書誌事項
- タイトル別名
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- Charge/Discharge Control of Wayside Batteries via Reinforcement Learning for Energy-Saving in Electrified Railway Systems
- デンキ テツドウ システム ノ ショウエネルギー ジツゲン ニ ムケタ キョウカ ガクシュウ ニ ヨル チジョウ チクデン ソウチ ノ ジュウホウデン セイギョ
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抄録
<p>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.</p>
収録刊行物
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- 電気学会論文誌D(産業応用部門誌)
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電気学会論文誌D(産業応用部門誌) 140 (11), 807-816, 2020-11-01
一般社団法人 電気学会
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詳細情報 詳細情報について
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- CRID
- 1391693801398472448
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- NII論文ID
- 130007934136
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- NII書誌ID
- AN10012320
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- ISSN
- 13488163
- 09136339
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- NDL書誌ID
- 030731880
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- 本文言語コード
- ja
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- データソース種別
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- JaLC
- NDL
- Crossref
- CiNii Articles
- KAKEN
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- 抄録ライセンスフラグ
- 使用不可