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

Le Xie, Yingzhong Gu, Xinxin Zhu, Marc G. Genton

Research output: Contribution to journalArticlepeer-review

152 Scopus citations

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.
Original languageEnglish (US)
Pages (from-to)511-520
Number of pages10
JournalIEEE Transactions on Smart Grid
Volume5
Issue number1
DOIs
StatePublished - Jan 2014

ASJC Scopus subject areas

  • Computer Science(all)

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