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A Nash bargaining-based cooperative planning and operation method for wind-hydrogen-heat multi-agent energy system

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  • Ma, Tengfei
  • Pei, Wei
  • Deng, Wei
  • Xiao, Hao
  • Yang, Yanhong
  • Tang, Chenghong

Abstract

Hydrogen production and clean heating from renewable power are promising methods to promote renewable energy consumption and reduce carbon emission. This paper is concentrated on the cooperative planning and operation problems of Wind-Hydrogen-Heat multi-agent energy system. A cooperative planning and operative model for the Wind-Hydrogen-Heating multi-agent energy system is proposed based on the Nash bargaining game theory. This cooperative model is transformed into two subproblems on the alliance planning and operation cost minimization problem and the payment bargaining problem. To protect the privacy of each participant, two distributed algorithms are proposed based on the Alternating Direction Method of Multipliers to solve the two subproblems. Finally, the effectiveness of the proposed cooperative model and distributed algorithms are demonstrated. Simulation results demonstrate that the proposed cooperative model can improve the benefits of both each participant and the cooperative alliance significantly. Furthermore, the wind power curtailment can be reduced through cooperation. And the decline of feed-in tariff of wind power will incentivize the cooperation of Wind-Hydrogen-Heat agents to improve their own benefits.

Suggested Citation

  • Ma, Tengfei & Pei, Wei & Deng, Wei & Xiao, Hao & Yang, Yanhong & Tang, Chenghong, 2022. "A Nash bargaining-based cooperative planning and operation method for wind-hydrogen-heat multi-agent energy system," Energy, Elsevier, vol. 239(PE).
    • Handle: RePEc:eee:energy:v:239:y:2022:i:pe:s0360544221026840
    DOI: 10.1016/j.energy.2021.122435
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    Cited by:

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    3. Han, Fengwu & Zeng, Jianfeng & Lin, Junjie & Zhao, Yunlong & Gao, Chong, 2023. "A stochastic hierarchical optimization and revenue allocation approach for multi-regional integrated energy systems based on cooperative games," Applied Energy, Elsevier, vol. 350(C).
    4. Chen, Yujia & Pei, Wei & Ma, Tengfei & Xiao, Hao, 2023. "Asymmetric Nash bargaining model for peer-to-peer energy transactions combined with shared energy storage," Energy, Elsevier, vol. 278(PB).
    5. Xie, Rui & Wei, Wei & Li, Mingxuan & Dong, ZhaoYang & Mei, Shengwei, 2023. "Sizing capacities of renewable generation, transmission, and energy storage for low-carbon power systems: A distributionally robust optimization approach," Energy, Elsevier, vol. 263(PA).
    6. Zhao, Leilei & Xue, Yixun & Sun, Hongbin & Du, Yuan & Chang, Xinyue & Su, Jia & Li, Zening, 2023. "Benefit allocation for combined heat and power dispatch considering mutual trust," Applied Energy, Elsevier, vol. 345(C).
    7. Xu, Jiazhu & Yi, Yuqin, 2023. "Multi-microgrid low-carbon economy operation strategy considering both source and load uncertainty: A Nash bargaining approach," Energy, Elsevier, vol. 263(PB).
    8. Zheng, Weiye & Lu, Hao & Zhu, Jizhong, 2023. "Incentivizing cooperative electricity-heat operation: A distributed asymmetric Nash bargaining mechanism," Energy, Elsevier, vol. 280(C).
    9. Han, Zhixin & Fang, Debin & Yang, Peiwen & Lei, Leyao, 2023. "Cooperative mechanisms for multi-energy complementarity in the electricity spot market," Energy Economics, Elsevier, vol. 127(PB).
    10. Wang, Yubin & Zheng, Yanchong & Yang, Qiang, 2023. "Nash bargaining based collaborative energy management for regional integrated energy systems in uncertain electricity markets," Energy, Elsevier, vol. 269(C).
    11. Khaligh, Vahid & Ghezelbash, Azam & Mazidi, Mohammadreza & Liu, Jay & Ryu, Jun-Hyung, 2023. "P-robust energy management of a multi-energy microgrid enabled with energy conversions under various uncertainties," Energy, Elsevier, vol. 271(C).

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