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Optimizing the layout of onshore wind farms to minimize noise

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  • Wu, Xiawei
  • Hu, Weihao
  • Huang, Qi
  • Chen, Cong
  • Jacobson, Mark Z.
  • Chen, Zhe

Abstract

As wind farm numbers and areas increase worldwide, it has become increasingly important to examine the impact of wind energy on the surrounding environment. One effect in some scenarios is noise, which depends on the type and age of the wind turbines and the distances between them and the residential buildings. Previous research on wind farm layout optimization has been generally aimed at achieving the minimum investment cost or maximum captured energy. This approach does not entirely align with minimizing noise. This paper focuses on an optimal layout for a wind farm considering its noise, without sacrificing power production. By optimizing the wind farm layout, the minimum noise is set as the basic objective, and both the wake effect and distances among wind turbines are considered. The basic particle swarm optimization algorithm and its evolutionary version are adopted and compared for better performance of calculation cost. Two strategies are presented to address the problems in various scenarios and to demonstrate the applicability of the proposed method and its effectiveness in designing layouts that minimize noise. Compared to a reference layout, a stringent noise control strategy could reduce the noise by 11%, even if minor, and increase the power production by 3.1%. A flexible strategy could reduce the noise by 5.7% and increase the power production by 3.1%.

Suggested Citation

  • Wu, Xiawei & Hu, Weihao & Huang, Qi & Chen, Cong & Jacobson, Mark Z. & Chen, Zhe, 2020. "Optimizing the layout of onshore wind farms to minimize noise," Applied Energy, Elsevier, vol. 267(C).
    • Handle: RePEc:eee:appene:v:267:y:2020:i:c:s0306261920304086
    DOI: 10.1016/j.apenergy.2020.114896
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    4. Glenk, Gunther & Meier, Rebecca & Reichelstein, Stefan, 2021. "Cost dynamics of clean energy technologies," ZEW Discussion Papers 21-054, ZEW - Leibniz Centre for European Economic Research.
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    7. Li, Sichen & Hu, Weihao & Cao, Di & Chen, Zhe & Huang, Qi & Blaabjerg, Frede & Liao, Kaiji, 2023. "Physics-model-free heat-electricity energy management of multiple microgrids based on surrogate model-enabled multi-agent deep reinforcement learning," Applied Energy, Elsevier, vol. 346(C).
    8. Cao, Jiufa & Nyborg, Camilla Marie & Feng, Ju & Hansen, Kurt S. & Bertagnolio, Franck & Fischer, Andreas & Sørensen, Thomas & Shen, Wen Zhong, 2022. "A new multi-fidelity flow-acoustics simulation framework for wind farm application," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
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    10. 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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