基于强化学习的车网电压调节：从单枞到多枞的协调及电池感知约束
摘要
本文提出一种基于强化学习（RL）的车网（V2G）协调框架。{基于软演员-评论家算法 developed an intelligent control strategy for voltage regulation through single and multi-hub charging systems while respecting realistic fleet constraints. A two-phase training approach integrates stability-focused learning with battery-aware deployment to ensure practical feasibility. Simulation studies on the IEEE 34-bus system validate the framework against a standard Volt-Var/Volt-Watt droop controller. Results indicate that the RL agent achieves performance comparable to the baseline control strategy in nominal scenarios. Under aggressive overloading, it provides robust voltage recovery (within 10% of the baseline) while prioritizing fleet availability and state-of-charge preservation, demonstrating the viability of constraint-aware learning for critical grid services.}
引用
@article{arxiv.2603.07237,
title = {Reinforcement Learning for Vehicle-to-Grid Voltage Regulation: Single-Hub to Multi-Hub Coordination with Battery-Aware Constraints},
author = {Jingbo Wang and Roshni Anna Jacob and Harshal D. Kaushik and Jie Zhang},
journal= {arXiv preprint arXiv:2603.07237},
year = {2026}
}