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Bipartite graph-based community-to-community matching in local energy market considering socially networked prosumers

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  • Xia, Yuanxing
  • Xu, Qingshan
  • Fang, Jicheng
  • Tang, Rongchuan
  • Du, Pengwei

Abstract

The mutual influences among different energy prosumers caused by the socially connected relationship have not been modeled explicitly in the current local energy market. Hence, we first develop an iterative market clearing framework to bridge the data-driven and model-driven models of heterogeneous market participants. Then, we consider the behavior difficulty, communication channel, depth of information process, social influence, and observed peer effects in the multi-agent deep reinforcement learning (MADRL) framework in the data-driven models. Finally, we calculate the marginal market value of each C2C energy transaction with a linearized distribution network power flow model and formulate the C2C matching as a bipartite graph. We propose a recurrent Hungarian algorithm to solve the best matching schemes of C2C trading. Case studies verify that the distribution network power flow model considered in C2C energy trading successfully constrain the unit values of nodal voltages between 0.9 and 1.1. The power flow values in distribution network are constrained within the upper limits of transmission capacity for each branch. The finite iteration number of MADRL in hybrid market-clearing algorithm guarantees the normal P2P energy market operation. Moreover, the social influences for the P2P energy trading results are verified by comparing P2P trading results in different scenarios. Our proposed model can better depict the trading behaviors in communities.

Suggested Citation

  • Xia, Yuanxing & Xu, Qingshan & Fang, Jicheng & Tang, Rongchuan & Du, Pengwei, 2024. "Bipartite graph-based community-to-community matching in local energy market considering socially networked prosumers," Applied Energy, Elsevier, vol. 353(PB).
    • Handle: RePEc:eee:appene:v:353:y:2024:i:pb:s0306261923016094
    DOI: 10.1016/j.apenergy.2023.122245
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    References listed on IDEAS

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    1. Guerrero, Jaysson & Gebbran, Daniel & Mhanna, Sleiman & Chapman, Archie C. & Verbič, Gregor, 2020. "Towards a transactive energy system for integration of distributed energy resources: Home energy management, distributed optimal power flow, and peer-to-peer energy trading," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    2. Ruan, Hebin & Gao, Hongjun & Qiu, Haifeng & Gooi, Hoay Beng & Liu, Junyong, 2023. "Distributed operation optimization of active distribution network with P2P electricity trading in blockchain environment," Applied Energy, Elsevier, vol. 331(C).
    3. Liu, Junhong & Long, Qinfei & Liu, Rong-Peng & Liu, Wenjie & Hou, Yunhe, 2023. "Online distributed optimization for spatio-temporally constrained real-time peer-to-peer energy trading," Applied Energy, Elsevier, vol. 331(C).
    4. Ableitner, Liliane & Tiefenbeck, Verena & Meeuw, Arne & Wörner, Anselma & Fleisch, Elgar & Wortmann, Felix, 2020. "User behavior in a real-world peer-to-peer electricity market," Applied Energy, Elsevier, vol. 270(C).
    5. Petrovich, Beatrice & Kubli, Merla, 2023. "Energy communities for companies: Executives’ preferences for local and renewable energy procurement," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    6. Sun, Chao & Liu, Yun & Li, Yuanzheng & Lin, Shunjiang & Gooi, Hoay Beng & Zhu, Jizhong, 2023. "Network-aware P2P multi-energy trading in decentralized electric-heat systems," Applied Energy, Elsevier, vol. 345(C).
    7. Lim, Xin-Le & Lam, Wei-Haur & Hashim, Roslan, 2015. "Feasibility of marine renewable energy to the Feed-in Tariff system in Malaysia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 708-719.
    8. Bandeiras, F. & Pinheiro, E. & Gomes, M. & Coelho, P. & Fernandes, J., 2020. "Review of the cooperation and operation of microgrid clusters," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    9. Alejandro Pena-Bello & David Parra & Mario Herberz & Verena Tiefenbeck & Martin K. Patel & Ulf J. J. Hahnel, 2022. "Integration of prosumer peer-to-peer trading decisions into energy community modelling," Nature Energy, Nature, vol. 7(1), pages 74-82, January.
    10. 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).
    11. Kimberly S. Wolske & Kenneth T. Gillingham & P. Wesley Schultz, 2020. "Peer influence on household energy behaviours," Nature Energy, Nature, vol. 5(3), pages 202-212, March.
    12. Li, Zhengmao & Wu, Lei & Xu, Yan & Wang, Luhao & Yang, Nan, 2023. "Distributed tri-layer risk-averse stochastic game approach for energy trading among multi-energy microgrids," Applied Energy, Elsevier, vol. 331(C).
    13. Dukovska, Irena & Slootweg, J.G. (Han) & Paterakis, Nikolaos G., 2023. "Introducing user preferences for peer-to-peer electricity trading through stochastic multi-objective optimization," Applied Energy, Elsevier, vol. 338(C).
    14. Lee, Won-Poong & Han, Dongjun & Won, Dongjun, 2022. "Grid-Oriented Coordination Strategy of Prosumers Using Game-theoretic Peer-to-Peer Trading Framework in Energy Community," Applied Energy, Elsevier, vol. 326(C).
    15. 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).
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    1. Zhang, Haoyang & Zhan, Sen & Kok, Koen & Paterakis, Nikolaos G., 2024. "Establishing a hierarchical local market structure using multi-cut Benders decomposition," Applied Energy, Elsevier, vol. 363(C).

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