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Optional Time-of-Use Prices for Electricity: Econometric Analysis of Surplus and Pareto Impacts

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  • Kenneth Train
  • Gil Mehrez

Abstract

We estimate a model of time-of-use (TOU) consumption and choice among optional TOU tariffs in which the customer's choice among tariffs is based on its demand parameters, which vary in the population. We simulate the impact on consumption, consumer surplus, and profit of several optional TOU rates that were offered experimentally in northern California. The analysis suggests that, with one possible exception, the offering of these optional rates did not constitute a Pareto improvement. However, total surplus, excluding measurement costs, is estimated to have risen by $1.41 to $2.55 per month per customer who chose the TOU rates.

Suggested Citation

  • Kenneth Train & Gil Mehrez, 1994. "Optional Time-of-Use Prices for Electricity: Econometric Analysis of Surplus and Pareto Impacts," RAND Journal of Economics, The RAND Corporation, vol. 25(2), pages 263-283, Summer.
    • Handle: RePEc:rje:randje:v:25:y:1994:i:summer:p:263-283
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    Cited by:

    1. Hobman, Elizabeth V. & Frederiks, Elisha R. & Stenner, Karen & Meikle, Sarah, 2016. "Uptake and usage of cost-reflective electricity pricing: Insights from psychology and behavioural economics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 455-467.
    2. Woo, C.K. & Liu, Y. & Zarnikau, J. & Shiu, A. & Luo, X. & Kahrl, F., 2018. "Price elasticities of retail energy demands in the United States: New evidence from a panel of monthly data for 2001–2016," Applied Energy, Elsevier, vol. 222(C), pages 460-474.
    3. Ariel Casarin, 2014. "Regulated price reforms and unregulated substitutes: the case of residential piped gas in Argentina," Journal of Regulatory Economics, Springer, vol. 45(1), pages 34-56, February.
    4. Dong Gu Choi & Michael K. Lim & Karthik Murali & Valerie M. Thomas, 2020. "Why Have Voluntary Time‐of‐Use Tariffs Fallen Short in the Residential Sector?," Production and Operations Management, Production and Operations Management Society, vol. 29(3), pages 617-642, March.
    5. Kim, Jihyo & Lee, Soomin & Jang, Heesun, 2022. "Lessons from residential electricity demand analysis on the time of use pricing experiment in South Korea," Energy Economics, Elsevier, vol. 113(C).
    6. Jang, Heesun & Moon, Seongman & Kim, Jihyo, 2024. "Effects of time-of-use pricing for residential customers and wholesale market consequences in South Korea," Energy Economics, Elsevier, vol. 134(C).
    7. Woo, C.K. & Shiu, A. & Liu, Y. & Luo, X. & Zarnikau, J., 2018. "Consumption effects of an electricity decarbonization policy: Hong Kong," Energy, Elsevier, vol. 144(C), pages 887-902.
    8. Allcott, Hunt, 2011. "Rethinking real-time electricity pricing," Resource and Energy Economics, Elsevier, vol. 33(4), pages 820-842.
    9. Lucinda, Claudio Ribeiro & Anuatti Neto, Francisco, 2014. "Non-linear Demand and Price: An Empirical Analysis of the Brazilian Industrial Electricity Consumption," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 34(2), November.
    10. Venizelou, Venizelos & Makrides, George & Efthymiou, Venizelos & Georghiou, George E., 2020. "Methodology for deploying cost-optimum price-based demand side management for residential prosumers," Renewable Energy, Elsevier, vol. 153(C), pages 228-240.
    11. Han, Xintong & Liu, Zimin & Wang, Tong, 2023. "Nonlinear pricing in multidimensional context: An empirical analysis of energy consumption," International Journal of Industrial Organization, Elsevier, vol. 91(C).
    12. Qiu, Yueming & Colson, Gregory & Wetzstein, Michael E., 2017. "Risk preference and adverse selection for participation in time-of-use electricity pricing programs," Resource and Energy Economics, Elsevier, vol. 47(C), pages 126-142.
    13. Miller, Reid & Golab, Lukasz & Rosenberg, Catherine, 2017. "Modelling weather effects for impact analysis of residential time-of-use electricity pricing," Energy Policy, Elsevier, vol. 105(C), pages 534-546.
    14. Takanori Ida & Wenjie Wang, 2014. "A Field Experiment on Dynamic Electricity Pricing in Los Alamos:Opt-in Versus Opt-out," Discussion papers e-14-010, Graduate School of Economics Project Center, Kyoto University.
    15. Sudarshan, Anant, 2013. "Deconstructing the Rosenfeld curve: Making sense of California's low electricity intensity," Energy Economics, Elsevier, vol. 39(C), pages 197-207.
    16. Schlereth, Christian & Skiera, Bernd & Schulz, Fabian, 2018. "Why do consumers prefer static instead of dynamic pricing plans? An empirical study for a better understanding of the low preferences for time-variant pricing plans," European Journal of Operational Research, Elsevier, vol. 269(3), pages 1165-1179.
    17. Tanaka, Makoto & Ida, Takanori, 2013. "Voluntary electricity conservation of households after the Great East Japan Earthquake: A stated preference analysis," Energy Economics, Elsevier, vol. 39(C), pages 296-304.
    18. Rufo, Michael W. & North, Alan S. & Owashi, Leslie D., 1997. "Determining the incremental value of residential energy information systems: a clear approach to an uncertain future," Utilities Policy, Elsevier, vol. 6(2), pages 137-149, June.
    19. Ericson, Torgeir, 2011. "Households' self-selection of dynamic electricity tariffs," Applied Energy, Elsevier, vol. 88(7), pages 2541-2547, July.
    20. Chu, Xuehao, 1999. "Alternative congestion pricing schedules," Regional Science and Urban Economics, Elsevier, vol. 29(6), pages 697-722, November.
    21. Rasanen, Mika & Ruusunen, Jukka & Hamalainen, Raimo P., 1997. "Optimal tariff design under consumer self-selection," Energy Economics, Elsevier, vol. 19(2), pages 151-167, May.

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