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dc.contributor.authorXie, Le
dc.contributor.authorGu, Yingzhong
dc.contributor.authorZhu, Xinxin
dc.contributor.authorGenton, Marc G.
dc.date.accessioned2015-08-03T11:45:23Z
dc.date.available2015-08-03T11:45:23Z
dc.date.issued2014-01
dc.identifier.citationXie, L., Gu, Y., Zhu, X., & Genton, M. G. (2014). Short-Term Spatio-Temporal Wind Power Forecast in Robust Look-ahead Power System Dispatch. IEEE Transactions on Smart Grid, 5(1), 511–520. doi:10.1109/tsg.2013.2282300
dc.identifier.issn19493053
dc.identifier.doi10.1109/TSG.2013.2282300
dc.identifier.urihttp://hdl.handle.net/10754/563308
dc.description.abstractWe propose a novel statistical wind power forecast framework, which leverages the spatio-temporal correlation in wind speed and direction data among geographically dispersed wind farms. Critical assessment of the performance of spatio-temporal wind power forecast is performed using realistic wind farm data from West Texas. It is shown that spatio-temporal wind forecast models are numerically efficient approaches to improving forecast quality. By reducing uncertainties in near-term wind power forecasts, the overall cost benefits on system dispatch can be quantified. We integrate the improved forecast with an advanced robust look-ahead dispatch framework. This integrated forecast and economic dispatch framework is tested in a modified IEEE RTS 24-bus system. Numerical simulation suggests that the overall generation cost can be reduced by up to 6% using a robust look-ahead dispatch coupled with spatio-temporal wind forecast as compared with persistent wind forecast models. © 2013 IEEE.
dc.description.sponsorshipThis work is supported in part by Power Systems Engineering Research Center, in part by NSF ECCS-1150944, and in part by KAUST-IAMCS Innovation Award. L. Xie and Y. Gu contributed equally to this work. Date of publication September 30, 2013; date of current version December 24, 2013. Paper no. TSG-00222-2013.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.subjectData-driven forecast
dc.subjectlook-ahead dispatch
dc.subjectspatio-temporal statistics
dc.subjectwind generation
dc.titleShort-term spatio-temporal wind power forecast in robust look-ahead power system dispatch
dc.typeArticle
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentSpatio-Temporal Statistics and Data Analysis Group
dc.contributor.departmentStatistics Program
dc.identifier.journalIEEE Transactions on Smart Grid
dc.contributor.institutionDepartment of Electrical and Computer Engineering, Texas A and M University, College Station, TX 77843, United States
dc.contributor.institutionDepartment of Statistics, Texas A and M University, College Station, TX 77843, United States
kaust.personGenton, Marc G.


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