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Trading off sound pressure level and average power production for wind farm layout optimization

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  • Tingey, Eric B.
  • Ning, Andrew

Abstract

This research explores the trade-offs between a wind farm's average power production and noise impact on nearby observers. Two specific wind farm designs were studied and optimized using the FLORIS wake model and an acoustic model based on semi-empirical turbine noise calculations. It was found in the two wind farms that the average power production could be increased, up to 8.01% in one and 3.63% in the other, ignoring sound level considerations. Including a noise restriction in the optimization had a minimal impact on the optimal average power production within about a five-decibel range. Past this range, sound limitations decreased the wind farm's power production significantly. By analyzing average power production and sound pressure level together, we can take advantage of the multi-modality of the optimization to find solutions were noise impact can be improved with an insignificant effect on power production.

Suggested Citation

  • Tingey, Eric B. & Ning, Andrew, 2017. "Trading off sound pressure level and average power production for wind farm layout optimization," Renewable Energy, Elsevier, vol. 114(PB), pages 547-555.
    • Handle: RePEc:eee:renene:v:114:y:2017:i:pb:p:547-555
    DOI: 10.1016/j.renene.2017.07.057
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Kyoungboo Yang & Kyungho Cho, 2019. "Simulated Annealing Algorithm for Wind Farm Layout Optimization: A Benchmark Study," Energies, MDPI, vol. 12(23), pages 1-15, November.
    2. Cao, Jiu Fa & Zhu, Wei Jun & Shen, Wen Zhong & Sørensen, Jens Nørkær & Sun, Zhen Ye, 2020. "Optimizing wind energy conversion efficiency with respect to noise: A study on multi-criteria wind farm layout design," Renewable Energy, Elsevier, vol. 159(C), pages 468-485.
    3. Azlan, F. & Kurnia, J.C. & Tan, B.T. & Ismadi, M.-Z., 2021. "Review on optimisation methods of wind farm array under three classical wind condition problems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    4. Yang, Kyoungboo & Kwak, Gyeongil & Cho, Kyungho & Huh, Jongchul, 2019. "Wind farm layout optimization for wake effect uniformity," Energy, Elsevier, vol. 183(C), pages 983-995.
    5. 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.
    6. 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).
    7. 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).
    8. Dinçer, A.E. & Demir, A. & Yılmaz, K., 2024. "Multi-objective turbine allocation on a wind farm site," Applied Energy, Elsevier, vol. 355(C).
    9. Shen, Wen Zhong & Zhu, Wei Jun & Barlas, Emre & Li, Ye, 2019. "Advanced flow and noise simulation method for wind farm assessment in complex terrain," Renewable Energy, Elsevier, vol. 143(C), pages 1812-1825.
    10. Han, Shuang & Qiao, Yanhui & Yan, Ping & Yan, Jie & Liu, Yongqian & Li, Li, 2020. "Wind turbine power curve modeling based on interval extreme probability density for the integration of renewable energies and electric vehicles," Renewable Energy, Elsevier, vol. 157(C), pages 190-203.

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