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Asymmetric Nash bargaining model for peer-to-peer energy transactions combined with shared energy storage

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  • Chen, Yujia
  • Pei, Wei
  • Ma, Tengfei
  • Xiao, Hao

Abstract

Distributed peer-to-peer (P2P) energy trading can promote the localized balancing of power supply and demand, improve grid utilization efficiency, and ensure fairness. Shared energy storage (SES) enables users to withdraw electrical energy from shared batteries. This paper proposes a P2P energy trading model combined with SES and studies a cooperative surplus distribution mechanism based on the asymmetric Nash bargaining (ANB) theory. First, a cooperative model is established for enabling cooperation among sellers and buyers in a P2P energy trading system, offering a cooperative surplus due to cooperation. Secondly, by using the Asymmetric Nash Bargaining theory, a profit distribution model for the participants is constructed to ensure that the cooperation surplus can be fairly distributed among buyers and sellers. Finally, case studies are presented to verify the effectiveness of the proposed model. The simulation results show that the proposed cooperative method can improve the benefits of each entity and the overall benefits of the cooperative alliance. The proposed profit distribution model can reflect the difference in the contributions of sellers and buyers in the P2P energy trading system and fairly distribute the cooperation surplus. Furthermore, SES can improve the overall economics of DGs and consumers.

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  • 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).
    • Handle: RePEc:eee:energy:v:278:y:2023:i:pb:s0360544223013749
    DOI: 10.1016/j.energy.2023.127980
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    Cited by:

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    3. Hussain, Sadam & Azim, M. Imran & Lai, Chunyan & Eicker, Ursula, 2023. "New coordination framework for smart home peer-to-peer trading to reduce impact on distribution transformer," Energy, Elsevier, vol. 284(C).
    4. Cai, Pengcheng & Mi, Yang & Ma, Siyuan & Li, Hongzhong & Li, Dongdong & Wang, Peng, 2023. "Hierarchical game for integrated energy system and electricity-hydrogen hybrid charging station under distributionally robust optimization," Energy, Elsevier, vol. 283(C).
    5. He, Ye & Wu, Hongbin & Wu, Andrew Y. & Li, Peng & Ding, Ming, 2024. "Optimized shared energy storage in a peer-to-peer energy trading market: Two-stage strategic model regards bargaining and evolutionary game theory," Renewable Energy, Elsevier, vol. 224(C).
    6. 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).
    7. Wang, Yifeng & Jiang, Aihua & Wang, Rui & Tian, Junyang, 2024. "A canonical coalitional game model incorporating motivational psychology analysis for incentivizing stable direct energy trading in smart grid," Energy, Elsevier, vol. 289(C).

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