Probabilistic bootstrap-based evolutionary algorithm for three-objective wind farm turbine position optimization

Abstract

Amid the worsening energy crisis, wind farm layout optimization (WFLO) to increase power generation, reduce costs, and mitigate potential environmental impacts is of great significance. This paper formulates three-objective wind farm layout optimization (TWFLO) which is rarely considered, aiming to effectively utilize existing information to optimize power output, land usage, and costs. We propose a new algorithm (MOEA/D-P) based on probability distributions to guide turbine placement and improve the performance of a Multiobjective Evolutionary Algorithm Based on Decomposition (MOEA/D). This algorithm addresses the issue of neglecting valuable information during layout optimization. Additionally, we make improvements to the traditional land-use function to avoid situations where non-convex layouts result in an area calculation of zero. The MOEA/D-P is tested on six different initial layouts and compared with five algorithms under two wind conditions. Results are evaluated using inverted generational distance, hypervolume, and scatter plot distributions. The impact of initial probability distribution on algorithm performance is discussed under four simple wind conditions. The results show that MOEA/D-P outperforms the other five algorithms in terms of performance.