Wavelet Neural Network-based Wind-power Forecasting in Economic Dispatch: A Differential Evolution, Bacterial Foraging Technology, and Primal Dual-interior Point Approach

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. The solution methods, namely primal dual interior point, differential evolution, and bacterial foraging technology, are used for solving the wind-thermal economic dispatch (ED). The feasibility of the proposed algorithms is demonstrated on three-unit, 13-unit, and 40-unit systems and their performances are compared in terms of the generation cost and execution time. The results show that the proposed algorithms are indeed capable of handling ED problems.