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Journal of Regulatory Economics

, Volume 38, Issue 2, pp 193–225 | Cite as

Household response to dynamic pricing of electricity: a survey of 15 experiments

  • Ahmad Faruqui
  • Sanem Sergici
Review Article

Abstract

Since the energy crisis of 2000–2001 in the western United States, much attention has been given to boosting demand response in electricity markets. One of the best ways to let that happen is to pass through wholesale energy costs to retail customers. This can be accomplished by letting retail prices vary dynamically, either entirely or partly. For the overwhelming majority of customers, that requires a change out of the metering infrastructure, which may cost as much as $40 billion for the US as a whole. While a good portion of this investment can be covered by savings in distribution system costs, about 40% may remain uncovered. This investment gap could be covered by reductions in power generation costs that could be brought about through demand response. Thus, state regulators in many states are investigating whether customers will respond to the higher prices by lowering demand and if so, by how much. To help inform this assessment, this paper surveys the evidence from the 15 most recent pilots, experiments and full-scale implementations of dynamic pricing of electricity. It finds conclusive evidence that households respond to higher prices by lowering usage. The magnitude of price response depends on several factors, such as the magnitude of the price increase, the presence of central air conditioning and the availability of enabling technologies such as two-way programmable communicating thermostats and always-on gateway systems that allow multiple end-uses to be controlled remotely. In addition, the design of the studies, the tools used to analyze the data and the geography of the assessment influence demand response. Across the range of experiments studied, time-of-use rates induce a drop in peak demand that ranges between 3 and 6% and critical-peak pricing (CPP) tariffs induce a drop in peak demand that ranges between 13 and 20%. When accompanied with enabling technologies, the latter set of tariffs lead to a reduction in peak demand in the 27–44% range.

Keywords

Dynamic pricing Price elasticity Elasticity of substitution Demand models Demand response Rate design 

JEL Classification

L51 L94 D00 

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References

  1. Aubin C., Fougere D., Husson E., Ivaldi M. (1995) Real-time pricing of electricity for residential customers: Econometric analysis of an experiment. Journal of Applied Econometrics 10: S171–S191CrossRefGoogle Scholar
  2. Borenstein, S., Jaske, M., & Rosenfeld, A. H. (2002, September). Dynamic pricing, advanced metering, and demand response in electricity markets. In Energy series: A Joint Project of the Hewlett Foundation & the Energy Foundation, California Energy Commission & the UC Energy Institute, San Francisco.Google Scholar
  3. Braithwait S.D. (2000) Residential TOU price response in the presence of interactive communication equipment. In: Faruqui A., Eakin B.K. (eds) Pricing in competitive electricity markets. Kluwer, BostonGoogle Scholar
  4. Caves D. W., Christensen L. R., Herriges J. A. (1984) Consistency of residential customer response in time-of-use electricity pricing experiments. Journal of Econometrics 26: 179–203CrossRefGoogle Scholar
  5. 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.
  6. Colebourn, H. (2006, December). Network price reform. Presented at BCSE energy infrastructure & sustainability conference.Google Scholar
  7. Energy Insights Inc. (2008a). Xcel Energy TOU Pilot Final Impact Report, March 2008a.Google Scholar
  8. Energy Insights Inc. (2008b). Experimental Residential Price Response Pilot Program March 2008 Update to the 2007 Final Report, March 2008b.Google Scholar
  9. Faruqui A., George S. S. (2003) Demise of PSE’s TOU program imparts lessons. Electric Light & Power 81(01): 14–15Google Scholar
  10. Faruqui A., George S.S. (2005) Quantifying customer response to dynamic pricing. The Electricity Journal 18: 53–63Google Scholar
  11. Faruqui A., Malko J. R. (1983) The residential demand for electricity by time-of-use: A survey of twelve experiments with peak load pricing. Energy 8(10): 781–795CrossRefGoogle Scholar
  12. Federal Energy Regulatory Commission. (2009). A national assessment of demand response potential. Staff Report, Washington, DC.Google Scholar
  13. Filippini M. (1995) Swiss residential demand for electricity by time-of-use: An application of the almost ideal demand system. Energy Journal 16(1): 27–39Google Scholar
  14. Giraud, D. (2004). The tempo tariff. In Efflocon Workshop, June 10. http://www.efflocom.com/pdf/EDF.pdf.
  15. Giraud, D., & Aubin, C. (1994). A new real-time tariff for residential customers. In Proceedings: 1994 innovative electricity pricing conference, EPRI TR-103629, February 1994.Google Scholar
  16. Herter K. (2007) Residential implementation of critical-peak pricing of electricity. Energy Policy 35(4): 2121–2130CrossRefGoogle Scholar
  17. Herter K., McAuliffe P., Rosenfeld A. (2007) An exploratory analysis of California residential customer response to critical peak pricing of electricity. Energy 32(1): 25–34CrossRefGoogle Scholar
  18. Idaho Power Company. (2006). Analysis of the residential time-of-day and energy watch pilot programs: Final report, December 2006.Google Scholar
  19. Kiesling, L. (2008). Digital technology, demand response, and customer choice: Efficiency benefits. In NARUC Winter Meetings, Washington, DC, 18 February 2008.Google Scholar
  20. Levy, R., Abbott, R., & Hadden, S. (2002). New principles for demand response planning. EPRI EP-P6035/C3047, March 2002.Google Scholar
  21. Matsukawa I. (2001) Household response to optional peak-load pricing of electricity. Journal of Regulatory Economics 20(3): 249–261CrossRefGoogle Scholar
  22. Ontario Energy Board. (2007). Ontario Energy Board Smart Price Pilot Final Report. Toronto, ON, July 2007.Google Scholar
  23. Pacific Northwest National Laboratory. (2007). Pacific Northwest GridWise Testbed Demonstration Projects, Part 1: Olympic Peninsula Project. Richland, Washington, October 2007.Google Scholar
  24. PSE&G and Summit Blue Consulting. (2007). Final report for the Mypower Pricing Segments Evaluation. Newark, NJ, December 2007.Google Scholar
  25. RLW Analytics. (2004). AmerenUE Residential TOU Pilot Study Load Research analysis: First look results, February 2004.Google Scholar
  26. Rocky Mountain Institute (2006). Automated demand response system pilot: Final report. Snowmass, CO, March 2006.Google Scholar
  27. Shadish W. R., Cook T. D., Campbell D. T. (2002) Experimental and quasi-experimental designs for generalized causal inference. Houghton Mifflin Company, BostonGoogle Scholar
  28. Summit Blue Consulting, LLC. (2006). Evaluation of the 2005 Energy-Smart Pricing Plan-Final Report. Boulder, CO, August 2006.Google Scholar
  29. Summit Blue Consulting, LLC. (2007). Evaluation of the 2006 Energy-Smart Pricing Plan—Final report. Boulder, CO.Google Scholar
  30. Voytas, R. (2006, June). AmerenUE critical peak pricing pilot. Presented at U.S. demand response research center conference, Berkeley, CA.Google Scholar
  31. Wellinghoff J., Morenoff D.M. (2007) Recognizing the importance of demand response: The second half of the wholesale electric market equation. Energy Law Journal 28(2): 389–419Google Scholar
  32. Wolak, F. A. (2006). Residential customer response to real-time pricing: The Anaheim Critical-Peak Pricing Experiment. http://www.stanford.edu/~wolak.

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.The Brattle GroupSan FranciscoUSA
  2. 2.The Brattle GroupCambridgeUSA

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