Comparing wind generation spatial profiles: A circular data approach

dc.contributor.authorMartins, A.
dc.contributor.authorSousa, J.
dc.contributor.authorCarvalho, A.
dc.date.accessioned2022-05-03T08:36:06Z
dc.date.available2022-05-03T08:36:06Z
dc.date.issued2015
dc.date.updated2022-05-03T09:34:25Z
dc.description.abstractThe importance of wind power energy for energy and environmental policies has been growing in past recent years. However, because of its random nature over time, the wind generation cannot be reliable dispatched and perfectly forecasted, becoming a challenge when integrating this production in power systems. In addition the wind energy has to cope with the diversity of production resulting from alternative wind power profiles located in different regions. In 2012, Portugal presented a cumulative installed capacity distributed over 223 wind farms [1]. In this work the circular data statistical methods are used to analyze and compare alternative spatial wind generation profiles. Variables indicating extreme situations are analyzed. The hour(s) of the day where the farm production attains its maximum daily production is considered. This variable was converted into circular variable, and the use of circular statistics enables to identify the daily hour distribution for different wind production profiles. This methodology was applied to a real case, considering data from the Portuguese power system regarding the year 2012 with a 15-minutes interval. Six geographical locations were considered, representing different wind generation profiles in the Portuguese system.In this work the circular data statistical methods are used to analyze and compare alternative spatial wind generation profiles. Variables indicating extreme situations are analyzed. The hour(s) of the day where the farm production attains its maximum daily production is considered. This variable was converted into circular variable, and the use of circular statistics enables to identify the daily hour distribution for different wind production profiles. This methodology was applied to a real case, considering data from the Portuguese power system regarding the year 2012 with a 15-minutes interval. Six geographical locations were considered, representing different wind generation profiles in the Portuguese system.eng
dc.description.versioninfo:eu-repo/semantics/acceptedVersion
dc.event.date2015
dc.event.locationLisboaeng
dc.event.typeConferênciapt
dc.identifier.doi10.1109/EEM.2015.7216766
dc.identifier.isbn978-1-4673-6692-2
dc.identifier.urihttp://hdl.handle.net/10071/25220
dc.journalConference Proceedings:12th International Conference on the European Energy Market (EEM)
dc.language.isoeng
dc.peerreviewedyes
dc.publisherIEEE
dc.relationinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID%2FCEC%2F50021%2F2013/PT
dc.rightsopen access
dc.subjectRenewable generationeng
dc.subjectWind power generationeng
dc.subjectCircular statisticseng
dc.subject.fosDomínio/Área Científica::Ciências Naturais::Ciências Físicaspor
dc.titleComparing wind generation spatial profiles: A circular data approacheng
dc.typeconferenceObject
degois.publication.locationLisboaeng
degois.publication.titleComparing wind generation spatial profiles: A circular data approacheng
dspace.entity.typePublicationen
iscte.alternateIdentifiers.wosWOS:000380377200167
iscte.identifier.cienciahttps://ciencia.iscte-iul.pt/id/ci-pub-28345

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