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

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.