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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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3Carnegie Mellon University
The FERC Assessment
650
700
750
800
850
900
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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4Carnegie Mellon University
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%
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
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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8Carnegie Mellon University
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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12Carnegie Mellon University
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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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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19Carnegie Mellon University
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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23Carnegie Mellon University
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.