An Adaptive Strategy-incorporated Integer Genetic Algorithm for Wind Farm Layout Optimization

Energy issues have always been one of the most significant concerns for scientists worldwide. With the ongoing over exploitation and continued outbreaks of wars, traditional energy sources face the threat of depletion. Wind energy is a readily available and sustainable energy source. Wind farm layout optimization problem, through scientifically arranging wind turbines, significantly enhances the efficiency of harnessing wind energy. Meta-heuristic algorithms have been widely employed in wind farm layout optimization. This paper introduces an Adaptive strategy-incorporated Integer Genetic Algorithm, referred to as AIGA, for optimizing wind farm layout problems. The adaptive strategy dynamically adjusts the placement of wind turbines, leading to a substantial improvement in energy utilization efficiency within the wind farm. In this study, AIGA is tested in four different wind conditions, alongside four other classical algorithms, to assess their energy conversion efficiency within the wind farm. Experimental results demonstrate a notable advantage of AIGA.