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Combined game model and investment decision making of power grid-distributed energy system

Author

Listed:
  • Yongxiu He

    (North China Electric Power University)

  • Wei Xiong

    (North China Electric Power University
    Changde Power Supply Branch of State Grid Hunan Power Co. LTD)

  • Binyou Yang

    (Changde Power Supply Branch of State Grid Hunan Power Co. LTD)

  • Hai-yan Yang

    (Changde Power Supply Branch of State Grid Hunan Power Co. LTD)

  • Jiu-fang Zhou

    (Changde Power Supply Branch of State Grid Hunan Power Co. LTD)

  • Ming-li Cui

    (China University of Geosciences)

  • Yan Li

    (China University of Geosciences)

Abstract

Distributed energy based on clean energy has gradually become a progressively important part of the energy system due to increasingly prominent environmental problems and energy crises. Presently, the development of distributed energy has the phenomena of investment chaos, resource waste, and information asymmetry. We propose a grid-distributed energy system joint decision-making model based on the alliance blockchain to solve problems including the power generation strategy of distributed energy users and the grid investment plan connected to the distributed system. Firstly, we construct the return models of the distributed energy system and the power grid system separately. Secondly, combined with the analysis of the game mechanism of different entities, we propose a multi-agent decision-making model of the distributed energy system based on the alliance blockchain. Thirdly, based on this model, we optimize the revenue of the entire system through the dynamic game and use the alliance chain and smart contract to automatically execute. Finally, the model is solved by the iterative search method, and the entire simulation process is implemented in Ethereum using python. Based on the idea of joint decision making, our study considers the interests of all participants, ensures that the participants maximize their benefits in the game process, optimizes the investment decisions of each entity, and improves the effectiveness of the grid-distributed energy system decision making.

Suggested Citation

  • Yongxiu He & Wei Xiong & Binyou Yang & Hai-yan Yang & Jiu-fang Zhou & Ming-li Cui & Yan Li, 2022. "Combined game model and investment decision making of power grid-distributed energy system," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(6), pages 8667-8690, June.
    • Handle: RePEc:spr:endesu:v:24:y:2022:i:6:d:10.1007_s10668-021-01804-3
    DOI: 10.1007/s10668-021-01804-3
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    References listed on IDEAS

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    1. Yu, Mengmeng & Hong, Seung Ho, 2017. "Incentive-based demand response considering hierarchical electricity market: A Stackelberg game approach," Applied Energy, Elsevier, vol. 203(C), pages 267-279.
    2. Wang, Can & Yan, Chao & Li, Gengfeng & Liu, Shiyu & Bie, Zhaohong, 2020. "Risk assessment of integrated electricity and heat system with independent energy operators based on Stackelberg game," Energy, Elsevier, vol. 198(C).
    3. Liang, Yile & Liu, Feng & Wang, Cheng & Mei, Shengwei, 2017. "Distributed demand-side energy management scheme in residential smart grids: An ordinal state-based potential game approach," Applied Energy, Elsevier, vol. 206(C), pages 991-1008.
    4. Jian Wang & Qianggang Wang & Niancheng Zhou & Yuan Chi, 2017. "A Novel Electricity Transaction Mode of Microgrids Based on Blockchain and Continuous Double Auction," Energies, MDPI, vol. 10(12), pages 1-22, November.
    5. Li, Yinan & Yang, Wentao & He, Ping & Chen, Chang & Wang, Xiaonan, 2019. "Design and management of a distributed hybrid energy system through smart contract and blockchain," Applied Energy, Elsevier, vol. 248(C), pages 390-405.
    6. Ahl, Amanda & Yarime, Masaru & Tanaka, Kenji & Sagawa, Daishi, 2019. "Review of blockchain-based distributed energy: Implications for institutional development," Renewable and Sustainable Energy Reviews, Elsevier, vol. 107(C), pages 200-211.
    7. Jiani Wu & Nguyen Khoi Tran, 2018. "Application of Blockchain Technology in Sustainable Energy Systems: An Overview," Sustainability, MDPI, vol. 10(9), pages 1-22, August.
    8. Noor, Sana & Yang, Wentao & Guo, Miao & van Dam, Koen H. & Wang, Xiaonan, 2018. "Energy Demand Side Management within micro-grid networks enhanced by blockchain," Applied Energy, Elsevier, vol. 228(C), pages 1385-1398.
    9. Shengnan Zhao & Beibei Wang & Yachao Li & Yang Li, 2018. "Integrated Energy Transaction Mechanisms Based on Blockchain Technology," Energies, MDPI, vol. 11(9), pages 1-19, September.
    10. Ning Wang & Weisheng Xu & Zhiyu Xu & Weihui Shao, 2018. "Peer-to-Peer Energy Trading among Microgrids with Multidimensional Willingness," Energies, MDPI, vol. 11(12), pages 1-22, November.
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