Review on the Application of Reinforcement Learning in Distribution Network Voltage Control
DOI:
https://doi.org/10.62051/064r5j52Keywords:
Reinforcement Learning; Distribution Network; Voltage Control; DistFlow Model; Multi-Agent Collaboration; Residual Learning; Renewable Energy with High Penetration Rate.Abstract
Based on the core of Survey on the Application of Reinforcement Learning in Distribution Network Voltage Control, this study systematically organizes RL's application system in distribution network voltage control: it establishes MDP and POMDP modeling frameworks, clarifying the design logic of state, action, and reward functions; classifies and reviews core schemes of value function-based, policy gradient-based, and residual RL, quantitatively comparing algorithm performance via unified benchmarks and evaluation metrics; analyzes engineering implementation challenges (safety, scalability, interpretability) and proposes solutions (hierarchical control, Sim-to-Real transfer, human-machine collaboration); finally, it outlines short-term engineering paths and medium-term technical directions, providing references for intelligent voltage control of distribution networks in the new power system.
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