DYNAMIC PRICING & CUSTOMER BEHAVIOR...2010/03/08 · dynamic pricing programs They also indicate that between 65-80 percent of customers would stay enrolled in dynamic pricing programs

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DYNAMIC PRICING & CUSTOMER BEHAVIOR

Ahmad Faruqui, Ph. D.Fourth Annual Electricity Conference

Carnegie Mellon UniversityMarch 9, 2010

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


The FERC Assessment

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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


The Top 10 states

Achievable Potential Peak Reduction from Pricing with Tech:Top 10 States

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Pricing Participants With Enabling Technology

Pricing Participants Without Enabling Technology


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


A bird’s eye view of the 70 tests

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Pricing Pilot

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The picture improves if results are sorted by pilot

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Pricing Pilot

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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


It also improves if the results are sorted by rate

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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

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Time-of-use(TOU)

Critical peak pricing(CPP)

Peak time rebate(PTR)

RTP


And it improves further with technology

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Pricing Pilot

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PTR CPP CPP w/ Tech

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TechPTR w/

Tech


The newest results come from the Northeast

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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


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


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

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Demand response varies with elasticity

Peak Reduction with Different Elasticities(Residential Customers on CPP Rate)

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Peak/Off Peak Price Ratio

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Elasticity = -0.13Elasticity = -0.122

Elasticity = -0.104Elasticity = -0.097Elasticity = -0.091

Elasticity = -0.073


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


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


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


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


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


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."


Sources II

Pilot Programs and Sources II


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.


Sources III

Pilot Programs and Sources III


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.


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 .


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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DYNAMIC PRICING & CUSTOMER BEHAVIOR...2010/03/08 · dynamic pricing programs They also indicate that between 65-80 percent of customers would stay enrolled in dynamic pricing programs