Decentralized Smart Charging of Large-Scale EVs using Adaptive
  Multi-Agent Multi-Armed Bandits
Sharyal Zafar Raphaël Feraud Anne Blavette Guy Camilleri Hamid Ben
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.