IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i21p8196-d961955.html
   My bibliography  Save this article

Modeling and Investigation of the Effect of a Wind Turbine on the Atmospheric Boundary Layer

Author

Listed:
  • Vladislav N. Kovalnogov

    (Laboratory of Inter-Disciplinary Problems in Energy Production, Ulyanovsk State Technical University, 32 Severny Venetz Street, 432027 Ulyanovsk, Russia)

  • Ruslan V. Fedorov

    (Laboratory of Inter-Disciplinary Problems in Energy Production, Ulyanovsk State Technical University, 32 Severny Venetz Street, 432027 Ulyanovsk, Russia)

  • Andrei V. Chukalin

    (Laboratory of Inter-Disciplinary Problems in Energy Production, Ulyanovsk State Technical University, 32 Severny Venetz Street, 432027 Ulyanovsk, Russia)

  • Ekaterina V. Tsvetova

    (Laboratory of Inter-Disciplinary Problems in Energy Production, Ulyanovsk State Technical University, 32 Severny Venetz Street, 432027 Ulyanovsk, Russia)

  • Mariya I. Kornilova

    (Laboratory of Inter-Disciplinary Problems in Energy Production, Ulyanovsk State Technical University, 32 Severny Venetz Street, 432027 Ulyanovsk, Russia)

Abstract

Wind power engineering is one of the environmentally safe areas of energy and certainly makes a significant contribution to the fight against CO 2 emissions. The study of the air masses movement in the zone of wind turbines and their influence on the boundary layer of the atmosphere is a fundamental basis for the efficient use of wind energy. The paper considers the theory of the movement of air masses in the rotation zone of a wind turbine, and presents an analytical review of applied methods for modeling the atmospheric boundary layer and its interaction with a wind turbine. The results of modeling the boundary layer in the wind turbine zone using the STAR CCM+ software product are presented. The wind speed and intensity of turbulence in the near and far wake of the wind turbine at nominal load parameters are investigated. There is a significant decrease in the average wind speed in the near wake of the wind generator by 3 m/s and an increase in turbulent intensity by 18.3%. When considering the long-distance track behind the wind turbine, there is a decrease in the average speed by 0.6 m/s, while the percentage taken from the average value of the turbulent intensity is 7.2% higher than in the section in front of the wind generator. The influence of a wind turbine on the change in the temperature stratification of the boundary layer is considered. The experiments revealed a temperature change (up to 0.5 K), which is insignificant, but at night the stratification reaches large values due to an increase in the temperature difference in the surface boundary layer. In the long term, the research will contribute to the sustainable and efficient development of regional wind energy.

Suggested Citation

  • Vladislav N. Kovalnogov & Ruslan V. Fedorov & Andrei V. Chukalin & Ekaterina V. Tsvetova & Mariya I. Kornilova, 2022. "Modeling and Investigation of the Effect of a Wind Turbine on the Atmospheric Boundary Layer," Energies, MDPI, vol. 15(21), pages 1-17, November.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:21:p:8196-:d:961955
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/21/8196/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/15/21/8196/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wang, Qiang & Luo, Kun & Wu, Chunlei & Fan, Jianren, 2019. "Impact of substantial wind farms on the local and regional atmospheric boundary layer: Case study of Zhangbei wind power base in China," Energy, Elsevier, vol. 183(C), pages 1136-1149.
    2. Tristan Revaz & Fernando Porté-Agel, 2021. "Large-Eddy Simulation of Wind Turbine Flows: A New Evaluation of Actuator Disk Models," Energies, MDPI, vol. 14(13), pages 1-22, June.
    3. Wang, Qiang & Luo, Kun & Wu, Chunlei & Zhu, Zhaofan & Fan, Jianren, 2022. "Mesoscale simulations of a real onshore wind power base in complex terrain: Wind farm wake behavior and power production," Energy, Elsevier, vol. 241(C).
    4. Narbel, Patrick A. & Hansen, Jan Petter, 2014. "Estimating the cost of future global energy supply," Discussion Papers 2014/14, Norwegian School of Economics, Department of Business and Management Science.
    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. Vladislav N. Kovalnogov & Ruslan V. Fedorov & Andrei V. Chukalin & Mariya I. Kornilova & Tamara V. Karpukhina & Anton V. Petrov, 2023. "Application of Intelligent and Digital Technologies to the Tasks of Wind Energy," Energies, MDPI, vol. 16(1), pages 1-16, January.

    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. Wiebe, Kirsten S. & Lutz, Christian, 2016. "Endogenous technological change and the policy mix in renewable power generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 739-751.
    2. van den Broek, Machteld & Berghout, Niels & Rubin, Edward S., 2015. "The potential of renewables versus natural gas with CO2 capture and storage for power generation under CO2 constraints," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 1296-1322.
    3. He, Yuhang & Han, Xingxing & Xu, Chang & Cheng, Zhe & Wang, Jincheng & Liu, Wei & Xu, Dong, 2023. "Sensitivity of simulated wind power under diverse spatial scales and multiple terrains using the weather research and forecasting model," Energy, Elsevier, vol. 285(C).
    4. Reddy, B. Sudhakara, 2018. "Economic dynamics and technology diffusion in indian power sector," Energy Policy, Elsevier, vol. 120(C), pages 425-435.
    5. Batista, N.C. & Melício, R. & Mendes, V.M.F. & Calderón, M. & Ramiro, A., 2015. "On a self-start Darrieus wind turbine: Blade design and field tests," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 508-522.
    6. Wang, Qiang & Luo, Kun & Wu, Chunlei & Zhu, Zhaofan & Fan, Jianren, 2022. "Mesoscale simulations of a real onshore wind power base in complex terrain: Wind farm wake behavior and power production," Energy, Elsevier, vol. 241(C).
    7. M. K. Islam & N. M. S. Hassan & M. G. Rasul & Kianoush Emami & Ashfaque Ahmed Chowdhury, 2023. "Forecasting of Solar and Wind Resources for Power Generation," Energies, MDPI, vol. 16(17), pages 1-23, August.
    8. Dara Vahidi & Fernando Porté-Agel, 2022. "A New Streamwise Scaling for Wind Turbine Wake Modeling in the Atmospheric Boundary Layer," Energies, MDPI, vol. 15(24), pages 1-18, December.
    9. Hansen, J.P. & Narbel, P.A. & Aksnes, D.L., 2017. "Limits to growth in the renewable energy sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 769-774.
    10. Wang, Qiang & Luo, Kun & Wu, Chunlei & Fan, Jianren, 2019. "Impact of substantial wind farms on the local and regional atmospheric boundary layer: Case study of Zhangbei wind power base in China," Energy, Elsevier, vol. 183(C), pages 1136-1149.
    11. Hannan, M.A. & Lipu, M.S. Hossain & Ker, Pin Jern & Begum, R.A. & Agelidis, Vasilios G. & Blaabjerg, F., 2019. "Power electronics contribution to renewable energy conversion addressing emission reduction: Applications, issues, and recommendations," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    12. Ignacio Herrera Anchustegui & Violeta S. Radovich, 2022. "Wind Energy on the High Seas: Regulatory Challenges for a Science Fiction Future," Energies, MDPI, vol. 15(23), pages 1-20, December.
    13. Jabir Ali Ouassou & Julian Straus & Marte Fodstad & Gunhild Reigstad & Ove Wolfgang, 2021. "Applying endogenous learning models in energy system optimization," Papers 2106.06373, arXiv.org.
    14. Vladislav N. Kovalnogov & Ruslan V. Fedorov & Andrei V. Chukalin & Vladimir N. Klyachkin & Vladimir P. Tabakov & Denis A. Demidov, 2024. "Applied Machine Learning to Study the Movement of Air Masses in the Wind Farm Area," Energies, MDPI, vol. 17(16), pages 1-27, August.
    15. Jabir Ali Ouassou & Julian Straus & Marte Fodstad & Gunhild Reigstad & Ove Wolfgang, 2021. "Applying Endogenous Learning Models in Energy System Optimization," Energies, MDPI, vol. 14(16), pages 1-21, August.
    16. Nicholas Christakis & Ioanna Evangelou & Dimitris Drikakis & George Kossioris, 2024. "A Computational Methodology for Assessing Wind Potential," Energies, MDPI, vol. 17(6), pages 1-23, March.
    17. Yongnian Zhao & Yu Xue & Shanhong Gao & Jundong Wang & Qingcai Cao & Tao Sun & Yan Liu, 2022. "Computation and Analysis of an Offshore Wind Power Forecast: Towards a Better Assessment of Offshore Wind Power Plant Aerodynamics," Energies, MDPI, vol. 15(12), pages 1-17, June.
    18. Wu, X.D. & Yang, Q. & Chen, G.Q. & Hayat, T. & Alsaedi, A., 2016. "Progress and prospect of CCS in China: Using learning curve to assess the cost-viability of a 2×600MW retrofitted oxyfuel power plant as a case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1274-1285.
    19. Wei, Max & Smith, Sarah J. & Sohn, Michael D., 2017. "Experience curve development and cost reduction disaggregation for fuel cell markets in Japan and the US," Applied Energy, Elsevier, vol. 191(C), pages 346-357.
    20. Wu, Chunlei & Luo, Kun & Wang, Qiang & Fan, Jianren, 2022. "Simulated potential wind power sensitivity to the planetary boundary layer parameterizations combined with various topography datasets in the weather research and forecasting model," Energy, Elsevier, vol. 239(PB).

    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:gam:jeners:v:15:y:2022:i:21:p:8196-:d:961955. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.