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Behavioral considerations for effective time-varying electricity prices

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  • SCHNEIDER, IAN
  • SUNSTEIN, CASS R.

Abstract

Wholesale prices for electricity vary significantly due to high fluctuations and low elasticity of short-run demand. End-use customers have typically paid flat retail rates for their electricity consumption, and time-varying prices (TVPs) have been proposed to help reduce peak consumption and lower the overall cost of servicing demand. Unfortunately, the general practice is an opt-in system: a default rule in favor of TVPs would be far better. A behaviorally informed analysis also shows that when transaction costs and decision biases are taken into account, the most cost-reflective policies are not necessarily the most efficient. On reasonable assumptions, real-time prices can result in less peak conservation of manually controlled devices than time-of-use or critical-peak prices. For that reason, the trade-offs between engaging automated and manually controlled loads must be carefully considered in time-varying rate design. The rate type and accompanying program details should be designed with the behavioral biases of consumers in mind, while minimizing price distortions for automated devices.

Suggested Citation

  • Schneider, Ian & Sunstein, Cass R., 2017. "Behavioral considerations for effective time-varying electricity prices," Behavioural Public Policy, Cambridge University Press, vol. 1(2), pages 219-251, November.
  • Handle: RePEc:cup:bpubpo:v:1:y:2017:i:02:p:219-251_00
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    Citations

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    Cited by:

    1. Jacobsen, Grant D. & Stewart, James I., 2022. "How do consumers respond to price complexity? Experimental evidence from the power sector," Journal of Environmental Economics and Management, Elsevier, vol. 116(C).
    2. Ambec, Stefan & Crampes, Claude, 2021. "Real-time electricity pricing to balance green energy intermittency," Energy Economics, Elsevier, vol. 94(C).
    3. Julien Lancelot Michellod & Declan Kuch & Christian Winzer & Martin K. Patel & Selin Yilmaz, 2022. "Building Social License for Automated Demand-Side Management—Case Study Research in the Swiss Residential Sector," Energies, MDPI, vol. 15(20), pages 1-25, October.
    4. Correia-da-Silva, João & Soares, Isabel & Fernández, Raquel, 2020. "Impact of dynamic pricing on investment in renewables," Energy, Elsevier, vol. 202(C).
    5. Rasool Bukhsh & Nadeem Javaid & Zahoor Ali Khan & Farruh Ishmanov & Muhammad Khalil Afzal & Zahid Wadud, 2018. "Towards Fast Response, Reduced Processing and Balanced Load in Fog-Based Data-Driven Smart Grid," Energies, MDPI, vol. 11(12), pages 1-21, November.
    6. Burns, Kelly & Mountain, Bruce, 2021. "Do households respond to Time-Of-Use tariffs? Evidence from Australia," Energy Economics, Elsevier, vol. 95(C).
    7. Atasoy, Ayse Tugba & Harmsen-van Hout, Marjolein & Madlener, Reinhard, 2018. "Strategic Demand Response to Dynamic Pricing: A Lab Experiment for the Electricity Market," FCN Working Papers 5/2018, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN), revised Jan 2020.

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