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<p>Wind energy has become a prominent alternative to fossil fuels, offering a sustainable solution for reducing the impacts of climate change in electricity production. Wind farm layouts must be optimized to maximize power output and minimize turbine wake effects, driving project profitability. This paper presents a comprehensive comparative analysis of two nature-inspired metaheuristic algorithms those are Symbiotic Organisms Search (SOS) and Sparrow Search Algorithm (SpSA) for optimizing wind turbine placement. Using identical problem data and constraints for fair comparison, these algorithms are evaluated against the traditional Particle Swarm Optimization (PSO) approach. The optimization framework simultaneously considers three key objectives: maximizing annual energy production (AEP), improving the wind farm's capacity factor, and minimizing wake effects between turbines. The study incorporates realistic constraints including terrain characteristics, wind resource variations, and minimum inter-turbine spacing requirements. Results demonstrate that the proposed algorithms achieve superior performance compared to PSO, with SOS showing 0.92% higher AEP, 0.3% improved capacity factor, and SpSA achieved a 0.8% increase in AEP and a 0.26% improvement in CF compared to PSO. The newly implemented algorithms also exhibited enhanced convergence characteristics and computational efficiency, demonstrating their potential for practical applications in wind farm design optimization.</p>

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Optimal Turbine Placement in Wind Power Plants Using Search Algorithms

  • 期刊论文

  • 发表日期：

    2025年2月26日

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  • Khoa Dang Nguyen,
  • Tinh Trung Tran,
  • Dieu Ngoc Vo

摘要

Wind energy has become a prominent alternative to fossil fuels, offering a sustainable solution for reducing the impacts of climate change in electricity production. Wind farm layouts must be optimized to maximize power output and minimize turbine wake effects, driving project profitability. This paper presents a comprehensive comparative analysis of two nature-inspired metaheuristic algorithms those are Symbiotic Organisms Search (SOS) and Sparrow Search Algorithm (SpSA) for optimizing wind turbine placement. Using identical problem data and constraints for fair comparison, these algorithms are evaluated against the traditional Particle Swarm Optimization (PSO) approach. The optimization framework simultaneously considers three key objectives: maximizing annual energy production (AEP), improving the wind farm's capacity factor, and minimizing wake effects between turbines. The study incorporates realistic constraints including terrain characteristics, wind resource variations, and minimum inter-turbine spacing requirements. Results demonstrate that the proposed algorithms achieve superior performance compared to PSO, with SOS showing 0.92% higher AEP, 0.3% improved capacity factor, and SpSA achieved a 0.8% increase in AEP and a 0.26% improvement in CF compared to PSO. The newly implemented algorithms also exhibited enhanced convergence characteristics and computational efficiency, demonstrating their potential for practical applications in wind farm design optimization.