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Decentralized coordinated operation model of VPP and P2H systems based on stochastic-bargaining game considering multiple uncertainties and carbon cost

Author

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  • Wang, Xuejie
  • zhao, Huiru
  • Lu, Hao
  • Zhang, Yuanyuan
  • Wang, Yuwei
  • Wang, Jingbo

Abstract

In order to achieve low-carbon development, the joint operation of virtual power plants (VPP) and clean power to hydrogen (P2H) is a hot issue. However, the conventional centralized optimization strategy ignores the information asymmetry between VPP and P2H, resulting in disorderly competition and low market efficiency. Based on this, this paper proposes a decentralized coordinated operation method of the VPP-P2H combined system considering the profits of each player. First, the stochastic optimization operation model of VPP and P2H is built involving the multiple uncertainties on both sides of the source and load. Secondly, based on the Nash-Harsanyi bargaining game theory, a multi-agent decentralized coordinated operation model of VPP-P2H is established. The model is further equivalent to the minimization of coordinated operation cost sub-problem and payment bargaining sub-problem. For the privacy of each player, the improved alternating direction multiplier method (ADMM) is used to solve the above two sub-problems. Finally, the effectiveness of the proposed decentralized coordinated operation model and distributed algorithm are verified. The simulation results show that compared with the individual operation mode, through coordinated operation, the operating costs of P2H1, P2H2, and VPP are reduced by 19.59%, 18.18%, and 16.61%, respectively. In addition, the improved-ADMM algorithm also improves the solution efficiency of the system.

Suggested Citation

  • Wang, Xuejie & zhao, Huiru & Lu, Hao & Zhang, Yuanyuan & Wang, Yuwei & Wang, Jingbo, 2022. "Decentralized coordinated operation model of VPP and P2H systems based on stochastic-bargaining game considering multiple uncertainties and carbon cost," Applied Energy, Elsevier, vol. 312(C).
    • Handle: RePEc:eee:appene:v:312:y:2022:i:c:s0306261922002069
    DOI: 10.1016/j.apenergy.2022.118750
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    12. Wang, Zhuo & Hou, Hui & Zhao, Bo & Zhang, Leiqi & Shi, Ying & Xie, Changjun, 2024. "Risk-averse stochastic capacity planning and P2P trading collaborative optimization for multi-energy microgrids considering carbon emission limitations: An asymmetric Nash bargaining approach," Applied Energy, Elsevier, vol. 357(C).
    13. Li, Xiangyu & Luo, Fengji & Li, Chaojie, 2024. "Multi-agent deep reinforcement learning-based autonomous decision-making framework for community virtual power plants," Applied Energy, Elsevier, vol. 360(C).
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    15. Wang, Beibei & Xu, Lun & Wang, Jialei, 2023. "A privacy-preserving trading strategy for blockchain-based P2P electricity transactions," Applied Energy, Elsevier, vol. 335(C).

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