IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v322y2025ics0360544225012113.html

Large eddy simulation of wind farm performance in horizontally and vertically staggered layouts

Author

Listed:
  • Bin Shahadat, Muhammad Rubayat
  • Doranehgard, Mohammad Hossein
  • Cai, Weibing
  • Li, Zheng

Abstract

This numerical investigation employs Large Eddy Simulation (LES) coupled with Actuator Disk Model (ADM) to evaluate wind farm layout optimization strategies. The study presents a systematic analysis of aligned, horizontal staggering, vertical staggering, and mixed (combination of horizontal and vertical) staggering configurations, aiming to establish optimal design parameters for enhanced power production. The investigation examines key performance metrics including mean velocity distributions, turbulence intensity characteristics, and power generation efficiency. Results demonstrate better performance of both horizontal and vertical staggering patterns compared to conventional aligned configurations, with horizontal staggering exhibiting notably higher power output than vertical arrangements. Our findings also suggest that mixed configurations, incorporating both horizontal and vertical staggering, can offer optimal performance characteristics. This research advances the understanding of wake interactions in complex wind farm layouts and provides design guidelines for maximizing wind farm power generation efficiency through strategic turbine positioning.

Suggested Citation

  • Bin Shahadat, Muhammad Rubayat & Doranehgard, Mohammad Hossein & Cai, Weibing & Li, Zheng, 2025. "Large eddy simulation of wind farm performance in horizontally and vertically staggered layouts," Energy, Elsevier, vol. 322(C).
    • Handle: RePEc:eee:energy:v:322:y:2025:i:c:s0360544225012113
    DOI: 10.1016/j.energy.2025.135569
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544225012113
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2025.135569?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Thiébot, Jérôme & Guillou, Nicolas & Guillou, Sylvain & Good, Andrew & Lewis, Michael, 2020. "Wake field study of tidal turbines under realistic flow conditions," Renewable Energy, Elsevier, vol. 151(C), pages 1196-1208.
    2. Faraz Bhurt & Aamir Ali & Muhammad U. Keerio & Ghulam Abbas & Zahoor Ahmed & Noor H. Mugheri & Yun-Su Kim, 2023. "Stochastic Multi-Objective Optimal Reactive Power Dispatch with the Integration of Wind and Solar Generation," Energies, MDPI, vol. 16(13), pages 1-22, June.
    3. Leonardo P. Chamorro & Fernando Porté-Agel, 2011. "Turbulent Flow Inside and Above a Wind Farm: A Wind-Tunnel Study," Energies, MDPI, vol. 4(11), pages 1-21, November.
    4. Feifei Xue & Heping Duan & Chang Xu & Xingxing Han & Yanqing Shangguan & Tongtong Li & Zhefei Fen, 2022. "Research on the Power Capture and Wake Characteristics of a Wind Turbine Based on a Modified Actuator Line Model," Energies, MDPI, vol. 15(1), pages 1-20, January.
    5. Sun, Chong & Tian, Tian & Zhu, Xiaocheng & Hua, Ouyang & Du, Zhaohui, 2021. "Investigation of the near wake of a horizontal-axis wind turbine model by dynamic mode decomposition," Energy, Elsevier, vol. 227(C).
    6. Ti, Zilong & Deng, Xiao Wei & Yang, Hongxing, 2020. "Wake modeling of wind turbines using machine learning," Applied Energy, Elsevier, vol. 257(C).
    7. Stevens, Richard J.A.M. & Martínez-Tossas, Luis A. & Meneveau, Charles, 2018. "Comparison of wind farm large eddy simulations using actuator disk and actuator line models with wind tunnel experiments," Renewable Energy, Elsevier, vol. 116(PA), pages 470-478.
    8. Dou, Bingzheng & Qu, Timing & Lei, Liping & Zeng, Pan, 2020. "Optimization of wind turbine yaw angles in a wind farm using a three-dimensional yawed wake model," Energy, Elsevier, vol. 209(C).
    9. Liu, Junbo & Cai, Chang & Song, Dongran & Zhong, Xiaohui & Shi, Kezhong & Chen, Yinpeng & Cheng, Shijie & Huang, Yupian & Jiang, Xue & Li, Qing'an, 2024. "Nonlinear model predictive control for maximum wind energy extraction of semi-submersible floating offshore wind turbine based on simplified dynamics model," Energy, Elsevier, vol. 311(C).
    10. Cheng, Biyi & Yao, Yingxue & Qu, Xiaobin & Zhou, Zhiming & Wei, Jionghui & Liang, Ertang & Zhang, Chengcheng & Kang, Hanwen & Wang, Hongjun, 2024. "Multi-objective parameter optimization of large-scale offshore wind Turbine's tower based on data-driven model with deep learning and machine learning methods," Energy, Elsevier, vol. 305(C).
    11. Liu, Yige & Zhao, Zhenzhou & Liu, Yan & Liu, Huiwen & Wei, Shangshang & Ma, Yuanzhuo & Ling, Ziyan & Luo, Qiao, 2024. "Combined wake control of aligned wind turbines for power optimization based on a 3D wake model considering secondary wake steering," Energy, Elsevier, vol. 308(C).
    12. Kuichao Ma & Huanqiang Zhang & Xiaoxia Gao & Xiaodong Wang & Heng Nian & Wei Fan, 2024. "Research on Evaluation Method of Wind Farm Wake Energy Efficiency Loss Based on SCADA Data Analysis," Sustainability, MDPI, vol. 16(5), pages 1-16, February.
    13. Adhikari, Jeevan & Prasanna, I.V. & Panda, S.K., 2016. "Power conversion system for high altitude wind power generation with medium voltage AC transmission," Renewable Energy, Elsevier, vol. 93(C), pages 562-578.
    14. 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.
    15. McKenna, Russell & Pfenninger, Stefan & Heinrichs, Heidi & Schmidt, Johannes & Staffell, Iain & Bauer, Christian & Gruber, Katharina & Hahmann, Andrea N. & Jansen, Malte & Klingler, Michael & Landwehr, 2022. "High-resolution large-scale onshore wind energy assessments: A review of potential definitions, methodologies and future research needs," Renewable Energy, Elsevier, vol. 182(C), pages 659-684.
    16. Ye, Maokun & Chen, Hamn-Ching & Koop, Arjen, 2023. "High-fidelity CFD simulations for the wake characteristics of the NTNU BT1 wind turbine," Energy, Elsevier, vol. 265(C).
    17. Wu, Chutian & Yang, Xiaolei & Zhu, Yaxin, 2021. "On the design of potential turbine positions for physics-informed optimization of wind farm layout," Renewable Energy, Elsevier, vol. 164(C), pages 1108-1120.
    18. Peng, H.Y. & Liu, H.J. & Yang, J.H., 2021. "A review on the wake aerodynamics of H-rotor vertical axis wind turbines," Energy, Elsevier, vol. 232(C).
    19. Mushtaq, Khurram & Waris, Asim & Zou, Runmin & Shafique, Uzma & Khan, Niaz B. & Khan, M. Ijaz & Jameel, Mohammed & Khan, Muhammad Imran, 2024. "A comprehensive approach to wind turbine power curve modeling: Addressing outliers and enhancing accuracy," Energy, Elsevier, vol. 304(C).
    20. He, Xingyue & He, Bitao & Qin, Tao & Lin, Chuan & Yang, Jing, 2024. "Ultra-short-term wind power forecasting based on a dual-channel deep learning model with improved coot optimization algorithm," Energy, Elsevier, vol. 305(C).
    21. Claire VerHulst & Charles Meneveau, 2015. "Altering Kinetic Energy Entrainment in Large Eddy Simulations of Large Wind Farms Using Unconventional Wind Turbine Actuator Forcing," Energies, MDPI, vol. 8(1), pages 1-17, January.
    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. Chen, Xinyu & Yu, Chuanjin & Wang, Xiong & Yuan, Shaoyang & Li, Yongle, 2025. "Data-driven characteristics and representation of vertical wind profiles in a mountainous region," Energy, Elsevier, vol. 338(C).
    2. Bin Shahadat, Muhammad Rubayat & Doranehgard, Mohammad Hossein & Cai, Weibing & Meneveau, Charles & Schafer, Benjamin & Li, Zheng, 2025. "An airfoil-based synthetic actuator disk model for wind turbine aerodynamic and structural analysis," Renewable Energy, Elsevier, vol. 255(C).

    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, Ruiyang & Sun, Haiying & Gao, Xiaoxia & Yang, Hongxing, 2022. "Wind tunnel tests for wind turbines: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 166(C).
    2. 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).
    3. 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.
    4. Yang, Kun & Zhang, Mingming & Yang, Shanghui & Song, Yuwei & Dong, Xinhui & Deng, Yanfei & Deng, Xiaowei, 2025. "Pareto frontier for multi-objective wind farm layout optimization balancing power production and turbine fatigue life," Renewable Energy, Elsevier, vol. 252(C).
    5. Yang, Shanghui & Deng, Xiaowei & Li, Qinglan, 2025. "A joint optimization framework for power and fatigue life based on cooperative wake steering of wind farm," Energy, Elsevier, vol. 319(C).
    6. Yang, Shanghui & Deng, Xiaowei & Ti, Zilong & Yan, Bowen & Yang, Qingshan, 2022. "Cooperative yaw control of wind farm using a double-layer machine learning framework," Renewable Energy, Elsevier, vol. 193(C), pages 519-537.
    7. Wang, Yize & Liu, Zhenqing & Yu, Zhongze, 2025. "An efficient optimization algorithm for active yaw control to increase wind farm power while considering load reduction," Energy, Elsevier, vol. 340(C).
    8. Sun, Haiying & Yang, Hongxing, 2023. "Wind farm layout and hub height optimization with a novel wake model," Applied Energy, Elsevier, vol. 348(C).
    9. Kale, Baris & Buckingham, Sophia & van Beeck, Jeroen & Cuerva-Tejero, Alvaro, 2023. "Comparison of the wake characteristics and aerodynamic response of a wind turbine under varying atmospheric conditions using WRF-LES-GAD and WRF-LES-GAL wind turbine models," Renewable Energy, Elsevier, vol. 216(C).
    10. Anagnostopoulos, Sokratis J. & Bauer, Jens & Clare, Mariana C.A. & Piggott, Matthew D., 2023. "Accelerated wind farm yaw and layout optimisation with multi-fidelity deep transfer learning wake models," Renewable Energy, Elsevier, vol. 218(C).
    11. Fan, Shuanglong & Liu, Zhenqing, 2025. "Investigation of fully coupled wind field simulations in complex terrain wind farms considering automatic upwind control of turbines," Renewable Energy, Elsevier, vol. 239(C).
    12. Zehtabiyan-Rezaie, Navid & Abkar, Mahdi, 2024. "An extended k−ɛ model for wake-flow simulation of wind farms," Renewable Energy, Elsevier, vol. 222(C).
    13. Mian, H.H. & Machot, F.A. & Ullah, H. & Keprate, A. & Siddiqui, M.S., 2025. "Advances in computational intelligence for floating offshore wind turbines aerodynamics: Current state review and future potential," Renewable and Sustainable Energy Reviews, Elsevier, vol. 224(C).
    14. Wang, Mingwei & Zhang, Mingming & Qin, Caiyan & Sun, Haiying & Deng, Xiaowei, 2026. "A data-driven double-Gaussian wake model reflecting the wake evolution process," Renewable Energy, Elsevier, vol. 257(C).
    15. Li, Rui & Zhang, Jincheng & Zhao, Xiaowei, 2022. "Dynamic wind farm wake modeling based on a Bilateral Convolutional Neural Network and high-fidelity LES data," Energy, Elsevier, vol. 258(C).
    16. Antonio Crespo, 2023. "Computational Fluid Dynamic Models of Wind Turbine Wakes," Energies, MDPI, vol. 16(4), pages 1-3, February.
    17. Victor P. Stein & Hans-Jakob Kaltenbach, 2022. "Validation of a Large-Eddy Simulation Approach for Prediction of the Ground Roughness Influence on Wind Turbine Wakes," Energies, MDPI, vol. 15(7), pages 1-25, April.
    18. Chen, Zhenyu & Lin, Zhongwei & Zhai, Xiaoya & Liu, Jizhen, 2022. "Dynamic wind turbine wake reconstruction: A Koopman-linear flow estimator," Energy, Elsevier, vol. 238(PB).
    19. Dai, Xuan & Xu, Da & Zhang, Mengqi & Stevens, Richard J.A.M., 2022. "A three-dimensional dynamic mode decomposition analysis of wind farm flow aerodynamics," Renewable Energy, Elsevier, vol. 191(C), pages 608-624.
    20. Zhang, Jincheng & Zhao, Xiaowei, 2021. "Spatiotemporal wind field prediction based on physics-informed deep learning and LIDAR measurements," Applied Energy, Elsevier, vol. 288(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:322:y:2025:i:c:s0360544225012113. 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.