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Adaptive incentive-based demand response with distributed non-compliance assessment

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  • Raman, Gururaghav
  • Zhao, Bo
  • Peng, Jimmy Chih-Hsien
  • Weidlich, Matthias

Abstract

Traditional residential incentive-based demand response (DR) programs use fixed incentive structures that do not incorporate closed-loop feedback to compensate for non-compliance by participants. In practice, such programs may not reliably meet their event goals. To address this challenge, real-time feedback can be used to adaptively modify the participants’ incentives, an approach which has not been proposed before. This paper proposes a flexible monitoring framework to detect potential non-compliance, whereby a second DR event is adaptively scheduled with higher incentives. In this context, constraints are presented to prevent over-compensation and gaming of the DR system by the participants. This novel dual-event design is implemented using a distributed event-stream monitoring framework to preserve scalability and ensure low monitoring costs. The merits of the proposed DR design are demonstrated at a utility-scale for 100,000 residents, while also considering the adoption of residential electric vehicles that are poised to increase the flexibility of the demand in the distribution system.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:326:y:2022:i:c:s0306261922012557
    DOI: 10.1016/j.apenergy.2022.119998
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    References listed on IDEAS

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    1. 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.
    2. Verena Tiefenbeck & Anselma Wörner & Samuel Schöb & Elgar Fleisch & Thorsten Staake, 2019. "Publisher Correction: Real-time feedback promotes energy conservation in the absence of volunteer selection bias and monetary incentives," Nature Energy, Nature, vol. 4(10), pages 891-891, October.
    3. Good, Nicholas, 2019. "Using behavioural economic theory in modelling of demand response," Applied Energy, Elsevier, vol. 239(C), pages 107-116.
    4. Maojiao Ye & Guoqiang Hu, 2015. "Game Design and Analysis for Price based Demand Response: An Aggregate Game Approach," Papers 1508.02636, arXiv.org, revised Feb 2016.
    5. Verena Tiefenbeck & Anselma Wörner & Samuel Schöb & Elgar Fleisch & Thorsten Staake, 2019. "Real-time feedback promotes energy conservation in the absence of volunteer selection bias and monetary incentives," Nature Energy, Nature, vol. 4(1), pages 35-41, January.
    6. Wang, Qi & Zhang, Chunyu & Ding, Yi & Xydis, George & Wang, Jianhui & Østergaard, Jacob, 2015. "Review of real-time electricity markets for integrating Distributed Energy Resources and Demand Response," Applied Energy, Elsevier, vol. 138(C), pages 695-706.
    7. Gils, Hans Christian, 2016. "Economic potential for future demand response in Germany – Modeling approach and case study," Applied Energy, Elsevier, vol. 162(C), pages 401-415.
    8. Verena Tiefenbeck & Anselma Wörner & Samuel Schöb & Elgar Fleisch & Thorsten Staake, 2019. "Publisher Correction: Real-time feedback promotes energy conservation in the absence of volunteer selection bias and monetary incentives," Nature Energy, Nature, vol. 4(4), pages 346-346, April.
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    Cited by:

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