再生可能エネルギーの大量導入に伴う課題解決のための機械学習の活用

カテゴリ: 論文誌(論文単位)

グループ名: 【B】電力・エネルギー部門

発行日: 2022/06/01

タイトル(英語): Machine Learning Applications on Integration of Renewable Energy to Power Systems

著者名: 占部　千由（東京大学生産技術研究所），Joao Gari da Silva Fonseca Junior（東京大学生産技術研究所），竹内　知哉（東京大学生産技術研究所）

著者名(英語): Chiyori T. Urabe (Institute of Industrial Science, University of Tokyo), Joao Gari da Silva Fonseca Junior (Institute of Industrial Science, University of Tokyo), Tomoya Takeuchi (Institute of Industrial Science, University of Tokyo)

キーワード: 再生可能エネルギー，機械学習，リザバーコンピューティング，アンサンブル学習　　renewable energy，machine learning，reservoir computing，ensemble learning

要約(英語): Introduction of variable renewable energy systems (VRE), such as solar and wind power, into power systems is growing rapidly around the world. While this growth provides a step towards more sustainable societies, it also brings challenges. One important challenge regards the VREs output weather-related variability. If deployed in large scale, such variability can cause issues in the balancing of power demand and supply. To deal with this challenge on the power system side, one possibility is to increase the system's flexibility, which means the capability to deal with potential mismatches between VRE and demand. For example, conventional power generators could be operated in such a way to compensate partially for the VREs output variability. On the VRE side, forecasting, curtailment, and battery-coupled operation are often considered. Regardless of the measures employed, they typically require tackling complex problems using massive databases, and modeling natural phenomena, such as the weather. Such problems are particularly fit for machine learning (ML) techniques, and they have been the front-runner in research and applications related with the integration of VREs to power systems. In this report, we introduce examples of the latest ML techniques, and the most recent trends regarding their applications in VREs.

本誌: 電気学会論文誌B（電力・エネルギー部門誌） Vol.142 No.6 （2022）

本誌掲載ページ: 283-286 p

原稿種別: 解説／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejpes/142/6/142_283/_article/-char/ja/