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Modeling and experimentally-driven sensitivity analysis of wake-induced power loss in offshore wind farms: Insights from Block Island Wind Farm

Author

Listed:
  • Shid-Moosavi, Sina
  • Di Cioccio, Fabrizio
  • Haghi, Rad
  • Tronci, Eleonora Maria
  • Moaveni, Babak
  • Liberatore, Sauro
  • Hines, Eric

Abstract

Wake effects present a major challenge in offshore wind farms where closely spaced turbines are often arranged in layouts that promote alignment and interaction. These aerodynamic disturbances, caused by upstream turbines, can significantly reduce power output and increase loads on downstream turbines, compromising the farm’s overall efficiency. This study focuses on the Block Island Wind Farm (BIWF), the first offshore wind farm in the United States, to perform a sensitivity analysis of key operational and model parameters — turbulence intensity, yaw misalignment, and power and thrust coefficients — using the FLOw Redirection and Induction in Steady State (FLORIS) framework. Based on experimental data from the BIWF, the analysis offers a robust evaluation of wake effects under real-world conditions. Initially, the focus is on aligned turbine pairs, enabling controlled observations of wake impacts on downstream performance. The results highlight the dominant role of turbulence intensity and its seasonal variation in shaping wake dynamics. The study then expands to a farm-level assessment, evaluating the FLORIS model’s accuracy in predicting wake effects and power losses across the entire wind farm. The goal is to identify and prioritize the most critical parameters affecting power loss, enhancing turbine modeling accuracy, and improving overall farm performance based on empirical data.

Suggested Citation

  • Shid-Moosavi, Sina & Di Cioccio, Fabrizio & Haghi, Rad & Tronci, Eleonora Maria & Moaveni, Babak & Liberatore, Sauro & Hines, Eric, 2025. "Modeling and experimentally-driven sensitivity analysis of wake-induced power loss in offshore wind farms: Insights from Block Island Wind Farm," Renewable Energy, Elsevier, vol. 241(C).
  • Handle: RePEc:eee:renene:v:241:y:2025:i:c:s0960148124021943
    DOI: 10.1016/j.renene.2024.122126
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    References listed on IDEAS

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    1. 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.
    2. Jian Teng & Corey D. Markfort, 2020. "A Calibration Procedure for an Analytical Wake Model Using Wind Farm Operational Data," Energies, MDPI, vol. 13(14), pages 1-19, July.
    3. Kim, Soo-Hyun & Shin, Hyung-Ki & Joo, Young-Chul & Kim, Keon-Hoon, 2015. "A study of the wake effects on the wind characteristics and fatigue loads for the turbines in a wind farm," Renewable Energy, Elsevier, vol. 74(C), pages 536-543.
    4. Rebecca J. Barthelmie & Kaitlyn E. Dantuono & Emma J. Renner & Frederick L. Letson & Sara C. Pryor, 2021. "Extreme Wind and Waves in U.S. East Coast Offshore Wind Energy Lease Areas," Energies, MDPI, vol. 14(4), pages 1-25, February.
    5. Yu-Ting Wu & Fernando Porté-Agel, 2012. "Atmospheric Turbulence Effects on Wind-Turbine Wakes: An LES Study," Energies, MDPI, vol. 5(12), pages 1-23, December.
    6. Jung-Tae Lee & Hyun-Goo Kim & Yong-Heack Kang & Jin-Young Kim, 2019. "Determining the Optimized Hub Height of Wind Turbine Using the Wind Resource Map of South Korea," Energies, MDPI, vol. 12(15), pages 1-13, July.
    7. Archer, Cristina L. & Vasel-Be-Hagh, Ahmadreza & Yan, Chi & Wu, Sicheng & Pan, Yang & Brodie, Joseph F. & Maguire, A. Eoghan, 2018. "Review and evaluation of wake loss models for wind energy applications," Applied Energy, Elsevier, vol. 226(C), pages 1187-1207.
    8. Gaurier, Benoît & Carlier, Clément & Germain, Grégory & Pinon, Grégory & Rivoalen, Elie, 2020. "Three tidal turbines in interaction: An experimental study of turbulence intensity effects on wakes and turbine performance," Renewable Energy, Elsevier, vol. 148(C), pages 1150-1164.
    9. Hines, Eric M. & Baxter, Christopher D.P. & Ciochetto, David & Song, Mingming & Sparrevik, Per & Meland, Henrik J. & Strout, James M. & Bradshaw, Aaron & Hu, Sau-Lon & Basurto, Jorge R. & Moaveni, Bab, 2023. "Structural instrumentation and monitoring of the Block Island Offshore Wind Farm," Renewable Energy, Elsevier, vol. 202(C), pages 1032-1045.
    10. Maarten T. van Beek & Axelle Viré & Søren J. Andersen, 2021. "Sensitivity and Uncertainty of the FLORIS Model Applied on the Lillgrund Wind Farm," Energies, MDPI, vol. 14(5), pages 1-21, February.
    11. 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.
    12. Deepu Dilip & Fernando Porté-Agel, 2017. "Wind Turbine Wake Mitigation through Blade Pitch Offset," Energies, MDPI, vol. 10(6), pages 1-17, May.
    13. Zhang, Jincheng & Zhao, Xiaowei, 2020. "Quantification of parameter uncertainty in wind farm wake modeling," Energy, Elsevier, vol. 196(C).
    14. 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.
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