The drastic growth of electric vehicles and photovoltaics can introduce new challenges, such as electrical current congestion and voltage limit violations due to peak load demands. These issues can be mitigated by controlling the operation of electric vehicles i.e., smart charging. Centralized smart charging solutions have already been proposed in the literature. But such solutions may lack scalability and suffer from inherent drawbacks of centralization, such as a single point of failure, and data privacy concerns. Decentralization can help tackle these challenges. In this paper, a fully decentralized smart charging system is proposed using the philosophy of adaptive multi-agent systems. The proposed system utilizes multi-armed bandit learning to handle uncertainties in the system. The presented system is decentralized, scalable, real-time, model-free, and takes fairness among different players into account. A detailed case study is also presented for performance evaluation.

翻译：电动汽车和光伏发电的急剧增长会带来新的挑战，例如尖峰负荷需求导致的电流拥堵和电压越限问题。通过控制电动汽车运行（即智能充电）可以缓解这些问题。现有文献已提出集中式智能充电方案，但此类方案可能缺乏可扩展性，且存在单点故障、数据隐私等集中式架构的固有缺陷。去中心化有助于应对这些挑战。本文基于自适应多智能体系统理念，提出了一种完全去中心化的智能充电系统。该系统采用多臂赌博机学习机制应对系统中的不确定性，具有去中心化、可扩展、实时性、无模型建模，并兼顾不同参与方公平性等特点。文中还通过详细案例研究对系统性能进行了评估。