A Double-Stage Optimization Approach for Wind Farm Layout Optimization

Wind farm layout optimization (WFLO) plays a key role in reducing wake effect energy losses and increasing annual energy production (AEP). This paper proposes a double-stage optimization approach that incorporates staggered grid-based optimization with coordinate-based local optimization. In the first stage, staggered grid-based optimization is performed to determine optimal turbine locations within predefined grid boundaries. In the second stage, turbine positions are locally optimized within bounded regions to improve AEP efficiently without extending the search across the entire wind farm. The modified electric charged particle optimization (MECPO) algorithm is applied to evaluate five optimization approaches, including two double-stage and three single-stage approaches. The framework is tested on a wind farm covering an area of 2000 m by 2000 m with 20 turbines under single-direction, uniform multi-directional, and spatially varying wind conditions. The proposed double-stage optimization approach achieves comparable or improved net AEP while significantly reducing computational cost across different wind conditions. The method provides up to 0.36% improvement in net AEP, reduces wake losses by up to 6.84%, and decreases computational time by up to 90% compared with the coordinate-based approach. These results confirm that the proposed approach significantly enhances computational efficiency while maintaining comparable energy performance. The findings indicate that integrating staggered grid-based optimization with coordinate-based local optimization provides an effective balance between solution quality and computational efficiency, offering a practical and scalable approach for WFLO.