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Coordinated Energy Scheduling of a Distributed Multi-Microgrid System Based on Multi-Agent Decisions

Author

Listed:
  • Yuyan Sun

    (School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China)

  • Zexiang Cai

    (School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China
    Zunyi Guihua Energy Technology Co., Ltd., Zunyi 563000, China
    Academician Work Center of Zunyi, Zunyi 563000, China)

  • Ziyi Zhang

    (School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China)

  • Caishan Guo

    (School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China)

  • Guolong Ma

    (School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China)

  • Yongxia Han

    (School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China
    Zunyi Guihua Energy Technology Co., Ltd., Zunyi 563000, China
    Academician Work Center of Zunyi, Zunyi 563000, China)

Abstract

Regarding the different ownerships and autonomy of microgrids (MGs) in the distributed multi-microgrid (MMG) system, this paper establishes a multi-stage energy scheduling model based on a multi-agent system (MAS). The proposed mechanism enables a microgrid agent (MGA), a central energy management agent (CEMA), and a coordination control agent (CCA) to cooperate efficiently during various stages including prescheduling, coordinated optimization, rescheduling and participation willingness analysis. Based on the limited information sharing between agents, energy scheduling models of agents and coordinated diagrams are constructed to demonstrate the different roles of agents and their interactions within the MMG system. Distributed schemes are introduced for MG internal operations considering demand response, while centralized schemes under the control of the CCA are proposed to coordinate MGAs. Participation willingness is defined to analyze the MGA’s satisfaction degree of the matchmaking. A hierarchical optimization algorithm is applied to solve the above nonlinear problem. The upper layer establishes a mixed-integer linear programming (MILP) model to optimize the internal operation problem of each MG, and the lower layer applies the particle swarm optimization (PSO) algorithm for coordination. The simulation with a three-MG system verifies the rationality and effectiveness of the proposed model and method.

Suggested Citation

  • Yuyan Sun & Zexiang Cai & Ziyi Zhang & Caishan Guo & Guolong Ma & Yongxia Han, 2020. "Coordinated Energy Scheduling of a Distributed Multi-Microgrid System Based on Multi-Agent Decisions," Energies, MDPI, vol. 13(16), pages 1-20, August.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:16:p:4077-:d:395586
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    References listed on IDEAS

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    1. Kong, Xiangyu & Liu, Dehong & Xiao, Jie & Wang, Chengshan, 2019. "A multi-agent optimal bidding strategy in microgrids based on artificial immune system," Energy, Elsevier, vol. 189(C).
    2. Afrasiabi, Mousa & Mohammadi, Mohammad & Rastegar, Mohammad & Kargarian, Amin, 2019. "Multi-agent microgrid energy management based on deep learning forecaster," Energy, Elsevier, vol. 186(C).
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    Cited by:

    1. Qingle Pang & Lin Ye & Houlei Gao & Xinian Li & Yang Zheng & Chenbin He, 2021. "Penalty Electricity Price-Based Optimal Control for Distribution Networks," Energies, MDPI, vol. 14(7), pages 1-16, March.
    2. Julia Morgan & Casey Canfield, 2021. "Comparing Behavioral Theories to Predict Consumer Interest to Participate in Energy Sharing," Sustainability, MDPI, vol. 13(14), pages 1-17, July.
    3. Yang, Kang & Li, Chunhua & Jing, Xu & Zhu, Zhiyu & Wang, Yuting & Ma, Haodong & Zhang, Yu, 2022. "Energy dispatch optimization of islanded multi-microgrids based on symbiotic organisms search and improved multi-agent consensus algorithm," Energy, Elsevier, vol. 239(PC).
    4. Regin Bose Kannaian & Belwin Brearley Joseph & Raja Prabu Ramachandran, 2023. "An Adaptive Centralized Protection and Relay Coordination Algorithm for Microgrid," Energies, MDPI, vol. 16(12), pages 1-18, June.
    5. Rovick Tarife & Yosuke Nakanishi & Yining Chen & Yicheng Zhou & Noel Estoperez & Anacita Tahud, 2022. "Optimization of Hybrid Renewable Energy Microgrid for Rural Agricultural Area in Southern Philippines," Energies, MDPI, vol. 15(6), pages 1-29, March.

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