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The spillover effect of peak pricing

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  • Guo, Bowei

Abstract

Understanding consumer behaviours is important in designing dynamic tariffs, which are usually considered the first-best solution when the conventional flat tariff does not reflect the varying cost of electricity generation. I estimate households’ own- and cross-price elasticities using dataset from a smart metering project, and investigate which household specific characteristics determine the impact of peak prices on electricity consumption. I find peak prices (17:00–20:00) reduce peak and post-peak consumption (20:00–23:00), indicating a spillover effect of peak prices. The underlying mechanisms that could be generating the spillover effect have been further discussed and investigated. Finally, I estimate dynamic tariffs’ distributional and welfare effects, and demonstrate that the spillover effect is crucial in determining the cost effectiveness of a smart metering programme.

Suggested Citation

  • Guo, Bowei, 2023. "The spillover effect of peak pricing," Journal of Environmental Economics and Management, Elsevier, vol. 121(C).
    • Handle: RePEc:eee:jeeman:v:121:y:2023:i:c:s0095069623000633
    DOI: 10.1016/j.jeem.2023.102845
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    References listed on IDEAS

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    1. Nojavan, Sayyad & Zare, Kazem & Mohammadi-Ivatloo, Behnam, 2017. "Optimal stochastic energy management of retailer based on selling price determination under smart grid environment in the presence of demand response program," Applied Energy, Elsevier, vol. 187(C), pages 449-464.
    2. Guo, Bowei & Weeks, Melvyn, 2022. "Dynamic tariffs, demand response, and regulation in retail electricity markets," Energy Economics, Elsevier, vol. 106(C).
    3. Guo, Bowei & Castagneto Gissey, Giorgio, 2021. "Cost pass-through in the British wholesale electricity market," Energy Economics, Elsevier, vol. 102(C).
    4. Gans, Will & Alberini, Anna & Longo, Alberto, 2013. "Smart meter devices and the effect of feedback on residential electricity consumption: Evidence from a natural experiment in Northern Ireland," Energy Economics, Elsevier, vol. 36(C), pages 729-743.
    5. Allcott, Hunt, 2011. "Rethinking real-time electricity pricing," Resource and Energy Economics, Elsevier, vol. 33(4), pages 820-842.
    6. Robin Greenwood & Andrei Shleifer, 2014. "Expectations of Returns and Expected Returns," The Review of Financial Studies, Society for Financial Studies, vol. 27(3), pages 714-746.
    7. Bryan K. Bollinger & Wesley R. Hartmann, 2020. "Information vs. Automation and Implications for Dynamic Pricing," Management Science, INFORMS, vol. 66(1), pages 290-314, January.
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    Cited by:

    1. Antweiler, Werner & Muesgens, Felix, 2024. "The new merit order: The viability of energy-only electricity markets with only intermittent renewable energy sources and grid-scale storage," Ruhr Economic Papers 1064, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.

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    More about this item

    Keywords

    Pricing; Demand response; Dynamic tariffs; Inattention;
    All these keywords.

    JEL classification:

    • D10 - Microeconomics - - Household Behavior - - - General
    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices

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