Reinforcement Learning for Vehicle-to-Grid Voltage Regulation: Single-Hub to Multi-Hub Coordination with Battery-Aware Constraints

TL;DR

This paper develops a reinforcement learning-based Vehicle-to-Grid control framework using soft actor-critic, enabling effective voltage regulation with battery constraints in single and multi-hub systems, validated on IEEE test systems.

Contribution

It introduces a novel RL-based V2G coordination method with a two-phase training process that incorporates battery-aware constraints for practical deployment.

Findings

RL agent achieves comparable performance to standard controllers in normal conditions.

Under overload, the RL approach ensures robust voltage recovery within 10% of baseline.

The framework demonstrates practical feasibility and constraint-aware learning for grid services.

Abstract

This paper presents a Vehicle-to-Grid (V2G) coordination framework using reinforcement learning (RL). {An intelligent control strategy based on the soft actor-critic algorithm is developed 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.}