in some tests and unequally in other tests
Customers respond to informational feedback about energy usage, prices and utility bills but by how much they respond remains uncertain and whether this response would persist over time is also uncertain
The variation in response across various technologies such as web portals, in-home displays and energy orbs is uncertain
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What we don’t know
Customer preferences for dynamic pricing over standard, flat rate pricing are poorly understood
Most of the evidence comes from focus groups, attitudinal surveys and pilots
In focus groups, customers who are first introduced to the notion of dynamic pricing articulate concerns about price volatility and higher bills
After they have participated in a pilot, most customers are satisfied or very satisfied with dynamic pricing rates
Attitudinal surveys of non-participants indicate that between 10-20 percent of customers would participate in well-designed and well-marketed opt-in dynamic pricing programs
They also indicate that between 65-80 percent of customers would stay enrolled in dynamic pricing programs that are offered on an opt-out basis
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Do we need more pilots?
Yes, because customer needs differ across regions and because they also change over time
But the next generation of pilots needs to focus on different issues than the previous generation
We also need some large-scale deployments to validate the experimental results
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Legend for the newest results (slide 10)
Index of Pilots and Rates
1 CL&P, TOU 15 Pepco DC, RTP2 CL&P, TOU-w/ technology 16 Pepco DC, PTR3 CL&P, TOU 17 Pepco DC, CPP4 CL&P, TOU-w/ technology 18 Pepco DC, CPP-w/ technology5 CL&P, PTR 19 BGE, PTR6 CL&P, CPP 20 BGE, CPP7 CL&P, PTR 21 BGE, PTR8 CL&P, PTR-w/ technology 22 BGE, PTR9 CL&P, CPP-w/ technology 23 BGE, PTR-w/ technology10 CL&P, CPP 24 BGE, PTR-w/ technology11 CL&P, PTR-w/ technology 25 BGE, PTR-w/ technology12 CL&P, CPP-w/ technology 26 BGE, CPP-w/ technology13 Pepco DC, RTP-w/ technology 27 BGE, PTR-w/ technology14 Pepco DC, PTR-w/ technology
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20Carnegie Mellon University
Sources of experimental results
Pilot Programs and Sources I
State/ Province Experiment Utility Sources
California Anaheim Critical Peak Pricing Experiment
Anaheim Public Utilities (APU)Wolak, Frank A. (2006). “Residential Customer Response to Real-Time Pricing: The Anaheim Critical-Peak Pricing Experiment.” Available from http://www.stanford.edu/~wolak.
California California Automated Demand Response System Pilot (ADRS)
Pacific Gas & Electric (PG&E), Southern California Edison (SCE) and San Diego Gas & Electric (SDG&E)
Rocky Mountain Institute (2006). “Automated Demand Response System Pilot: Final Report.” Snowmass, Colorado. March.
California California Statewide Pricing Pilot (SPP)
Pacific Gas & Electric (PG&E), Southern California Edison (SCE) and San Diego Gas & Electric (SDG&E)
Charles River Associates (2005). “Impact Evaluation of the California Statewide Pricing Pilot.” March 16. The report can be downloaded from:http://www.calmac.org/publications/2005-03-24_SPP_FINAL_REP.pdf.
ColoradoXcel Experimental Residential Price Response Pilot Program
Xcel Energy
Energy Insights Inc. (2008a). “Xcel Energy TOU Pilot Final Impact Report.” March.
Energy Insights Inc. (2008b). “Experimental Residential Price Response Pilot Program March 2008 Update to the 2007 Final Report.” March.
ConnecticutConnecticut Light & Power Plan-it Wise Energy Pilot program
Connecticut Light & Power Company (CL&P)
The Brattle Group (2009). "CL&P’s Plan-it Wise Program Summer 2009 Impact Evaluation". Prepared for Connecticut Light & Power (CL&P). November.
DCSmart Meter Pilot Project, Inc. (SMPPI) Pepco eMeter Strategic Consulting (2009). "PowerCentsDC™ Program: Interim Report."
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Sources II
Pilot Programs and Sources II
State/ Province Experiment Utility Sources
Florida The Gulf Power Select Program Gulf Power
Borenstein, Severin, Michael Jaske, and Arthur Rosenfeld (2002). “Dynamic Pricing, Advanced Metering and Demand Response in Electricity Markets.” Center for the Study of Electricity Markets, Paper CSEMWP 105, October 31.
Levy, Roger, Ralph Abbott and Stephen Hadden (2002). New Principles for Demand Response Planning. EPRI EP-P6035/C3047, March.
France Electricite de France (EDF) Tempo Program
Electricite de France (EDF)
Giraud, Denise. 2004. “The tempo tariff,” Efflocon Workshop, June 10. http://www.efflocom.com/pdf/EDF.pdf.
Giraud, Denise and Christophe Aubin. 1994. “A New Real-Time Tariff for Residential Customers,” in Proceedings: 1994 Innovative Electricity Pricing Conference, EPRI TR-103629, February.
Aubin, Christophe, Denis Fougere, Emmanuel Husson and Marc Ivaldi (1995). “Real-Time Pricing of Electricity for Residential Customers: Econometric Analysis of an Experiment,” Journal of Applied Econometrics, 10, S171-191.
Idaho Idaho Residential Pilot Program Idaho Power Company Idaho Power Company. 2006. “Analysis of the Residential Time-of-Day and Energy Watch Pilot Programs: Final Report.” December.
IllinoisThe Community Energy Cooperative's Energy-Smart Pricing Plan (ESPP)
Commonwealth Edison
Summit Blue Consulting, LLC. (2006). “Evaluation of the 2005 Energy-Smart Pricing Plan-Final Report.” Boulder, Colorado. August.
Summit Blue Consulting, LLC. (2007). “Evaluation of the 2006 Energy-Smart Pricing Plan-Final Report.” Boulder, Colorado.
MarylandBaltimore Gas & Electric Company's Smart Energy Pricing Pilot
Baltimore Gas & Electric CompanyThe Brattle Group (2009). "BGE's Smart Energy Pricing Pilot Summer 2008 Impact Evaluation". Prepared for Baltimore Gas & Electric Company. April.
MissouriAmerenUE Residential TOU Pilot Study AmerenUE
RLW Analytics (2004). “AmerenUE Residential TOU Pilot Study Load Research Analysis: First Look Results.” February.
Voytas, Rick (2006). “AmerenUE Critical Peak Pricing Pilot.” presented at U.S. Demand Response Research Center Conference, Berkeley, California, June.
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Sources III
Pilot Programs and Sources III
State/ Province Experiment Utility Sources
New Jersey GPU Pilot GPU Braithwait, S. D. (2000). “Residential TOU Price Response in the Presence of Interactive Communication Equipment.” In Faruqui and Eakin (2000).
New JerseyPublic Service Electric and Gas (PSE&G) Residential Pilot Program
Public Service Electric and Gas Company (PSE&G)
PSE&G and Summit Blue Consulting (2007). “Final Report for the Mypower Pricing Segments Evaluation.” Newark, New Jersey. December.
New South Wales (Australia)
Energy Australia’s Network Tariff Reform
Energy Australia Colebourn H. (2006). “Network Price Reform.” presented at BCSE Energy Infrastructure& Sustainability Conference. December.
Ontario (Canada)Ontario Energy Board Smart Price Pilot
Hydro Ottawa Ontario Energy Board. 2007. “Ontario Energy Board Smart Price Pilot Final Report.” Toronto, Ontario, July.
WashingtonPuget Sound Energy (PSE)’s TOU Program Puget Sound Energy
Faruqui, Ahmad and Stephen S. George. 2003. “Demise of PSE’s TOU Program Imparts Lessons.” Electric Light & Power Vol. 81.01:14-15.
Washington and Oregon Olympic Peninsula Project
Bonneville Power Administration, Clallam County PUD, The City of Port Angeles, Portland General Electric, and PacifiCorp
Pacific Northwest National Laboratory. 2007. “Pacific Northwest GridWise Testbed Demonstration Projects Part 1: Olympic Peninsula Project.” Richland, Washington. October.
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Reading list
Faruqui, Ahmad, Ryan Hledik and Sanem Sergici, “Rethinking pricing: the changing architecture of demand response,” The Public Utilities Fortnightly, January 2010.
Faruqui, Ahmad, Ryan Hledik, and Sanem Sergici, “Piloting the smart grid,” The Electricity Journal, August/September, 2009.
Faruqui, Ahmad and Sanem Sergici, “Household response to dynamic pricing of electricity–a survey of the experimental evidence,” January 10, 2009. http://www.hks.harvard.edu/hepg/
FERC, “A National Assessment of Demand Response Potential,”June 2009, http://www.ferc.gov/legal/staff-reports/06-09-demand-response.pdf .
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Biography
Ahmad Faruqui led FERC’s state-by-state assessment of the potential for demand response, co-authored EPRI’s national assessment of the potential for energy efficiency and co-authored EEI’s report on quantifying the benefits of dynamic pricing. He has assessed the benefits of dynamic pricing for the New York Independent System Operator, worked on fostering economic demand response for the Midwest ISO and ISO New England, reviewed demand forecasts for the PJM Interconnection and assisted the California Energy Commission in developing load management standards. He has performed cost-benefit analysis of demand response options for utilities in nearly dozen states and testified before several state commissions and legislative bodies. He has designed and evaluated some of the nation’s best known pilot programs and his early experimental work is cited in Bonbright’s canon. The author, co-author or editor of four books and more than a hundred articles and papers, he holds a doctoral degree in economics from the University of California at Davis.
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