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Energy trading and scheduling in networked microgrids using fuzzy bargaining game theory and distributionally robust optimization

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  • Mohseni, Shayan
  • Pishvaee, Mir Saman

Abstract

To ensure the robust performance of networked microgrids (MGs) against uncertainty and provide a fair energy trading scheme, this paper proposes a decentralized bi-level energy trading and scheduling framework equipped with an innovative incentive mechanism. The upper-level determines the robust decisions of internal scheduling within MGs and peer-to-peer (P2P) energy trading between MGs using a distributionally robust optimization model under an ambiguity set formed by principal component analysis (PCA). The ambiguity set accurately captures distributional information from renewable power generation data, reducing the unnecessary conservatism of robust solutions. The lower-level develops an asymmetric Nash bargaining game model with a new index, named fuzzy bargaining power (FBP), to fairly allocate trading benefits to MGs. This fuzzy index incentivizes MGs to proactively trade energy throughout the entire day, not just when energy selling or buying is in their interest. The upper and lower level problems are solved in a privacy-preserving manner by proposing a decentralized optimization algorithm based on the asynchronous alternating direction method of multipliers (ADMM). Numerical tests demonstrate the effectiveness of the proposed models in terms of solution robustness, profit distribution fairness, and computational performance.

Suggested Citation

  • Mohseni, Shayan & Pishvaee, Mir Saman, 2023. "Energy trading and scheduling in networked microgrids using fuzzy bargaining game theory and distributionally robust optimization," Applied Energy, Elsevier, vol. 350(C).
    • Handle: RePEc:eee:appene:v:350:y:2023:i:c:s0306261923011121
    DOI: 10.1016/j.apenergy.2023.121748
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    References listed on IDEAS

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    1. Wang, Jingjing & Yao, Liangzhong & Liang, Jun & Wang, Jun & Cheng, Fan, 2025. "Distributed optimization strategy for networked microgrids based on network partitioning," Applied Energy, Elsevier, vol. 378(PB).
    2. Elimam, Mohamed & El Moursi, Mohamed Shawki & EL-Fouly, Tarek H.M. & Al-Durra, Ahmed & Al Hosani, Khalifa Hassan, 2025. "Transactive energy trading among multi-microgrids in a distribution network with fair loss sharing," Applied Energy, Elsevier, vol. 381(C).
    3. Tang, Wenhu & Xie, Xuehua & Qian, Tong & Li, Weiwei & Li, Xiuzhang & Jin, Xin & Cao, Wangzhang & Pan, Tingzhe, 2025. "Energy-carbon coordination of transactive multi-community integrated energy systems under imperfect communication networks," Energy, Elsevier, vol. 319(C).
    4. Kong, Feng & Zhang, Dongyue & Song, Minghao & Zhou, Xuecong & Wang, Yuwei, 2024. "Collaborative scheduling and benefit allocation for waste-to-energy, hydrogen storage, and power-to-gas under uncertainties with temporal relevance," Energy, Elsevier, vol. 307(C).
    5. Li, Fei & Wang, Dong & Guo, Hengdao & Zhang, Jianhua, 2024. "Distributionally Robust Optimization for integrated energy system accounting for refinement utilization of hydrogen and ladder-type carbon trading mechanism," Applied Energy, Elsevier, vol. 367(C).
    6. Li, Ping & Chen, Jiajia & Yang, Hui & Lin, Zhenjia, 2026. "Peer-to-peer power trading and pricing for rental energy storage shared community microgrid: A coordinated Stackelberg and cooperative game," Renewable Energy, Elsevier, vol. 256(PB).
    7. Yanfang Hou & Hui Tian, 2023. "Research on the Dynamic Characteristics of Photovoltaic Power Production and Sales Based on Game Theory," Sustainability, MDPI, vol. 15(19), pages 1-19, October.
    8. Tang, Yingzhao & Ding, Shixing & Lu, Zhigang & Lu, Shuai & Zhang, Jiangyong & Cai, Yao & Guo, Xiaoqiang, 2026. "Multi-objective hybrid game-theoretic framework with distributionally robust chance constraints for multi-energy microgrid energy management," Applied Energy, Elsevier, vol. 404(C).
    9. Mohammad Javad Bordbari & Fuzhan Nasiri, 2024. "Networked Microgrids: A Review on Configuration, Operation, and Control Strategies," Energies, MDPI, vol. 17(3), pages 1-28, February.

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