Assessment of Wind Energy Resources in Jordan Using Different Optimization Techniques

Abstract

Wind energy has become one of the world’s most renewable energy sources in recent years. It is regarded as a clean energy source because it produces no greenhouse gas emissions. The assessment of wind energy resources is an important step in the development of any wind energy conversion system (WECS). As a result, this article examines the wind energy potential of nine Jordanian wind locations: Queen Alia Airport, Civil Amman Airport, King Hussein Airport, Irbid, Mafraq, Ma’an, Ghor Al Safi, Safawi, and Irwaished. The available wind speed data were implemented using three statistical distribution models, Weibull, Rayleigh, and Gamma distributions, and one traditional estimation method, the Maximum Likelihood Method (MLM). Three optimization techniques were used to assign parameters to each distribution model: Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), and Whale Optimization Algorithm (WOA). To determine the optimal distribution model, the performance of these distribution models was tested. According to the findings, King Hussein Airport features the highest wind power density, followed by Queen Alia Airport, while Irbid features the lowest, followed by Ghor Al Safi.