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An Optimization Method for the Distributed Collaborative Operation of Multilateral Entities Considering Dynamic Time-of-Use Electricity Price in Active Distribution Network

Author

Listed:
  • Gang Liang

    (School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
    Jinghai Power Supply Branch of State Grid Tianjin Electric Power Company, Tianjin 301600, China)

  • Yu Wang

    (Dongli Power Supply Branch of State Grid Tianjin Electric Power Company, Tianjin 300308, China)

  • Bing Sun

    (School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China)

  • Zheng Zhang

    (School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China)

Abstract

More and more microgrids, energy storage systems, and other emerging entities are integrated into active distribution networks. However, a microgrid is characterized by autonomous operation and privacy protection. The rapid development of equipment such as shared energy storage brings strong uncertainty to a traditional dispatcher. The observability and controllability of the distribution system decrease, and traditional regulatory methods are no longer applicable. To deal with the above challenges, a distributed collaborative operation optimization method of multilateral participants is proposed. Guided by the dynamic time-of-use electricity price, the collaborative operation of multilateral participants can be realized. Firstly, the cooperative operation architecture is established considering the dynamic time-of-use electricity price. In this architecture, the residual capacity of shared energy storage is used for arbitrage by storing electricity at low electricity prices and generating electricity at high electricity prices. Then, the optimization operation models of a microgrid alliance, shared energy storage, and an active distribution network are established. The final operation scheme and the dynamic time-of-use price of the distribution network are formulated through the cyclic iteration among the three participants. Finally, a case study is carried out to analyze the optimization effect of each participant with the proposed method. It is found that the overall interests and the interests of each participant can be taken into account effectively and the consumption of renewable energy can be promoted by the method proposed in the paper. In addition, an oscillation phenomenon is found during the distributed collaborative operation, and the strategy to eliminate the oscillation phenomenon is given.

Suggested Citation

  • Gang Liang & Yu Wang & Bing Sun & Zheng Zhang, 2024. "An Optimization Method for the Distributed Collaborative Operation of Multilateral Entities Considering Dynamic Time-of-Use Electricity Price in Active Distribution Network," Energies, MDPI, vol. 17(2), pages 1-19, January.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:2:p:359-:d:1316765
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    References listed on IDEAS

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    1. Cao, Wenzhi & Xiao, Jiang-Wen & Cui, Shi-Chang & Liu, Xiao-Kang, 2022. "An efficient and economical storage and energy sharing model for multiple multi-energy microgrids," Energy, Elsevier, vol. 244(PB).
    2. Guo, Tianyu & Guo, Qi & Huang, Libin & Guo, Haiping & Lu, Yuanhong & Tu, Liang, 2023. "Microgrid source-network-load-storage master-slave game optimization method considering the energy storage overcharge/overdischarge risk," Energy, Elsevier, vol. 282(C).
    3. Mansouri, S.A. & Ahmarinejad, A. & Nematbakhsh, E. & Javadi, M.S. & Esmaeel Nezhad, A. & Catalão, J.P.S., 2022. "A sustainable framework for multi-microgrids energy management in automated distribution network by considering smart homes and high penetration of renewable energy resources," Energy, Elsevier, vol. 245(C).
    4. Ma, Mingtao & Huang, Huijun & Song, Xiaoling & Peña-Mora, Feniosky & Zhang, Zhe & Chen, Jie, 2022. "Optimal sizing and operations of shared energy storage systems in distribution networks: A bi-level programming approach," Applied Energy, Elsevier, vol. 307(C).
    5. Lu, Zhiming & Gao, Yan & Xu, Chuanbo, 2021. "Evaluation of energy management system for regional integrated energy system under interval type-2 hesitant fuzzy environment," Energy, Elsevier, vol. 222(C).
    6. Wang, Chutong & Zhang, Xiaoyan & Wang, Yucui & Xiong, Houbo & Ding, Xi & Guo, Chuangxin, 2023. "Pricing method of electric-thermal heterogeneous shared energy storage service," Energy, Elsevier, vol. 281(C).
    7. Zhang, Tianhan & Qiu, Weiqiang & Zhang, Zhi & Lin, Zhenzhi & Ding, Yi & Wang, Yiting & Wang, Lianfang & Yang, Li, 2023. "Optimal bidding strategy and profit allocation method for shared energy storage-assisted VPP in joint energy and regulation markets," Applied Energy, Elsevier, vol. 329(C).
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    1. Jiangping Liu & Wei Zhang & Guang Hu & Bolun Xu & Xue Cui & Xue Liu & Jun Zhao, 2025. "Master–Slave Game Pricing Strategy of Time-of-Use Electricity Price of Electricity Retailers Considering Users’ Electricity Utility and Satisfaction," Sustainability, MDPI, vol. 17(7), pages 1-19, March.

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