Abstract
The development of wind power generation has rapidly progressed over the last decade. With the advancement in wind turbine technology, wind energy has become competitive with other fuel-based resources. The fluctuation of wind, however, makes it difficult to optimize the usage of wind power. The current practice ignores wind generation capacity in the unit commitment (UC), which discounts its usable capacity and may cause operational issues when the installation of wind generation equipment increases. To ensure system reliability, the forecasting uncertainty must be considered in the incorporation of wind power capacity into generation planning. This paper discusses the development of an artificial-neural-network-based wind power forecaster and the integration of wind forecast results into UC scheduling considering forecasting uncertainty by the probabilistic concept of confidence interval. The data from a wind farm located in Lawton City, OK, is used in this paper.
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