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A Residential Demand Charge: Evidence from the Duke Power Time-of-Day Pricing Experiment

Author

Listed:
  • Thomas N. Taylor
  • Peter M. Schwarz

Abstract

Demand charges account for one-third to one-half of industrial and commercial electricity bills, and yet they have been virtually ignored, both theoretically and practically, as a component of residential tariffs. Our objective here is twofold: (1) to model and test the effects of a time-of-use demand charge on residential consumer behavior and (2) to evaluate, theoretically and empirically, its influence on utility system peak. Among the pragmatic issues are the effects of sustained hot weather on household response and the effects of the charge on demand at time of system peak compared to billing demand.

Suggested Citation

  • Thomas N. Taylor & Peter M. Schwarz, 1986. "A Residential Demand Charge: Evidence from the Duke Power Time-of-Day Pricing Experiment," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 135-151.
    • Handle: RePEc:aen:journl:1986v07-02-a10
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    Cited by:

    1. Bernard, Jean-Thomas & Roland, Michel, 2000. "Load management programs, cross-subsidies and transaction costs: the case of self-rationing," Resource and Energy Economics, Elsevier, vol. 22(2), pages 161-188, May.
    2. Robert H. Patrick & Frank A. Wolak, 2001. "Estimating the Customer-Level Demand for Electricity Under Real-Time Market Prices," NBER Working Papers 8213, National Bureau of Economic Research, Inc.
    3. Brown, David P. & Sappington, David E.M., 2018. "On the role of maximum demand charges in the presence of distributed generation resources," Energy Economics, Elsevier, vol. 69(C), pages 237-249.
    4. Lanot, Gauthier & Vesterberg, Mattias, 2021. "The price elasticity of electricity demand when marginal incentives are very large," Energy Economics, Elsevier, vol. 104(C).
    5. Öhrlund, Isak & Schultzberg, Mårten & Bartusch, Cajsa, 2019. "Identifying and estimating the effects of a mandatory billing demand charge," Applied Energy, Elsevier, vol. 237(C), pages 885-895.
    6. Seung-Ju Lee & Yourim Yoon, 2020. "Electricity Cost Optimization in Energy Storage Systems by Combining a Genetic Algorithm with Dynamic Programming," Mathematics, MDPI, vol. 8(9), pages 1-20, September.
    7. Gopinath Subramani & Vigna K. Ramachandaramurthy & Sanjeevikumar Padmanaban & Lucian Mihet-Popa & Frede Blaabjerg & Josep M. Guerrero, 2017. "Grid-Tied Photovoltaic and Battery Storage Systems with Malaysian Electricity Tariff—A Review on Maximum Demand Shaving," Energies, MDPI, vol. 10(11), pages 1-17, November.

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    JEL classification:

    • F0 - International Economics - - General

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