Bulk power system availability assessment with multiple wind power plants

dc.contributor.authorAngie C. Cepeda
dc.contributor.authorMario A. Ríos
dc.coverage.spatialBolivia
dc.date.accessioned2026-03-22T14:20:13Z
dc.date.available2026-03-22T14:20:13Z
dc.date.issued2020
dc.descriptionCitaciones: 12
dc.description.abstractThe use of renewable non-conventional energy sources, as wind electric power energy and photovoltaic solar energy, has introduced uncertainties in the performance of bulk power systems. The power system availability has been employed as a useful tool for planning power systems; however, traditional methodologies model generation units as a component with two states: in service or out of service. Nevertheless, this model is not useful to model wind power plants for availability assessment of the power system. This paper used a statistical representation to model the uncertainty of power injection of wind power plants based on the central moments: mean value, variance, skewness and kurtosis. In addition, this paper proposed an availability assessment methodology based on application of this statistical model, and based on the 2m+1 point estimate method the availability assessment is performed. The methodology was tested on the IEEE-RTS assuming the connection of two wind power plants and different correlation among the behavior of these plants.
dc.identifier.doi10.11591/ijece.v11i1.pp27-36
dc.identifier.urihttps://doi.org/10.11591/ijece.v11i1.pp27-36
dc.identifier.urihttps://andeanlibrary.org/handle/123456789/45919
dc.language.isoen
dc.publisherInstitute of Advanced Engineering and Science (IAES)
dc.relation.ispartofInternational Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering
dc.sourceUniversidad de Los Andes
dc.subjectWind power
dc.subjectRenewable energy
dc.subjectElectric power system
dc.subjectKurtosis
dc.subjectComputer science
dc.subjectReliability engineering
dc.subjectPhotovoltaic system
dc.subjectPower (physics)
dc.subjectEnvironmental science
dc.subjectAutomotive engineering
dc.titleBulk power system availability assessment with multiple wind power plants
dc.typearticle

Files