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<p>This study examines the impact of grid cell size on Wind Farm Layout Optimization (WFLO). We optimized turbine location (solely) and location-yaw angle (jointly) for two wind farms—dense and sparse packed—across four examples, each with two grid scenarios: (1) grid cell dimensions equal to or larger than the minimum turbine spacing, and (2) refined grid cells smaller than the spacing with our proposed Inter-Distance Algorithm (IDA). Refined cells with IDA improved Annual Energy Production (AEP) by 2–21% and reduced computational costs by up to 99.6%, particularly in densely packed farms. We also evaluated nine recently introduced, less/never been applied and notably cited metaheuristic algorithms: Hunger Games Search (HGS), Harris&#xa0;Hawks Optimization, Slime Mould Algorithm, Equilibrium Optimizer, Sparrow Search Algorithm, Arithmetic Optimization Algorithm, Aquila Optimizer, RUN Beyond the Metaphor, and Reptile Search Algorithm—and, for benchmarking purposes, three classical baselines: Genetic Algorithm, Particle Swarm Optimization, and Differential Evolution. With diverse search topologies, these algorithms improved grid size reliability and enabled performance comparisons across varied wind farm examples and scenarios. HGS emerged as the most effective, achieving the highest AEP with 70% less computation time. This dual study highlights the role of grid refinement, IDA, and algorithm selection in WFLO, offering insights for wind farm design.</p>

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Exploring the effectiveness of cell size criteria and comparison of nine recently developed metaheuristic algorithms for wind farm layout optimization

  • 期刊论文

  • 发表日期：

    2025年11月8日

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  • Amir Semnani,
  • Guowu Yang,
  • Wenzhong Shen,
  • Ju Feng

摘要

This study examines the impact of grid cell size on Wind Farm Layout Optimization (WFLO). We optimized turbine location (solely) and location-yaw angle (jointly) for two wind farms—dense and sparse packed—across four examples, each with two grid scenarios: (1) grid cell dimensions equal to or larger than the minimum turbine spacing, and (2) refined grid cells smaller than the spacing with our proposed Inter-Distance Algorithm (IDA). Refined cells with IDA improved Annual Energy Production (AEP) by 2–21% and reduced computational costs by up to 99.6%, particularly in densely packed farms. We also evaluated nine recently introduced, less/never been applied and notably cited metaheuristic algorithms: Hunger Games Search (HGS), Harris Hawks Optimization, Slime Mould Algorithm, Equilibrium Optimizer, Sparrow Search Algorithm, Arithmetic Optimization Algorithm, Aquila Optimizer, RUN Beyond the Metaphor, and Reptile Search Algorithm—and, for benchmarking purposes, three classical baselines: Genetic Algorithm, Particle Swarm Optimization, and Differential Evolution. With diverse search topologies, these algorithms improved grid size reliability and enabled performance comparisons across varied wind farm examples and scenarios. HGS emerged as the most effective, achieving the highest AEP with 70% less computation time. This dual study highlights the role of grid refinement, IDA, and algorithm selection in WFLO, offering insights for wind farm design.