# Dynamic Load Balancing for EV Charging Stations Using Reinforcement Learning and Demand Prediction

arXiv: https://arxiv.org/abs/2503.06370 · HTML: https://mlsanity.com/paper/2503.06370/

## Key facts

- Cited by 1 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 4 of its 16 references that are in the map.
- First posted on arXiv 9 Mar 2025.

## 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, 4 of 4)

- [A physics-informed and attention-based graph learning approach for regional electric vehicle charging demand prediction](https://mlsanity.com/paper/2309.05259/) (2023)
- [Learning to Operate an Electric Vehicle Charging Station Considering Vehicle-grid Integration](https://mlsanity.com/paper/2111.01294/) (2021)
- [Demand Forecasting for Electric Vehicle Charging Stations using Multivariate Time-Series Analysis](https://mlsanity.com/paper/2502.16365/) (2025)
- [Dynamic Pricing for Electric Vehicle Charging](https://mlsanity.com/paper/2408.14169/) (2024)

## Built on by (most cited first, 1 of 1)

- [Optimizing Electric Vehicle Charging Station Placement Using Reinforcement Learning and Agent-Based Simulations](https://mlsanity.com/paper/2511.01218/) (2025)

## Abstract (arXiv)

This paper presents a method for load balancing and dynamic pricing in electric vehicle (EV) charging networks, utilizing reinforcement learning (RL) to enhance network performance. The proposed framework integrates a pre-trained graph neural network to predict demand elasticity and inform pricing decisions. The spatio-temporal EV charging demand prediction (EVCDP) dataset from Shenzhen is utilized to capture the geographic and temporal characteristics of the charging stations. The RL model dynamically adjusts prices at individual stations based on occupancy, maximum station capacity, and demand forecasts, ensuring an equitable network load distribution while preventing station overloads. By leveraging spatially-aware demand predictions and a carefully designed reward function, the framework achieves efficient load balancing and adaptive pricing strategies that respond to localized demand and global network dynamics, ensuring improved network stability and user satisfaction. The efficacy of the approach is validated through simulations on the dataset, showing significant improvements in load balancing and reduced overload as the RL agent iteratively interacts with the environment and learns to dynamically adjust pricing strategies based on real-time demand patterns and station constraints. The findings highlight the potential of adaptive pricing and load-balancing strategies to address the complexities of EV infrastructure, paving the way for scalable and user-centric solutions.