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dc.contributor.authorSeguin, Sara
dc.contributor.authorFleten, Stein-Erik
dc.contributor.authorCote, Pascal
dc.contributor.authorPichler, Alois
dc.contributor.authorAudet, Charles
dc.date.accessioned2017-12-14T09:20:35Z
dc.date.available2017-12-14T09:20:35Z
dc.date.created2017-01-13T14:51:20Z
dc.date.issued2016
dc.identifier.citationEuropean Journal of Operational Research. 2016, 259 (3), 1156-1168.nb_NO
dc.identifier.issn0377-2217
dc.identifier.urihttp://hdl.handle.net/11250/2471589
dc.description.abstractThis paper presents an optimization approach to solve the short-term hydropower unit commitment and loading problem with uncertain inflows. A scenario tree is built based on a forecasted fan of inflows, which is developed using the weather forecast and the historical weather realizations. The tree-building approach seeks to minimize the nested distance between the stochastic process of historical inflow data and the multistage stochastic process represented in the scenario tree. A two-phase multistage stochastic model is used to solve the problem. The proposed approach is tested on a 31 day rolling-horizon with daily forecasted inflows for three power plants situated in the province of Quebec, Canada, that belong to the company Rio Tinto.nb_NO
dc.language.isoengnb_NO
dc.publisherElseviernb_NO
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.no*
dc.titleStochastic short-term hydropower planning with inflow scenario treesnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionacceptedVersionnb_NO
dc.source.pagenumber1156-1168nb_NO
dc.source.volume259nb_NO
dc.source.journalEuropean Journal of Operational Researchnb_NO
dc.source.issue3nb_NO
dc.identifier.doi10.1016/j.ejor.2016.11.028
dc.identifier.cristin1426915
dc.relation.projectNorges forskningsråd: 209697nb_NO
dc.description.localcode© 2016. This is the authors’ accepted and refereed manuscript to the article. Locked until 18.11.2018 due to copyright restrictions. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/nb_NO
cristin.unitcode194,60,25,0
cristin.unitnameInstitutt for industriell økonomi og teknologiledelse
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode2


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Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal