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Using Satellite SAR to Characterize the Wind Flow around Offshore Wind Farms

Author

Listed:
  • Charlotte Bay Hasager

    (Technical University of Denmark, Wind Energy Department, Frederiksborgvej 399, Roskilde 4000, Denmark)

  • Pauline Vincent

    (Collecte Localisation Satellites, Avenue La Pérouse, Bâtiment le Ponant, Plouzané 29280, France
    These authors contributed equally to this work.)

  • Jake Badger

    (Technical University of Denmark, Wind Energy Department, Frederiksborgvej 399, Roskilde 4000, Denmark
    These authors contributed equally to this work.)

  • Merete Badger

    (Technical University of Denmark, Wind Energy Department, Frederiksborgvej 399, Roskilde 4000, Denmark
    These authors contributed equally to this work.)

  • Alessandro Di Bella

    (Technical University of Denmark, Wind Energy Department, Frederiksborgvej 399, Roskilde 4000, Denmark
    These authors contributed equally to this work.)

  • Alfredo Peña

    (Technical University of Denmark, Wind Energy Department, Frederiksborgvej 399, Roskilde 4000, Denmark
    These authors contributed equally to this work.)

  • Romain Husson

    (Collecte Localisation Satellites, Avenue La Pérouse, Bâtiment le Ponant, Plouzané 29280, France
    These authors contributed equally to this work.)

  • Patrick J. H. Volker

    (Technical University of Denmark, Wind Energy Department, Frederiksborgvej 399, Roskilde 4000, Denmark
    These authors contributed equally to this work.)

Abstract

Offshore wind farm cluster effects between neighboring wind farms increase rapidly with the large-scale deployment of offshore wind turbines. The wind farm wakes observed from Synthetic Aperture Radar (SAR) are sometimes visible and atmospheric and wake models are here shown to convincingly reproduce the observed very long wind farm wakes. The present study mainly focuses on wind farm wake climatology based on Envisat ASAR. The available SAR data archive covering the large offshore wind farms at Horns Rev has been used for geo-located wind farm wake studies. However, the results are difficult to interpret due to mainly three issues: the limited number of samples per wind directional sector, the coastal wind speed gradient, and oceanic bathymetry effects in the SAR retrievals. A new methodology is developed and presented. This method overcomes effectively the first issue and in most cases, but not always, the second. In the new method all wind field maps are rotated such that the wind is always coming from the same relative direction. By applying the new method to the SAR wind maps, mesoscale and microscale model wake aggregated wind-fields results are compared. The SAR-based findings strongly support the model results at Horns Rev 1.

Suggested Citation

  • Charlotte Bay Hasager & Pauline Vincent & Jake Badger & Merete Badger & Alessandro Di Bella & Alfredo Peña & Romain Husson & Patrick J. H. Volker, 2015. "Using Satellite SAR to Characterize the Wind Flow around Offshore Wind Farms," Energies, MDPI, vol. 8(6), pages 1-27, June.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:6:p:5413-5439:d:50765
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    Citations

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    Cited by:

    1. Pryor, Sara C. & Barthelmie, Rebecca J., 2024. "Wind shadows impact planning of large offshore wind farms," Applied Energy, Elsevier, vol. 359(C).
    2. Majidi Nezhad, M. & Groppi, D. & Marzialetti, P. & Fusilli, L. & Laneve, G. & Cumo, F. & Garcia, D. Astiaso, 2019. "Wind energy potential analysis using Sentinel-1 satellite: A review and a case study on Mediterranean islands," Renewable and Sustainable Energy Reviews, Elsevier, vol. 109(C), pages 499-513.
    3. Wang, Qiang & Luo, Kun & Wu, Chunlei & Fan, Jianren, 2019. "Impact of substantial wind farms on the local and regional atmospheric boundary layer: Case study of Zhangbei wind power base in China," Energy, Elsevier, vol. 183(C), pages 1136-1149.
    4. Cemil Yigit, 2020. "Effect of Air-Ducted Blade Design on Horizontal Axis Wind Turbine Performance," Energies, MDPI, vol. 13(14), pages 1-15, July.
    5. Vincenzo Dovì & Antonella Battaglini, 2015. "Energy Policy and Climate Change: A Multidisciplinary Approach to a Global Problem," Energies, MDPI, vol. 8(12), pages 1-8, November.
    6. Jong-Hyeon Shin & Jong-Hwi Lee & Se-Myong Chang, 2019. "A Simplified Numerical Model for the Prediction of Wake Interaction in Multiple Wind Turbines," Energies, MDPI, vol. 12(21), pages 1-14, October.
    7. Cuevas-Figueroa, Gabriel & Stansby, Peter K. & Stallard, Timothy, 2022. "Accuracy of WRF for prediction of operational wind farm data and assessment of influence of upwind farms on power production," Energy, Elsevier, vol. 254(PB).
    8. Charlotte Bay Hasager & Nicolai Gayle Nygaard & Patrick J. H. Volker & Ioanna Karagali & Søren Juhl Andersen & Jake Badger, 2017. "Wind Farm Wake: The 2016 Horns Rev Photo Case," Energies, MDPI, vol. 10(3), pages 1-24, March.

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