An Efficient Distributed Multi-Agent Reinforcement Learning for EV
  Charging Network Control
Amin Shojaeighadikolaei Morteza Hashemi
The increasing trend in adopting electric vehicles (EVs) will significantly
impact the residential electricity demand, which results in an increased risk
of transformer overload in the distribution grid. To mitigate such risks, there
are urgent needs to develop effective EV charging controllers. Currently, the
majority of the EV charge controllers are based on a centralized approach for
managing individual EVs or a group of EVs. In this paper, we introduce a
decentralized Multi-agent Reinforcement Learning (MARL) charging framework that
prioritizes the preservation of privacy for EV owners. We employ the
Centralized Training Decentralized Execution-Deep Deterministic Policy Gradient
(CTDE-DDPG) scheme, which provides valuable information to users during
training while maintaining privacy during execution. Our results demonstrate
that the CTDE framework improves the performance of the charging network by
reducing 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 of
transformer overload during the peak hours.