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Charge/discharge control of wayside batteries via reinforcement learning for energy‐conservation in electrified railway systems
Author(s) -
Yoshida Yasuhiro,
Arai Sachiyo,
Kobayashi Hiroyasu,
Kondo Keiichiro
Publication year - 2021
Publication title -
electrical engineering in japan
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.136
H-Index - 28
eISSN - 1520-6416
pISSN - 0424-7760
DOI - 10.1002/eej.23319
Subject(s) - charge control , energy conservation , charge (physics) , control (management) , reinforcement learning , engineering , electrical engineering , automotive engineering , computer science , battery (electricity) , physics , artificial intelligence , power (physics) , quantum mechanics
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.