Skip to main content

Advertisement

Log in

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

  • Review Article
  • Published:
Journal of Regulatory Economics Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • 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–S191

    Article  Google Scholar 

  • 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.

  • 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, Boston

    Google Scholar 

  • 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: 179–203

    Article  Google Scholar 

  • 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.

  • Colebourn, H. (2006, December). Network price reform. Presented at BCSE energy infrastructure & sustainability conference.

  • Energy Insights Inc. (2008a). Xcel Energy TOU Pilot Final Impact Report, March 2008a.

  • Energy Insights Inc. (2008b). Experimental Residential Price Response Pilot Program March 2008 Update to the 2007 Final Report, March 2008b.

  • Faruqui A., George S. S. (2003) Demise of PSE’s TOU program imparts lessons. Electric Light & Power 81(01): 14–15

    Google Scholar 

  • Faruqui A., George S.S. (2005) Quantifying customer response to dynamic pricing. The Electricity Journal 18: 53–63

    Google Scholar 

  • 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): 781–795

    Article  Google Scholar 

  • Federal Energy Regulatory Commission. (2009). A national assessment of demand response potential. Staff Report, Washington, DC.

  • Filippini M. (1995) Swiss residential demand for electricity by time-of-use: An application of the almost ideal demand system. Energy Journal 16(1): 27–39

    Google Scholar 

  • Giraud, D. (2004). The tempo tariff. In Efflocon Workshop, June 10. http://www.efflocom.com/pdf/EDF.pdf.

  • 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.

  • Herter K. (2007) Residential implementation of critical-peak pricing of electricity. Energy Policy 35(4): 2121–2130

    Article  Google Scholar 

  • Herter K., McAuliffe P., Rosenfeld A. (2007) An exploratory analysis of California residential customer response to critical peak pricing of electricity. Energy 32(1): 25–34

    Article  Google Scholar 

  • Idaho Power Company. (2006). Analysis of the residential time-of-day and energy watch pilot programs: Final report, December 2006.

  • Kiesling, L. (2008). Digital technology, demand response, and customer choice: Efficiency benefits. In NARUC Winter Meetings, Washington, DC, 18 February 2008.

  • Levy, R., Abbott, R., & Hadden, S. (2002). New principles for demand response planning. EPRI EP-P6035/C3047, March 2002.

  • Matsukawa I. (2001) Household response to optional peak-load pricing of electricity. Journal of Regulatory Economics 20(3): 249–261

    Article  Google Scholar 

  • Ontario Energy Board. (2007). Ontario Energy Board Smart Price Pilot Final Report. Toronto, ON, July 2007.

  • Pacific Northwest National Laboratory. (2007). Pacific Northwest GridWise Testbed Demonstration Projects, Part 1: Olympic Peninsula Project. Richland, Washington, October 2007.

  • PSE&G and Summit Blue Consulting. (2007). Final report for the Mypower Pricing Segments Evaluation. Newark, NJ, December 2007.

  • RLW Analytics. (2004). AmerenUE Residential TOU Pilot Study Load Research analysis: First look results, February 2004.

  • Rocky Mountain Institute (2006). Automated demand response system pilot: Final report. Snowmass, CO, March 2006.

  • Shadish W. R., Cook T. D., Campbell D. T. (2002) Experimental and quasi-experimental designs for generalized causal inference. Houghton Mifflin Company, Boston

    Google Scholar 

  • Summit Blue Consulting, LLC. (2006). Evaluation of the 2005 Energy-Smart Pricing Plan-Final Report. Boulder, CO, August 2006.

  • Summit Blue Consulting, LLC. (2007). Evaluation of the 2006 Energy-Smart Pricing Plan—Final report. Boulder, CO.

  • Voytas, R. (2006, June). AmerenUE critical peak pricing pilot. Presented at U.S. demand response research center conference, Berkeley, CA.

  • 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): 389–419

    Google Scholar 

  • Wolak, F. A. (2006). Residential customer response to real-time pricing: The Anaheim Critical-Peak Pricing Experiment. http://www.stanford.edu/~wolak.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ahmad Faruqui.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Faruqui, A., Sergici, S. Household response to dynamic pricing of electricity: a survey of 15 experiments. J Regul Econ 38, 193–225 (2010). https://doi.org/10.1007/s11149-010-9127-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11149-010-9127-y

Keywords

JEL Classification

Navigation