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A bargaining game-based profit allocation method for the wind-hydrogen-storage combined system

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  • Wang, Xuejie
  • Li, Bingkang
  • Wang, Yuwei
  • Lu, Hao
  • Zhao, Huiru
  • Xue, Wanlei

Abstract

Aiming at the coexistence of multiple players in the wind-hydrogen-storage combined system, a new profit allocation mechanism is proposed. The combination of multiple stakeholders such as wind power plant (WT), hydrogen energy system (HE), and energy storage system (ES) can achieve the purpose of promoting renewable energy consumption by using renewable energy to produce hydrogen, so as to improve overall system benefits. However, WT, HE, and ES belong to different stakeholders, and wind output is uncertain, which affects the efficient operation of the wind-hydrogen-storage combined system. Based on this, firstly, the Wasserstein metric is used to characterize the ambiguity set of the probability distribution of wind output forecast error, and a distributionally robust optimization model considering the uncertainty of wind output and demand response is constructed to maximize the benefits of the wind-hydrogen-storage combined system. Secondly, in order to balance the profits of multiple players in the combined system, a profit allocation model considering the real contribution of each player is proposed based on the Nash-Harsanyi bargaining game theory. Finally, the effectiveness of the proposed distributionally robust optimization operation model and profit allocation method are verified by simulation in a typical wind-hydrogen-storage combined system.

Suggested Citation

  • Wang, Xuejie & Li, Bingkang & Wang, Yuwei & Lu, Hao & Zhao, Huiru & Xue, Wanlei, 2022. "A bargaining game-based profit allocation method for the wind-hydrogen-storage combined system," Applied Energy, Elsevier, vol. 310(C).
    • Handle: RePEc:eee:appene:v:310:y:2022:i:c:s0306261921016962
    DOI: 10.1016/j.apenergy.2021.118472
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    References listed on IDEAS

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    Cited by:

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    2. Yan Gao & Long Gao & Pei Zhang & Qiang Wang, 2023. "Two-Stage Optimization Scheduling of Virtual Power Plants Considering a User-Virtual Power Plant-Equipment Alliance Game," Sustainability, MDPI, vol. 15(18), pages 1-28, September.
    3. Zheng, Weiye & Lu, Hao & Zhu, Jizhong, 2023. "Incentivizing cooperative electricity-heat operation: A distributed asymmetric Nash bargaining mechanism," Energy, Elsevier, vol. 280(C).
    4. Abadie, Luis Mª & Chamorro, José M., 2023. "Investment in wind-based hydrogen production under economic and physical uncertainties," Applied Energy, Elsevier, vol. 337(C).
    5. Elsir, Mohamed & Al-Sumaiti, Ameena Saad & El Moursi, Mohamed Shawky & Al-Awami, Ali Taleb, 2023. "Coordinating the day-ahead operation scheduling for demand response and water desalination plants in smart grid," Applied Energy, Elsevier, vol. 335(C).
    6. 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).
    7. Wang, Yuwei & Song, Minghao & Jia, Mengyao & Shi, Lin & Li, Bingkang, 2023. "TimeGAN based distributionally robust optimization for biomass-photovoltaic-hydrogen scheduling under source-load-market uncertainties," Energy, Elsevier, vol. 284(C).
    8. Fang, Xiaolun & Dong, Wei & Wang, Yubin & Yang, Qiang, 2022. "Multiple time-scale energy management strategy for a hydrogen-based multi-energy microgrid," Applied Energy, Elsevier, vol. 328(C).

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