An analysis of unconventional grid sizes for wind farm layout optimization using a genetic algorithm

Wind energy has emerged as an effective alternative to fossil fuels. At a commercial level, wind energy is harnessed through wind farms, which consist of several wind turbines arranged in a specific layout. Identifying the optimal layout of a wind farm is vital for maximum energy absorption from wind. Due to the sheer complexity of the problem, evolutionary and swarm intelligence algorithms have been employed for wind farm layout optimization. The performance of the optimal layout design is governed by several physical parameters, such as the wind speed, wind direction, and grid size. Most existing studies have focused on the effects of wind speed and wind direction on layout optimization. However, limited attention has been given to studying the impact of grid size. This study analyzes the effect of various unconventional grid sizes of higher dimension, ranging from 16×16, 17×17, up to 20×20, while using the genetic algorithm as the test bench. For a more comprehensive comparison, results of 10×10 to 15×15 were adopted from an earlier study. In contrast to previous studies, which focused on the assessment of the quality of results, a novel aspect of the present study also considers execution time as a performance measure. Another novel aspect is to analyze the performance of the generic algorithm for different grid sizes. Furthermore, unlike past studies, which mostly used hypothetical data, the present study employs real data from a potential site in Saudi Arabia. Results indicate that the maximum average conversion efficiency of 0.992 was obtained with a grid size of 19×19, while a grid size of 10×10 produced the minimum average conversion efficiency of 0.813. These results signify that an increase in the grid size positively affects conversion efficiency, which is used as a measure of the quality of the solution. However, the change in grid size has a negligible impact on the run time of the genetic algorithm. The findings of the study potentially pave the way for wind farm developers to select the best grid size for a given site while considering several practical issues, such as maintenance and operational costs, demographic and topographic structures, among other factors.