Utilizing Metaheuristics to Estimate Wind Energy Integration in Smart Grids With A Comparative Analysis of Ten Distributions

Abstract

Renewable energy presents the most favorable approach to address the escalating challenge of greenhouse gas emissions while simultaneously guaranteeing the safeguarding of the environment. This article utilizes ten different distributions to approximate the wind energy integration in smart grids. The employed distributions are Rayleigh, Poisson, Weibull, Normal, Gamma, Laplace, LogNormal, Nakagami, Birnbaum Saunders, and Burr. The parameters of each distribution are calculated based on metaheuristic methods such as particle swarm optimization and genetic algorithms. Six error criteria have been employed to evaluate the precision of introduced distributions and metaheuristic methods. The approximation is performed by utilizing the wind data collected over three years hourly in the Marmara region of Turkiye. The empirical findings indicate that Gamma, Burr, and Weibull distributions exhibit more significant superiority than the remaining distributions across all datasets.