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High-resolution numerical simulation of the performance of vertical axis wind turbines in urban area: Part I, wind turbines on the side of single building

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  • Xu, Wenhao
  • Li, Gaohua
  • Zheng, Xiaobo
  • Li, Ye
  • Li, Shoutu
  • Zhang, Chen
  • Wang, Fuxin

Abstract

In urban areas, vertical axis wind turbines have been installed on both sides of the road and rooftop. Compared with rooftop, vertical axis wind turbines mounted on the side of a building have a larger swept area with longer blades, and the effect of tip vortices can be reduced at the same time. Around the building, there are several high wind speed regions that can provide more wind energy. In this paper, a high-resolution numerical method composed of overset grid and adaptive mesh refinement techniques is adopted to verify the feasibility of vertical axis wind turbines mounted on the side of the building. Firstly, the complex flow field around the building is simplified into the flow field around the cylindrical building. Five typical flow regions with different flow conditions are determined: windward zone, crosswind zone, tailwind zone, recirculation zone and leeward zone. Secondly, wind turbines are installed evenly around the building. The simulation results show that the energy output of wind turbines in tailwind zone can be increased by 120% compared with the reference value. Thirdly, multiple wind turbines are coupled with the building. In order to get the optimal arrangement, power outputs of four arrangements are compared for the best choice. The results indicate that the arrangement with six wind turbines in crosswind zone and tailwind zone can achieve the best performance. Through changing the rotational directions, the forward blades of wind turbines can be close to the wall, and the energy output of all the wind turbines around the building can be improved.

Suggested Citation

  • Xu, Wenhao & Li, Gaohua & Zheng, Xiaobo & Li, Ye & Li, Shoutu & Zhang, Chen & Wang, Fuxin, 2021. "High-resolution numerical simulation of the performance of vertical axis wind turbines in urban area: Part I, wind turbines on the side of single building," Renewable Energy, Elsevier, vol. 177(C), pages 461-474.
    • Handle: RePEc:eee:renene:v:177:y:2021:i:c:p:461-474
    DOI: 10.1016/j.renene.2021.04.071
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    1. Xu, Wenhao & Li, Ye & Li, Gaohua & Li, Shoutu & Zhang, Chen & Wang, Fuxin, 2021. "High-resolution numerical simulation of the performance of vertical axis wind turbines in urban area: Part II, array of vertical axis wind turbines between buildings," Renewable Energy, Elsevier, vol. 176(C), pages 25-39.
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    5. Isabel Cristina Gil-García & María Socorro García-Cascales & Angel Molina-García, 2022. "Urban Wind: An Alternative for Sustainable Cities," Energies, MDPI, vol. 15(13), pages 1-20, June.
    6. Dar, Arslan Salim & Armengol Barcos, Guillem & Porté-Agel, Fernando, 2022. "An experimental investigation of a roof-mounted horizontal-axis wind turbine in an idealized urban environment," Renewable Energy, Elsevier, vol. 193(C), pages 1049-1061.
    7. Luca Salvadori & Annalisa Di Bernardino & Giorgio Querzoli & Simone Ferrari, 2021. "A Novel Automatic Method for the Urban Canyon Parametrization Needed by Turbulence Numerical Simulations for Wind Energy Potential Assessment," Energies, MDPI, vol. 14(16), pages 1-22, August.
    8. Meng, Fantai & Sergiienko, Nataliia & Ding, Boyin & Zhou, Binzhen & Silva, Leandro Souza Pinheiro Da & Cazzolato, Benjamin & Li, Ye, 2023. "Co-located offshore wind–wave energy systems: Can motion suppression and reliable power generation be achieved simultaneously?," Applied Energy, Elsevier, vol. 331(C).
    9. Zhang, Lijun & Li, Ye & Xu, Wenhao & Gao, Zhiteng & Fang, Long & Li, Rongfu & Ding, Boyin & Zhao, Bin & Leng, Jun & He, Fenglan, 2022. "Systematic analysis of performance and cost of two floating offshore wind turbines with significant interactions," Applied Energy, Elsevier, vol. 321(C).
    10. Ogliari, Emanuele & Guilizzoni, Manfredo & Giglio, Alessandro & Pretto, Silvia, 2021. "Wind power 24-h ahead forecast by an artificial neural network and an hybrid model: Comparison of the predictive performance," Renewable Energy, Elsevier, vol. 178(C), pages 1466-1474.

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