Decentralized Smart Charging of Large-Scale EVs using Adaptive Multi-Agent Multi-Armed Bandits

TL;DR

This paper proposes a decentralized, scalable, real-time smart charging system for large-scale electric vehicles using adaptive multi-agent multi-armed bandit learning to address peak load challenges while ensuring fairness.

Contribution

It introduces a novel decentralized approach leveraging multi-armed bandits for adaptive, fair, and scalable EV smart charging, overcoming limitations of centralized solutions.

Findings

System is decentralized, scalable, and real-time.

Effective handling of uncertainties with multi-armed bandits.

Ensures fairness among EV users.

Abstract

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.