期刊
IEEE TRANSACTIONS ON SMART GRID
卷 5, 期 1, 页码 511-520出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSG.2013.2282300
关键词
Data-driven forecast; look-ahead dispatch; spatio-temporal statistics; wind generation
资金
- Power Systems Engineering Research Center
- NSF [ECCS-1150944]
- KAUST-IAMCS
- Directorate For Engineering
- Div Of Electrical, Commun & Cyber Sys [1150944] Funding Source: National Science Foundation
- Division Of Mathematical Sciences
- Direct For Mathematical & Physical Scien [1007504] Funding Source: National Science Foundation
- Div Of Industrial Innovation & Partnersh
- Directorate For Engineering [0968810] Funding Source: National Science Foundation
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.
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