Optimized Energy Management for EV Charging Stations Using Soft Actor-Critic Reinforcement Learning

This paper presents a novel intelligent energy management system based on Soft Actor-Critic (SAC) reinforcement learning for Electric Vehicle (EV) charging stations. The proposed approach addresses the complexity of dynamic EV charging and uncertain renewable energy generation by employing a scalable state aggregation method, which reduces the dimensionality of the system state space at the expense of system information. This capability allows the SAC algorithm to learn robust control policies in the continuous action space. Realistic constraint integration and better exploration mechanisms further enhance the algorithm to attain superior convergence and optimized performance. Results from the simulation are validated with simulated data, showing that the proposed SAC-based method significantly outperforms rule-based methods in reducing electricity cost by 13 percent, balancing energy supply with supply demand, and lowering grid dependence at peak demand time. It shows that SAC-based deep reinforcement learning is much more capable than rule-based approaches in solving the challenges of operating complex and uncertain energy systems and designing an efficient and sustainable EV charging infrastructure. The presented results suggest further research and the application of adaptive and intelligent energy management strategies for the widespread adoption of EVs.