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Multi-objective lightning search algorithm applied to wind farm layout optimization

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  • Moreno, Sinvaldo Rodrigues
  • Pierezan, Juliano
  • Coelho, Leandro dos Santos
  • Mariani, Viviana Cocco

Abstract

The wind farm layout design is an expansive and complex task involving a wide knowledge. Usually, attention is given on high energy efficiency, considering as active constraints the energy loss due to wake effect. Nevertheless, future advancements have identified that highly efficient and maximum power can be formulated as a multiobjective optimization problem. Thus in this study is proposed a novel multi-objective based on lightning search algorithm (MO-LSA) to design the wind farm layout more efficiently, considering to minimize three objectives including the cost of annual energy production, the overall wind farm’s area, and the wake effect’ losses. Also, to develop a realistic model are applied concepts of the convex hull, to provide an accurate assessment related to the overall land area. Different wind speed scenarios are evaluated with constant and variable wind direction. To compare the performance from MO-LSA three multi-objective optimization algorithms from the literature were used. In terms of results, the MO-LSA provided the best Pareto front for the scenarios analyzed regarding the metrics applied to evaluate them, also with well-distributed solutions along of all searching space, reflecting in alternative wind park layouts with better efficiency in terms of power output and investment costs.

Suggested Citation

  • Moreno, Sinvaldo Rodrigues & Pierezan, Juliano & Coelho, Leandro dos Santos & Mariani, Viviana Cocco, 2021. "Multi-objective lightning search algorithm applied to wind farm layout optimization," Energy, Elsevier, vol. 216(C).
    • Handle: RePEc:eee:energy:v:216:y:2021:i:c:s0360544220323215
    DOI: 10.1016/j.energy.2020.119214
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    Cited by:

    1. Cao, Lichao & Ge, Mingwei & Gao, Xiaoxia & Du, Bowen & Li, Baoliang & Huang, Zhi & Liu, Yongqian, 2022. "Wind farm layout optimization to minimize the wake induced turbulence effect on wind turbines," Applied Energy, Elsevier, vol. 323(C).
    2. Yu, Xiaobing & Lu, Yangchen, 2023. "Reinforcement learning-based multi-objective differential evolution for wind farm layout optimization," Energy, Elsevier, vol. 284(C).
    3. Moreno, Sinvaldo Rodrigues & Seman, Laio Oriel & Stefenon, Stefano Frizzo & Coelho, Leandro dos Santos & Mariani, Viviana Cocco, 2024. "Enhancing wind speed forecasting through synergy of machine learning, singular spectral analysis, and variational mode decomposition," Energy, Elsevier, vol. 292(C).
    4. He, Ruiyang & Sun, Haiying & Gao, Xiaoxia & Yang, Hongxing, 2022. "Wind tunnel tests for wind turbines: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 166(C).
    5. Ling, Ziyan & Zhao, Zhenzhou & Liu, Yige & Liu, Huiwen & Ali, Kashif & Liu, Yan & Wen, Yifan & Wang, Dingding & Li, Shijun & Su, Chunhao, 2024. "Multi-objective layout optimization for wind farms based on non-uniformly distributed turbulence and a new three-dimensional multiple wake model," Renewable Energy, Elsevier, vol. 227(C).
    6. 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).
    7. Dinçer, A.E. & Demir, A. & Yılmaz, K., 2024. "Multi-objective turbine allocation on a wind farm site," Applied Energy, Elsevier, vol. 355(C).

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