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A multi-agent optimal bidding strategy in microgrids based on artificial immune system

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  • Kong, Xiangyu
  • Liu, Dehong
  • Xiao, Jie
  • Wang, Chengshan

Abstract

The utilization of distributed energy resources (DERs) is growing worldwide, and the commercial prospects of microgrids (MGs) are clear. To optimally coordinate the power outputs of DERs owned by different owners while considering uncertainties in the commercial MG, a multi-agent optimal bidding strategy based on the artificial immune system (AIS) is proposed. The method takes the multi-agent system (MAS) control structure of the MG, distributing the profits of different owners through the market mechanism to conduct the optimization. A novel AIS is established and integrated into the MAS to help DERs participate in the optimal bidding operation of MG. The antigen is transformed by the environmental information, the price of the main grid, other DERs’ bidding strategies, and the predicted deviation coefficient while accounting for the uncertainties of DER facilities, which is solved by AIS to find the optimal bidding strategy. A mixed-integer programming model is solved by the bidding manager agent to get bidding results, which are fed back to the DERs to help them form the next round of strategies until the result reaches the equilibrium. Results show that the proposed method is efficient in coordinating the power generation with uncertainty and maximizing the interests of each investor.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:189:y:2019:i:c:s0360544219318493
    DOI: 10.1016/j.energy.2019.116154
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    References listed on IDEAS

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    1. Wakui, Tetsuya & Hashiguchi, Moe & Sawada, Kento & Yokoyama, Ryohei, 2019. "Two-stage design optimization based on artificial immune system and mixed-integer linear programming for energy supply networks," Energy, Elsevier, vol. 170(C), pages 1228-1248.
    2. Elsied, Moataz & Oukaour, Amrane & Youssef, Tarek & Gualous, Hamid & Mohammed, Osama, 2016. "An advanced real time energy management system for microgrids," Energy, Elsevier, vol. 114(C), pages 742-752.
    3. Obara, Shin'ya & Sato, Katsuaki & Utsugi, Yuta, 2018. "Study on the operation optimization of an isolated island microgrid with renewable energy layout planning," Energy, Elsevier, vol. 161(C), pages 1211-1225.
    4. Wang, Jianxiao & Zhong, Haiwang & Tang, Wenyuan & Rajagopal, Ram & Xia, Qing & Kang, Chongqing & Wang, Yi, 2017. "Optimal bidding strategy for microgrids in joint energy and ancillary service markets considering flexible ramping products," Applied Energy, Elsevier, vol. 205(C), pages 294-303.
    5. Ferruzzi, Gabriella & Cervone, Guido & Delle Monache, Luca & Graditi, Giorgio & Jacobone, Francesca, 2016. "Optimal bidding in a Day-Ahead energy market for Micro Grid under uncertainty in renewable energy production," Energy, Elsevier, vol. 106(C), pages 194-202.
    6. Chettibi, N. & Mellit, A., 2018. "Intelligent control strategy for a grid connected PV/SOFC/BESS energy generation system," Energy, Elsevier, vol. 147(C), pages 239-262.
    7. Li, Qiang & Gao, Mengkai & Lin, Houfei & Chen, Ziyu & Chen, Minyou, 2019. "MAS-based distributed control method for multi-microgrids with high-penetration renewable energy," Energy, Elsevier, vol. 171(C), pages 284-295.
    8. Seung Wan Kim & Jip Kim & Young Gyu Jin & Yong Tae Yoon, 2016. "Optimal Bidding Strategy for Renewable Microgrid with Active Network Management," Energies, MDPI, vol. 9(1), pages 1-15, January.
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