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Demand-side management by electric utilities in Switzerland: Analyzing its impact on residential electricity demand

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Abstract

In this paper we use panel data from a survey conducted on 30 Swiss utilities to estimate the impact of demand-side management (DSM) activities on residential electricity demand using DSM spending and an energy efficiency score. Using the variation in DSM activities within utilities and across utilities over time we identify the impact of these programs and find that their presence reduce per customer residential electricity consumption by around 5%. If we consider monetary spending, the effect of a 10% increase in DSM spending causes around a 0.14% reduction in per customer residential electricity consumption. The cost of saving a kilowatt hour is around CHF 0.04 while the average cost of producing and distributing electricity in Switzerland is around CHF 0.18 per kilowatt hour. We conclude that current DSM practices in Switzerland have a statistically significant effect on reducing the demand for residential electricity.

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  • Nina Boogen & Souvik Datta & Massimo Filippini, 2016. "Demand-side management by electric utilities in Switzerland: Analyzing its impact on residential electricity demand," CER-ETH Economics working paper series 16/247, CER-ETH - Center of Economic Research (CER-ETH) at ETH Zurich.
    • Handle: RePEc:eth:wpswif:16-247
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    Cited by:

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    2. Hyun, Minwoo & Kim, Yeong Jae & Eom, Jiyong, 2020. "Assessing the impact of a demand-resource bidding market on an electricity generation portfolio and the environment," Energy Policy, Elsevier, vol. 147(C).
    3. O’Reilly, Ryan & Cohen, Jed & Reichl, Johannes, 2024. "Achievable load shifting potentials for the European residential sector from 2022–2050," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    4. Terlouw, Tom & AlSkaif, Tarek & Bauer, Christian & van Sark, Wilfried, 2019. "Optimal energy management in all-electric residential energy systems with heat and electricity storage," Applied Energy, Elsevier, vol. 254(C).
    5. Wang, Yuanping & Hou, Lingchun & Cai, Weiguang & Zhou, Zhaoyin & Bian, Jing, 2023. "Exploring the drivers and influencing mechanisms of urban household electricity consumption in China - Based on longitudinal data at the provincial level," Energy, Elsevier, vol. 273(C).
    6. Terlouw, Tom & AlSkaif, Tarek & Bauer, Christian & van Sark, Wilfried, 2019. "Multi-objective optimization of energy arbitrage in community energy storage systems using different battery technologies," Applied Energy, Elsevier, vol. 239(C), pages 356-372.
    7. 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.
    8. Kang, Jieyi & Reiner, David M., 2022. "What is the effect of weather on household electricity consumption? Empirical evidence from Ireland," Energy Economics, Elsevier, vol. 111(C).
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    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C36 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Instrumental Variables (IV) Estimation
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy

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