Transcript Slide 1
Cost-Benefit Analysis of Smart
Metering and Smart Pricing
Ahmad Faruqui, Ph. D.
NARUC Annual Convention
Miami Beach, Florida
November 14, 2006
A framework for quantifying costs and benefits
• Identify and measure costs
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Deploying advanced metering infrastructure (AMI)
■ Advanced meters
■ Two-way communication links
■ Meter data management system
■ Billing system
Offering dynamic pricing signals
■ Administrative costs
■ Marketing costs
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Framework (concluded)
• Identify and measure benefits
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Operational benefits of AMI
Demand response (DR) benefits of dynamic pricing
• Operational benefits
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Avoided meter reading costs
Faster outage detection
Remotely connect/disconnect service
• DR benefits
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See next several slides
• Develop a present value of net benefits
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Quantifying DR benefits
• Primary benefits = Quantity of DR (MW) * Value of
avoided MW
• Quantity of DR = kW reduction per participant * Number
of participants
• Value of avoided load = Cost of peaking capacity net of
energy profits
• Secondary benefits = Reduction in wholesale prices
+ increased system reliability + reduced
planning reserves + customer choice of rates
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Will customers exhibit DR?
• Even a mild time-of-use (TOU) rate caused peak loads to drop by
5 % in Puget Sound
• Additional evidence is beginning to emerge from other pilots
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AmerenUE, Missouri
Anaheim Public Utilities, California
BC Hydro, British Columbia, Canada
Commonwealth Edison, Illinois
Hawaiian Electric, Hawaii
Idaho Power, Idaho
Ontario, Canada
Pepco, Washington, D.C.
Public Service Electric & Gas, New Jersey
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The most comprehensive evidence comes from California
• Two state commissions and three investor-owned utilities
conducted a scientifically designed experiment with 2,500
residential and small commercial and industrial customers in
2003-05
• Impacts were estimated for standard time-of-use (TOU) and
dynamic critical peak pricing (CPP) rates
• Customers on TOU rates dropped peak loads by 5 %, when
prices doubled
• Customers on CPP rates dropped loads by 13 %, when prices
quintupled
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30% of the customers accounted for 80% of the impact
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Price responsiveness varies by customer characteristics
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Elasticity of Substitution
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Enabling technologies boost the drop in critical peak
loads
Type of technology
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Percent drop in critical peak load
Smart Meter
Smart
Thermostat
Gateway
Systems
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Weighted
Average
Dynamic prices have a substantial impact in a hot climate
(Central Valley)
Figure 11
Hourly Load Shape - Complex Daily Share Model - Zone 4
2.5
kW Load
2.0
1.5
1.0
<-Peak Period->
0.5
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Hour
Control 4
Treatment 4
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Dynamic prices even have an impact in a mild climate
(San Francisco)
Figure 8
Hourly Load Shape - Complex Daily Share Model - Zone 1
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kW Load
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Hour
Control 1
Treatment 1
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California’s utilities are developing advanced metering
infrastructure (AMI) business cases
• PG&E’s $1.7 billion AMI filing was unanimously approved by the
CPUC in July
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Almost 90% of the benefits come from operational savings
By 2011, the utility projects more than 500 MW of demand response if
a third of its customers with central air conditioning adopt dynamic
pricing tariffs
It is proceeding to deploy five million electric meters and four million gas
meters
• SDG&E’s AMI filing is currently in hearings before the CPUC
• SCE has filed a Phase I feasibility report
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It plans to file an application next year
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Can others make use of the California results?
• Magnitude of response is driven by several factors
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Existing rate design
New dynamic rate design
Existing load shape
Saturation of central air conditioning
Weather conditions
• Once these “initial conditions” are specified, the California
pricing model can be used to make preliminary forecasts of
dynamic pricing impacts in other regions
• Responses may be more transferable across regions than is
generally believed
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In the mid-1980s, EPRI pooled data from five pricing experiments and
showed that customer response patterns were consistent across
California, Connecticut, North Carolina and Wisconsin
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Percent drop in critical-peak load will vary with price and
climate
Figure 1-2
Percent Reduction in Peak-Period Energy Use on Critical Days
Average Summer, 2003/04
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% Impact (kWh/Hour)
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Peak Price ($/kWh)
Zone 1
Zone 2
Zone 3
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Zone 4
State-wide
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0.90
1.00
Putting it all together in five easy steps
• 1: Develop a dynamic pricing rate and estimate its impact per
customer
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Ball park estimate: 10-30 % per participant
• 2: Identify the number of participants and associated marketing
costs
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Ball park estimate: 10 – 30 % of the target market
• 3: Compute aggregate DR impact
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Ball park estimate: 1 to 9 % of peak demand
• 4: Estimate value of avoided costs
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Ball park estimate: $52 – 85 /kW-yr
• 5: Estimate the present value of benefits with the present value
of costs and derive an estimate of net benefits
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Additional reading
• Ahmad Faruqui, “2050: A pricing odyssey,” The Electricity
Journal, October 2006
• Roger Levy, “A vision of demand response: 2016,” The
Electricity Journal, October 2006
• Plexus Research, Inc., Deciding on Smart Meters, Edison
Electric Institute, September 2006
• FERC, Demand Response and Advanced Metering, Staff Report,
August 2006
• Robert Earle and Ahmad Faruqui, “Toward a new paradigm for
valuing demand response,” The Electricity Journal, May 2006
• US Department of Energy, Benefits of Demand Response in
Electricity Markets, February 2006
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Contact information
Ahmad Faruqui, Ph. D.
Principal
The Brattle Group
353 Sacramento Street, Suite 1140
San Francisco, CA 94111
Voice: 415.217.1026
Fax: 415.217.1099
Cell: 925.408.0149
Email: [email protected]
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