Offshore wind resource estimation for wind energy
Alfredo Peñavisibility
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Satellite remote sensing from active and passive microwave instruments is used to estimate the offshore wind resource in the Northern European Seas in the EU-Norsewind project. The satellite data include 8 years of Envisat ASAR, 10 years of QuikSCAT, and 23 years of SSM/I. The satellite observations are compared to selected offshore meteorological masts in the Baltic Sea and North Sea. The overall aim of the Norsewind project is a state-of-the-art wind atlas at 100 m height. The satellite winds are all valid at 10 m above sea level. Extrapolation to higher heights is a challenge. Mesoscale modeling of the winds at hub height will be compared to data from wind lidars observing at 100 m above sea level. Plans are also to compare mesoscale model results and satellite-based estimates of the offshore wind resource.
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Offshore wind resource estimation for wind energy
Hasager, Charlotte Bay; Badger, Merete; Mouche, A.; Karagali, Ioanna; Astrup, Poul; Nielsen, Morten;
Bingöl, Ferhat; Pena Diaz, Alfredo; Larsén, Xiaoli Guo; Badger, Jake
Total number of authors:
13
Published in:
Proceedings (CD-ROM)
Publication date:
2010
Document Version
Publisher's PDF, also known as Version of record
Link back to DTU Orbit
Citation (APA):
Hasager, C. B., Badger, M., Mouche, A., Karagali, I., Astrup, P., Nielsen, M., Bingöl, F., Pena Diaz, A., Larsén,
X. G., Badger, J., Hahmann, A. N., Mikkelsen, T., & Gryning, S-E. (2010). Offshore wind resource estimation for
wind energy. In Proceedings (CD-ROM) (pp. 005). Techno-Ocean Network.
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Offshore wind resource estimation for wind energy
C. B. Hasager
Risø National Laboratory for Sustainable Energy, Technical University Denmark
M. Badger
Risø National Laboratory for Sustainable Energy, Technical University Denmark
A. Mouche
CLS, France
I. Karagali
Risø National Laboratory for Sustainable Energy, Technical University Denmark
P. Astrup
Risø National Laboratory for Sustainable Energy, Technical University Denmark
M. Nielsen
Risø National Laboratory for Sustainable Energy, Technical University Denmark
F. Bingöl
Risø National Laboratory for Sustainable Energy, Technical University Denmark
A. Peña
Risø National Laboratory for Sustainable Energy, Technical University Denmark
X.G. Larsén
Risø National Laboratory for Sustainable Energy, Technical University Denmark
J. Badger
Risø National Laboratory for Sustainable Energy, Technical University Denmark
A.Hahmann
Risø National Laboratory for Sustainable Energy, Technical University Denmark
T. Mikkelsen
Risø National Laboratory for Sustainable Energy, Technical University Denmark
S.-E.Gryning
Risø National Laboratory for Sustainable Energy, Technical University Denmark
I. INTRODUCTION
Abstract – Satellite remote sensing from active and passive
microwave instruments is used to estimate the offshore wind
resource in the Northern European Seas in the EU-Norsewind
project. The satellite data include 8 years of Envisat ASAR,
10 years of QuikSCAT, and 23 years of SSM/I. The satellite
observations
are
compared
to
selected
offshore
meteorological masts in the Baltic Sea and North Sea. The
overall aim of the Norsewind project is a state-of-the-art wind
atlas at 100 m height. The satellite winds are all valid at 10 m
above sea level. Extrapolation to higher heights is a challenge.
Mesoscale modeling of the winds at hub height will be
compared to data from wind lidars observing at 100 m above
sea level. Plans are also to compare mesoscale model results
and satellite-based estimates of the offshore wind resource.
Satellite remote sensing offer several options on
mapping of ocean winds. Satellite-based winds are used in
oceanography and forecasting. Recently offshore wind
energy resource mapping has taken advantage of satellite
remote sensing data. In the project EU-Norsewind satellite
remote sensing is used in combination with ground-based
wind lidars and meteorological mast data(1). The collected
data are used for either comparison to atmospheric model
results or as input to the meteorological modeling. Satellite
ocean wind maps are valid at 10 m above sea level.
Offshore wind turbines operate at around 100 m. Therefore
the vertical extrapolation of winds – the wind profile- is
important to assess the wind resource.
1
mapping at shorter distance to the coast using ASCAT as
the basic input to this product.
The QuikSCAT ocean wind products include a 10-year
time series. For wind resource mapping the particular
advantage is that wind vectors are available. The
twice-daily observational pattern allows the morning
versus evening passes to be compared. This reveal diurnal
wind variations over the ocean. Fig. 2 shows an example
from the Northern European Seas.
RESULTS
Passive microwave
The longest series of satellite ocean winds are from the
passive microwave radiometer, SSM/I. Examples of
resource mapping using SSM/I in the North Sea and Baltic
Sea are presented in (2; 3). The series encompass 23 years
of data. Some have been compared to the FINO-1
meteorological station http://www.fino-offshore.de/ in the
North Sea. The calculation of the Weibull scale and shape
parameters (4) has been done using SSM/I and comparing
to the FINO-1 meteorological data. It appears that the
distributions are similar, see Fig. 1.
Weibull curves based on SSM/I data and 10 minutes
FINO1 data from 2004 to 2007. WAsP method.
0.1
ssmi15
ssmi14
ssmi13
FINO1
0.08
Fig. 2. Mean wind speed difference observed from
10-years of QuikSCAT ocean wind maps from REMSS.
The variation in diurnal wind speed from the morning
passes minus the afternoon passes show up to 0.5 m/s near
the UK whereas the difference is down to -0.5 m/s at some
locations in the Baltic Sea. The mean wind speed in the
area is shown in Fig. 3. The variation is from around 6.5
m/s to 9.5 m/s in the area.
0.06
0.04
0.02
0
0
2
4
6
8
10
12
14
16
18
20
22
24
26
Wind speed [m/s]
Fig. 1. Weibull curves calculated from SSM/I satellite
observations from F-13, F-14 and F-15 and from FINO-1
meteorological data.
The variations at longer timescale, annual and monthly,
are also quantified. For the inter-annual variability it is
found that the actual power production of the wind farms
in Denmark vary approximately with the variation of the
SSM/I-based wind-index (2). At Dogger Banke in the
North Sea where the largest offshore wind farm cluster is
planned in the UK Round 3 is investigated from SSM/I.
The wind speed variation at the hourly timescale is not
very large far out in the North Sea at Dogger Banke and
the inter-annual variability is moderate (3). SSM/I only
map ocean winds rather far from the coastline.
Fig. 3. Mean wind speed observed from 10-years of
QuikSCAT ocean wind maps from REMSS.
Synthetic Aperture Radar (SAR)
SAR ocean wind mapping has the advantage of higher
spatial resolution compared to passive microwave and
scatterometers. Thereby ocean wind mapping is possible at
much shorter distances from the coastline. Background on
ocean wind mapping from satellite SAR is provide in (5;
6).
In the Baltic Sea the SAR-based ocean wind mapping
has been compared to the FINO-2 meteorological data
http://212.201.38.20/fino2/. The mast is located
approximately 38 km from land between Germany and
Sweden at the position 13.154167 E, 55.006944 N.
A map of satellite ocean wind from SAR, from the
European Space Agency (ESA) satellite Envisat is shown.
The winds are retrieved using the CMOD5 (7) and with
input of wind direction from the NOGAPS model in the
Scatterometer
Scatterometers in space also have a long history.
Several scatterometers have been and are now flown
on-board satellite platforms. Currently the ASCAT is the
operational scatterometer for ocean wind vector mapping
used
in
operational
forecasting
http://www.knmi.nl/scatterometer/. Furthermore, the new
coastal ocean wind mapping product allow improved
2
10 m. This was done using the neutral logarithmic profile
and the roughness of Charnock with the constant set at
0.0144 following (11).
ANSWRS system (8) from Johns Hopkins University,
Applied Physics Laboratory. The map in Fig. 4 covers the
southwestern part of the Baltic Sea. The winds are coming
from the east with up to 15 m/s wind speed and down to 5
m/s in the Danish interior seas. Lee effects of islands and
other land areas are clear.
Fig. 4. Satellite SAR ocean wind map from Envisat
ASAR Wide Swath Mode shows part of the Baltic Sea and
interior Danish Seas. The image is from 8 November 2009
at 20.45 UTC.
178 SAR wind maps based on Envisat ASAR Wide
Swath Mode images collected from December 2007 to
September 2009 are collocated with hourly averages
centered near the satellite observational time from the
FINO-2 mast. The average wind speed from the
meteorological mast is 8.16 m/s.
Two pixel-averaging methods in the SAR wind maps
are tested. One is the footprint function for area-averaging
following Gash (9), the other is a simplified ellipse shape
following (10). The footprint of Gash has a physical basis,
and the averaging of pixels follows the probability density
function upwind of the location of interest (here set as the
90% limit). The simple ellipse on the other hand averages
all pixels within the ellipse with equal weight (according to
the size of area).
The result on average wind speed from the Gash
footprint is 8.19 m/s and from the ellipse-footprint 8.11
m/s.
Linear regression results between the two data sets are
also calculated. The result is y = 1.0386x - 0.2839 with R²
= 0.7729 for the Gash footprint and y = 1.0278x - 0.3198
with R² = 0.7351 for the ellipse footprint. Thus the best
result is found using the Gash footprint for the comparison
at the FINO-2 mast using 178 collocated pairs. Scatterplots
of data are shown in Fig. 5.
Fig 5. Comparison of wind speed observed at FINO-2
meteorological mast and from satellite SAR using 178
collocated Envisat images. Upper panel shows linear
regression results using the Gash footprint for averaging in
the SAR wind maps, lower panel similar for an ellipse
footprint averaging.
The Weibull scale and shape parameters are calculated
using all available SAR wind maps, i.e. 409 at the FINO-2
location. The images are collected from 2003 to 2010 from
Envisat ASAR. The Weibull scale parameter is found to be
9.21 m/s (uncertainty 0.25 m/s) and the shape parameter
1.93 (uncertainty 0.07). This result is found using the
fitting function of the maximum likelihood estimator.
Using the same data set and the WAsP fitting function the
result is Weibull scale 9.03 m/s (uncertainty 0.26 m/s) and
Weibull shape 1.84 (uncertainty 0.08). Using all
meteorological observations from the mast, extrapolated to
10 m as described above and using the WAsP fitting
method gives Weibull scale of 8.49 m/s and Weibull shape
of 2.31.
The observations from the mast are from a shorter
period 1 year 9 months but sampled every 10 minutes,
The lowest level of the FINO-2 mast is at 32 m above
sea level. The SAR-based ocean winds are valid at 10 m
above sea level. Thus before comparing the two data sets,
the FINO-2 meteorological data are extrapolated down to
3
whereas the satellite observations are from 7 years but
sampled very infrequently. Each satellite wind map
represent around 1-hour time slot, though taken within 15
seconds. The natural variations in wind speed during
seasons and year introduce uncertainty to wind resource
estimates.
Results in the North Sea show good comparison
between three meteorological masts: Horns Rev, FINO-1
and Høvsøre coastal masts, comparing SAR-based wind
resource statistics and meteorological observations (12;
13).
The future aim is to also compare the results to
mesoscale model results in the North Sea and the Baltic
Sea.
Roskilde, 2000.
[5]
Christiansen, M. B., Hasager, C. B., Thompson, D.
R., and Monaldo, F., "Ocean winds from synthetic
aperture radar," Ocean remote Sensing: Recent
Techniques and Applications., edited by R. Niclos
and V. Caselles Research Singpost Editorial, 2008,
pp. 31-54.
[6] Monaldo, F., Thompson, D. R., Winstead, N. S.,
Pichel, W., Clemente-Colón, and Christiansen, M.
B., "Ocean wind field mapping from synthetic
aperture radar and its application to research and
applied problems," Johns Hopkins Apl.Tech.Dig.,
Vol. 26, 2005, pp. 102-113.
CONCLUSION
Satellite remote sensing of ocean winds is readily
available for the analysis. The challenges are to assess the
difference in winds at 10 m above sea level and the winds
at higher heights, the height of operation wind turbines,
and to fit the probability functions to the observations. The
EU-Norsewind project aims to quantify the differences
through atmospheric modeling and comparison to
observations. The work is in progress. The preliminary
results indicate that satellite winds compare well to
individual meteorological observations, and that the
fitting method chosen for wind resource calculation can
change the result.
[7]
Hersbach, H., Stoffelen, A., and de Haan, S., "An
improved
C-band
scatterometer
ocean
geophysical model function:CMOD5," Journal of
Geophysical Research, Vol. 112, 2007.
[8] Monaldo, F., "The Alaska SAR demonstration
and near-real-time synthetic aperture radar winds,"
John Hopkins APL Technical Digest, Vol. 21, 2000,
pp. 75-79.
[9] Gash, J. H. C., "A note on estimating the effect of
a limited fetch on micrometeorological evaporation
measurements," Boundary-Layer Meteorology, Vol.
35, 1986, pp. 409-413.
ACKNOWLEDGEMENTS
Funding from EU-Norsewind TREN-FP7EN-219048,
met-data from DONG energy, Vattenfall and FINO, and
satellite data from the European Space Agency and
Remote Sensing Systems are acknowledged.
[10] Hasager, C. B., Dellwik, E., Nielsen, M., and
Furevik, B., "Validation of ERS-2 SAR offshore
wind-speed maps in the North Sea," International
Journal of Remote Sensing, 2002.
REFERENCES
References
[11]
[1] Hasager, C. B., Mouche, A., Badger, M., Astrup, P.,
and
Nielsen,
M.,
"Satellite
winds
in
EU-Norsewind,"
European
Wind
Energy
Conference (EWEC) 2008, Marseilles (FR), 2009.
[12] Badger, M., Badger, J., Nielsen, M., Hasager, C. B.,
and Peña, A., "Wind class sampling of satellite SAR
imagery
for offshore wind resource mapping," JOURNAL OF APPLIED
METEOROLOGY AND CLIMATOLOGY, 2010.
[2] Hasager, C. B., Peña, A., Christiansen, M. B.,
Astrup, P., Nielsen, M., Monaldo, F. M., Thompson,
D. R., and Nielsen, P.,
"Remote sensing
observation used in offshore wind energy," IEEE
Journal of Selected Topics in Applied Earth
Observations and Remote Sensing, Vol. 1, No. 1,
2008, pp. 67-79.
[3]
Garratt, J. R., "Review of drag coefficients over
oceans and continents," Monthly Weather Review,
Vol. 105, No. 7, 1977, pp. 915929.
[13] Badger, M., Badger, J., Nielsen, M., Hasager, C. B.,
and Peña, A., "A hybrid method for offshore wind
resource assessment," 2010.
Hasager, C. B., Karagali, I., Astrup, P., Badger, M.,
Mouche, A., and Stoffelen, A., "Offshore wind atlas
for Northern European Seas," 2010.
[4] Mortensen, N., Heathfield, D. N., Landberg, L.,
Rathmann, O., TROEN, I., and Petersen, E. L.,
"Wind Atlas Analysis and Wind Atlas Analysis and
Application program:WAsP 7.0 Help Facility," Risø
National
Laboratory,
ISBN
87-550-2667-2,
4
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