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A data-driven evolutionary algorithm for wind farm layout optimization

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  • Long, Huan
  • Li, Peikun
  • Gu, Wei

Abstract

The wind farm layout model is to optimize the location of wind turbines to maximize the power output of the wind farm. Due to the complexity of the wind farm layout problem, the computation of objective function costs lots of time. To reduce the high computational cost while maintaining the solution performance, a data-driven evolutionary algorithm is proposed. An adaptive differential evolution algorithm (ADE) is proposed as the solver of the wind farm layout model. The adaption mechanism of ADE benefits the automatic adjustment of parameters in the mutation and crossover operators to achieve the optimal solution. The general regression neural network (GRNN) algorithm builds the data-driven surrogate model. The data-driven surrogate model is trained and updated using the data generated by the evolutionary algorithm throughout the evolution process. Through the data-driven surrogate model, the objective function is fast approximated and the bad candidate solutions are identified. The algorithm efficiency is greatly improved by fast filtering the bad candidate solutions. The ADE-GRNN is compared to other three conventional optimization methods based on two different wind scenarios. The results show the super-performance of ADE-GRNN in complex situations in terms of power output and execution time.

Suggested Citation

  • Long, Huan & Li, Peikun & Gu, Wei, 2020. "A data-driven evolutionary algorithm for wind farm layout optimization," Energy, Elsevier, vol. 208(C).
    • Handle: RePEc:eee:energy:v:208:y:2020:i:c:s0360544220314171
    DOI: 10.1016/j.energy.2020.118310
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    References listed on IDEAS

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    Cited by:

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    2. Houssem R. E. H. Bouchekara & Yusuf A. Sha’aban & Mohammad S. Shahriar & Makbul A. M. Ramli & Abdullahi A. Mas’ud, 2023. "Wind Farm Layout Optimization/Expansion with Real Wind Turbines Using a Multi-Objective EA Based on an Enhanced Inverted Generational Distance Metric Combined with the Two-Archive Algorithm 2," Sustainability, MDPI, vol. 15(3), pages 1-32, January.
    3. Song, Dongran & Shen, Xutao & Gao, Yang & Wang, Lei & Du, Xin & Xu, Zhiliang & Zhang, Zhihong & Huang, Chaoneng & Yang, Jian & Dong, Mi & Joo, Young Hoo, 2023. "Application of surrogate-assisted global optimization algorithm with dimension-reduction in power optimization of floating offshore wind farm," Applied Energy, Elsevier, vol. 351(C).
    4. Froese, Gabrielle & Ku, Shan Yu & Kheirabadi, Ali C. & Nagamune, Ryozo, 2022. "Optimal layout design of floating offshore wind farms," Renewable Energy, Elsevier, vol. 190(C), pages 94-102.
    5. Masoudi, Seiied Mohsen & Baneshi, Mehdi, 2022. "Layout optimization of a wind farm considering grids of various resolutions, wake effect, and realistic wind speed and wind direction data: A techno-economic assessment," Energy, Elsevier, vol. 244(PB).

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