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.

翻译：随着电动汽车（EV）普及趋势的不断增长，住宅用电需求将受到显著影响，进而导致配电网变压器过载风险增加。为缓解此类风险，亟需开发有效的电动汽车充电控制器。目前，多数电动汽车充电控制器采用集中式方法管理单个或成组电动汽车。本文提出一种优先保护电动汽车车主隐私的分散式多智能体强化学习（MARL）充电框架。我们采用集中训练分散执行-深度确定性策略梯度（CTDE-DDPG）方案，该方案在训练阶段向用户提供有价值信息，同时在执行阶段保持隐私性。结果表明，CTDE框架通过降低网络成本提升了充电网络性能。此外，我们证明总需求的峰均比（PAR）得到降低，从而降低了高峰时段变压器过载的风险。