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

Yasuhiro Yoshida*, Sachiyo Arai, Hiroyasu Kobayashi, Keiichiro Kondo

*この研究の対応する著者

研究成果: Article査読

抄録

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.

寄稿の翻訳タイトルCharge/Discharge Control of Wayside Batteries via Reinforcement Learning for Energy-Saving in Electrified Railway Systems
本文言語Japanese
ページ(範囲)807-816
ページ数10
ジャーナルieej transactions on industry applications
140
11
DOI
出版ステータスPublished - 2020 11 1

Keywords

  • Charge/Discharge control
  • Electrified railways
  • Regenerative power
  • Reinforcement learning

ASJC Scopus subject areas

  • 産業および生産工学
  • 電子工学および電気工学

フィンガープリント

「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」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル