The effective utilization of regenerative power generated by trains has attracted the attention of engineers due to its promising potential in energy conservation for electrified railways. Charge control by wayside battery batteries is an effective method of utilizing this regenerative power. Wayside batteries requires saving energy by utilizing the minimum storage capacity of energy storage devices. However, because current control policies are rule‐based, based on human empirical knowledge, it is difficult to decide the rules appropriately considering the battery's state of charge. Therefore, in this paper, we introduce reinforcement learning with an actor‐critic algorithm to acquire an effective control policy, which had been previously difficult to derive as rules using experts’ knowledge. The proposed algorithm, which can autonomously learn the control policy, stabilizes the balance of power supply and demand. Through several computational simulations, we demonstrate that the proposed method exhibits a superior performance compared to existing ones.

中文翻译：

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通过强化学习在电气化铁路系统中通过节能学习来控制路边电池的充电/放电

火车产生的可再生能源的有效利用，因其在电气化铁路节能方面的潜力巨大，引起了工程师的关注。通过路旁电池的充电控制是利用这种再生功率的有效方法。路边电池需要通过利用能量存储设备的最小存储容量来节省能量。但是，由于当前的控制策略是基于规则的，因此基于人类的经验知识，很难根据电池的充电状态来适当地确定规则。因此，在本文中，我们引入了一种基于行为批评算法的强化学习，以获取有效的控制策略，而以前很难利用专家的知识将其作为规则来推导。提出的算法，可以自主学习控制策略，稳定电力供需平衡。通过几次计算仿真，我们证明了所提出的方法与现有方法相比具有优越的性能。