Federated Reinforcement Learning for Electric Vehicles Charging Control
on Distribution Networks
- URL: http://arxiv.org/abs/2308.08792v1
- Date: Thu, 17 Aug 2023 05:34:46 GMT
- Title: Federated Reinforcement Learning for Electric Vehicles Charging Control
on Distribution Networks
- Authors: Junkai Qian and Yuning Jiang and Xin Liu and Qing Wang and Ting Wang
and Yuanming Shi and Wei Chen
- Abstract summary: Multi-agent deep reinforcement learning (MADRL) has proven its effectiveness in EV charging control.
Existing MADRL-based approaches fail to consider the natural power flow of EV charging/discharging in the distribution network.
This paper proposes a novel approach that combines multi-EV charging/discharging with a radial distribution network (RDN) operating under optimal power flow.
- Score: 42.04263644600909
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: With the growing popularity of electric vehicles (EVs), maintaining power
grid stability has become a significant challenge. To address this issue, EV
charging control strategies have been developed to manage the switch between
vehicle-to-grid (V2G) and grid-to-vehicle (G2V) modes for EVs. In this context,
multi-agent deep reinforcement learning (MADRL) has proven its effectiveness in
EV charging control. However, existing MADRL-based approaches fail to consider
the natural power flow of EV charging/discharging in the distribution network
and ignore driver privacy. To deal with these problems, this paper proposes a
novel approach that combines multi-EV charging/discharging with a radial
distribution network (RDN) operating under optimal power flow (OPF) to
distribute power flow in real time. A mathematical model is developed to
describe the RDN load. The EV charging control problem is formulated as a
Markov Decision Process (MDP) to find an optimal charging control strategy that
balances V2G profits, RDN load, and driver anxiety. To effectively learn the
optimal EV charging control strategy, a federated deep reinforcement learning
algorithm named FedSAC is further proposed. Comprehensive simulation results
demonstrate the effectiveness and superiority of our proposed algorithm in
terms of the diversity of the charging control strategy, the power fluctuations
on RDN, the convergence efficiency, and the generalization ability.
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