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arXiv · 2210.01452

Federated Reinforcement Learning for Real-Time Electric Vehicle Charging and Discharging Control

Zixuan Zhang·Yuning Jiang·Yuanming Shi·Ye Shi·Wei Chen
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Abstract

With the recent advances in mobile energy storage technologies, electric vehicles (EVs) have become a crucial part of smart grids. When EVs participate in the demand response program, the charging cost can be significantly reduced by taking full advantage of the real-time pricing signals. However, many stochastic factors exist in the dynamic environment, bringing significant challenges to design an optimal charging/discharging control strategy. This paper develops an optimal EV charging/discharging control strategy for different EV users under dynamic environments to maximize EV users' benefits. We first formulate this problem as a Markov decision process (MDP). Then we consider EV users with different behaviors as agents in different environments. Furthermore, a horizontal federated reinforcement learning (HFRL)-based method is proposed to fit various users' behaviors and dynamic environments. This approach can learn an optimal charging/discharging control strategy without sharing users' profiles. Simulation results illustrate that the proposed real-time EV charging/discharging control strategy can perform well among various stochastic factors.

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Zixuan Zhang, Yuning Jiang, Yuanming Shi, Ye Shi, Wei Chen. 2022-10-04. Federated Reinforcement Learning for Real-Time Electric Vehicle Charging and Discharging Control. https://arxiv.org/abs/2210.01452

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