Scheduling Method of Wind Power Generation for Electricity Market Using State-of-charge Transition and Forecast Error Journal of International Council on Electrical Engineering

Material type: 文書・図像類

Author: Aki, Kikuchiほか

Publisher: Taylor & Francis

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Note (General)：: This study focuses on the trade of power from wind power plants (WPPs) at an electricity market. To trade power, operators should produce generation s...

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Material Type: 文書・図像類
Title: Scheduling Method of Wind Power Generation for Electricity Market Using State-of-charge Transition and Forecast Error Journal of International Council on Electrical Engineering
Author/Editor: Aki, Kikuchi
Masakazu, Ito
Yasuhiro, Hayashi
Author Heading: Aki, Kikuchi
Masakazu, Ito
Yasuhiro, Hayashi
Publication, Distribution, etc.: Taylor & Francis
Periodical title: Journal of International Council on Electrical Engineering
No. or year of volume/issue: 9 1
Volume: 9
Issue: 1
Pages: 123-132
ISSN (Periodical Title): ISSN : 2234-8972
Text Language Code: eng
Subject Heading: Energy storage system
forecast error
generation scheduling
machine learning
state-of-charge transition
wind power
Target Audience: 一般
Note (General): This study focuses on the trade of power from wind power plants (WPPs) at an electricity market. To trade power, operators should produce generation schedules for bids and then supply the same amount of energy as the schedules. However, power from WPPs tends to fluctuate, which complicates scheduling. This paper proposes three scheduling methods with an energy storage system (ESS) to solve this problem. The first method considers state-of-charge (SOC) transition to maintain the appropriate SOC and minimize the imbalance between supplied and scheduled energy. The second and third methods consider SOC transition and forecast errors. The second method uses a linear regression model to estimate forecast errors. The third method adopts a bagged trees model, which is a machine learning method, to directly estimate the adjusted forecast data considering errors. Five patterns of the rated power of the ESS are assumed, and these three methods are simulated on each power. In comparison with the basic method, whose schedules are the same as the forecast, the third method can reduce 84% of the imbalance from the schedules when the rated power of the ESS is the minimum. The proposed methods help develop further correct and practical scheduling methods.
Source: https://u-fukui.repo.nii.ac.jp/?action=repository_action_common_download&item_id=24656&item_no=1&attribute_id=22&file_no=1
https://u-fukui.repo.nii.ac.jp/?action=repository_action_common_download&item_id=24656&item_no=1&attribute_id=22&file_no=1
Access Restrictions: 限定公開
Related Material (DOI): 10.1080/22348972.2020.1712018
Data Provider (Database): 国立情報学研究所 : 学術機関リポジトリデータベース（IRDB）（機関リポジトリ）
https://irdb.nii.ac.jp
Original Data Provider (Database): 福井大学 : 福井大学学術機関リポジトリ