# Learning to Operate an Electric Vehicle Charging Station Considering Vehicle-grid Integration

arXiv: https://arxiv.org/abs/2111.01294 · HTML: https://mlsanity.com/paper/2111.01294/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 51 references that are in the map.
- First posted on arXiv 1 Nov 2021.

## Builds on (most cited first, 5 of 5)

- [Deep Reinforcement Learning in Large Discrete Action Spaces](https://mlsanity.com/paper/1512.07679/) (2015)
- [Definition and evaluation of model-free coordination of electrical vehicle charging with reinforcement learning](https://mlsanity.com/paper/1809.10679/) (2018)
- [A Model Predictive Control Approach for Low-Complexity Electric Vehicle Charging Scheduling: Optimality and Scalability](https://mlsanity.com/paper/1502.01456/) (2015)
- [Electrical Vehicle Charging Station Profit Maximization: Admission, Pricing, and Online Scheduling](https://mlsanity.com/paper/1705.02116/) (2017)
- [Reinforcement Learning Based Algorithm for the Maximization of EV Charging Station Revenue](https://mlsanity.com/paper/1407.1291/) (2014)

## Built on by (most cited first, 7 of 7)

- [A Deep Reinforcement Learning-Based Charging Scheduling Approach with Augmented Lagrangian for Electric Vehicle](https://mlsanity.com/paper/2209.09772/) (2022)
- [MARL for Decentralized Electric Vehicle Charging Coordination with V2V Energy Exchange](https://mlsanity.com/paper/2308.14111/) (2023)
- [Dynamic Load Balancing for EV Charging Stations Using Reinforcement Learning and Demand Prediction](https://mlsanity.com/paper/2503.06370/) (2025)
- [Optimizing Electric Bus Charging Scheduling with Uncertainties Using Hierarchical Deep Reinforcement Learning](https://mlsanity.com/paper/2505.10296/) (2025)
- [Chargax: A JAX Accelerated EV Charging Simulator](https://mlsanity.com/paper/2507.01522/) (2025)
- [Safe and Sustainable Electric Bus Charging Scheduling with Constrained Hierarchical DRL](https://mlsanity.com/paper/2512.03059/) (2025)
- [Emergent Charging Coordination in Electric Delivery Fleets](https://mlsanity.com/paper/2609.07689/) (2026)

## Abstract (arXiv)

The rapid adoption of electric vehicles (EVs) calls for the widespread installation of EV charging stations. To maximize the profitability of charging stations, intelligent controllers that provide both charging and electric grid services are in great need. However, it is challenging to determine the optimal charging schedule due to the uncertain arrival time and charging demands of EVs. In this paper, we propose a novel centralized allocation and decentralized execution (CADE) reinforcement learning (RL) framework to maximize the charging station's profit. In the centralized allocation process, EVs are allocated to either the waiting or charging spots. In the decentralized execution process, each charger makes its own charging/discharging decision while learning the action-value functions from a shared replay memory. This CADE framework significantly improves the scalability and sample efficiency of the RL algorithm. Numerical results show that the proposed CADE framework is both computationally efficient and scalable, and significantly outperforms the baseline model predictive control (MPC). We also provide an in-depth analysis of the learned action-value function to explain the inner working of the reinforcement learning agent.