Optimization of wind power and its variability with a computational intelligence approach

An optimization model is presented for maximizing the generation of wind power while minimizing its variability. In the optimization model, data-driven approaches are used to model the wind-power generation process based on industrial data. A new constraint is developed for governing the data-driven wind-power generation model based on physics and statistical process control theory. Since the wind-power model is nonparametric, computational intelligence algorithms are utilized to solve the optimization model. Computer experiments are designed to compare the performance of computational intelligence algorithms. The improvement in the generated wind power and its variability is demonstrated with the computational results.