An Efficient Distributed Multi-Agent Reinforcement Learning for EV Charging Network Control
This addresses grid stability issues for utility operators and EV owners, but is incremental as it builds on existing CTDE-DDPG methods.
The paper tackles the problem of transformer overload risk from increasing EV adoption by developing a decentralized multi-agent reinforcement learning framework for EV charging control, which reduces network costs and peak-to-average ratio of demand.
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.