IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v214y2021ics0360544220321587.html
   My bibliography  Save this article

Changes in wind turbine power characteristics and annual energy production due to atmospheric stability, turbulence intensity, and wind shear

Author

Listed:
  • Kim, Dae-Young
  • Kim, Yeon-Hee
  • Kim, Bum-Suk

Abstract

The power generation of wind turbines varies depending on external environmental conditions. To present universal correlations between conditions that affect wind speed and wind turbine power, this study analyzed the effects of three atmospheric factors—atmospheric stability, turbulence intensity (TI), and wind shear exponent (WSE)—on the power performance and annual energy production (AEP) of wind turbines. Additionally, the horizontal and vertical speed components of the 3D sonic anemometer were investigated. At two measurement heights, unstable regimes showed an ascending air current, whereas stable regimes simultaneously showed both ascending and descending currents. Power curves calculated for each atmospheric factor regime revealed that atmospheric stability (200 kW–11 m/s) exhibited the greatest difference, followed by TI (91 kW–11 m/s) and WSE (32 kW–10.5 m/s). Finally, AEP was calculated at the annual mean wind speed of the study site and exhibited variations of 1.4–4% according to the factor regime. The AEP difference due to the change in atmospheric stability was greatest, and AEP was highest in moderately unstable atmospheric conditions. Clearer understanding of the effects of atmospheric factors on the power characteristics and AEP of wind turbines is expected to deliver practical benefits for wind-resource assessment and power-production prediction.

Suggested Citation

  • Kim, Dae-Young & Kim, Yeon-Hee & Kim, Bum-Suk, 2021. "Changes in wind turbine power characteristics and annual energy production due to atmospheric stability, turbulence intensity, and wind shear," Energy, Elsevier, vol. 214(C).
    • Handle: RePEc:eee:energy:v:214:y:2021:i:c:s0360544220321587
    DOI: 10.1016/j.energy.2020.119051
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544220321587
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2020.119051?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. Sanz Rodrigo, J. & Borbón Guillén, F. & Gómez Arranz, P. & Courtney, M.S. & Wagner, R. & Dupont, E., 2013. "Multi-site testing and evaluation of remote sensing instruments for wind energy applications," Renewable Energy, Elsevier, vol. 53(C), pages 200-210.
    2. Optis, Mike & Perr-Sauer, Jordan, 2019. "The importance of atmospheric turbulence and stability in machine-learning models of wind farm power production," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 27-41.
    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. Hu, Weicheng & Yang, Qingshan & Chen, Hua-Peng & Yuan, Ziting & Li, Chen & Shao, Shuai & Zhang, Jian, 2021. "Wind field characteristics over hilly and complex terrain in turbulent boundary layers," Energy, Elsevier, vol. 224(C).
    2. 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).
    3. Zhang, Jincheng & Zhao, Xiaowei, 2021. "Three-dimensional spatiotemporal wind field reconstruction based on physics-informed deep learning," Applied Energy, Elsevier, vol. 300(C).
    4. Liao, Ding & Zhu, Shun-Peng & Correia, José A.F.O. & De Jesus, Abílio M.P. & Veljkovic, Milan & Berto, Filippo, 2022. "Fatigue reliability of wind turbines: historical perspectives, recent developments and future prospects," Renewable Energy, Elsevier, vol. 200(C), pages 724-742.
    5. Geon Hwa Ryu & Young-Gon Kim & Sung Jo Kwak & Man Soo Choi & Moon-Seon Jeong & Chae-Joo Moon, 2022. "Atmospheric Stability Effects on Offshore and Coastal Wind Resource Characteristics in South Korea for Developing Offshore Wind Farms," Energies, MDPI, vol. 15(4), pages 1-23, February.
    6. Christy Pérez & Michel Rivero & Mauricio Escalante & Victor Ramirez & Damien Guilbert, 2023. "Influence of Atmospheric Stability on Wind Turbine Energy Production: A Case Study of the Coastal Region of Yucatan," Energies, MDPI, vol. 16(10), pages 1-20, May.
    7. Xu, Zongyuan & Gao, Xiaoxia & Zhang, Huanqiang & Lv, Tao & Han, Zhonghe & Zhu, Xiaoxun & Wang, Yu, 2023. "Analysis of the anisotropy aerodynamic characteristics of downstream wind turbine considering the 3D wake expansion based on coupling method," Energy, Elsevier, vol. 263(PD).
    8. Xiangjie Liu & Le Feng & Xiaobing Kong, 2022. "A Comparative Study of Robust MPC and Stochastic MPC of Wind Power Generation System," Energies, MDPI, vol. 15(13), pages 1-22, June.
    9. Davide Astolfi & Raymond Byrne & Francesco Castellani, 2021. "Estimation of the Performance Aging of the Vestas V52 Wind Turbine through Comparative Test Case Analysis," Energies, MDPI, vol. 14(4), pages 1-25, February.
    10. Antoine Chrétien & Antoine Tahan & Philippe Cambron & Adaiton Oliveira-Filho, 2023. "Operational Wind Turbine Blade Damage Evaluation Based on 10-min SCADA and 1 Hz Data," Energies, MDPI, vol. 16(7), pages 1-18, March.
    11. Paweł Ligęza, 2022. "Dynamic Error Correction Method in Tachometric Anemometers for Measurements of Wind Energy," Energies, MDPI, vol. 15(11), pages 1-9, June.
    12. Pan He & Jian Xia, 2022. "Study on the Influence of Low-Level Jet on the Aerodynamic Characteristics of Horizontal Axis Wind Turbine Rotor Based on the Aerodynamics–Controller Interaction Method," Energies, MDPI, vol. 15(8), pages 1-18, April.
    13. He, J.Y. & Chan, P.W. & Li, Q.S. & Lee, C.W., 2022. "Characterizing coastal wind energy resources based on sodar and microwave radiometer observations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    14. Tang, Xinzi & Yuan, Keren & Gu, Nengwei & Li, Pengcheng & Peng, Ruitao, 2022. "An interval quantification-based optimization approach for wind turbine airfoil under uncertainties," Energy, Elsevier, vol. 244(PA).

    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. He, J.Y. & Chan, P.W. & Li, Q.S. & Lee, C.W., 2022. "Characterizing coastal wind energy resources based on sodar and microwave radiometer observations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    2. Cheng, Biyi & Du, Jianjun & Yao, Yingxue, 2022. "Machine learning methods to assist structure design and optimization of Dual Darrieus Wind Turbines," Energy, Elsevier, vol. 244(PA).
    3. Yang, Mao & Wang, Da & Xu, Chuanyu & Dai, Bozhi & Ma, Miaomiao & Su, Xin, 2023. "Power transfer characteristics in fluctuation partition algorithm for wind speed and its application to wind power forecasting," Renewable Energy, Elsevier, vol. 211(C), pages 582-594.
    4. Cheng-Yu Ho & Ke-Sheng Cheng & Chi-Hang Ang, 2023. "Utilizing the Random Forest Method for Short-Term Wind Speed Forecasting in the Coastal Area of Central Taiwan," Energies, MDPI, vol. 16(3), pages 1-18, January.
    5. Guo, Nai-Zhi & Shi, Ke-Zhong & Li, Bo & Qi, Liang-Wen & Wu, Hong-Hui & Zhang, Zi-Liang & Xu, Jian-Zhong, 2022. "A physics-inspired neural network model for short-term wind power prediction considering wake effects," Energy, Elsevier, vol. 261(PA).
    6. Elena Roibas-Millan & Javier Cubas & Santiago Pindado, 2017. "Studies on Cup Anemometer Performances Carried out at IDR/UPM Institute. Past and Present Research," Energies, MDPI, vol. 10(11), pages 1-17, November.
    7. Cheng, Biyi & Yao, Yingxue, 2023. "Machine learning based surrogate model to analyze wind tunnel experiment data of Darrieus wind turbines," Energy, Elsevier, vol. 278(PA).
    8. 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).
    9. Liu, Chenyu & Zhang, Xuemin & Mei, Shengwei & Zhen, Zhao & Jia, Mengshuo & Li, Zheng & Tang, Haiyan, 2022. "Numerical weather prediction enhanced wind power forecasting: Rank ensemble and probabilistic fluctuation awareness," Applied Energy, Elsevier, vol. 313(C).
    10. Kim, Dae-Young & Kim, Bum-Suk, 2022. "Differences in wind farm energy production based on the atmospheric stability dissipation rate: Case study of a 30 MW onshore wind farm," Energy, Elsevier, vol. 239(PE).
    11. Xiaoxun, Zhu & Zixu, Xu & Yu, Wang & Xiaoxia, Gao & Xinyu, Hang & Hongkun, Lu & Ruizhang, Liu & Yao, Chen & Huaxin, Liu, 2023. "Research on wind speed behavior prediction method based on multi-feature and multi-scale integrated learning," Energy, Elsevier, vol. 263(PA).
    12. Ejigu Tefera Habtemariam & Kula Kekeba & María Martínez-Ballesteros & Francisco Martínez-Álvarez, 2023. "A Bayesian Optimization-Based LSTM Model for Wind Power Forecasting in the Adama District, Ethiopia," Energies, MDPI, vol. 16(5), pages 1-22, February.
    13. Sasser, Christiana & Yu, Meilin & Delgado, Ruben, 2022. "Improvement of wind power prediction from meteorological characterization with machine learning models," Renewable Energy, Elsevier, vol. 183(C), pages 491-501.
    14. Pei-Chi Chang & Ray-Yeng Yang & Chi-Ming Lai, 2015. "Potential of Offshore Wind Energy and Extreme Wind Speed Forecasting on the West Coast of Taiwan," Energies, MDPI, vol. 8(3), pages 1-16, February.
    15. Tian, Linlin & Song, Yilei & Xiao, Pengcheng & Zhao, Ning & Shen, Wenzhong & Zhu, Chunling, 2022. "A new three-dimensional analytical model for wind turbine wake turbulence intensity predictions," Renewable Energy, Elsevier, vol. 189(C), pages 762-776.
    16. Chen, Hao, 2022. "Cluster-based ensemble learning for wind power modeling from meteorological wind data," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    17. Radünz, William Corrêa & Sakagami, Yoshiaki & Haas, Reinaldo & Petry, Adriane Prisco & Passos, Júlio César & Miqueletti, Mayara & Dias, Eduardo, 2021. "Influence of atmospheric stability on wind farm performance in complex terrain," Applied Energy, Elsevier, vol. 282(PA).

    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:energy:v:214:y:2021:i:c:s0360544220321587. 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/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.