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The Potential of Advanced Scatterometer (ASCAT) 12.5 km Coastal Observations for Offshore Wind Farm Site Selection in Irish Waters

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  • Tiny Remmers

    (MaREI Centre for Marine and Renewable Energy, Beaufort Building, Environmental Research Institute, University College Cork, Ringaskiddy, P43 C573, Ireland)

  • Fiona Cawkwell

    (Department of Geography, University College Cork, College Road, Cork, T12 ND89, Ireland)

  • Cian Desmond

    (MaREI Centre for Marine and Renewable Energy, Beaufort Building, Environmental Research Institute, University College Cork, Ringaskiddy, P43 C573, Ireland)

  • Jimmy Murphy

    (MaREI Centre for Marine and Renewable Energy, Beaufort Building, Environmental Research Institute, University College Cork, Ringaskiddy, P43 C573, Ireland)

  • Eirini Politi

    (Odermatt & Brockmann GmbH, CH-8006 Zürich, Switzerland)

Abstract

The offshore wind industry has seen unprecedented growth over the last few years. In line with this growth, there has been a push towards more exposed sites, farther from shore, in deeper water with consequent increased investor risk. There is therefore a growing need for accurate, reliable, met-ocean data to identify suitable sites, and from which to base preliminary design and investment decisions. This study investigates the potential of hyper-temporal satellite remote sensing Advanced Scatterometer (ASCAT) data in generating information necessary for the optimal site selection of offshore renewable energy infrastructure, and hence providing a cost-effective alternative to traditional techniques, such as in situ data from public or private entities and modelled data. Five years of the ASCAT 12.5 km wind product were validated against in situ weather buoys and showed a strong correlation with a Pearson coefficient of 0.95, when the in situ measurements were extrapolated with the log law. Temporal variations depicted by the ASCAT wind data followed the same inter-seasonal and intra-annual variations as the in situ measurements. A small diurnal bias of 0.12 m s −1 was observed between the descending swath (10:00 to 12:00) and the ascending swath (20:30 to 22:30), indicating that Ireland’s offshore wind speeds are slightly stronger in the daytime, especially in the nearshore areas. Seasonal maps showed that the highest spatial variability in offshore wind speeds are exhibited in winter and summer. The mean wind speed extrapolated at 80 m above sea level showed that Ireland’s mean offshore wind speeds at hub height ranged between 9.6 m s −1 and 12.3 m s −1 . To best represent the offshore wind resource and its spatial distribution, an operational frequency map and a maximum yield frequency map were produced based on the ASCAT wind product in an offshore zone between 20 km and 200 km from the coast. The operational frequency indicates the percentage of time during which the observed local wind speed is between cut-in (3 m/s) and cut-out (25 m/s) for a standard turbine. The operational frequency map shows that the frequency of the wind speed within the cut-in and cut-off range of wind turbines was between 92.4% and 97.2%, while the maximum yield frequency map showed that between 40.6% and 59.5% of the wind speed frequency was included in the wind turbine rated power range. The results showed that the hyper-temporal ASCAT 12.5 km wind speed product (five consecutive years, two observations daily per satellite, two satellites) is representative of wind speeds measured by in situ measurements in Irish waters, and that its ability to depict temporal and spatial variability can assist in the decision-making process for offshore wind farm site selection in Ireland.

Suggested Citation

  • Tiny Remmers & Fiona Cawkwell & Cian Desmond & Jimmy Murphy & Eirini Politi, 2019. "The Potential of Advanced Scatterometer (ASCAT) 12.5 km Coastal Observations for Offshore Wind Farm Site Selection in Irish Waters," Energies, MDPI, vol. 12(2), pages 1-16, January.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:2:p:206-:d:196328
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    References listed on IDEAS

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    2. O’Connell, Ross & de Montera, Louis & Peters, Jared L. & Horion, Stéphanie, 2020. "An updated assessment of Ireland’s wave energy resource using satellite data assimilation and a revised wave period ratio," Renewable Energy, Elsevier, vol. 160(C), pages 1431-1444.
    3. Mingcan Li & Hanbin Xiao & Lin Pan & Chengjun Xu, 2019. "Study of Generalized Interaction Wake Models Systems with ELM Variation for Off-Shore Wind Farms," Energies, MDPI, vol. 12(5), pages 1-32, March.
    4. Nezhad, M. Majidi & Neshat, M. & Heydari, A. & Razmjoo, A. & Piras, G. & Garcia, D. Astiaso, 2021. "A new methodology for offshore wind speed assessment integrating Sentinel-1, ERA-Interim and in-situ measurement," Renewable Energy, Elsevier, vol. 172(C), pages 1301-1313.
    5. Arun Kumar, Surisetty V.V. & Nagababu, Garlapati & Kumar, Raj, 2019. "Comparative study of offshore winds and wind energy production derived from multiple scatterometers and met buoys," Energy, Elsevier, vol. 185(C), pages 599-611.
    6. Majidi Nezhad, M. & Heydari, A. & Groppi, D. & Cumo, F. & Astiaso Garcia, D., 2020. "Wind source potential assessment using Sentinel 1 satellite and a new forecasting model based on machine learning: A case study Sardinia islands," Renewable Energy, Elsevier, vol. 155(C), pages 212-224.

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