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Offshore wind energy planning map for the Aegean Sea based on Envisat ASAR

Hasager, Charlotte Bay; Badger, Merete

Publication date:2015

Document VersionPeer reviewed version

Link back to DTU Orbit

Citation (APA):Hasager, C. B., & Badger, M. (2015). Offshore wind energy planning map for the Aegean Sea based on EnvisatASAR. Poster session presented at IOVWST Meeting 2015, Portland, OR, United States.

The Aegean Sea is an area with promising wind potential for offshore wind farms. The Greek authorities therefore have focus on this. Our study contributes details on the wind resource of relevance for early planning of wind farms. More specifically we use satellite remote sensing of ocean surface winds combined with Numerical Weather Prediction (NWP) modeling to estimate the wind resource at hub-height. Envisat Advanced Synthetic Aperture Radar (ASAR) scenes from 10 years have been retrieved, calibrated and processed using the geophysical model function CMOD5.N (Fig. 1). Wind direction from a global atmospheric model is used to retrieve wind fields at around 1 km resolution. The 10-m wind resource map based on SAR is calculated through Weibull statistics (Hasager et al. 2015). The spatial resolution of the SAR based wind resource map is 0.02° latitude and longitude (Fig. 3). The purpose of this analysis is to bring 10-m mean winds retrieved from SAR up to higher levels in the atmosphere where wind turbines operate. This requires information about the atmospheric stability from a different source – in this case from the Weather Research and Forecasting (WRF) model (Hahmann et al. 2014) (Fig. 2). Previous work has shown that a correction of the vertical wind profile for atmospheric stability effects can be done on the basis of WRF data. However, the method is only robust if the long-term average rather than the instant effect of atmospheric stability is considered (Peña & Hahmann, 2012). Our approach is therefore to combine the 10-m mean wind speed from satellite SAR data with a long-term average stability correction based on WRF simulations in order to calculate profiles of the mean wind speed up to 100 m. Our calculation of the long-term atmospheric stability correction follows the method described by Kelly & Gryning (2010). Once the stability correction parameter, Ψ is known, the wind profile is calculated as follows: where u is wind speed, u* is friction velocity, κ is von Karman’s constant, z is the height, and z0 the aerodynamic roughness length. The brackets denote long-term mean values. Maps of the 100-m mean wind speed are shown with and without a correction for atmospheric stability effects from WRF (Figure 4. and 5). Finally, 10-m wind resource statistics and vertical profiles of the long-term stability correction parameter and the mean wind speed are shown for specific points, as example we show for Anatoliki Kriti II (Fig. 7). The Hellenic Wind Energy Association (HWEA) has supplied a list of polygons, which indicate the location of future bottom mounted wind farms (Phase I) and floating wind farms (Phase II) in the Aegean Sea (Fig. 6).

Offshore wind energy planning map for the Aegean Sea based on Envisat ASAR Charlotte Hasager and Merete Badger, DTU Wind Energy, Technical University of Denmark, Risø Campus, 4000 Roskilde, Denmark,

Meteorology www.vindenergi.dtu.dk

DTU Wind Energy Risø Campus Frederiksborgvej 399 DK-4000 Roskilde, Denmark

Hahmann, A.N., Vincent, C.L., Peña, A., Lange, J., & Hasager, C. B. (2014). Wind climate estimation using WRF model output: Method and model sensitivities over the sea. Journal of Climatology, doi: 10.1002/joc.4217. Hasager, C. B., Mouche, A., Badger, M., Bingöl, F., Karagali, I., Driesenaar, T., Stoffelen, A., Longépé, N. (2015). Offshore wind climatology based on synergetic use of Envisat ASAR, ASCAT and QuikSCAT. Remote Sensing of Environment, 156, 247–263. Kelly, M., & Gryning, S.-E. (2010). Long-Term Mean Wind Profiles Based on Similarity Theory. Boundary-Layer Meteorology, 136(3), 377–390. Peña, A., & Hahmann, A. (2012). Atmospheric stability and turbulence fluxes at Horns Rev—an intercomparison of sonic, bulk and WRF model data. Wind Energy. doi:10.1002/we. Acknowledgement: ESA for Envisat ASAR scenes, software from Johns Hopkins University Applied Physics Laboratory, information from HWEA about planning of wind farms and funding from ESA ResGrow project (http://resgrow.eu/wind/) in kindly acknowledged.

Fig. 1. Overlapping satellite SAR scenes for the Aegean Sea (based on more than 2,000 scenes)

Fig. 2. Maps showing (left) an overview of the three nested WRF domains and (right) the inner domain simulated at 4 km x 4 km spatial resolution which is used for the extrapolation of SAR winds.

Fig. 3. Map showing the mean wind speed (ENW) from SAR for the longitude range 19-29°E and the latitude range 33-42°N. The map coverage and color scaling is approximately the same as for results provided by HWEA (not shown here).

Fig. 5. Map showing the mean SAR wind speed extrapolated to the 100-m level taking atmospheric stability effects into account. This gives the Stability Dependent Wind speed (SDW). The applied long-term atmospheric stability correction is calculated from WRF output data.

Fig. 4. Map showing the mean SAR wind speed extrapolated to the 100-m level without any consideration of atmospheric stability effects. This gives the Equivalent Neutral Wind speed (ENW).

Fig. 6. Map showing the Phase I wind farm locations (left) and the Phase II wind farm locations (right) in the Aegean Sea where SAR wind extrapolation to 100 m has been performed. Image courtesy Google Earth.

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0zz

uzu ψκ

−

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*

Fig. 7. Example of wind resource statistics (left), profile of stability correction (middle) and mean wind profiles (right).