Federated Reinforcement Learning for Real-Time Electric Vehicle Charging and Discharging Control
This work addresses the challenge of real-time control for EV users in smart grids, offering a privacy-preserving solution, but it appears incremental as it builds on existing federated and reinforcement learning techniques.
The paper tackled the problem of optimizing electric vehicle charging and discharging control in dynamic smart grid environments to maximize user benefits, and the proposed federated reinforcement learning method achieved effective performance in simulations with various stochastic factors.
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.