ADAPTATION OF ANT COLONY OPTIMIZATION FOR WIND FARM LAYOUT OPTIMIZATION: METHODOLOGY AND THEORETICAL ANALYSIS
Keywords:
ACO, renewable energy, wind farm, wind turbine, WFLOAbstract
Wind Farm Layout Optimization (WFLO) is a complex renewable energy problem involving nonlinear, nonconvex objectives, where gradient-based methods often fail to find the global optimum due to numerous local optima. This study introduces a continuous Ant Colony Optimization (ACO) approach to address the WFLO problem by determining turbine layouts that maximize power output while considering wake effects, boundary constraints, and minimum spacing requirements. In contrast to conventional ACO methods that operate in discrete spaces, the proposed approach combines pheromone-guided region selection with the continuous generation of turbine coordinates. This continuous ACO approach enables direct optimization in continuous space, rather than at discrete locations, thereby enhancing flexibility and effectiveness in wind turbine layout design. The continuous ACO framework enables direct optimization of turbine locations in a continuous search space, offering near-infinite placement resolution and a stronger potential to mitigate wake interactions than discrete formulations. This study focuses on establishing the theoretical foundation for applying continuous ACO to the WFLO problem and identifies a promising direction for the future development of advanced optimization techniques in renewable energy systems.
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