SAR for offshore wind energy Tobias Ahsbahs1, Charlotte Hasager1, Caroline Draxl2, Galen MacLaurin2, Alex Newcombe3, Nicolai G. Nygaard3, and Merete Badger1 1) Technical University of Denmark, Wind Energy Risø, Roskilde, Denmark 2) National Renewable Energy Laboratory, Golden, Colorado, USA 3) Ørsted Wind Power, Fredericia, Denmark

Outline Offshore wind – some key numbers Application examples SAR for wind energy Wind resource variation Wind farm wake detection

Development of offshore wind farms in Europe Offshore wind is growing fast Larger wind farms New markets (US, Taiwan, China…)

Where are offshore wind farms planned? Europe: North Sea Baltic Sea Close to shore USA: East Coast www.4coffshore.com

What makes SAR wind fields attractive? High spatial resolution Large swath widths Open data access via Copernicus

SAR wind archive at DTU Wind Energy SAR winds available at: https://satwinds.windenergy.dtu.dk/ NRT processing of Sentinel-1 and archive of Envisat

SAR for wind resource assessment Wind climatology (wind atlas) from SAR wind fields High accuracy needed: 𝐸𝑛𝑒𝑟𝑔𝑦 ~ 𝑈 3 Long time series necessary Use multiple sensor (Envisat, Radarsat-1, Sentinel-1A, and B) Need to inter-calibrate for consistency (Badger et. al .(2019), in review)

WRF SAR Mean wind speed maps US East Coast 25 designated areas Meso-scale model WRF is available for planning of wind farm projects. Satellite (SAR) and meso-scale model (WRF) based mean wind maps. Gradients in the mean wind speed with the distance to shore. More spatial variability in the mean wind speed from SAR. WRF data from WIND Toolkit Draxl et.al. (2015) SAR derived mean wind speed map of the US East Coast (a) Mean wind speed from 7 year WRF model (b) Increase of wind speed with distance to shore How is this useful for wind energy? To be published: Ahsbahs et.al. (2019)

Variation of wind resources within each wind farm SAR WRF Extract mean wind speed within each potential wind farm area Larger variation of wind resources from SAR than model Hint towards underestimating spatial variability of wind resources? What is shown? Violin plot X-axis: different potential wind farm locations Y-axis: mean wind speed Distributions of SAR and WRF mean wind speeds over the region What does this mean? Span of mean wind speeds higher from SAR Explanation: Higher resolution SAR, edges and artefacts, lower sampling => SAR likely upper boundary To be published: Ahsbahs et.al. (2019)

Wind turbine and farm wakes What is the wind farm wake? Energy extracted Decreased wind speed Increased turbulence Wind farm wakes in SAR: SAR observes the sea surface Wakes at hub height vertical extrapolation U(z)

SAR wind fields in the presence of wind farm wakes Wind direction English East Coast Wind farms Wakes visible Wind farms SAR image of the English East Coast (27th of April 2018) Easterly wind direction Wakes clearly visible as reduced wind speed But: Can structures in the wake be resolved? Is the wind retrieval accurate under wakes conditions?

Large scale reference data in the wake – the BEACon experiment Two Doppler radars Large scale measurements Collocated SAR images Doppler radar wind measurements provided by Ørsted’s BEACon experiment

Large scale reference data in the wake – the BEACon experiment SAR Doppler radar SAR backscatter image (a) Wind field at 100 m (b) Features of the wake similarly present Singular wakes detectable in SAR with correct position Velocity deficit downstream (c) Observation:

Large scale reference data in the wake – the BEACon experiment Velocity deficit characterizes reduced winds in the wake. Structure of the wake similar in SAR and Doppler radar. First comparison at full scale wind farm. Features of the wake similarly present Singular wakes detectable in SAR with correct position

Summary High resolution wind resource maps Identify variation in wind resources Compare with meso-scale model outputs Wind farm wakes offshore Wakes are visible in SAR images Structure of the wind farm wake can also be observed

Challenges SAR observation at the surface vs. atmospheric processes higher in the atmosphere. Wind turbines operate up to 250m height Limited sampling rate and sampling biases from SAR. The interaction of wind farm wakes with the ocean surface needs to be better understood.

Acknowledgements Satellite SAR data from the ESA, Copernicus, and NOAA. The SAR Ocean Products System (SAROPS) by the Johns Hopkins University, Applied Physics Laboratory and the US National Atmospheric and Oceanographic Administration (NOAA). This work received funding from the EU H2020 program under grant agreement no. 730030 (CEASELESS project) Access to Doppler radar wind observation from the BEACon experiment provided by Ørsted

References Draxl, C., Clifton, A., Hodge, B., McCaa, J.: The Wind Integration National Dataset ( WIND ) Toolkit, Appl. Energy, 151(August), 355–366, doi:10.1016/j.apenergy.2015.03.121, 2015. Ahsbahs, T., Maclaurin, G., Draxl, C, Jackson, C., Monaldo, F., Badger, M.: US East Coast synthetic aperture radar wind atlas for offshore wind Energy, submitted to Wind Energy Science. Badger, M., Ahsbahs, T., Maule, P., Karagali, I. Inter-calibration of SAR data series for offshore wind resource assessment, Remote Sensing of Environment (in review), 2019