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

Translated title of the contribution: Charge/Discharge Control of Wayside Batteries via Reinforcement Learning for Energy-Saving in Electrified Railway Systems

Yasuhiro Yoshida, Sachiyo Arai, Hiroyasu Kobayashi, Keiichiro Kondo

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Translated title of the contributionCharge/Discharge Control of Wayside Batteries via Reinforcement Learning for Energy-Saving in Electrified Railway Systems
Original languageJapanese
Pages (from-to)807-816
Number of pages10
Journalieej transactions on industry applications
Volume140
Issue number11
DOIs
Publication statusPublished - 2020 Nov 1

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

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