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Voltage Hierarchical Control Strategy for Distribution Networks Based on Regional Autonomy and Photovoltaic-Storage Coordination

Author

Listed:
  • Jiang Wang

    (Hubei Key Laboratory of Power Equipment & System Security for Integrated Energy, Wuhan 430072, China
    School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China)

  • Jinchen Lan

    (State Grid Fujian Electric Power Co., Ltd. Electric Power Science Research Institute, Fuzhou 350007, China)

  • Lianhui Wang

    (State Grid Fujian Electric Power Co., Ltd., Fuzhou 350007, China)

  • Yan Lin

    (State Grid Fujian Electric Power Co., Ltd. Electric Power Science Research Institute, Fuzhou 350007, China)

  • Meimei Hao

    (State Grid Fujian Electric Power Co., Ltd., Fuzhou 350007, China)

  • Yan Zhang

    (State Grid Fujian Electric Power Co., Ltd., Fuzhou 350007, China)

  • Yang Xiang

    (Hubei Key Laboratory of Power Equipment & System Security for Integrated Energy, Wuhan 430072, China
    School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China)

  • Liang Qin

    (Hubei Key Laboratory of Power Equipment & System Security for Integrated Energy, Wuhan 430072, China
    School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China)

Abstract

High-penetration photovoltaic (PV) integration into a distribution network can cause serious voltage overruns. This study proposes a voltage hierarchical control method based on active and reactive power coordination to enhance the regional voltage autonomy of an active distribution network and improve the sustainability of new energy consumption. First, considering the reactive power margin and spatiotemporal characteristics of distributed photovoltaics, a reactive voltage modularity function is proposed to divide a distribution grid into voltage regions. Voltage region types and their weak points are then defined, and the voltage characteristics and governance needs of different regions are obtained through photovoltaic voltage regulation. Subsequently, a dual-layer optimal configuration model of energy storage that accounts for regional voltage regulation is established. The upper-layer model focuses on planned configurations to minimize the annual comprehensive operating cost of the energy storage system (ESS), while the lower-layer model focuses on optimal dispatch to achieve the best regional voltage quality. KKT conditions and the Big-M method are employed to convert the dual-layer model into a single-layer linear model for optimization and solution. Finally, an IEEE 33-node system with high-penetration photovoltaics is modeled using MATLAB (2022a). A comparative analysis of four scenarios shows that the comprehensive cost of an ESS decreased by 8.49%, total revenue increased by 19.36%, and the overall voltage deviation in the distribution network was reduced to 0.217%.

Suggested Citation

  • Jiang Wang & Jinchen Lan & Lianhui Wang & Yan Lin & Meimei Hao & Yan Zhang & Yang Xiang & Liang Qin, 2024. "Voltage Hierarchical Control Strategy for Distribution Networks Based on Regional Autonomy and Photovoltaic-Storage Coordination," Sustainability, MDPI, vol. 16(16), pages 1-23, August.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:16:p:6758-:d:1451606
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    References listed on IDEAS

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    1. Han, Rushuai & Hu, Qinran & Cui, Hantao & Chen, Tao & Quan, Xiangjun & Wu, Zaijun, 2022. "An optimal bidding and scheduling method for load service entities considering demand response uncertainty," Applied Energy, Elsevier, vol. 328(C).
    2. Xiao He & Seiji Hashimoto & Wei Jiang & Jicheng Liu & Takahiro Kawaguchi, 2023. "Design and Implementation of a Low-Voltage Photovoltaic System Integrated with Battery Energy Storage," Energies, MDPI, vol. 16(7), pages 1-20, March.
    3. Liu, Jingkun & Zhang, Ning & Kang, Chongqing & Kirschen, Daniel & Xia, Qing, 2017. "Cloud energy storage for residential and small commercial consumers: A business case study," Applied Energy, Elsevier, vol. 188(C), pages 226-236.
    4. K Vidhya & K Krishnamoorthi, 2024. "A hybrid technique for optimal power quality enhancement in grid-connected photovoltaic interleaved inverter," Energy & Environment, , vol. 35(1), pages 244-274, February.
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    Cited by:

    1. Peng Y. Lak & Jin-Woo Lim & Soon-Ryul Nam, 2025. "Deep Neural Network-Based Optimal Power Flow for Active Distribution Systems with High Photovoltaic Penetration," Energies, MDPI, vol. 18(17), pages 1-17, September.

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