4.7 Article

On the design of potential turbine positions for physics-informed optimization of wind farm layout

Journal

RENEWABLE ENERGY
Volume 164, Issue -, Pages 1108-1120

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.renene.2020.10.060

Keywords

Wind farm layout optimization; Physics-informed; Potential turbine positions; Jensen wake model; Genetic algorithm

Funding

  1. NSFC Basic Science Center Program for Multiscale Problems in Nonlinear Mechanics [11988102]

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Wind farm layout optimization is crucial in wind energy project design, with physical understanding and different mesh approaches used to determine optimal turbine positions. Staggered arrangement is more efficient in extracting energy from wind compared to aligned arrangement. Different mesh approaches show varying performance under different wind conditions, and the sunflower method generally performs well overall.
Wind farm layout optimization is a critical step in the design of a wind energy project. In the literature, the potential turbine positions employed in the layout optimization are often obtained by discretizing the field using a Cartesian mesh. In this work, physical understanding is proposed to incorporate in the design of potential turbine positions. Specifically, the known knowledge, that a staggered arrangement is more efficient for extracting energy from wind than an aligned arrangement, is employed and implemented using the staggered mesh approach, the unstructured mesh approach and the sunflower mesh approach. Different mesh approaches are tested using two cases, i.e. case I, unidirectional uniform wind, and case II, uniform wind with variable wind direction. The optimal layout obtained from the staggered mesh approach performs the best for case I. For case II, the farm performance from different layouts is similar. The performance of the layouts under off-design conditions is also tested for case I. For all considered cases, the optimal layout obtained from the sunflower approach shows an overall good performance. (c) 2020 Elsevier Ltd. All rights reserved.

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