Profit Maximization for Electric Vehicle Charging Stations Using Multiagent Reinforcement Learning

TL;DR

This paper introduces a multi-agent reinforcement learning framework for EV charging stations that optimizes profit through cooperative energy management, energy trading, and handling uncertainties in demand and renewable generation.

Contribution

It proposes a Double Hypernetwork QMIX-based MARL approach that improves profit maximization and decision-making efficiency for EVCSs with renewable energy and energy trading capabilities.

Findings

Achieves 5.3% and 12.7% higher profits in real-world data experiments.

Maintains robust performance under demand and renewable energy fluctuations.

Mitigates overestimation bias in value estimation.

Abstract

Electric vehicles (EVs) are increasingly integrated into power grids, offering economic and environmental benefits but introducing challenges due to uncoordinated charging. This study addresses the profit maximization problem for multiple EV charging stations (EVCSs) equipped with energy storage systems (ESS) and renewable energy sources (RES), with the capability for energy trading. We propose a Double Hypernetwork QMIX-based multi-agent reinforcement learning (MARL) framework to optimize cooperative energy management under uncertainty in EV demand, renewable generation, and real-time electricity prices. The framework mitigates overestimation bias in value estimation, enables distributed decision-making, and incorporates an internal energy trading mechanism. Numerical experiments using real-world data demonstrate that, compared to standard QMIX, the proposed method achieves approximately 5.3% and 12.7% higher total profit for the two regions, respectively, highlighting its economic and operational efficiency. Additionally, the approach maintains robust performance under varying levels of EV demand uncertainty and renewable energy fluctuations.