Please use this identifier to cite or link to this item: http://repositorio.inesctec.pt/handle/123456789/3416
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dc.contributor.authorHrvoje Kekoen
dc.contributor.authorLeonel Magalhães Carvalhoen
dc.contributor.authorRicardo Jorge Bessaen
dc.contributor.authorDiego Issicabaen
dc.contributor.authorJianhui Wangen
dc.contributor.authorAudun Botteruden
dc.contributor.authorVladimiro Mirandaen
dc.contributor.authorJean Sumailien
dc.date.accessioned2017-11-17T12:48:04Z-
dc.date.available2017-11-17T12:48:04Z-
dc.date.issued2011en
dc.identifier.urihttp://repositorio.inesctec.pt/handle/123456789/3416-
dc.description.abstractIn this paper, we investigate the representation of wind power forecasting (WPF) uncertainty in the unit commitment (UC) problem. While deterministic approaches use a point forecast of wind power output, WPF uncertainty in the stochastic UC alternative is captured by a number of scenarios that include cross-temporal dependency. A comparison among a diversity of UC strategies (based on a set of realistic experiments) is presented. The results indicate that representing WPF uncertainty with wind power scenarios that rely on stochastic UC has advantages over deterministic approaches that mimic the classical models. Moreover, the stochastic model provides a rational and adaptive way to provide adequate spinning reserves at every hour, as opposed to increasing reserves to predefined, fixed margins that cannot account either for the system's costs or its assumed risks.en
dc.languageengen
dc.relation5164en
dc.relation4811en
dc.relation4882en
dc.relation4971en
dc.relation5073en
dc.relation208en
dc.rightsinfo:eu-repo/semantics/embargoedAccessen
dc.titleWind Power Forecasting Uncertainty and Unit Commitmenten
dc.typearticleen
dc.typePublicationen
Appears in Collections:CPES - Articles in International Journals

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