Balancing EV demand at charging stations using multi-agent reinforcement learning

Abstract

This paper proposes a method for optimising the routing of electric vehicles (EVs) to charging stations via a multi-agent reinforcement learning (MARL) demand balancing system in order to reduce queuing time. This is achieved through simulations via the SUMO simulator to train and test agents to reduce demand by applying reinforcement learning algorithms. Q-learning, PPO and DQN experiments have been conducted to determine a suitable algorithm. The approaches were run on multiple test road networks and a real-world Berlin network with ten charging stations to validate the findings. Varied learning strategies are also explored to determine the appropriate behaviour patterns between the agents, including competitive and cooperative learning as well as a mix of the two. The results of the most promising DQN cooperative implementation applied to the Berlin network achieved an 88.09% reduction in the mean wait times when compared with a greedy approach. The findings of this paper demonstrate the potential for practical benefits of applying MARL systems to real-world environments.

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Identifiers

ORCID for Sebastian Stein: orcid.org/0000-0003-2858-8857

ORCID for Jan Buermann: orcid.org/0000-0002-4981-6137

ORCID for Suleiman Sharkh: orcid.org/0000-0001-7335-8503

ORCID for Andrew Cruden: orcid.org/0000-0003-3236-2535

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