Multi-Agent Multi-Armed Bandits: application to EVs smart charging with grid constraints

Electrification of energy uses with renewable sources is a major lever of decarbonization. To maximize the use of renewable energy, and tackle its variability and uncertainty, flexible entities such as electric vehicles can be used. This paper presents a scalable, decentralized multi-agent system, where each electric vehicle (EV) seeks the best instants to charge to satisfy its mobility needs, while favoring photovoltaic energy and avoiding congesting the electric network. Each EV makes autonomous decisions using information from its environment, using contextual multi-armed bandit algorithms based on Thompson sampling. Four types of bandit-based algorithms are compared to a baseline through simulations with 55 homogeneous EV agents, achieving a distance of only 14% from an optimal omniscient centralized algorithm. The system’s real-world practicality is demonstrated through minimal computational requirements (200 µs per EV per timestep) and limited information sharing, maintaining user privacy while effectively managing grid congestion.