基于强化学习的插电式电动汽车充电自适应控制

Abdullah Al Zishan, Moosa Moghimi Haji, Omid Ardakanian
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引用次数: 12

摘要

针对插电式电动汽车在电力系统中的控制充电问题,提出了一种类似于自适应加-增-乘-减(AIMD)算法。该算法是分散和无模型的,并依赖于从部署在网络上的传感器接收的拥塞信号来避免拥塞。我们使用多智能体强化学习来动态调整自适应AIMD算法的参数,假设充电点是独立的智能体。我们采用模仿学习对这些智能体进行预训练,并采用脱轨行为者-批评家深度强化学习算法来确定在线环境下的最优控制。在多个充电点停车站的仿真结果表明,该算法在避免线路或变压器过载的情况下,密切跟踪网络的可用容量,在利用率方面优于AIMD算法和其他基准。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Adaptive Control of Plug-in Electric Vehicle Charging with Reinforcement Learning
This paper proposes an adaptive additive-increase multiplicative-decrease (AIMD)-like algorithm for controlled charging of plug-in electric vehicles in a power system. The proposed algorithm is decentralized and model-free, and relies on congestion signals received from sensors deployed across the network to avoid congestion. We use multi-agent reinforcement learning to dynamically adjust the parameters of the adaptive AIMD algorithm assuming that charging points are independent agents. We adopt imitation learning to pre-train these agents and an off-policy actor-critic deep reinforcement learning algorithm to determine the optimal control in the online setting. Simulation results obtained in a parking station with several charging points corroborate that the proposed algorithm closely tracks the available capacity of the network while avoiding line or transformer overloading, and outperforms the AIMD algorithm and other baselines in terms of utilization.
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