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Development of a Dynamic Operational Scheduling Algorithm for an Independent Micro-Grid with Renewable Energy
https://kitami-it.repo.nii.ac.jp/records/7469
https://kitami-it.repo.nii.ac.jp/records/746913aae283-f4e4-4e19-a27d-d4e9114a8666
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
03_11obara.pdf (455.0 kB)
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| Item type | 学術雑誌論文 / Journal Article(1) | |||||
|---|---|---|---|---|---|---|
| 公開日 | 2010-04-08 | |||||
| タイトル | ||||||
| タイトル | Development of a Dynamic Operational Scheduling Algorithm for an Independent Micro-Grid with Renewable Energy | |||||
| 言語 | en | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | Micro-Grid | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | Operation Planning | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | Energy Storage | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | Renewable Energy | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | Neural Network | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | Weather Prediction | |||||
| 資源タイプ | ||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||
| 資源タイプ | journal article | |||||
| アクセス権 | ||||||
| アクセス権 | open access | |||||
| アクセス権URI | http://purl.org/coar/access_right/c_abf2 | |||||
| 著者 |
OBARA, Shinya
× OBARA, Shinya |
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| 著者別名 | ||||||
| 識別子Scheme | WEKO | |||||
| 識別子 | 44843 | |||||
| 識別子Scheme | KAKEN | |||||
| 識別子URI | https://nrid.nii.ac.jp/ja/nrid/1000010342437 | |||||
| 識別子 | 10342437 | |||||
| 姓名 | 小原, 伸哉 | |||||
| 言語 | ja | |||||
| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | A micro-grid with the capacity for sustainable energy is expected to be a distributed energy system that exhibits quite a small environmental impact. In an independent micro-grid, "green energy," which is typically thought of as unstable, can be utilized effectively by introducing a battery. In the past study, the production-of-electricity prediction algorithm (PAS) of the solar cell was developed. In PAS, a layered neural network is made to learn based on past weather data and the operation plan of the compound system of a solar cell and other energy systems was examined using this prediction algorithm. In this paper, a dynamic operational scheduling algorithm is developed using a neural network (PAS) and a genetic algorithm (GA) to provide predictions for solar cell power output. We also do a case study analysis in which we use this algorithm to plan the operation of a system that connects nine houses in Sapporo to a micro-grid composed of power equipment and a polycrystalline silicon solar cell. In this work, the relationship between the accuracy of output prediction of the solar cell and the operation plan of the micro-grid was clarified. Moreover, we found that operating the micro-grid according to the plan derived with PAS was far superior, in terms of equipment hours of operation, to that using past average weather data. | |||||
| 書誌情報 |
Journal of Thermal Science and Technology 巻 3, 号 3, p. 474-485, 発行日 2008 |
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| DOI | ||||||
| 識別子タイプ | DOI | |||||
| 関連識別子 | http://doi.org/10.1299/jtst.3.474 | |||||
| フォーマット | ||||||
| 内容記述タイプ | Other | |||||
| 内容記述 | application/pdf | |||||
| 出版者 | ||||||
| 出版者 | 日本機械学会 | |||||
| 著者版フラグ | ||||||
| 値 | publisher | |||||
| 出版タイプ | ||||||
| 出版タイプ | VoR | |||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||
