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dc.contributor.authorHågenvik, Hans Olaf
dc.contributor.authorMathisen, Siri Gulaker
dc.contributor.authorHelseth, Arild
dc.contributor.authorMo, Birger
dc.date.accessioned2022-07-21T10:21:01Z
dc.date.available2022-07-21T10:21:01Z
dc.date.created2022-07-19T11:25:23Z
dc.date.issued2022
dc.identifier.citation2022 17th International Conference on Probabilistic Methods Applied to Power Systems - PMAPSen_US
dc.identifier.isbn978-1-6654-1211-7
dc.identifier.urihttps://hdl.handle.net/11250/3007499
dc.description.abstractDue to a higher share of power production from renewable sources with high short-term variation, hydro systems must more often operate closer to their components' physical limits. To simulate system behaviour, a hydropower system simulator must therefore include most physical details. We present a simulator for hydropower investment analysis that combines a medium-term production planning model based on stochastic dual dynamic programming principles with a detailed and deterministic short-term hydro scheduling model. To reduce computation times, the system description for the short-term model may include only a snipped subset of the plants and reservoirs without deteriorating the results. The simulator is verified in a case study where an investment decision has been analysed for a Norwegian hydropower producer. The combination of medium-term optimization and short-term, detailed simulation is a useful decision support tool and provides both economic results and detailed physical information about the system behaviour.en_US
dc.description.abstractA Comprehensive Simulator for Hydropower Investment Decisionsen_US
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.relation.ispartof2022 17th International Conference on Probabilistic Methods Applied to Power Systems - PMAPS
dc.titleA Comprehensive Simulator for Hydropower Investment Decisionsen_US
dc.title.alternativeA Comprehensive Simulator for Hydropower Investment Decisionsen_US
dc.typeChapteren_US
dc.typePeer revieweden_US
dc.description.versionacceptedVersionen_US
dc.rights.holder© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_US
dc.identifier.cristin2038760
dc.relation.projectNorges forskningsråd: 257588en_US
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1


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