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Distributed PV Bearing Capacity Assessment Method Based on Source–Load Coupling Scenarios

Author

Listed:
  • Yalu Sun

    (Economic and Technological Research Institute, State Grid Gansu Electric Power Company, Lanzhou 730000, China)

  • Zhou Wang

    (Economic and Technological Research Institute, State Grid Gansu Electric Power Company, Lanzhou 730000, China)

  • Yongcheng Liu

    (Economic and Technological Research Institute, State Grid Gansu Electric Power Company, Lanzhou 730000, China)

  • Yi Jiang

    (School of Mechanical and Electrical Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China)

  • Yalong Li

    (School of Mechanical and Electrical Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China)

Abstract

To address the insufficient consideration of system static voltage stability and PV–load coupling in distributed photovoltaic (PV) hosting capacity assessment, this study first investigates the impact of distributed PV integration on power system transient voltage stability based on a typical power supply system. Building on this analysis, we propose a Static Grid Stability Margin (SGSM) index. Subsequently, leveraging historical PV and load data, the copula function is introduced to establish a joint distribution function characterizing their correlation. Massive evaluation scenarios are generated through sampling, with robust clustering methods employed to form representative evaluation scenarios. Finally, a distributed PV bearing capacity assessment model is established with the objectives of maximizing PV bearing capacity, optimizing economic efficiency, and enhancing static voltage stability. Through simulation verification, the power system has a higher capacity for distributed PV when distributed PV is integrated into nodes with weak static voltage stability and a decentralized integration scheme is adopted.

Suggested Citation

  • Yalu Sun & Zhou Wang & Yongcheng Liu & Yi Jiang & Yalong Li, 2025. "Distributed PV Bearing Capacity Assessment Method Based on Source–Load Coupling Scenarios," Energies, MDPI, vol. 18(20), pages 1-22, October.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:20:p:5520-:d:1775271
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    References listed on IDEAS

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    1. Youzhuo Zheng & Kun Zhou & Yekui Yang & Hanbin Diao & Long Hua & Renzhi Wang & Kang Liu & Qi Guo, 2025. "Robust Assessment Method for Hosting Capacity of Distribution Network in Mountainous Areas for Distributed Photovoltaics," Energies, MDPI, vol. 18(9), pages 1-17, May.
    2. Jude Suchithra & Amin Rajabi & Duane A. Robinson, 2024. "Enhancing PV Hosting Capacity of Electricity Distribution Networks Using Deep Reinforcement Learning-Based Coordinated Voltage Control," Energies, MDPI, vol. 17(20), pages 1-27, October.
    3. Jaramillo-Leon, Brian & Zambrano-Asanza, Sergio & Franco, John F. & Soares, João & Leite, Jonatas B., 2024. "Allocation and smart inverter setting of ground-mounted photovoltaic power plants for the maximization of hosting capacity in distribution networks," Renewable Energy, Elsevier, vol. 223(C).
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