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A Reinforcement Learning-Based Approach for Distributed Photovoltaic Carrying Capacity Analysis in Distribution Grids

Author

Listed:
  • Shumin Sun

    (State Grid Shandong Electric Power Research Institute, No. 2000 Wangyue Road, Shizhong District, Jinan 250002, China)

  • Song Yang

    (State Grid Shandong Electric Power Research Institute, No. 2000 Wangyue Road, Shizhong District, Jinan 250002, China)

  • Peng Yu

    (State Grid Shandong Electric Power Research Institute, No. 2000 Wangyue Road, Shizhong District, Jinan 250002, China)

  • Yan Cheng

    (State Grid Shandong Electric Power Research Institute, No. 2000 Wangyue Road, Shizhong District, Jinan 250002, China)

  • Jiawei Xing

    (State Grid Shandong Electric Power Research Institute, No. 2000 Wangyue Road, Shizhong District, Jinan 250002, China)

  • Yuejiao Wang

    (State Grid Shandong Electric Power Research Institute, No. 2000 Wangyue Road, Shizhong District, Jinan 250002, China)

  • Yu Yi

    (State Grid Shandong Electric Power Research Institute, No. 2000 Wangyue Road, Shizhong District, Jinan 250002, China)

  • Zhanyang Hu

    (School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China)

  • Liangzhong Yao

    (School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China)

  • Xuanpei Pang

    (School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China)

Abstract

Driven by the “double carbon” goals, the penetration rate of distributed photovoltaics (PV) in distribution networks has increased rapidly. However, the continuous growth of distributed PV installed capacity poses significant challenges to the carrying capacity of distribution networks. Reinforcement learning (RL), with its capability to handle high-dimensional nonlinear problems, plays a critical role in analyzing the carrying capacity of distribution networks. This study constructs an evaluation model for distributed PV carrying capacity and proposes a corresponding quantitative evaluation index system by analyzing the core factors influencing it. An optimization scheme based on deep reinforcement learning is adopted, introducing the Deep Deterministic Policy Gradient (DDPG) algorithm to solve the evaluation model. Finally, simulations on the IEEE-33 bus system validate the good feasibility of the reinforcement learning approach for this problem.

Suggested Citation

  • Shumin Sun & Song Yang & Peng Yu & Yan Cheng & Jiawei Xing & Yuejiao Wang & Yu Yi & Zhanyang Hu & Liangzhong Yao & Xuanpei Pang, 2025. "A Reinforcement Learning-Based Approach for Distributed Photovoltaic Carrying Capacity Analysis in Distribution Grids," Energies, MDPI, vol. 18(18), pages 1-19, September.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:18:p:5029-:d:1754889
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    References listed on IDEAS

    as
    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 & Duane Robinson & Amin Rajabi, 2023. "Hosting Capacity Assessment Strategies and Reinforcement Learning Methods for Coordinated Voltage Control in Electricity Distribution Networks: A Review," Energies, MDPI, vol. 16(5), pages 1-28, March.
    Full references (including those not matched with items on IDEAS)

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