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Determinants of Demand Response Program Participation: Contingent Valuation Evidence from a Smart Thermostat Program

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  • Jesse Kaczmarski

    (Department of Economics, The University of New Mexico, 1915 Roma Ave. NE 1019 ECON 1006E, Albuquerque, NM 87131, USA)

  • Benjamin Jones

    (Department of Economics, The University of New Mexico, 1915 Roma Ave. NE 1019 ECON 1006E, Albuquerque, NM 87131, USA)

  • Janie Chermak

    (Department of Economics, The University of New Mexico, 1915 Roma Ave. NE 1019 ECON 1006E, Albuquerque, NM 87131, USA)

Abstract

As renewable electricity generation continues to increase in the United States (US), considerable effort goes into matching heterogeneous supply to demand at a subhour time-step. As a result, some electric providers offer incentive-based programs for residential consumers that aim to reduce electric demand during high-demand periods. There is little research into determinants of consumer response to incentive-based programs beyond typical sociodemographic characteristics. To add to this body of literature, this paper presents the findings of a dichotomous choice contingent valuation (CV) survey targeting US ratepayers’ participation in a direct-load-control scheme utilizing a smart thermostat designed to reallocate consumer electricity demand on summer days when grid stress is high. Our results show approximately 50% of respondents are willing to participate at a median willingness-to-accept (WTA) figure of USD 9.50 (95% CI: 3.74, 15.25) per month that lasts for one summer (June through August)—or slightly less than USD 30 per annum. Participation is significantly affected by a respondent’s attitudes and preferences surrounding various environmental and institutional perspectives, but not by sociodemographic characteristics. These findings suggest utilities designing direct-load-control programs may improve participation by designing incentives specific to customers’ attitudes and preferences.

Suggested Citation

  • Jesse Kaczmarski & Benjamin Jones & Janie Chermak, 2022. "Determinants of Demand Response Program Participation: Contingent Valuation Evidence from a Smart Thermostat Program," Energies, MDPI, vol. 15(2), pages 1-18, January.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:2:p:590-:d:724787
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    References listed on IDEAS

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    1. Katrina Jessoe & David Rapson, 2014. "Knowledge Is (Less) Power: Experimental Evidence from Residential Energy Use," American Economic Review, American Economic Association, vol. 104(4), pages 1417-1438, April.
    2. Allcott, Hunt, 2011. "Rethinking real-time electricity pricing," Resource and Energy Economics, Elsevier, vol. 33(4), pages 820-842.
    3. Frank A. Wolak, 2011. "Do Residential Customers Respond to Hourly Prices? Evidence from a Dynamic Pricing Experiment," American Economic Review, American Economic Association, vol. 101(3), pages 83-87, May.
    4. Herter, Karen, 2007. "Residential implementation of critical-peak pricing of electricity," Energy Policy, Elsevier, vol. 35(4), pages 2121-2130, April.
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    Cited by:

    1. António Gomes Martins & Luís Pires Neves & José Luís Sousa, 2023. "Electricity Demand Side Management," Energies, MDPI, vol. 16(16), pages 1-3, August.
    2. Georgia K. Roberts & Dominique J. Pride & Joseph M. Little & Julie M. Mueller, 2023. "Willingness to Pay for Renewably-Sourced Home Heating in the Fairbanks North Star Borough," Energies, MDPI, vol. 16(8), pages 1-14, April.
    3. So-Yeon Park & Ju-Hee Kim & Jungkwan Seo & Seung-Hoon Yoo, 2022. "Evaluating the Economic Benefits of Tightening Regulations on the Use of Toluene, a Hazardous Chemical, in South Korea," Sustainability, MDPI, vol. 14(11), pages 1-15, May.
    4. Pourramezan, Ali & Samadi, Mahdi, 2023. "A system dynamics investigation on the long-term impacts of demand response in generation investment planning incorporating renewables," Renewable and Sustainable Energy Reviews, Elsevier, vol. 171(C).

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