Methodology for optimal wind farm location and internal layout optimization using PSO with queue swarm.
Wind Energy; PSO Metaheuristic; Queue Swarm; Site Prospecting; Optimization.
The search for efficient solutions in the prospection of areas for wind power projects drives the development of new computational approaches. This work presents an innovative methodology based on a variation of the Particle Swarm Optimization (PSO) algorithm, named “queue swarm”. This approach was designed to optimally identify the most suitable regions for installing wind farms across large geographic areas, addressing the needs of Eletrobras. The methodology uses wind power density data derived from atlases generated in WindPro, processed and integrated in QGIS, in addition to layers of environmental and social constraints. The “queue swarm” algorithm organizes wind turbines into structured formations that evolve iteratively toward the most promising areas, while respecting technical criteria such as minimum spacing and exclusion zones. As a distinguishing feature, the methodology also includes the complementary optimization of the internal turbine layout, by adjusting the positioning of the rows to enhance the local wind resource utilization. The codes implemented in Scilab integrate PSO principles with this adaptation to improve the efficiency and accuracy of the prospection process. The expected results include the identification of high technical and economic feasibility sites, promoting more effective allocation of resources and renewable energy generation. The study stands out for its practical application and its potential to deliver significant gains for the wind energy sector.