電気鉄道システムの省エネルギー実現に向けた強化学習による地上蓄電装置の充放電制御

カテゴリ: 論文誌(論文単位)

グループ名: 【D】産業応用部門

発行日: 2020/11/01

タイトル(英語): Charge/Discharge Control of Wayside Batteries via Reinforcement Learning for Energy-Saving in Electrified Railway Systems

著者名: 吉田　賢央（千葉大学融合理工学府 地球環境科学専攻都市環境システムコース），荒井　幸代（千葉大学融合理工学府 地球環境科学専攻都市環境システムコース），小林　宏泰（早稲田大学先進理工学研究科 電気・情報生命専攻），近藤　圭一郎（早稲田大学先進理工学研究科 電気・情報生命専攻）

著者名(英語): Yasuhiro Yoshida (Department of Urban Environment Systems, Division of Earth and Environmental Sciences,Graduate School of Science and Engineering, Chiba University), Sachiyo Arai (Department of Urban Environment Systems, Division of Earth and Environmental Sciences,Graduate School of Science and Engineering, Chiba University), Hiroyasu Kobayashi (Department of Electrical Engineering and Bioscience, Faculty of Science and Engineering, Waseda University), Keiichiro Kondo (Department of Electrical Engineering and Bioscience, Faculty of Science and Engineering, Waseda University)

キーワード: 回生電力，電気鉄道，強化学習，充放電制御　　regenerative power，electrified railways，reinforcement learning，Charge/Discharge control

要約(英語): 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.

本誌: 電気学会論文誌D（産業応用部門誌） Vol.140 No.11 （2020）

本誌掲載ページ: 807-816 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejias/140/11/140_807/_article/-char/ja/