IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v306y2022ipas0306261921012575.html
   My bibliography  Save this article

Layout optimization of offshore wind farm considering spatially inhomogeneous wave loads

Author

Listed:
  • Zilong, Ti
  • Xiao Wei, Deng

Abstract

Monopile-supported wind turbines are usually placed in a harsh ocean environment and wave load becomes a major consideration in the nearshore wind farm design. Affected by the coastal geography and seabed terrain, the wave field inside a wind farm is usually spatially inhomogeneous and the wave load on each turbine can be greatly different. However, current practice for wind farm layout design is based on a passive strategy in which the layout is determined first, then followed by the wave load calculation and support structure design. Such a passive design strategy does not take inhomogeneous wave loads into consideration and may inappropriately place some turbines into the intense wave load regions, e.g., high wave or breaking wave zones, which is uneconomic and even risky for structural safety. In this paper, a novel coupling approach considering inhomogeneous wave loads on monopile-supported wind turbines in wind farm layout optimization is presented. The main goal is to arrange all turbines in an optimal layout, in which the total wave load of the wind farm is minimized while simultaneously maintaining the favorable AEP (annual energy production). The inhomogeneous wave field is characterized using either an analytical wave model or a numerical wave model. The wind energy production and wave loads are investigated and coupled in the layout optimization. A user-defined knockdown coefficient is employed in the coupling optimization to allow the users to customize the exact tradeoffs between AEP and wave load. The optimization is driven by the Multistart algorithm to efficiently approximate the best layout. It is discovered that the coupling optimization shows apparent benefits in producing satisfactory layouts, in which the total wave loads are significantly reduced by 20.1%-40.5% while maintaining a favorable amount of AEP. The presented coupling approach provides a useful alternative optimization strategy for offshore wind farm layout design to reduce the costs related to wave loads, especially in the harsh sea regions.

Suggested Citation

  • Zilong, Ti & Xiao Wei, Deng, 2022. "Layout optimization of offshore wind farm considering spatially inhomogeneous wave loads," Applied Energy, Elsevier, vol. 306(PA).
    • Handle: RePEc:eee:appene:v:306:y:2022:i:pa:s0306261921012575
    DOI: 10.1016/j.apenergy.2021.117947
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261921012575
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2021.117947?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. Brogna, Roberto & Feng, Ju & Sørensen, Jens Nørkær & Shen, Wen Zhong & Porté-Agel, Fernando, 2020. "A new wake model and comparison of eight algorithms for layout optimization of wind farms in complex terrain," Applied Energy, Elsevier, vol. 259(C).
    2. Chowdhury, Souma & Zhang, Jie & Messac, Achille & Castillo, Luciano, 2013. "Optimizing the arrangement and the selection of turbines for wind farms subject to varying wind conditions," Renewable Energy, Elsevier, vol. 52(C), pages 273-282.
    3. Ti, Zilong & Deng, Xiao Wei & Yang, Hongxing, 2020. "Wake modeling of wind turbines using machine learning," Applied Energy, Elsevier, vol. 257(C).
    4. Changshui, Zhang & Guangdong, Hou & Jun, Wang, 2011. "A fast algorithm based on the submodular property for optimization of wind turbine positioning," Renewable Energy, Elsevier, vol. 36(11), pages 2951-2958.
    5. Cao, Jiu Fa & Zhu, Wei Jun & Shen, Wen Zhong & Sørensen, Jens Nørkær & Sun, Zhen Ye, 2020. "Optimizing wind energy conversion efficiency with respect to noise: A study on multi-criteria wind farm layout design," Renewable Energy, Elsevier, vol. 159(C), pages 468-485.
    6. Lozano-Minguez, E. & Kolios, A.J. & Brennan, F.P., 2011. "Multi-criteria assessment of offshore wind turbine support structures," Renewable Energy, Elsevier, vol. 36(11), pages 2831-2837.
    7. Wu, Yu-Ting & Porté-Agel, Fernando, 2015. "Modeling turbine wakes and power losses within a wind farm using LES: An application to the Horns Rev offshore wind farm," Renewable Energy, Elsevier, vol. 75(C), pages 945-955.
    8. DuPont, Bryony & Cagan, Jonathan & Moriarty, Patrick, 2016. "An advanced modeling system for optimization of wind farm layout and wind turbine sizing using a multi-level extended pattern search algorithm," Energy, Elsevier, vol. 106(C), pages 802-814.
    9. Wu, Xiawei & Hu, Weihao & Huang, Qi & Chen, Cong & Jacobson, Mark Z. & Chen, Zhe, 2020. "Optimizing the layout of onshore wind farms to minimize noise," Applied Energy, Elsevier, vol. 267(C).
    10. Hevia-Koch, Pablo & Klinge Jacobsen, Henrik, 2019. "Comparing offshore and onshore wind development considering acceptance costs," Energy Policy, Elsevier, vol. 125(C), pages 9-19.
    11. V. Aboobacker & P. Vethamony & K. Sudheesh & S. Rupali, 2009. "Spectral characteristics of the nearshore waves off Paradip, India during monsoon and extreme events," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 49(2), pages 311-323, May.
    12. Bastankhah, Majid & Porté-Agel, Fernando, 2014. "A new analytical model for wind-turbine wakes," Renewable Energy, Elsevier, vol. 70(C), pages 116-123.
    13. Zhixin, Wang & Chuanwen, Jiang & Qian, Ai & Chengmin, Wang, 2009. "The key technology of offshore wind farm and its new development in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(1), pages 216-222, January.
    14. Kusiak, Andrew & Song, Zhe, 2010. "Design of wind farm layout for maximum wind energy capture," Renewable Energy, Elsevier, vol. 35(3), pages 685-694.
    15. Zsolt Ugray & Leon Lasdon & John Plummer & Fred Glover & James Kelly & Rafael Martí, 2007. "Scatter Search and Local NLP Solvers: A Multistart Framework for Global Optimization," INFORMS Journal on Computing, INFORMS, vol. 19(3), pages 328-340, August.
    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. Song, Jeonghwan & Kim, Taewan & You, Donghyun, 2023. "Particle swarm optimization of a wind farm layout with active control of turbine yaws," Renewable Energy, Elsevier, vol. 206(C), pages 738-747.
    2. Zilong, Ti & Yubing, Song & Xiaowei, Deng, 2022. "Spatial-temporal wave height forecast using deep learning and public reanalysis dataset," Applied Energy, Elsevier, vol. 326(C).
    3. Cao, Feifei & Yu, Mingqi & Han, Meng & Liu, Bing & Wei, Zhiwen & Jiang, Juan & Tian, Huiyuan & Shi, Hongda & Li, Yanni, 2023. "WECs microarray effect on the coupled dynamic response and power performance of a floating combined wind and wave energy system," Renewable Energy, Elsevier, vol. 219(P2).
    4. Muhammad Nabeel Hussain & Nadeem Shaukat & Ammar Ahmad & Muhammad Abid & Abrar Hashmi & Zohreh Rajabi & Muhammad Atiq Ur Rehman Tariq, 2022. "Micro-Siting of Wind Turbines in an Optimal Wind Farm Area Using Teaching–Learning-Based Optimization Technique," Sustainability, MDPI, vol. 14(14), pages 1-24, July.

    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. Masoudi, Seiied Mohsen & Baneshi, Mehdi, 2022. "Layout optimization of a wind farm considering grids of various resolutions, wake effect, and realistic wind speed and wind direction data: A techno-economic assessment," Energy, Elsevier, vol. 244(PB).
    2. Azlan, F. & Kurnia, J.C. & Tan, B.T. & Ismadi, M.-Z., 2021. "Review on optimisation methods of wind farm array under three classical wind condition problems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    3. Yang, Kun & Deng, Xiaowei & Ti, Zilong & Yang, Shanghui & Huang, Senbin & Wang, Yuhang, 2023. "A data-driven layout optimization framework of large-scale wind farms based on machine learning," Renewable Energy, Elsevier, vol. 218(C).
    4. Cao, Lichao & Ge, Mingwei & Gao, Xiaoxia & Du, Bowen & Li, Baoliang & Huang, Zhi & Liu, Yongqian, 2022. "Wind farm layout optimization to minimize the wake induced turbulence effect on wind turbines," Applied Energy, Elsevier, vol. 323(C).
    5. Tao, Siyu & Xu, Qingshan & Feijóo, Andrés & Zheng, Gang & Zhou, Jiemin, 2020. "Nonuniform wind farm layout optimization: A state-of-the-art review," Energy, Elsevier, vol. 209(C).
    6. Amiri, Mojtaba Maali & Shadman, Milad & Estefen, Segen F., 2024. "A review of physical and numerical modeling techniques for horizontal-axis wind turbine wakes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 193(C).
    7. Cao, Jiufa & Nyborg, Camilla Marie & Feng, Ju & Hansen, Kurt S. & Bertagnolio, Franck & Fischer, Andreas & Sørensen, Thomas & Shen, Wen Zhong, 2022. "A new multi-fidelity flow-acoustics simulation framework for wind farm application," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    8. Ti, Zilong & Deng, Xiao Wei & Zhang, Mingming, 2021. "Artificial Neural Networks based wake model for power prediction of wind farm," Renewable Energy, Elsevier, vol. 172(C), pages 618-631.
    9. Pawar, Suraj & Sharma, Ashesh & Vijayakumar, Ganesh & Bay, Chrstopher J. & Yellapantula, Shashank & San, Omer, 2022. "Towards multi-fidelity deep learning of wind turbine wakes," Renewable Energy, Elsevier, vol. 200(C), pages 867-879.
    10. Wang, Longyan & Cholette, Michael E. & Zhou, Yunkai & Yuan, Jianping & Tan, Andy C.C. & Gu, Yuantong, 2018. "Effectiveness of optimized control strategy and different hub height turbines on a real wind farm optimization," Renewable Energy, Elsevier, vol. 126(C), pages 819-829.
    11. Kaldellis, John K. & Triantafyllou, Panagiotis & Stinis, Panagiotis, 2021. "Critical evaluation of Wind Turbines’ analytical wake models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    12. Wu, Yan & Xia, Tianqi & Wang, Yufei & Zhang, Haoran & Feng, Xiao & Song, Xuan & Shibasaki, Ryosuke, 2022. "A synchronization methodology for 3D offshore wind farm layout optimization with multi-type wind turbines and obstacle-avoiding cable network," Renewable Energy, Elsevier, vol. 185(C), pages 302-320.
    13. Zhang, Jincheng & Zhao, Xiaowei, 2020. "A novel dynamic wind farm wake model based on deep learning," Applied Energy, Elsevier, vol. 277(C).
    14. Zhang, Shaohai & Gao, Xiaoxia & Ma, Wanli & Lu, Hongkun & Lv, Tao & Xu, Shinai & Zhu, Xiaoxun & Sun, Haiying & Wang, Yu, 2023. "Derivation and verification of three-dimensional wake model of multiple wind turbines based on super-Gaussian function," Renewable Energy, Elsevier, vol. 215(C).
    15. Amin Niayifar & Fernando Porté-Agel, 2016. "Analytical Modeling of Wind Farms: A New Approach for Power Prediction," Energies, MDPI, vol. 9(9), pages 1-13, September.
    16. Yang, Shanghui & Deng, Xiaowei & Yang, Kun, 2024. "Machine-learning-based wind farm optimization through layout design and yaw control," Renewable Energy, Elsevier, vol. 224(C).
    17. Matthias Ritter & Simone Pieralli & Martin Odening, 2017. "Neighborhood Effects in Wind Farm Performance: A Regression Approach," Energies, MDPI, vol. 10(3), pages 1-16, March.
    18. Purohit, Shantanu & Ng, E.Y.K. & Syed Ahmed Kabir, Ijaz Fazil, 2022. "Evaluation of three potential machine learning algorithms for predicting the velocity and turbulence intensity of a wind turbine wake," Renewable Energy, Elsevier, vol. 184(C), pages 405-420.
    19. Cao, Jiu Fa & Zhu, Wei Jun & Shen, Wen Zhong & Sørensen, Jens Nørkær & Sun, Zhen Ye, 2020. "Optimizing wind energy conversion efficiency with respect to noise: A study on multi-criteria wind farm layout design," Renewable Energy, Elsevier, vol. 159(C), pages 468-485.
    20. Tao, Siyu & Xu, Qingshan & Feijóo, Andrés & Zheng, Gang & Zhou, Jiemin, 2020. "Wind farm layout optimization with a three-dimensional Gaussian wake model," Renewable Energy, Elsevier, vol. 159(C), pages 553-569.

    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:appene:v:306:y:2022:i:pa:s0306261921012575. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.