IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i2p710-d1028264.html
   My bibliography  Save this article

Review of Key Technologies for Offshore Floating Wind Power Generation

Author

Listed:
  • Bowen Zhou

    (College of Information Science and Engineering, Northeastern University, NO. 3-11, Wenhua Road, Heping District, Shenyang 110819, China
    Key Laboratory of Integrated Energy Optimization and Secure Operation of Liaoning Province, Northeastern University, Shenyang 110819, China)

  • Zhibo Zhang

    (College of Information Science and Engineering, Northeastern University, NO. 3-11, Wenhua Road, Heping District, Shenyang 110819, China
    Key Laboratory of Integrated Energy Optimization and Secure Operation of Liaoning Province, Northeastern University, Shenyang 110819, China)

  • Guangdi Li

    (College of Information Science and Engineering, Northeastern University, NO. 3-11, Wenhua Road, Heping District, Shenyang 110819, China
    Key Laboratory of Integrated Energy Optimization and Secure Operation of Liaoning Province, Northeastern University, Shenyang 110819, China)

  • Dongsheng Yang

    (College of Information Science and Engineering, Northeastern University, NO. 3-11, Wenhua Road, Heping District, Shenyang 110819, China
    Key Laboratory of Integrated Energy Optimization and Secure Operation of Liaoning Province, Northeastern University, Shenyang 110819, China)

  • Matilde Santos

    (Institute of Knowledge Technology, University Complutense of Madrid, 28040 Madrid, Spain)

Abstract

In recent years, due to the global energy crisis, increasingly more countries have recognized the importance of developing clean energy. Offshore wind energy, as a basic form of clean energy, has become one of the current research priorities. In the future, offshore wind farms will be developed in deep and distant sea areas. In these areas, there is a new trend of floating offshore wind platforms replacing fixed wind power platforms, due to their low cost, ease of installation, and independence from the water depth. However, the stability of offshore floating platforms is poor and their power fluctuations are significant; furthermore, they are more prone to failure because of sea wind, waves, and currents. This paper summarizes and analyzes the current research progress and critical technical issues of offshore floating wind power generation, such as stability control technology, integrated wind storage technology, wind power energy management, and long-distance transmission of electricity for floating wind power generation at sea. Finally, future research directions for key offshore wind power technologies are presented.

Suggested Citation

  • Bowen Zhou & Zhibo Zhang & Guangdi Li & Dongsheng Yang & Matilde Santos, 2023. "Review of Key Technologies for Offshore Floating Wind Power Generation," Energies, MDPI, vol. 16(2), pages 1-26, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:2:p:710-:d:1028264
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/2/710/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/2/710/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Meng, Yongqing & Yan, Shuhao & Wu, Kang & Ning, Lianhui & Li, Xuan & Wang, Xiuli & Wang, Xifan, 2021. "Comparative economic analysis of low frequency AC transmission system for the integration of large offshore wind farms," Renewable Energy, Elsevier, vol. 179(C), pages 1955-1968.
    2. Bing Wang & Zhen Tang & Xiang Gao & Weiyang Liu & Xianhui Chen, 2019. "Distributed Control Strategy of the Leader-Follower for Offshore Wind Farms under Fault Conditions," Sustainability, MDPI, vol. 11(8), pages 1-20, April.
    3. Antonio Galán-Lavado & Matilde Santos, 2021. "Analysis of the Effects of the Location of Passive Control Devices on the Platform of a Floating Wind Turbine," Energies, MDPI, vol. 14(10), pages 1-19, May.
    4. Zhang, Jinhua & Yan, Jie & Infield, David & Liu, Yongqian & Lien, Fue-sang, 2019. "Short-term forecasting and uncertainty analysis of wind turbine power based on long short-term memory network and Gaussian mixture model," Applied Energy, Elsevier, vol. 241(C), pages 229-244.
    5. Hedi Basbas & Yong-Chao Liu & Salah Laghrouche & Mickaël Hilairet & Franck Plestan, 2022. "Review on Floating Offshore Wind Turbine Models for Nonlinear Control Design," Energies, MDPI, vol. 15(15), pages 1-27, July.
    6. Christos S. Ioakimidis & Konstantinos N. Genikomsakis, 2018. "Integration of Seawater Pumped-Storage in the Energy System of the Island of São Miguel (Azores)," Sustainability, MDPI, vol. 10(10), pages 1-14, September.
    7. Li, Xuan & Zhang, Wei, 2020. "Long-term fatigue damage assessment for a floating offshore wind turbine under realistic environmental conditions," Renewable Energy, Elsevier, vol. 159(C), pages 570-584.
    8. Srikanth Bashetty & Selahattin Ozcelik, 2021. "Review on Dynamics of Offshore Floating Wind Turbine Platforms," Energies, MDPI, vol. 14(19), pages 1-30, September.
    9. Domínguez-García, José Luis & Rogers, Daniel J. & Ugalde-Loo, Carlos E. & Liang, Jun & Gomis-Bellmunt, Oriol, 2012. "Effect of non-standard operating frequencies on the economic cost of offshore AC networks," Renewable Energy, Elsevier, vol. 44(C), pages 267-280.
    10. Liao, Hao & Hu, Weihao & Wu, Xiawei & Wang, Ni & Liu, Zhou & Huang, Qi & Chen, Cong & Chen, Zhe, 2020. "Active power dispatch optimization for offshore wind farms considering fatigue distribution," Renewable Energy, Elsevier, vol. 151(C), pages 1173-1185.
    11. Luo, Xing & Wang, Jihong & Dooner, Mark & Clarke, Jonathan, 2015. "Overview of current development in electrical energy storage technologies and the application potential in power system operation," Applied Energy, Elsevier, vol. 137(C), pages 511-536.
    12. Chen, Ziwen & Wang, Xiaodong & Guo, Yize & Kang, Shun, 2021. "Numerical analysis of unsteady aerodynamic performance of floating offshore wind turbine under platform surge and pitch motions," Renewable Energy, Elsevier, vol. 163(C), pages 1849-1870.
    13. Hahn, Henning & Hau, Daniel & Dick, Christian & Puchta, Matthias, 2017. "Techno-economic assessment of a subsea energy storage technology for power balancing services," Energy, Elsevier, vol. 133(C), pages 121-127.
    14. Li, Liang & Liu, Yuanchuan & Yuan, Zhiming & Gao, Yan, 2018. "Wind field effect on the power generation and aerodynamic performance of offshore floating wind turbines," Energy, Elsevier, vol. 157(C), pages 379-390.
    15. Weiwei Yao & Changhong Deng & Dinglin Li & Man Chen & Peng Peng & Hao Zhang, 2019. "Optimal Sizing of Seawater Pumped Storage Plant with Variable-Speed Units Considering Offshore Wind Power Accommodation," Sustainability, MDPI, vol. 11(7), pages 1-18, April.
    16. Lu, Hongfang & Ma, Xin & Huang, Kun & Azimi, Mohammadamin, 2020. "Prediction of offshore wind farm power using a novel two-stage model combining kernel-based nonlinear extension of the Arps decline model with a multi-objective grey wolf optimizer," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    17. Shahram Hanifi & Xiaolei Liu & Zi Lin & Saeid Lotfian, 2020. "A Critical Review of Wind Power Forecasting Methods—Past, Present and Future," Energies, MDPI, vol. 13(15), pages 1-24, July.
    18. Foley, Aoife M. & Leahy, Paul G. & Marvuglia, Antonino & McKeogh, Eamon J., 2012. "Current methods and advances in forecasting of wind power generation," Renewable Energy, Elsevier, vol. 37(1), pages 1-8.
    19. Wen, Binrong & Tian, Xinliang & Dong, Xingjian & Peng, Zhike & Zhang, Wenming, 2017. "Influences of surge motion on the power and thrust characteristics of an offshore floating wind turbine," Energy, Elsevier, vol. 141(C), pages 2054-2068.
    20. Yang, Kyoungboo & Kwak, Gyeongil & Cho, Kyungho & Huh, Jongchul, 2019. "Wind farm layout optimization for wake effect uniformity," Energy, Elsevier, vol. 183(C), pages 983-995.
    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. Fei Xie & Junxue Zhang & Guodong Wu & Chunxia Zhang & Hechi Wang, 2023. "The Environmental Sustainability Study of an Airport Building System Based on an Integrated LCA-Embodied Energy (Emergy)-ANN Analysis," Sustainability, MDPI, vol. 15(9), pages 1-19, May.

    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. 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.
    2. Wen, Binrong & Jiang, Zhihao & Li, Zhanwei & Peng, Zhike & Dong, Xingjian & Tian, Xinliang, 2022. "On the aerodynamic loading effect of a model Spar-type floating wind turbine: An experimental study," Renewable Energy, Elsevier, vol. 184(C), pages 306-319.
    3. Wang, Xinbao & Cai, Chang & Cai, Shang-Gui & Wang, Tengyuan & Wang, Zekun & Song, Juanjuan & Rong, Xiaomin & Li, Qing'an, 2023. "A review of aerodynamic and wake characteristics of floating offshore wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 175(C).
    4. Sun, Chuan & Chen, Yueyi & Cheng, Cheng, 2021. "Imputation of missing data from offshore wind farms using spatio-temporal correlation and feature correlation," Energy, Elsevier, vol. 229(C).
    5. Meng, Anbo & Zhu, Zibin & Deng, Weisi & Ou, Zuhong & Lin, Shan & Wang, Chenen & Xu, Xuancong & Wang, Xiaolin & Yin, Hao & Luo, Jianqiang, 2022. "A novel wind power prediction approach using multivariate variational mode decomposition and multi-objective crisscross optimization based deep extreme learning machine," Energy, Elsevier, vol. 260(C).
    6. Kisvari, Adam & Lin, Zi & Liu, Xiaolei, 2021. "Wind power forecasting – A data-driven method along with gated recurrent neural network," Renewable Energy, Elsevier, vol. 163(C), pages 1895-1909.
    7. Hunt, Julian David & Zakeri, Behnam & Falchetta, Giacomo & Nascimento, Andreas & Wada, Yoshihide & Riahi, Keywan, 2020. "Mountain Gravity Energy Storage: A new solution for closing the gap between existing short- and long-term storage technologies," Energy, Elsevier, vol. 190(C).
    8. Takuji Matsumoto & Yuji Yamada, 2023. "Improving the Efficiency of Hedge Trading Using Higher-Order Standardized Weather Derivatives for Wind Power," Energies, MDPI, vol. 16(7), pages 1-22, March.
    9. Javed, Muhammad Shahzad & Ma, Tao & Jurasz, Jakub & Amin, Muhammad Yasir, 2020. "Solar and wind power generation systems with pumped hydro storage: Review and future perspectives," Renewable Energy, Elsevier, vol. 148(C), pages 176-192.
    10. Wen, Binrong & Tian, Xinliang & Dong, Xingjian & Li, Zhanwei & Peng, Zhike & Zhang, Wenming & Wei, Kexiang, 2020. "Design approaches of performance-scaled rotor for wave basin model tests of floating wind turbines," Renewable Energy, Elsevier, vol. 148(C), pages 573-584.
    11. Gu, Bo & Zhang, Tianren & Meng, Hang & Zhang, Jinhua, 2021. "Short-term forecasting and uncertainty analysis of wind power based on long short-term memory, cloud model and non-parametric kernel density estimation," Renewable Energy, Elsevier, vol. 164(C), pages 687-708.
    12. Yakoub, Ghali & Mathew, Sathyajith & Leal, Joao, 2023. "Intelligent estimation of wind farm performance with direct and indirect ‘point’ forecasting approaches integrating several NWP models," Energy, Elsevier, vol. 263(PD).
    13. Marta Poncela-Blanco & Pilar Poncela, 2021. "Improving Wind Power Forecasts: Combination through Multivariate Dimension Reduction Techniques," Energies, MDPI, vol. 14(5), pages 1-16, March.
    14. Shahram Hanifi & Saeid Lotfian & Hossein Zare-Behtash & Andrea Cammarano, 2022. "Offshore Wind Power Forecasting—A New Hyperparameter Optimisation Algorithm for Deep Learning Models," Energies, MDPI, vol. 15(19), pages 1-21, September.
    15. Upma Singh & Mohammad Rizwan & Muhannad Alaraj & Ibrahim Alsaidan, 2021. "A Machine Learning-Based Gradient Boosting Regression Approach for Wind Power Production Forecasting: A Step towards Smart Grid Environments," Energies, MDPI, vol. 14(16), pages 1-21, August.
    16. Yang, Mao & Wang, Da & Zhang, Wei, 2023. "A short-term wind power prediction method based on dynamic and static feature fusion mining," Energy, Elsevier, vol. 280(C).
    17. Muhammad Yaqoob Javed & Iqbal Ahmed Khurshid & Aamer Bilal Asghar & Syed Tahir Hussain Rizvi & Kamal Shahid & Krzysztof Ejsmont, 2022. "An Efficient Estimation of Wind Turbine Output Power Using Neural Networks," Energies, MDPI, vol. 15(14), pages 1-22, July.
    18. Frate, Guido Francesco & Ferrari, Lorenzo & Desideri, Umberto, 2021. "Energy storage for grid-scale applications: Technology review and economic feasibility analysis," Renewable Energy, Elsevier, vol. 163(C), pages 1754-1772.
    19. Shahram Hanifi & Xiaolei Liu & Zi Lin & Saeid Lotfian, 2020. "A Critical Review of Wind Power Forecasting Methods—Past, Present and Future," Energies, MDPI, vol. 13(15), pages 1-24, July.
    20. Meftah Elsaraiti & Adel Merabet, 2021. "A Comparative Analysis of the ARIMA and LSTM Predictive Models and Their Effectiveness for Predicting Wind Speed," Energies, MDPI, vol. 14(20), pages 1-16, October.

    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:2:p:710-:d:1028264. 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.