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Incremental incentive mechanism design for diversified consumers in demand response

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  • Liu, Di
  • Qin, Zhaoming
  • Hua, Haochen
  • Ding, Yi
  • Cao, Junwei

Abstract

Demand response has been proven to be an effective way to improve energy utilization efficiency. It is notable that the diversified characteristics of residential consumers, which many greatly affect its performance in demand response, have not been fully considered in existing incentive mechanisms. In this paper, an incremental incentive mechanism for incentive-based demand response (IBDR) is proposed, in which consumers obtain different incentives according to the increment of response, so that the incentive can follow the change of consumers' marginal cost. We theoretically illustrate that the proposed incremental incentive mechanism can effectively improve the profit of load service entity (LSE), as well as the benefit of highly flexible consumers, compared with other existing incentive mechanism. In practice, LSE's bidding strategy in the day ahead market is affected by the intraday IBDR strategy that cannot be known in advance. In order to solve the bidding problem with incomplete information in the day ahead market, we propose an asynchronous double-interaction deep reinforcement learning (DRL) algorithm to maximize LSE’s cumulative profit of multiple time slots throughout the day. Numerical simulation results show that the proposed mechanism can improve the consumers' response depth while reducing the unit incentive cost, and the proposed DRL algorithm has relatively stable and satisfactory performance even in highly uncertain environment.

Suggested Citation

  • Liu, Di & Qin, Zhaoming & Hua, Haochen & Ding, Yi & Cao, Junwei, 2023. "Incremental incentive mechanism design for diversified consumers in demand response," Applied Energy, Elsevier, vol. 329(C).
    • Handle: RePEc:eee:appene:v:329:y:2023:i:c:s0306261922014970
    DOI: 10.1016/j.apenergy.2022.120240
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    References listed on IDEAS

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    1. Wen, Lulu & Zhou, Kaile & Li, Jun & Wang, Shanyong, 2020. "Modified deep learning and reinforcement learning for an incentive-based demand response model," Energy, Elsevier, vol. 205(C).
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    4. Wang, Zhaohua & Li, Hao & Deng, Nana & Cheng, Kaiwei & Lu, Bin & Zhang, Bin & Wang, Bo, 2020. "How to effectively implement an incentive-based residential electricity demand response policy? Experience from large-scale trials and matching questionnaires," Energy Policy, Elsevier, vol. 141(C).
    5. Lin, Jin & Dong, Jun & Dou, Xihao & Liu, Yao & Yang, Peiwen & Ma, Tongtao, 2022. "Psychological insights for incentive-based demand response incorporating battery energy storage systems: A two-loop Stackelberg game approach," Energy, Elsevier, vol. 239(PC).
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