IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i13p5169-d1187214.html
   My bibliography  Save this article

Blade Design and Aerodynamic Performance Analysis of a 20 MW Wind Turbine for LCoE Reduction

Author

Listed:
  • Kang-Ho Jang

    (Department of Aerospace Engineering, Graduate School, Jeonbuk National University, 567 Baekje-daero, Deokjin-gu, Jeonju 54896, Jeonbuk, Republic of Korea)

  • Ki-Wahn Ryu

    (Department of Aerospace Engineering, Jeonbuk National University, 567 Baekje-daero, Deokjin-gu, Jeonju 54896, Jeonbuk, Republic of Korea)

Abstract

The aim of this study is to develop a blade mass model that incorporates a low-induction rotor (LIR) and a low-specific power concept to reduce aerodynamic loads and lower the Levelized Cost of Energy (LCoE). This blade mass model replaces the traditional simple scaling rule and incorporates the concept of LCoE reduction, presenting not only the mass distribution in the blade span direction but also the stiffness distribution. In order to achieve the desired reduction in LCoE, we developed a mathematical model that expresses blade mass as a function of the axial induction factor, which influences the aerodynamic load on the blade. We used this model to determine geometries of various low-induction rotors for 20 MW class horizontal axis wind turbine, and to identify the axial induction factor that correlates with the lowest blade mass. The chord length and twist angle in the spanwise direction of the blade were determined using PROPID’s reverse design process, based on the specified axial induction factor. Since the low-induction concept is not aerodynamically optimal, a low-specific power design approach was also adopted. This involved increasing the blade length and shifting the power curve to the left. By doing so, the AEP is increased, directly contributing to a reduction in the LCoE. Mass per unit length of the blade was presented, reflecting the distribution of airfoil type, blade geometry, and shapes of internal structures such as spars and webs.

Suggested Citation

  • Kang-Ho Jang & Ki-Wahn Ryu, 2023. "Blade Design and Aerodynamic Performance Analysis of a 20 MW Wind Turbine for LCoE Reduction," Energies, MDPI, vol. 16(13), pages 1-24, July.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:13:p:5169-:d:1187214
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/13/5169/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/13/5169/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Qin, Chao (Chris) & Loth, Eric & Zalkind, Daniel S. & Pao, Lucy Y. & Yao, Shulong & Griffith, D. Todd & Selig, Michael S. & Damiani, Rick, 2020. "Downwind coning concept rotor for a 25 MW offshore wind turbine," Renewable Energy, Elsevier, vol. 156(C), pages 314-327.
    2. Ashuri, T. & Zaaijer, M.B. & Martins, J.R.R.A. & van Bussel, G.J.W. & van Kuik, G.A.M., 2014. "Multidisciplinary design optimization of offshore wind turbines for minimum levelized cost of energy," Renewable Energy, Elsevier, vol. 68(C), pages 893-905.
    Full references (including those not matched with items on IDEAS)

    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. Escalera Mendoza, Alejandra S. & Griffith, D. Todd & Jeong, Michael & Qin, Chris & Loth, Eric & Phadnis, Mandar & Pao, Lucy & Selig, Michael S., 2023. "Aero-structural rapid screening of new design concepts for offshore wind turbines," Renewable Energy, Elsevier, vol. 219(P2).
    2. Reddy, Sohail R., 2021. "A machine learning approach for modeling irregular regions with multiple owners in wind farm layout design," Energy, Elsevier, vol. 220(C).
    3. Chen, Z.J. & Stol, K.A. & Mace, B.R., 2017. "Wind turbine blade optimisation with individual pitch and trailing edge flap control," Renewable Energy, Elsevier, vol. 103(C), pages 750-765.
    4. Michael K. McWilliam & Antariksh C. Dicholkar & Frederik Zahle & Taeseong Kim, 2022. "Post-Optimum Sensitivity Analysis with Automatically Tuned Numerical Gradients Applied to Swept Wind Turbine Blades," Energies, MDPI, vol. 15(9), pages 1-19, April.
    5. Kaminski, Meghan & Simpson, Juliet & Loth, Eric & Fingersh, Lee Jay & Scholbrock, Andy & Johnson, Nick & Johnson, Kathryn & Pao, Lucy & Griffith, Todd, 2023. "Gravo-aeroelastically-scaled demonstrator field tests to represent blade response of a flexible extreme-scale downwind turbine," Renewable Energy, Elsevier, vol. 218(C).
    6. Haojie Kang & Bofeng Xu & Xiang Shen & Zhen Li & Xin Cai & Zhiqiang Hu, 2023. "Comparison of Blade Aeroelastic Responses between Upwind and Downwind of 10 MW Wind Turbines under the Shear Wind Condition," Energies, MDPI, vol. 16(6), pages 1-13, March.
    7. Gonzalez Silva, Jean & Ferrari, Riccardo & van Wingerden, Jan-Willem, 2023. "Wind farm control for wake-loss compensation, thrust balancing and load-limiting of turbines," Renewable Energy, Elsevier, vol. 203(C), pages 421-433.
    8. Al-Nassar, W.K. & Neelamani, S. & Al-Salem, K.A. & Al-Dashti, H.A., 2019. "Feasibility of offshore wind energy as an alternative source for the state of Kuwait," Energy, Elsevier, vol. 169(C), pages 783-796.
    9. Ozan Gözcü & Taeseong Kim & David Robert Verelst & Michael K. McWilliam, 2022. "Swept Blade Dynamic Investigations for a 100 kW Small Wind Turbine," Energies, MDPI, vol. 15(9), pages 1-22, April.
    10. van Dijk, Mike T. & van Wingerden, Jan-Willem & Ashuri, Turaj & Li, Yaoyu, 2017. "Wind farm multi-objective wake redirection for optimizing power production and loads," Energy, Elsevier, vol. 121(C), pages 561-569.
    11. James Roetzer & Xingjie Li & John Hall, 2024. "Review of Data-Driven Models in Wind Energy: Demonstration of Blade Twist Optimization Based on Aerodynamic Loads," Energies, MDPI, vol. 17(16), pages 1-20, August.
    12. Hailun Xie & Lars Johanning, 2023. "A Hierarchical Met-Ocean Data Selection Model for Fast O&M Simulation in Offshore Renewable Energy Systems," Energies, MDPI, vol. 16(3), pages 1-20, February.
    13. Mohammad Barooni & Turaj Ashuri & Deniz Velioglu Sogut & Stephen Wood & Shiva Ghaderpour Taleghani, 2022. "Floating Offshore Wind Turbines: Current Status and Future Prospects," Energies, MDPI, vol. 16(1), pages 1-28, December.
    14. Manchado, Cristina & Gomez-Jauregui, Valentin & Lizcano, Piedad E. & Iglesias, Andres & Galvez, Akemi & Otero, Cesar, 2019. "Wind farm repowering guided by visual impact criteria," Renewable Energy, Elsevier, vol. 135(C), pages 197-207.
    15. Sedaghat, Ahmad & Hassanzadeh, Arash & Jamali, Jamaloddin & Mostafaeipour, Ali & Chen, Wei-Hsin, 2017. "Determination of rated wind speed for maximum annual energy production of variable speed wind turbines," Applied Energy, Elsevier, vol. 205(C), pages 781-789.
    16. Koh, J.H. & Ng, E.Y.K., 2016. "Downwind offshore wind turbines: Opportunities, trends and technical challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 797-808.
    17. Chen, Jincheng & Wang, Feng & Stelson, Kim A., 2018. "A mathematical approach to minimizing the cost of energy for large utility wind turbines," Applied Energy, Elsevier, vol. 228(C), pages 1413-1422.
    18. Zhu, Jie & Zhou, Zhong & Cai, Xin, 2020. "Multi-objective aerodynamic and structural integrated optimization design of wind turbines at the system level through a coupled blade-tower model," Renewable Energy, Elsevier, vol. 150(C), pages 523-537.
    19. Zhang, Bin & E, Jiaqiang & Gong, Jinke & Yuan, Wenhua & Zuo, Wei & Li, Yu & Fu, Jun, 2016. "Multidisciplinary design optimization of the diesel particulate filter in the composite regeneration process," Applied Energy, Elsevier, vol. 181(C), pages 14-28.
    20. Ahmed, Ahsan & Nadeem, Talha Bin & Naqvi, Asad A. & Siddiqui, Mubashir Ali & Khan, Muhammad Hamza & Bin Zahid, Muhammad Saad & Ammar, Syed Muhammad, 2022. "Investigation of PV utilizability on university buildings: A case study of Karachi, Pakistan," Renewable Energy, Elsevier, vol. 195(C), pages 238-251.

    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:16:y:2023:i:13:p:5169-:d:1187214. 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.