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Demand Response Strategy Based on the Multi-Agent System and Multiple-Load Participation

Author

Listed:
  • Pingliang Zeng

    (Department of Electrical Engineering and Automation, School of Automation, Xiasha Campus, Hangzhou Dianzi University, Hangzhou 310018, China)

  • Jin Xu

    (Department of Electrical Engineering and Automation, School of Automation, Xiasha Campus, Hangzhou Dianzi University, Hangzhou 310018, China)

  • Minchen Zhu

    (Department of Electrical Engineering and Automation, School of Automation, Xiasha Campus, Hangzhou Dianzi University, Hangzhou 310018, China)

Abstract

In order to improve the utilization of user-side power resources in the distribution network and promote energy conservation, this paper designs a distributed system suitable for power demand response (DR), considering multi-agent system (MAS) technology and consistency algorithms. Due to the frequent changes in the power system structure caused by changes in the load of a large number of users, this paper proposes using cluster partitioning indicators as communication weights between agents, enabling agents to utilize the distribution network for collaborative optimization. In order to achieve the integration of multiple load-side power resources and improve the refinement level of demand-side management (DSM), two types of agents with load aggregator (LA) functions are provided, which adopt the demand response strategies of Time-of-Use (TOU) or Direct Load Control (DLC) and model the uncertainty of individual device states using Monte Carlo method, so that the two typical flexible loads can achieve the target load-reduction requirements under the MAS framework. The research results demonstrate that this method achieves complementary advantages of the two types of loads participating in DR on a time scale, reducing the costs of power companies and saving customers’ electricity bills while peak shaving.

Suggested Citation

  • Pingliang Zeng & Jin Xu & Minchen Zhu, 2024. "Demand Response Strategy Based on the Multi-Agent System and Multiple-Load Participation," Sustainability, MDPI, vol. 16(2), pages 1-21, January.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:2:p:902-:d:1323250
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    References listed on IDEAS

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    1. Alipour, Manijeh & Mohammadi-Ivatloo, Behnam & Moradi-Dalvand, Mohammad & Zare, Kazem, 2017. "Stochastic scheduling of aggregators of plug-in electric vehicles for participation in energy and ancillary service markets," Energy, Elsevier, vol. 118(C), pages 1168-1179.
    2. Wu, Hongbin & Wang, Jingjie & Lu, Junhua & Ding, Ming & Wang, Lei & Hu, Bin & Sun, Ming & Qi, Xianjun, 2022. "Bilevel load-agent-based distributed coordination decision strategy for aggregators," Energy, Elsevier, vol. 240(C).
    3. Rocha, Helder R.O. & Honorato, Icaro H. & Fiorotti, Rodrigo & Celeste, Wanderley C. & Silvestre, Leonardo J. & Silva, Jair A.L., 2021. "An Artificial Intelligence based scheduling algorithm for demand-side energy management in Smart Homes," Applied Energy, Elsevier, vol. 282(PA).
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