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

Tiny Remmers, Fiona Cawkwell, Cian Desmond, Jimmy Murphy and Eirini Politi
Additional contact information
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

Energies, 2019, vol. 12, issue 2, 1-16

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.

Keywords: Ireland; offshore wind energy; operational frequency; scatterometers; ASCAT satellite; hyper-temporal data (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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