Probability Density Function Characterization for Aggregated Large-Scale Wind Power Based on Weibull Mixtures

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dc.contributor.authorG©mez-L©zaro, E.en_US
dc.contributor.authorBueso, M. C.en_US
dc.contributor.authorKessler, M.en_US
dc.contributor.authorMart©n-Mart©nez, S.en_US
dc.contributor.authorZhang, Jieen_US
dc.contributor.authorHodge, B. -Men_US
dc.contributor.authorMolina-Garc©a, A.en_US
dc.contributor.utdAuthorZhang, Jieen_US
dc.date.accessioned2016-09-26T19:20:07Z
dc.date.accessioned2019-09-25T16:47:36Z
dc.date.available2016-09-26T19:20:07Z
dc.date.available2019-09-25T16:47:36Z
dc.date.created2016-02-02en_US
dc.date.issued2016-02-02en_US
dc.description.abstractThe Weibull probability distribution has been widely applied to characterize wind speeds for wind energy resources. Wind power generation modeling is different, however, due in particular to power curve limitations, wind turbine control methods, and transmission system operation requirements. These differences are even greater for aggregated wind power generation in power systems with high wind penetration. Consequently, models based on one-Weibull component can provide poor characterizations for aggregated wind power generation. With this aim, the present paper focuses on discussing Weibull mixtures to characterize the probability density function (PDF) for aggregated wind power generation. PDFs of wind power data are firstly classified attending to hourly and seasonal patterns. The selection of the number of components in the mixture is analyzed through two well-known different criteria: the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). Finally, the optimal number of Weibull components for maximum likelihood is explored for the defined patterns, including the estimated weight, scale, and shape parameters. Results show that multi-Weibull models are more suitable to characterize aggregated wind power data due to the impact of distributed generation, variety of wind speed values and wind power curtailment.en_US
dc.description.sponsorshipThis work was supported by ”Ministerio de Economía y Competitividad” and the European Union —ENE2012-34603—, Fulbright/Spanish Ministry of Education Visiting Scholar —PRX14/00694—, and by the U.S. Department of Energy under Contract No. DE-AC36-08-GO28308 with the National Renewable Energy Laboratory.en_US
dc.identifier.bibliographicCitationGómez-Lázaro, E., M. C. Bueso, M. Kessler, S. Martín-Martínez, et al. 2016. "Probability density function characterization for aggregated large-scale wind power based on Weibull mixtures." Energies 9(2), doi:10.3390/en9020091.en_US
dc.identifier.issn1996-1073en_US
dc.identifier.issue2en_US
dc.identifier.urihttps://hdl.handle.net/10735.1/5072
dc.identifier.volume9en_US
dc.language.isoenen_US
dc.publisherMDPI Agen_US
dc.relation.urihttp://dx.doi.org/10.3390/en9020091
dc.rightsCC BY 4.0 (Attribution)en_US
dc.rights©2016 The Authorsen_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_US
dc.sourceEnergies
dc.subjectAkaike information criterionen_US
dc.subjectBayesian statistical decision theoryen_US
dc.subjectWeibull distributionen_US
dc.subjectWind poweren_US
dc.titleProbability Density Function Characterization for Aggregated Large-Scale Wind Power Based on Weibull Mixturesen_US
dc.type.genreArticleen_US

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