IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v143y2019icp1812-1825.html
   My bibliography  Save this article

Advanced flow and noise simulation method for wind farm assessment in complex terrain

Author

Listed:
  • Shen, Wen Zhong
  • Zhu, Wei Jun
  • Barlas, Emre
  • Li, Ye

Abstract

A new wind farm during its development phase requires a technical evaluation of its annual power prediction and noise issue in the area in and around the wind farm. In this paper, for the first time, an advanced numerical method for wind farm assessment is developed, which consists of a Reynolds averaged Navier-Stokes - Actuator Disc model for the flow modelling, a semi-engineering model for the noise source modelling, and a parabolic wave equation model for the sound propagation modelling. The developed method can evaluate both annual energy production of wind farm and noise emission at receivers nearby. The wind farm considered in this study is located in a typical mountainous area. The energy production of the wind turbines is simulated and compared with measured data. The flow simulations over the complex terrain are performed using the in-house developed Navier-Stokes solver. A wind farm noise map is created by solving the parabolic wave equation. The obtained flow results are the inputs to the parabolic wave equation solver for sound propagation. The numerical computations are performed on a standard high-performance computer cluster. The developed numerical method provides a reliable assessment method for wind farm about its energy efficiency and noise features.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:renene:v:143:y:2019:i:c:p:1812-1825
    DOI: 10.1016/j.renene.2019.05.140
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148119308134
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.renene.2019.05.140?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zhu, Wei Jun & Shen, Wen Zhong & Barlas, Emre & Bertagnolio, Franck & Sørensen, Jens Nørkær, 2018. "Wind turbine noise generation and propagation modeling at DTU Wind Energy: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 133-150.
    2. Wilson, Dennis & Rodrigues, Silvio & Segura, Carlos & Loshchilov, Ilya & Hutter, Frank & Buenfil, Guillermo López & Kheiri, Ahmed & Keedwell, Ed & Ocampo-Pineda, Mario & Özcan, Ender & Peña, Sergio Iv, 2018. "Evolutionary computation for wind farm layout optimization," Renewable Energy, Elsevier, vol. 126(C), pages 681-691.
    3. Yan, Shu & Shi, Shaoping & Chen, Xinming & Wang, Xiaodong & Mao, Linzhi & Liu, Xiaojie, 2018. "Numerical simulations of flow interactions between steep hill terrain and large scale wind turbine," Energy, Elsevier, vol. 151(C), pages 740-747.
    4. Zhu, Wei Jun & Shen, Wen Zhong & Sørensen, Jens Nørkær & Yang, Hua, 2017. "Verification of a novel innovative blade root design for wind turbines using a hybrid numerical method," Energy, Elsevier, vol. 141(C), pages 1661-1670.
    5. Tian, Linlin & Zhu, Weijun & Shen, Wenzhong & Song, Yilei & Zhao, Ning, 2017. "Prediction of multi-wake problems using an improved Jensen wake model," Renewable Energy, Elsevier, vol. 102(PB), pages 457-469.
    6. 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.
    7. Guo, Yue & Ru, Peng & Su, Jun & Anadon, Laura Diaz, 2015. "Not in my backyard, but not far away from me: Local acceptance of wind power in China," Energy, Elsevier, vol. 82(C), pages 722-733.
    8. Husien, Walid & El-Osta, Wedad & Dekam, Elhadi, 2013. "Effect of the wake behind wind rotor on optimum energy output of wind farms," Renewable Energy, Elsevier, vol. 49(C), pages 128-132.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Xiaoxia, Gao & Luqing, Li & Shaohai, Zhang & Xiaoxun, Zhu & Haiying, Sun & Hongxing, Yang & Yu, Wang & Hao, Lu, 2022. "LiDAR-based observation and derivation of large-scale wind turbine's wake expansion model downstream of a hill," Energy, Elsevier, vol. 259(C).
    2. Dong, Xinghui & Li, Jia & Gao, Di & Zheng, Kai, 2020. "Wind speed modeling for cascade clusters of wind turbines part 1: The cascade clusters of wind turbines," Energy, Elsevier, vol. 205(C).
    3. Li, Shoutu & Chen, Qin & Li, Ye & Pröbsting, Stefan & Yang, Congxin & Zheng, Xiaobo & Yang, Yannian & Zhu, Weijun & Shen, Wenzhong & Wu, Faming & Li, Deshun & Wang, Tongguang & Ke, Shitang, 2022. "Experimental investigation on noise characteristics of small scale vertical axis wind turbines in urban environments," Renewable Energy, Elsevier, vol. 200(C), pages 970-982.
    4. 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).
    5. Gyatso, Ngawang & Li, Ye & Gao, Zhiteng & Wang, Qiang & Li, Shoutu & Yin, Qiang & Chen, Junbo & Jin, Peng & Liu, Zhengshu & Ma, Zengyi & Chen, Xuefeng & Feng, Jiajia & Dorje,, 2023. "Wind power performance assessment at high plateau region: A case study of the wind farm field test on the Qinghai-Tibet plateau," Applied Energy, Elsevier, vol. 336(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Wang, Qiang & Luo, Kun & Yuan, Renyu & Zhang, Sanxia & Fan, Jianren, 2019. "Wake and performance interference between adjacent wind farms: Case study of Xinjiang in China by means of mesoscale simulations," Energy, Elsevier, vol. 166(C), pages 1168-1180.
    3. Qi, Wen-Hui & Qi, Ming-Liang & Ji, Ya-Min, 2020. "The effect path of public communication on public acceptance of nuclear energy," Energy Policy, Elsevier, vol. 144(C).
    4. Wang, Yu & Gu, Jibao & Wu, Jianlin, 2020. "Explaining local residents’ acceptance of rebuilding nuclear power plants: The roles of perceived general benefit and perceived local benefit," Energy Policy, Elsevier, vol. 140(C).
    5. Siyu Tao & Andrés Feijóo & Jiemin Zhou & Gang Zheng, 2020. "Topology Design of an Offshore Wind Farm with Multiple Types of Wind Turbines in a Circular Layout," Energies, MDPI, vol. 13(3), pages 1-16, January.
    6. Jiufa Cao & Weijun Zhu & Xinbo Wu & Tongguang Wang & Haoran Xu, 2018. "An Aero-acoustic Noise Distribution Prediction Methodology for Offshore Wind Farms," Energies, MDPI, vol. 12(1), pages 1-16, December.
    7. 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).
    8. Dhunny, A.Z. & Timmons, D.S. & Allam, Z. & Lollchund, M.R. & Cunden, T.S.M., 2020. "An economic assessment of near-shore wind farm development using a weather research forecast-based genetic algorithm model," Energy, Elsevier, vol. 201(C).
    9. Xinkai Li & Ke Yang & Hao Hu & Xiaodong Wang & Shun Kang, 2019. "Effect of Tailing-Edge Thickness on Aerodynamic Noise for Wind Turbine Airfoil," Energies, MDPI, vol. 12(2), pages 1-25, January.
    10. Jong-Hyeon Shin & Jong-Hwi Lee & Se-Myong Chang, 2019. "A Simplified Numerical Model for the Prediction of Wake Interaction in Multiple Wind Turbines," Energies, MDPI, vol. 12(21), pages 1-14, October.
    11. He, Zhengxia & Cao, Changshuai & Kuai, Leyi & Zhou, Yanqing & Wang, Jianming, 2022. "Impact of policies on wind power innovation at different income levels: Regional differences in China based on dynamic panel estimation," Technology in Society, Elsevier, vol. 71(C).
    12. Sturge, D. & While, A. & Howell, R., 2014. "Engineering and energy yield: The missing dimension of wind turbine assessment," Energy Policy, Elsevier, vol. 65(C), pages 245-250.
    13. Zemo, Kahsay Haile & Panduro, Toke Emil & Termansen, Mette, 2019. "Impact of biogas plants on rural residential property values and implications for local acceptance," Energy Policy, Elsevier, vol. 129(C), pages 1121-1131.
    14. Pérez Albornoz, C. & Escalante Soberanis, M.A. & Ramírez Rivera, V. & Rivero, M., 2022. "Review of atmospheric stability estimations for wind power applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    15. Emin Sertaç Ari & Cevriye Gencer, 2020. "Proposal of a novel mixed integer linear programming model for site selection of a wind power plant based on power maximization with use of mixed type wind turbines," Energy & Environment, , vol. 31(5), pages 825-841, August.
    16. Mounir Alliche & Redha Rebhi & Noureddine Kaid & Younes Menni & Houari Ameur & Mustafa Inc & Hijaz Ahmad & Giulio Lorenzini & Ayman A. Aly & Sayed K. Elagan & Bassem F. Felemban, 2021. "Estimation of the Wind Energy Potential in Various North Algerian Regions," Energies, MDPI, vol. 14(22), pages 1-13, November.
    17. Zerrahn, Alexander, 2017. "Wind Power and Externalities," Ecological Economics, Elsevier, vol. 141(C), pages 245-260.
    18. Alphan, H., 2021. "Modelling potential visibility of wind turbines: A geospatial approach for planning and impact mitigation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
    19. Santos, Maria João & Ferreira, Paula & Araújo, Madalena, 2016. "A methodology to incorporate risk and uncertainty in electricity power planning," Energy, Elsevier, vol. 115(P2), pages 1400-1411.
    20. Bertsch, Valentin & Hyland, Marie & Mahony, Michael, 2017. "What drives people's opinions of electricity infrastructure? Empirical evidence from Ireland," Energy Policy, Elsevier, vol. 106(C), pages 472-497.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:renene:v:143:y:2019:i:c:p:1812-1825. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.