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Households' response to changes in electricity pricing schemes: Bridging microeconomic and engineering principles

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  • Matar, Walid

Abstract

This paper presents a computational take on households' response to price changes. Many studies use assumptions (i.e., price elasticities) that were estimated using historical information; however, if a price change has not been experienced in the past, the response may not be statistically predicted. While other papers have explored price response behavior internally through microeconomic principles, many factors affect a household's electricity use, including the construction of the dwelling, outdoor air temperature, and efficiency of the air conditioner. We have superimposed a physical model, which determines hourly power loads, with a utility maximization component.

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  • Matar, Walid, 2018. "Households' response to changes in electricity pricing schemes: Bridging microeconomic and engineering principles," Energy Economics, Elsevier, vol. 75(C), pages 300-308.
    • Handle: RePEc:eee:eneeco:v:75:y:2018:i:c:p:300-308
    DOI: 10.1016/j.eneco.2018.08.028
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Javier Bueno & Desiderio Romero-Jordán & Pablo del Río, 2020. "Analysing the Drivers of Electricity Demand in Spain after the Economic Crisis," Energies, MDPI, vol. 13(20), pages 1-18, October.
    2. Aldubyan, Mohammad & Gasim, Anwar, 2021. "Energy price reform in Saudi Arabia: Modeling the economic and environmental impacts and understanding the demand response," Energy Policy, Elsevier, vol. 148(PB).
    3. Jerzy Andruszkiewicz & Józef Lorenc & Agnieszka Weychan, 2019. "Demand Price Elasticity of Residential Electricity Consumers with Zonal Tariff Settlement Based on Their Load Profiles," Energies, MDPI, vol. 12(22), pages 1-22, November.
    4. Andruszkiewicz, Jerzy & Lorenc, Józef & Weychan, Agnieszka, 2020. "Seasonal variability of price elasticity of demand of households using zonal tariffs and its impact on hourly load of the power system," Energy, Elsevier, vol. 196(C).
    5. Mahmoud Shaban & Mohammed F. Alsharekh, 2022. "Design of a Smart Distribution Panelboard Using IoT Connectivity and Machine Learning Techniques," Energies, MDPI, vol. 15(10), pages 1-17, May.
    6. E. Ruben van Beesten & Daan Hulshof, 2022. "Economic incentives for capacity reductions on interconnectors in the day-ahead market," Papers 2210.07129, arXiv.org.
    7. E. Ruben van Beesten & Ole Kristian r{A}dnanes & Hr{a}kon Morken Linde & Paolo Pisciella & Asgeir Tomasgard, 2022. "Welfare compensation in international transmission expansion planning under uncertainty," Papers 2205.05978, arXiv.org.
    8. van Beesten, E. Ruben & Hulshof, Daan, 2023. "Economic incentives for capacity reductions on interconnectors in the day-ahead market," Applied Energy, Elsevier, vol. 341(C).

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    More about this item

    Keywords

    Demand response; Electricity use; Households; Physical factors; Electricity price;
    All these keywords.

    JEL classification:

    • B21 - Schools of Economic Thought and Methodology - - History of Economic Thought since 1925 - - - Microeconomics
    • B41 - Schools of Economic Thought and Methodology - - Economic Methodology - - - Economic Methodology
    • C00 - Mathematical and Quantitative Methods - - General - - - General
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D11 - Microeconomics - - Household Behavior - - - Consumer Economics: Theory

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