Safety-Aware Reinforcement Learning for Electric Vehicle Charging Station Management in Distribution Network
This addresses grid stability issues for electric vehicle integration, though it appears incremental as it builds on existing RL approaches with specific enhancements.
The paper tackles the problem of managing electric vehicle charging stations in distribution networks with uncertain solar generation and energy prices, presenting a safety-aware reinforcement learning algorithm that eliminates penalty tuning and outperforms traditional RL methods in simulations.
The increasing integration of electric vehicles (EVs) into the grid can pose a significant risk to the distribution system operation in the absence of coordination. In response to the need for effective coordination of EVs within the distribution network, this paper presents a safety-aware reinforcement learning (RL) algorithm designed to manage EV charging stations while ensuring the satisfaction of system constraints. Unlike existing methods, our proposed algorithm does not rely on explicit penalties for constraint violations, eliminating the need for penalty coefficient tuning. Furthermore, managing EV charging stations is further complicated by multiple uncertainties, notably the variability in solar energy generation and energy prices. To address this challenge, we develop an off-policy RL algorithm to efficiently utilize data to learn patterns in such uncertain environments. Our algorithm also incorporates a maximum entropy framework to enhance the RL algorithm's exploratory process, preventing convergence to local optimal solutions. Simulation results demonstrate that our algorithm outperforms traditional RL algorithms in managing EV charging in the distribution network.