DYNAMIC PRICING
& CUSTOMER BEHAVIOR
Copyright © 2009 The Brattle Group, Inc.
Ahmad Faruqui, Ph. D.
Fourth Annual Electricity Conference
Carnegie Mellon University
March 9, 2010
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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
1,000
BAU
1.7% AAGR
950
No DR (NERC)
1.7% AAGR
38 GW,
4% of
GW
900
82 GW,
9% of
850
138 GW,
14% of
800
188 GW,
20% of
750
700
650
2009
Expanded
BAU
1.3% AAGR
2011
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Achievable
Participation
0.6% AAGR
2013
Full
Participation
0.0% AAGR
2015
2017
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2019
The Top 10 states
Achievable Potential Peak Reduction from Pricing with Tech:
Top 10 States
18%
Pricing Participants With Enabling Technology
16%
Pricing Participants Without Enabling Technology
Peak Reduction
14%
12%
10%
8%
6%
4%
2%
0%
AZ
NV
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GA
FL
NC
4
MD
TN
ID
SC
TX
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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70
% Reduction in Peak Load
A bird’s eye view of the 70 tests
60%
50%
40%
30%
20%
10%
0%
Pricing Pilot
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The picture improves if results are sorted by pilot
60%
Colorado
Ontario, New
Canada Jersey
Connecticut
Maryland
Calif.
DC
Calif. Miss. OP GP Others
ADRS
% Reduction in Peak Load
50%
40%
30%
20%
10%
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0%
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.
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Pricing Pilot
It also improves if the results are sorted by rate
60%
Time-of-use
(TOU)
Critical peak pricing
(CPP)
Peak time rebate
(PTR)
RTP
% Reduction in Peak Load
50%
40%
30%
20%
10%
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0%
Rate Design Tested
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% Reduction in Peak Load
And it improves further with technology
60%
TOU
TOU w/
Tech
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PTR
PTR
w/
Tech
CPP
Pricing Pilot
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CPP w/
Tech
RTP w/
Tech
RTP
50%
40%
30%
20%
10%
0%
The newest results come from the Northeast
40%
DC
CT
MD
35%
% Reduction in Peak Load
30%
25%
20%
15%
10%
5%
Pricing Pilot
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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 energy
consumption that are induced by changes in rate level
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The Zen of PRISMetrics
Avoided
Capacity
Dynamic
Rate
Weather
Data
PRISM
Load
Shape
CAC
Saturation
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CustomerLevel
Demand
Response
Customer
Participation
Forecast
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System-wide
Peak
Reduction
Avoided
Energy
Market
Price
Mitigation
Additional
Benefits
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
30%
Residential
Small General Service
Medium General Service
Peak Reduction
25%
20%
15%
10%
5%
0%
0
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10
Peak/Off Peak Price Ratio
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Demand response varies with elasticity
Peak Reduction with Different Elasticities
(Residential Customers on CPP Rate)
30%
Peak Reduction
25%
20%
15%
Elasticity = -0.13
10%
Elasticity = -0.122
Elasticity = -0.104
Elasticity = -0.097
5%
Elasticity = -0.091
Elasticity = -0.073
0%
0
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9
Peak/Off Peak Price Ratio
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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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What we know imperfectly
Customers respond equally to peak time rebates and critical peak
pricing 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
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CL&P, TOU
CL&P, TOU-w/ technology
CL&P, TOU
CL&P, TOU-w/ technology
CL&P, PTR
CL&P, CPP
CL&P, PTR
CL&P, PTR-w/ technology
CL&P, CPP-w/ technology
CL&P, CPP
CL&P, PTR-w/ technology
CL&P, CPP-w/ technology
Pepco DC, RTP-w/ technology
Pepco DC, PTR-w/ technology
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Pepco DC, RTP
Pepco DC, PTR
Pepco DC, CPP
Pepco DC, CPP-w/ technology
BGE, PTR
BGE, CPP
BGE, PTR
BGE, PTR
BGE, PTR-w/ technology
BGE, PTR-w/ technology
BGE, PTR-w/ technology
BGE, CPP-w/ technology
BGE, 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
Rocky Mountain Institute (2006). “Automated Demand Response System Pilot: Final
California Edison (SCE) and San Diego Gas
Report.” Snowmass, Colorado. March.
& Electric (SDG&E)
California
Charles River Associates (2005). “Impact Evaluation of the California Statewide
Pacific Gas & Electric (PG&E), Southern
California Statewide Pricing Pilot
California Edison (SCE) and San Diego Gas Pricing Pilot.” March 16. The report can be downloaded from:
(SPP)
http://www.calmac.org/publications/2005-03-24_SPP_FINAL_REP.pdf.
& Electric (SDG&E)
Colorado
Xcel Experimental Residential
Price Response Pilot Program
Connecticut
Connecticut Light & Power Plan-it Connecticut Light & Power Company
Wise Energy Pilot program
(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.
DC
Smart Meter Pilot Project, Inc.
(SMPPI)
eMeter Strategic Consulting (2009). "PowerCentsDC™ Program: Interim Report."
Energy Insights Inc. (2008a). “Xcel Energy TOU Pilot Final Impact Report.” March.
Carnegie Mellon University
Xcel Energy
Energy Insights Inc. (2008b). “Experimental Residential Price Response Pilot
Program March 2008 Update to the 2007 Final Report.” March.
Pepco
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Sources II
Pilot Programs and Sources II
State/ Province
Florida
Experiment
The Gulf Power Select Program
Utility
Sources
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.
Gulf Power
Levy, Roger, Ralph Abbott and Stephen Hadden (2002). New Principles for Demand
Response Planning. EPRI EP-P6035/C3047, March.
Giraud, Denise. 2004. “The tempo tariff,” Efflocon Workshop, June 10.
http://www.efflocom.com/pdf/EDF.pdf.
France
Electricite de France (EDF)
Tempo Program
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.
Electricite de France (EDF)
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 Power Company. 2006. “Analysis of the Residential Time-of-Day and Energy
Watch Pilot Programs: Final Report.” December.
Idaho
Idaho Residential Pilot Program
Illinois
The Community Energy
Cooperative's Energy-Smart
Pricing Plan (ESPP)
Commonwealth Edison
Baltimore Gas & Electric
Company's Smart Energy Pricing
Pilot
Baltimore Gas & Electric Company
Maryland
Missouri
Idaho Power Company
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.
RLW Analytics (2004). “AmerenUE Residential TOU Pilot Study Load Research
Analysis: First Look Results.” February.
AmerenUE Residential TOU Pilot
AmerenUE
Study
Carnegie Mellon University
The Brattle Group (2009). "BGE's Smart Energy Pricing Pilot Summer 2008 Impact
Evaluation". Prepared for Baltimore Gas & Electric Company. April.
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 Jersey
Public 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
Energy Australia
Reform
Colebourn H. (2006). “Network Price Reform.” presented at BCSE Energy
Infrastructure& Sustainability Conference. December.
Ontario (Canada)
Ontario Energy Board Smart Price
Hydro Ottawa
Pilot
Ontario Energy Board. 2007. “Ontario Energy Board Smart Price Pilot Final
Report.” Toronto, Ontario, July.
Washington
Puget Sound Energy (PSE)’s TOU
Puget Sound Energy
Program
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
Carnegie Mellon University
Bonneville Power Administration, Clallam
County PUD, The City of Port Angeles,
Portland General Electric, and PacifiCorp
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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-demandresponse.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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