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

Shumin Sun, Song Yang, Peng Yu, Yan Cheng, Jiawei Xing, Yuejiao Wang, Yu Yi, Zhanyang Hu (), Liangzhong Yao and Xuanpei Pang
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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

Energies, 2025, vol. 18, issue 18, 1-19

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.

Keywords: distribution grid; distributed photovoltaic carrying capacity; reinforcement learning; deep deterministic policy gradient (DDPG) algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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