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The increasing trend in adopting electric vehicles (EVs) will significantlyimpact the residential electricity demand, which results in an increased riskof transformer overload in the distribution grid. To mitigate such risks, thereare urgent needs to develop effective EV charging controllers. Currently, themajority of the EV charge controllers are based on a centralized approach formanaging individual EVs or a group of EVs. In this paper, we introduce adecentralized Multi-agent Reinforcement Learning (MARL) charging framework thatprioritizes the preservation of privacy for EV owners. We employ theCentralized Training Decentralized Execution-Deep Deterministic Policy Gradient(CTDE-DDPG) scheme, which provides valuable information to users duringtraining while maintaining privacy during execution. Our results demonstratethat the CTDE framework improves the performance of the charging network byreducing the network costs. Moreover, we show that the Peak-to-Average Ratio(PAR) of the total demand is reduced, which, in turn, reduces the risk oftransformer overload during the peak hours.

An Efficient Distributed Multi-Agent Reinforcement Learning for EV Charging Network Control