An Efficient Forecasting-Optimization Scheme for the Intraday Unit Commitment Process Under Significant Wind and Solar Power

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dc.contributor.authorCordova, Samuel
dc.contributor.authorRudnick, Hugh
dc.contributor.authorLorca Galvez, Álvaro Hugo
dc.contributor.authorMartinez Aranza, Victor Julio
dc.date.accessioned2022-05-18T14:39:50Z
dc.date.available2022-05-18T14:39:50Z
dc.date.issued2018
dc.description.abstractDue to their uncertain and variable nature, the large-scale integration of wind and solar power poses significant challenges to the generator scheduling process in power systems. To support this process, system operators require using repeatedly updated forecasts of the best possible quality for renewable power. Motivated by this, the present work aims to study the benefits of incorporating spatiotemporal dependence and seasonalities into probabilistic forecasts for the intraday unit commitment (UC) process. With this purpose, a highly efficient forecasting-optimization scheme is proposed, which is composed of a detrended periodic vector autoregressive model and a technology-clustered interval UC model. The proposed approach is tested on a 120-GW power system with 210 conventional generators using real wind and solar measurements and compared to existing deterministic and stochastic UC techniques alongside standard forecasting methods. Extensive computational experiments show that the incorporation of spatiotemporal dependence and seasonalities into forecasts translates in a reduction of up to 1.55% in operational costs for a daily UC relative to standard practice, the application of intraday instead of daily UC runs further reduces operational costs in up to 1.51%, and the proposed forecasting-optimization scheme takes less than 10 h to simulate a whole year.
dc.fuente.origenIEEE
dc.identifier.doi10.1109/TSTE.2018.2818979
dc.identifier.issn1949-3037
dc.identifier.urihttps://doi.org/10.1109/TSTE.2018.2818979
dc.identifier.urihttps://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8323248
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/64174
dc.information.autorucEscuela de ingeniería ; Cordova, Samuel ; S/I ; 193758
dc.information.autorucEscuela de ingeniería ; Rudnick, Hugh ; S/I ; 99167
dc.information.autorucEscuela de ingeniería ; Lorca Galvez, Álvaro Hugo ; S/I ; 148348
dc.information.autorucEscuela de ingeniería ; Martínez Aranza, Victor Julio ; S/I ; 181794
dc.issue.numero4
dc.language.isoen
dc.nota.accesoContenido parcial
dc.pagina.final1909
dc.pagina.inicio1899
dc.revistaIEEE Transactions on Sustainable Energy
dc.rightsacceso restringido
dc.subjectUncertainty
dc.subjectWind power generation
dc.subjectOptimization
dc.subjectForecasting
dc.subjectAutoregressive processes
dc.subjectSolar power generation
dc.subject.ddc620
dc.subject.deweyIngeniería
dc.titleAn Efficient Forecasting-Optimization Scheme for the Intraday Unit Commitment Process Under Significant Wind and Solar Poweres_ES
dc.typeartículo
dc.volumen9
sipa.codpersvinculados193758
sipa.codpersvinculados99167
sipa.codpersvinculados148348
sipa.codpersvinculados181794
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