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Inaccurate consumer perceptions of monetary savings in a demand-side response programme predict programme acceptance

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  • Lee V. White

    (University of Southern California
    The Ohio State University)

  • Nicole D. Sintov

    (The Ohio State University)

Abstract

Demand-side response (DSR) measures are critical to integrating variable renewable generation into electric grids. Time-of-use rates (TOU) are a common DSR mechanism that seeks to shift electricity use to low-demand times using financial instruments. However, consumers generally have a poor understanding of their electricity use and bills, raising questions about the extent to which TOU participation is driven by perceptions of savings versus actual savings. We find that among 8,702 residents who opted into a pilot TOU programme, the TOU treatment decreases on-peak use compared to a control group, but this effect is small. Perceived savings is the strongest predictor of intent to remain on TOU, over and above actual savings, even though it is only weakly related to actual changes in bills and usage. Residents may thus join DSR programmes based on perceived savings without achieving actual monetary or energy use savings, which may undermine the goals of these programmes.

Suggested Citation

  • Lee V. White & Nicole D. Sintov, 2018. "Inaccurate consumer perceptions of monetary savings in a demand-side response programme predict programme acceptance," Nature Energy, Nature, vol. 3(12), pages 1101-1108, December.
    • Handle: RePEc:nat:natene:v:3:y:2018:i:12:d:10.1038_s41560-018-0285-y
    DOI: 10.1038/s41560-018-0285-y
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    Cited by:

    1. Raman, Gururaghav & Zhao, Bo & Peng, Jimmy Chih-Hsien & Weidlich, Matthias, 2022. "Adaptive incentive-based demand response with distributed non-compliance assessment," Applied Energy, Elsevier, vol. 326(C).
    2. Stelmach, Greg & Zanocco, Chad & Flora, June & Rajagopal, Ram & Boudet, Hilary S., 2020. "Exploring household energy rules and activities during peak demand to better determine potential responsiveness to time-of-use pricing," Energy Policy, Elsevier, vol. 144(C).
    3. Xu, Jiuping & Liu, Tingting, 2020. "Technological paradigm-based approaches towards challenges and policy shifts for sustainable wind energy development," Energy Policy, Elsevier, vol. 142(C).
    4. Fikru, Mahelet G., 2019. "Estimated electricity bill savings for residential solar photovoltaic system owners: Are they accurate enough?," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    5. Zhaohua Wang & Bin Lu & Bo Wang & Yueming (Lucy) Qiu & Han Shi & Bin Zhang & Jingyun Li & Hao Li & Wenhui Zhao, 2023. "Incentive based emergency demand response effectively reduces peak load during heatwave without harm to vulnerable groups," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    6. Gong, Yuanchao & Cai, Bo-feng & Sun, Yan, 2020. "Perceived fiscal subsidy predicts rural residential acceptance of clean heating: Evidence from an indoor-survey in a pilot city in China," Energy Policy, Elsevier, vol. 144(C).
    7. Wang, Bohong & Guo, Qinglai & Yu, Yang, 2022. "Mechanism design for data sharing: An electricity retail perspective," Applied Energy, Elsevier, vol. 314(C).
    8. Li, Longxi, 2021. "Coordination between smart distribution networks and multi-microgrids considering demand side management: A trilevel framework," Omega, Elsevier, vol. 102(C).

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