# Safety-Aware Reinforcement Learning for Electric Vehicle Charging Station Management in Distribution Network

arXiv: https://arxiv.org/abs/2403.13236 · HTML: https://mlsanity.com/paper/2403.13236/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 2 of its 14 references that are in the map.
- First posted on arXiv 20 Mar 2024.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 2 of 2)

- [Smart Online Charging Algorithm for Electric Vehicles via Customized Actor-Critic Learning](https://mlsanity.com/paper/2106.00854/) (2021)
- [Reducing the Impact of EV Charging Operations on the Distribution Network](https://mlsanity.com/paper/1509.07056/) (2015)

## Built on by (most cited first, 2 of 2)

- [Physics-Informed Reinforcement Learning for Large-Scale EV Smart Charging Considering Distribution Network Voltage Constraints](https://mlsanity.com/paper/2510.12335/) (2025)
- [Emission-Aware Reinforcement Learning for Sustainable Electric Vehicle Charging and Carbon Dioxide Reduction Under Varying Renewable Penetration](https://mlsanity.com/paper/2605.24543/) (2026)

## Abstract (arXiv)

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.