Smart charging of large fleets of Electric Vehicles: Independent Multi-Agent Reinforcement Learning approaches

2026-06-30

AI Summary

This study addresses the challenges posed by large-scale electric vehicle (EV) integration—namely, peak load amplification, voltage fluctuations, and curtailment of renewable energy—by proposing a decentralized intelligent charging scheduling framework. Leveraging independent multi-agent reinforcement learning, each EV autonomously makes charging decisions based solely on local information, such as dynamic electricity prices, state of charge, and temporal constraints, while implicitly coordinating with others to jointly minimize user costs and ensure grid safety. For the first time, the performance of contextual combinatorial bandits and policy gradient algorithms is systematically compared within a heterogeneous multi-agent setting under real photovoltaic-driven dynamic pricing. Experimental results demonstrate that both approaches significantly reduce user expenses and alleviate line overloads across varying levels of grid congestion, thereby validating the feasibility, robustness, and practicality of the proposed decentralized strategy.

Abstract

The electrification of transportation through electric vehicles introduces new challenges for power grid management, such as increased peak demand, voltage fluctuations, line overloads, and the integration of variable renewable energy sources. To enable efficient integration of EVs while minimizing costs for users and avoiding network overloads, implicit coordination between EVs is required. This work compares two independent multi-agent reinforcement learning approaches for optimizing such decentralized EV charging: contextual combinatorial bandits and policy gradient algorithms. Using a realistic simulation environment with autonomous agents making decisions based on local environmental information (including price signals, state-of-charge, and temporal constraints), we evaluate their performance across varying congestion levels, and mixed-strategy configurations with heterogeneous agent groups under dynamic electricity pricing derived from real photovoltaic production data.

Problem

• electric vehicles
• smart charging
• power grid management
• multi-agent reinforcement learning
• renewable energy integration

Innovation

• multi-agent reinforcement learning
• electric vehicle charging
• contextual combinatorial bandits
• decentralized coordination
• dynamic electricity pricing