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: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Summary form only given. 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.
Original languageEnglish (US)
Title of host publication2016 IEEE Power and Energy Society General Meeting (PESGM)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISBN (Print)9781509041688
DOIs
StatePublished - Jul 2016

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01

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