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A Distribution Network Planning Method Considering the Distributed Energy Resource Flexibility of Virtual Power Plants

Author

Listed:
  • Zhichun Yang

    (Electric Power Research Institute of State Grid Hubei Co., Ltd., Wuhan 430037, China)

  • Gang Han

    (Electric Power Research Institute of State Grid Hubei Co., Ltd., Wuhan 430037, China)

  • Fan Yang

    (Electric Power Research Institute of State Grid Hubei Co., Ltd., Wuhan 430037, China)

  • Yu Shen

    (Electric Power Research Institute of State Grid Hubei Co., Ltd., Wuhan 430037, China)

  • Yu Liu

    (Electric Power Research Institute of State Grid Hubei Co., Ltd., Wuhan 430037, China)

  • Huaidong Min

    (Electric Power Research Institute of State Grid Hubei Co., Ltd., Wuhan 430037, China)

  • Zhiqiang Zhou

    (State Grid Hubei Electric Power Co., Ltd., Wuhan 430037, China)

  • Bin Zhou

    (State Grid Hubei Electric Power Co., Ltd., Wuhan 430037, China)

  • Wei Hu

    (Electric Power Research Institute of State Grid Hubei Co., Ltd., Wuhan 430037, China)

  • Yang Lei

    (Electric Power Research Institute of State Grid Hubei Co., Ltd., Wuhan 430037, China)

Abstract

To solve the overload problem caused by the high proportion of renewable energy into the power system, it is particularly important to find a suitable distribution network planning scheme. Existing studies have effectively reduced the planning cost by incorporating virtual power plants into the distribution planning process, but there is no quantitative analysis of the flexible resources inside the virtual power plant. At the same time, the traditional planning process does not pay much attention to the acquisition of photovoltaic and load data. Therefore, in this paper, we propose a distribution network planning method considering the flexibility of distributed energy resources in virtual power plants. Firstly, taking the distribution network planning including the virtual power plant as the research object, the flexibility of the distributed energy resource of the virtual power plant was quantified. Then, in order to achieve the goal of minimizing the operating cost of system planning, a distribution network planning model considering the flexibility of distributed energy resources in the virtual power plant is established. In this model, the impact of virtual power plants flexibility on the distribution network planning process is mainly considered. Secondly, this paper uses the improved k-means clustering algorithm to obtain the typical data of PV and load. The algorithm effectively overcomes the impact of PV and load output fluctuations on the planning process. Finally, the simulation results show that the proposed planning model can effectively reduce the operation cost of system planning by using distributed energy storage system and distributed energy resource flexibility. At the same time, the PV absorption rate of the PV power station inside the distribution network is improved.

Suggested Citation

  • Zhichun Yang & Gang Han & Fan Yang & Yu Shen & Yu Liu & Huaidong Min & Zhiqiang Zhou & Bin Zhou & Wei Hu & Yang Lei, 2023. "A Distribution Network Planning Method Considering the Distributed Energy Resource Flexibility of Virtual Power Plants," Sustainability, MDPI, vol. 15(19), pages 1-17, September.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:19:p:14399-:d:1251520
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    References listed on IDEAS

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    1. Moradi-Sarvestani, Sajjad & Jooshaki, Mohammad & Fotuhi-Firuzabad, Mahmud & Lehtonen, Matti, 2023. "Incorporating direct load control demand response into active distribution system planning," Applied Energy, Elsevier, vol. 339(C).
    2. He, Shuaijia & Gao, Hongjun & Liu, Junyong & Zhang, Xi & Chen, Zhe, 2022. "Distribution system planning considering peak shaving of energy station," Applied Energy, Elsevier, vol. 312(C).
    3. Weifeng Xu & Bing Yu & Qing Song & Liguo Weng & Man Luo & Fan Zhang, 2022. "Economic and Low-Carbon-Oriented Distribution Network Planning Considering the Uncertainties of Photovoltaic Generation and Load Demand to Achieve Their Reliability," Energies, MDPI, vol. 15(24), pages 1-15, December.
    4. Fan, Vivienne Hui & Dong, Zhaoyang & Meng, Ke, 2020. "Integrated distribution expansion planning considering stochastic renewable energy resources and electric vehicles," Applied Energy, Elsevier, vol. 278(C).
    5. Dong, Lianxin & Fan, Shuai & Wang, Zhihua & Xiao, Jucheng & Zhou, Huan & Li, Zuyi & He, Guangyu, 2021. "An adaptive decentralized economic dispatch method for virtual power plant," Applied Energy, Elsevier, vol. 300(C).
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