Decentralized Smart Charging of Large-Scale EVs using Adaptive Multi-Agent Multi-Armed Bandits
This work addresses grid congestion and voltage issues for EV and photovoltaic integration, offering a decentralized alternative to centralized methods, though it appears incremental as it builds on existing multi-agent and bandit approaches.
The paper tackles the problem of peak load demands and grid instability from large-scale electric vehicle (EV) charging by proposing a fully decentralized smart charging system using adaptive multi-agent multi-armed bandits, resulting in a scalable, real-time, and fair solution that addresses uncertainties without centralized control.
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.