Short-term spatio-temporal wind power forecast in robust look-ahead power system dispatch
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Applied Mathematics and Computational Science Program
Spatio-Temporal Statistics and Data Analysis Group
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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.
SponsorsThis 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.
JournalIEEE Transactions on Smart Grid