Short-term spatio-temporal wind power forecast in robust look-ahead power system dispatch

Handle URI:
http://hdl.handle.net/10754/563308
Title:
Short-term spatio-temporal wind power forecast in robust look-ahead power system dispatch
Authors:
Xie, Le; Gu, Yingzhong; Zhu, Xinxin; Genton, Marc G. ( 0000-0001-6467-2998 )
Abstract:
We 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.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Applied Mathematics and Computational Science Program; Spatio-Temporal Statistics and Data Analysis Group
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
IEEE Transactions on Smart Grid
Issue Date:
Jan-2014
DOI:
10.1109/TSG.2013.2282300
Type:
Article
ISSN:
19493053
Sponsors:
This 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.
Appears in Collections:
Articles; Applied Mathematics and Computational Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorXie, Leen
dc.contributor.authorGu, Yingzhongen
dc.contributor.authorZhu, Xinxinen
dc.contributor.authorGenton, Marc G.en
dc.date.accessioned2015-08-03T11:45:23Zen
dc.date.available2015-08-03T11:45:23Zen
dc.date.issued2014-01en
dc.identifier.issn19493053en
dc.identifier.doi10.1109/TSG.2013.2282300en
dc.identifier.urihttp://hdl.handle.net/10754/563308en
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.en
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.en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.subjectData-driven forecasten
dc.subjectlook-ahead dispatchen
dc.subjectspatio-temporal statisticsen
dc.subjectwind generationen
dc.titleShort-term spatio-temporal wind power forecast in robust look-ahead power system dispatchen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentApplied Mathematics and Computational Science Programen
dc.contributor.departmentSpatio-Temporal Statistics and Data Analysis Groupen
dc.identifier.journalIEEE Transactions on Smart Griden
dc.contributor.institutionDepartment of Electrical and Computer Engineering, Texas A and M University, College Station, TX 77843, United Statesen
dc.contributor.institutionDepartment of Statistics, Texas A and M University, College Station, TX 77843, United Statesen
kaust.authorGenton, Marc G.en
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