Abstract
Wind speed and wind power generation are characterized by their inherent variability and uncertainty. To overcome this drawback, an accurate prediction of wind speed is essential. The purpose of this paper is to develop a hybrid Wavelet Neural Network model for wind speed forecasting and thus, in turn, for wind power generation. The combined optimal economic scheduling of the wind generators and conventional generators has also been investigated in this paper. Three solution techniques, viz., Primal Dual Interior Point, Differential Evolution and Bacterial Foraging algorithms have been employed for optimal scheduling and tested utilizing the IEEE 118 bus system. A realistic example case, Indian utility 66 bus system is also presented. Using the simulation results, the performance of the methods proposed are compared and analyzed.
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