{"product_id":"ieej-smf17050","title":"Model Predictive Control for Microgrid Power Management under Forecast Uncertainties","description":"\u003cp\u003e\u003cstrong\u003eカテゴリ: \u003c\/strong\u003e研究会(論文単位)\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003e論文No: \u003c\/strong\u003eSMF17050\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eグループ名: \u003c\/strong\u003e【D】産業応用部門 スマートファシリティ研究会\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003e発行日: \u003c\/strong\u003e2017\/11\/10\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eタイトル(英語): \u003c\/strong\u003eModel Predictive Control for Microgrid Power Management under Forecast Uncertainties\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003e著者名: \u003c\/strong\u003eMildt Dominik(Keio University),Cupelli Marco(RWTH Aachen),Kubo Ryogo(Keio University)\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003e著者名(英語): \u003c\/strong\u003eDominik Mildt(Keio University),Marco Cupelli(RWTH Aachen),Ryogo Kubo(Keio University)\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eキーワード: \u003c\/strong\u003eMicrogrids|Optimization|Model Predictive Control|Forecast Uncertainty|Demand Response|Microgrids|Optimization|Model Predictive Control|Forecast Uncertainty|Demand Response\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003e要約(日本語): \u003c\/strong\u003eThe allocation of energy resources in distribution grids into microgrids allows for feasible optimal control on a local scale. This paper presents and implements the active and reactive power management of a benchmark microgrid system that includes distributed generation, energy storages, thermo-electric coupling and demand response strategies using optimization for several control objectives, while taking into account operational and network constraints. To counteract forecast uncertainties of electric and thermal load, as well as solar and wind generation, a receding horizon approach based on the concept of Model Predictive Control (MPC) is used.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003e要約(英語): \u003c\/strong\u003eThe allocation of energy resources in distribution grids into microgrids allows for feasible optimal control on a local scale. This paper presents and implements the active and reactive power management of a benchmark microgrid system that includes distributed generation, energy storages, thermo-electric coupling and demand response strategies using optimization for several control objectives, while taking into account operational and network constraints. To counteract forecast uncertainties of electric and thermal load, as well as solar and wind generation, a receding horizon approach based on the concept of Model Predictive Control (MPC) is used.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003e原稿種別: \u003c\/strong\u003e英語\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003ePDFファイルサイズ: \u003c\/strong\u003e835 Kバイト\u003c\/p\u003e","brand":"IEEJ-PDF","offers":[{"title":"PDFダウンロード（一般価格330円\/会員価格220円） \/ A4 \/ 6","offer_id":46388659323119,"sku":"IEEJ-SMF17050-PDF","price":330.0,"currency_code":"JPY","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0718\/9512\/2159\/files\/IEEJ-PDF_bcde4b30-3f37-49eb-9ae6-a1393c2153b6.png?v=1744513655","url":"https:\/\/ieej.bookpark.ne.jp\/products\/ieej-smf17050","provider":"電気学会 電子図書館","version":"1.0","type":"link"}