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Influence of orography on wind resource assessment programs

Article · January 2007

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 Influence of orography on wind resource assessment programs

A. Llombart1, A. Mallet1, N. Burillo1, O. Alvarez1 and A. Talayero1

1
Fundación CIRCE, Department of Electrical Engineering Zaragoza University
C/ Maria de Luna 3, 50018 Zaragoza (Spain)
Phone/Fax number:+34 976 762398, e-mail: llombart@unizar.es
Phone/Fax number:+34 976 762424, e-mail: malletad@unizar.es

Abstract. In order to provide optimal sitting of wind RIX coefficient value is too high, recirculation processes
turbines, a reliable estimate of the wind resource over a given (formation of vortexes) could appear. This comportment is not
area is required. This paper compares the performance of two taken into account by the 2D linear equations which govern
models, WAsP and WindSim predicting the power production WAsP calculations and consequently would justify the use of a
of individual turbines. Wind models are potentially important at CFD model to simulate wind flow.
complex terrain sites in the wind resource assessment since
measurements can only be afforded at selected positions, and This analysis gives a general picture of how well the two
the wind variations may be large over short distances. The models are able to predict wind speed and direction, trying to
purpose of this paper is to compare the accuracy with regard to focus on the causes of the generated differences and
wind resource assessment of the commonly used WASP consequently the eventual necessity of the CFD solver in some
package with the WindSim package which uses a more situations.
complete Navier-Stokes solver based on the PHOENICS code
which should give more accurate results in complex terrain. Due to the constant increscent of wind turbines size and
therefore wind farm dimensions, it is absolutely necessary be
able to predict the error committed by the wind assessment
Key words software extrapolating wind speed in both horizontal and
vertical directions. These analysis will consequently focuses
WAsP, WindSim, RIX coefficient, wind shear, cross checking. first on the way that each software model the wind shear at
three determined sites of the three considered terrains. Then,
1. Introduction considering common series of wind data, using cross-checking
methodology [5], comparing the wind speed obtained at one
met mast site from the data registered at another met mast, it
One of the challenges to effective sitting of wind turbines is the has been evaluated how each model would perform the
ability to make reliable and accurate predictions of the wind horizontal extrapolation of wind data and then its reliability to
power resource. Wind models are potentially important at model wind behavior in each determined terrain configuration.
complex terrain sites in the wind resource assessment since
measurements can only be afforded at selected positions, and Some overall conclusions are then drawn as to the relative
the wind variations may be large over short distances. For accuracy of the two models and what factors affect the degree
micro-scale flow (spatial scales of 1 m to 5 km), the WAsP of accuracy.
model [1] is most commonly used in the wind resource
analysis, but in areas with flow separation the model is not very
well suited for resource assessment. Simple terrain corrections 2. The wind resource evaluation methods
(in function of the so called RIX-coefficient ) have been applied
with success to the WAsP simulation for quite complicated Perhaps the first serious software package that has been
terrain. developed in order to assess wind resource was the WAsP
However, there is increasing interest in applying more complete program . This is generally considered to be the reference wind
Navier Stokes models to flow in complex terrain, given that the resource program at the present time.
required computing power is now more affordable and can
produce results in a reasonable time-frame with an acceptable The program is based on similar boundary layer physics to the
spatial resolution. MS-Micro package using the 3D extension by Mason and
The purpose of this paper is to compare the accuracy with Sykes of the original linear 2D equations by Jackson and Hunt
regard to wind resource assessment of the commonly used for wind flow over low hills [6]. In addition, the WAsP model
WAsP package with the WindSim package [2] which uses a includes a roughness change and shelter model to cater for the
more complete Navier Stokes solver based on the PHOENICS effect of obstacles. The model employs a zooming polar
code which should give more accurate results in complex coordinate grid for optimum resolution at the point of interest.
terrain.
Recent research has been carried out using more complete
To study the limitations of the different software packages have Navier-Stokes solvers. One of the first commercial programs to
in modelling the wind comportment at a site, several European use such a solver is the WindSim model developed by Vector
wind farms sites in Southern-Europe situated presenting AS. This package uses the computational fluid dynamics (CFD)
varying degrees of orography have been studied. In order to model PHOENICS as it core solver. Potentially, the program is
evaluate the real necessity of more complex model, such as capable of more accurate results over steep hills than the WAsP
Computational Fluid Dynamics (CFD) [3], it is necessary to model.
identify zones where the WAsP model could commit important
errors. The RIX coefficient [4], based on a steepness ratio can Both software allows the conversion of time history wind
be used to evaluate the accuracy of an estimation. When the measurements (speed and direction). However, they do not
integrate any temporal verification, considering data as annual The wind rose obtained at met mast A1 and A2 presents two
complete cycles. Thus, it is necessary to analyse the wind data main E-W wind direction as it is shown in the following
measured, clean them and select the study data series. Beside, figure 2.
one of the main characteristics of wind is its variability annual,
diurnal and situational. That means that its mean velocity could
change a lot from one year to another and consequently the
wind potential of a determined site. In order to well evaluate an
elected zone wind potential, it is necessary to get a large set of
annually wind data [7]. Scale

3. Selected wind farms sites description A1 Wind rose A1 Weibull.Fit

To study the limitations of the different software packages have

in modelling the wind comportment at a site, three European
wind farms sites situated in varying degrees of complex terrain
were considered in the present project. The RIX coefficient,
defined by the RISO laboratory based on a steepness ratio is
used as reference parameter for the accuracy comparison of
each one of the two previously described models. A2 Wind rose A2 Weibull-Fit
Wind farms selected to perform this comparison process have
been selected according to the level of complexity of their Figure 2: Site A; Wind rose and Weibull distribution
orography at meteorological masts locations, wind orientation
and average wind speed. 3.2 Site B
For each project, two different meteorological mast were
available. At site A2, B2 and C2 measurements were carried out The B wind farm site is located in a semi-complex terrain, with
with a single height (so-called HH) tubular tower while a two an relatively important increscent of elevation in the area where
measurement height (LW and HH) were employed at site A1, are situated both met mast B1 and B2.
B1 and C1. Consequently, vertical wind profile can be The following figure 3 shows the RIX map obtained in this
calculated from measurement only for these three previously case, underlining important speed-ups for both westerly and
mentioned met mast. southerly flow.
The following points present the characteristic of each project
considered in the present study.
RIX Coeff. scale

3.1 Site A 0.6

0.56

The first wind farm site studied is located in a relatively flat 0.52

terrain. As can be seen on the following RIX map, met mast A1 0.48

0.44
and A2 are situated in a very flat area, presenting a RIX 0.4

coefficient almost null. 0.36

However, it is necessary to consider but this site has several 0.32

0.28
roughness areas, such as villages, different landscapes, not 0.24
taken into account. This assumption might affect the results 0.2

obtained at lower altitude, such as met mast A2 measurement 0.16

0.12
height (Higher Height HH: 10m). 0.08

Figure 3: Site B; RIX map

RIX Coeff. scale
The annual average wind speed of this site at met mast A2 at its
0.6 higher height (40 m) is 6.2 m/s.
0.56
The distribution of wind has a classic Weibull distribution
0.52

0.48
(close to Raleigh distribution) with few calms. As it is shown in
0.44 following figure 4, the Weibull-fit is very good, presenting a
0.4 high value of shape parameter K.
0.36

0.32
Wind rose obtained at site B presents a dominance of winds
0.28 coming from the NW but not defined as clear as site A. Indeed,
0.24 the wind frequency is generally distributed all over direction-
0.2
binned sectors from W to NNW. This characteristic will be
0.16

0.12
taken into account in the further analysis of wind shear
0.08

Figure 1: Site A; RIX map

The annual average wind speed of this site at met mast A1 at its
higher height (40 m) is 6.6 m/s. However, the high number of Scale
calms cumulated with a low dispersion of high wind speeds
would generate a bad adjustment of the Weibull distribution fit,
which would introduce an increscent of uncertainties. B1 Wind rose B1 Weibull
 4. Micro-scale model set-up

4.1 WAsP

WasP version 8.3 was employed in this study. Height contours

for each 5 m were available for the three wind farms sites
B2 Wind rose B2 Weibull considered.
Table 1presents the dimensions of the map used to perform all
Figure 4: Site B; Wind rose and Weibull distribution simulations in each case.

3.3 Site C Table 1: Set-up of WAsP simulations for sites A,B and C.
Site_A Site_B Site_C

Site C presents the most complex terrain of the three considered Size of map (Xkm x Ykm) 38.4 x 28.2 4x7 12.2 x 9

wind farm sites. Contours (m) 10 10 5

Both met masts C1 and C2 are located on the top of north-south Roughness length (m) 0.05 0.03 0.03
high ridge presenting strong slopes and consequently
considerable speed-up for both westerly and easterly flow. As As roughness was not considered as the influent parameter to
shown in figure 5, RIX coefficient can reach values close to study in details in this project, a fixed roughness height was set
60 % in the previously mentioned wind directions. Then, up for each one of the three terrains.
important deflections can be assumed between both met mast However, it is necessary to keep in mind that in the case of
locations. Site A, presenting a very flat terrain with little forest and
villages, little changes in roughness might generate a strong
RIX Coeff. scale affection to the wind speed estimation a low height, such as
Mast A2.
0.6

0.56

0.52
4.2 Windsim
0.48

0.44
Windsim version 4.6.1 was employed in this study. The details
0.4

0.36
of the models set-up for the three wind farms sites considered
0.32 are shown in the following Table 2.
0.28

0.24

0.2
Table 2: Set-up of Windsim simulations for sites A,B and C.
Site_A Site_B Site_C
0.16

0.12
Size of model domain (km2) 15 · 10 4·7 9· 9

0.08 Size of refinement area (km2) 4.5 ·4 2.5 · 3.5 -

Figure 5: Site C; RIX map Minimum grid spacing (m) 46.2 · 47.1 33.8 · 33.2 60 · 60

Total number of cells 791616 490176 928650

The annual average wind speed of this site at met mast C1 at its Height (m) of the lowest layers 10, 34, 64, 100 4, 18, 43, 79 15.4, 49.8, 91.4, 140
higher height (40 m) is 6.2 m/s. Number of cells vertically 30 35 40
Due to difference of orientation of the highest slopes, there are
important differences in flow orientation characterized by the
difference of the wind rose shown in figure 6. Windsim, as it was said previously, is based on the 3D
The Weibull fit is not good because there are a lot of values Reynolds Averaged Navier-Stokes equations. This model
around the 4-5 m/s that decompensate the distribution. solves the atmospheric flow for a “steady state” case for a
chosen wind direction. The total number of direction-binned
sector are 16. Simulating for several different wind directions,
an average wind speed value can be consequently generated
weighting each sector average wind speed by wind frequency.
The models are run for a given set of constructed boundary and
Scale initial conditions common to the three terrains considered in
this study : a 10 m/s horizontal wind speed above the boundary
layer and a logarithmic vertical wind profile (corresponding to
C1 Wind rose C1 Weibull neutral stability conditions) for the lateral boundary conditions
are guessed. The standard k-ε turbulence closure scheme is
applied in both models.

5. Data analysis and discussion

5.1 Wind shear

C2 Wind rose C2 Weibull To perform the wind shear comparison study, only met mast
presenting various levels of measurements, in order to be able
Figure 6: Site C; Wind rose and Weibull distribution to estimate the vertical evolution of wind speed from theoretical
measured values. Met mast elected for the wind shear study
were the highest met masts with several measurement levels
available for each one of the three terrain configuration.
The following process was realized in each one of the two
model studied to estimate the wind shear at mast A1, B and C1.
Various wind turbines were modeled at meteorological mast in function of the wind direction. Therefore, it has been chosen
location, with different hub height, in order to estimate the to study the vertical evolution of wind for the predominant
vertical evolution of wind direction and speed. direction-binned sectors.

In order to perform a suitable comparison until heights up to Site A1: The following figure 8 presents the wind shear
100 meters, it has been chosen to extrapolate the wind speed calculated theoretically, and computed respectively by WasP
registered at met mast measurement levels applying the so- and Windsim for the predominant sectors W and E.
called potential law, known to be appropriate in flat terrain or .
gentle hills. As the mast permits two level of measurements, it
is possible to determine the alpha coefficient defined by the
120
Power Law following equation, which is commonly used in
most of the cases that require height extrapolation.
α 100
U(h1) ⎛ (h1) ⎞
=⎜ ⎟
U(h2) ⎜⎝ (h2) ⎟⎠ Equation(1)
80

The figure 7 presents the wind shear obtained from met mast

Height [m]
measurements, from the WASP model and finally from the 60
Windsim model.
40
120

20
100

0
80 4 5 6 7 8 9 10 11 12 13 14
Wind Speed [m/s]
Height [m]

A1 Met Mast_E A1 WAsP Model_E A1 Windsim Model_E

60 A1 Met Mast_W A1 WAsP Model_W A1 Windsim Model_W

40 Figure 8: Site A1; Wind shear in the main wind directions

20
The closest wind shears to the theoretical measured one is
surprisingly given by Windsim, being however overestimated.
The vertical wind profile given by measurement present for
0 both met mast a more vertical tendency. The inaccuracy of both
5.00 6.00 7.00 8.00
Wind Speed [m/s] numerical models can be due to the absence of roughness
A1 Met Mast A1 WAsP Model A1 Windsim Model
which, in this type of terrain, influence significantly the
B1 Met Mast B1 WAsP Model B1 Windsim Model evolution of wind speed, especially at lower height. Indeed, we
C1 Met Mast C1 WAsP Model C1 Windsim Model
can see that the difference at heights lower than 40m are much
Figure 7: shows the results obtained for mast A1, B1 and C1 higher than at higher heights, underlining the importance of
between the heights of 20 and 100 m: roughness model in very flat terrain where influence of
orography is negligible.
Site A1: Despite of the low changes in orography; both software
overestimate the wind shear at met mast site, underlining the The following table 3, (predominant directions being underlined
importance of an accurate assessment of the roughness at met in yellow), shows the wind shear calculated by both model
mast site, especially in the case of very low measurement height assuming a “power law coefficient” obtained using a potential
and very flat terrain. Moreover, in this case, site A being regression between wind speeds calculated between 20 and 100
located very close to sea, atmospheric stability is another m height for each model in function of deflection and RIX
parameter which could have generated inaccuracy for both coefficient by sectors.
models. It can be easily seen that in this case orography has no influence
of the difference between WAsP and Windsim models.
Site B1: In this case, results obtained with the WASP model is
closer to measurements. The Windsim model tends to produce a Table 3: Sector power Law coefficient estimation for WAsP
too vertical wind shear. The lower influence of roughness and Windsim model in function of defection, Speed-up and
changes, the no very complex orography and the good Weibull RIX. Site A
Sector Frequency alfa Windsim alfa WAsP Speed up [%] Deviation [º] RIX
fit could explain the improvement of WASP model respect to N 0.015 0.174 0.201 3% -4% 0%
NNE 0.015 0.106 0.206 -2% -2% 0%
Site A1. NE 0.016 0.495 0.195 -3% 1% 0%
ENE 0.022 0.657 0.171 0% 3% 0%
E 0.264 0.113 0.121 5% 4% 1%
Site C1: As the logarithmic corrected model of WASP is not ESE 0.068 0.034 0.037 10% 2% 4%
0.023 -0.015 0.084 11% -1% 0%
valid in very complex terrain, as expected, the values obtained SE
SSE 0.036 0.123 0.139 8% -3% 0%
with the CFD model are much closer to real measured wind S 0.051 0.147 0.184 2% -4% 1%
SSW 0.049 0.175 0.217 -2% -2% 2%
speeds. The inverted wind profile modelled by Windsim SW 0.046 0.171 0.206 -3% 1% 3%
0.111 0.157 0.204 0% 4% 1%
represent with much accuracy the comportment of wind in top WSW
W 0.139 0.138 0.144 5% 4% 0%
of a strong hill, as site C1. For this so complex terrain wasp is WNW 0.053 0.096 0.101 10% 2% 0%
NW 0.064 0.122 0.161 11% -1% 0%
not enough to evaluate the site. NNW 0.026 0.141 0.216 8% -3% 0%
Global -- 0.126 0.141 1%

Due to the difference of elevation change in function of the

wind direction, wind shear could present very different profiles
Site B1: The following figure 9 presents the wind shear Site C1: The following figure 10 presents the wind shear
calculated theoretically, and computed respectively by WasP calculated theoretically, and computed respectively by WasP
and Windsim for the predominant sectors SSE, S, WNW and and Windsim for the predominant sectors ENE, NE, SW and
NNW. SSW.

120

120

100

100

80

80
Height [m]

60

Height [m]
60

40
40

20
20

0
5 6 7 8 0
Wind Speed [m/s] 2 3 4 5 6 7 8 9 10 11 12 13 14
C1 Met Mast_SSE C1 WAsP Model_SSE C1 Windsim Model_SSE Wind Speed [m/s]
C1 Met Mast_S C1 WAsP Model_S C1 Windsim Model_S
C1 Met Mast_WNW C1 WAsP Model_WNW C1 Windsim Model_WNW B1 Met Mast_ENE B1 WAsP Model_ENE B1 Windsim Model_ENE
C1 Met Mast_NW C1 WAsP Model_NW C1 Windsim Model_NW
C1 Met Mast_NNW C1 WAsP Model_NNW C1 Windsim Model_NNW B1 Met Mast_SW B1 WAsP Model_SW B1 Windsim Model_SW
B1 Met Mast_WSW B1 WAsP Model_WSW B1 Windsim Model_WSW
B1 Met Mast_NE B1 WAsP Model_NE B1 Windsim Model_NE

Figure 9: Site B1; Wind shear in the main wind directions

Figure 10: Site C1; Wind shear in the main wind directions
The following table 4 (predominant directions being underlined
in yellow) shows the wind shear calculated by both model The following table 4 (predominant directions being underlined
assuming a “power law coefficient” obtained using a potential in yellow) shows the wind shear calculated by both model
regression between wind speeds calculated between 20 and 100 assuming a “power law coefficient” obtained using a potential
m height for each model in function of deflection and RIX regression between wind speeds calculated between 20 and 100
coefficient by sectors. m height for each model in function of deflection and RIX
coefficient by sectors.
Table 4: Sector power Law coefficient estimation for WAsP
and Windsim model in function of defection, Speed-up and Table 5: Sector power Law coefficient estimation for WAsP
RIX. Site B and Windsim model in function of defection, Speed-up and
Sector Frequency alfa Windsim alfa WAsP Speed up [%] Deviation [º] RIX RIX. Site C
N 0.053 0.053 0.096 51% -9% 9% Sector Frequency alfa Windsim alfa WAsP Speed up [%] Deviation [º] RIX
NNE 0.036 0.073 0.124 33% -7% 10% N 0.023 0.121 0.282 -17% 12% 39%
NE 0.040 0.090 0.153 26% 1% 12% NNE 0.028 0.289 0.623 13% 22% 17%
ENE 0.040 0.070 0.133 35% 8% 14% NE 0.122 0.000 0.160 46% 17% 40%
E 0.043 0.039 0.091 54% 9% 17% ENE 0.148 -0.060 0.034 65% 6% 49%
ESE 0.041 0.061 0.068 70% 6% 16% E 0.078 -0.060 0.009 65% -6% 57%
SE 0.043 0.037 0.049 75% -1% 23% ESE 0.020 0.003 0.159 46% -17% 65%
SSE 0.087 0.043 0.069 68% -6% 33% SE 0.020 0.020 0.140 13% -22% 51%
S 0.088 0.064 0.098 51% -9% 21% SSE 0.030 0.198 0.170 -17% -12% 26%
SSW 0.058 0.122 0.127 33% -7% 10% S 0.066 0.176 0.217 -17% 12% 18%
SW 0.041 0.120 0.151 26% 1% 15% SSW 0.063 -0.010 0.157 13% 22% 27%
WSW 0.046 0.108 0.133 35% 8% 15% SW 0.136 0.015 0.220 46% 17% 45%
W 0.072 0.078 0.102 54% 9% 15% WSW 0.174 -0.040 0.043 65% 6% 43%
WNW 0.107 0.052 0.071 70% 6% 14% W 0.028 -0.220 -0.042 65% -6% 30%
NW 0.117 0.046 0.053 75% -1% 19% WNW 0.018 -0.050 0.215 46% -17% 58%
NNW 0.089 0.055 0.067 68% -6% 11% NW 0.022 0.021 0.349 13% -22% 54%
Global -- 0.062 0.091 16% NNW 0.022 0.090 0.263 -17% -12% 53%
Global -- -0.020 0.074 42%

In most of the sectors presented in the previous figure 9, WAsP

tends to produce a wind shears closer to the calculated from It is possible to see in the previous figure 10 that in any of the
measurements. Windsim has a slight tendency to give “too main direction none of the models represent correctly the
vertical” wind profiles. However, if we observe sectors vertical evolution of the wind speed, representing well the
presenting the highest RIX coefficients (sectors SSE and S inversion of the wind shear. However, it can be seen that, in the
with a respective value of 33% and 21%), Windsim reproduce case of sectors ENE, SW and NE, presenting the highest RIX
in a better way the inversion of the wind shear in that direction, coefficients (respectively 40 %, 45% and 49 %) the wind shear
the logarithmic vertical profile applied by Wasp being too modelled by Windsim is closer to the wind shear calculated
optimistic in that cases. from measurements, reproducing in a more realistic way
For the most energetic sectors, which would influence relatively its inverted tendency. Nevertheless, in the most
significantly on the average wind speed, WAsP produce a more energetic sector, surprisingly Windsim behaviour is worse than
realistic wind shear, Windsim being really conservative. It is WAsP, Windsim being to conservative.
necessary to take into account that the RIX coefficient in this
direction is lower than in the SSE and S directions (11%). Referring to the results presented in Figure 7, Windsim
The little size of the map (cf. Table 2) could be a reason of the reproduce with accuracy the wind shear at site C1, and shows
difficulties met by Windsim to produce a realistic model of site that it is more able to describe the vertical evolution of wind
B. With a larger area of calculus, the CFD model could be more flows in a terrain presenting high slopes where processes such
capable to reproduce the wind flow in this case. as recirculation processes can appear.

4.2 Cross checking

 The following tables the deviation of average wind speed
A cross-checking process has been performed to evaluate the respect to the theoretical calculated value from measurements
ability of each software to reproduce changes in wind speed and for each model.
direction over the type of terrain considered in function of their
RIX coefficient map. Table 8: Site B. Wind speed deviation. WAsP model
Reference met mast
For each one the three selected wind farms site, common
B 1_HH B 1_LH B 2_LH

Modelled
temporal series of wind data (wind speed and direction) or
registered by the both met mast considered has been set as input B 1_HH 0.2% -1.7% 4.0%
in each software in order to evaluate the ability of each model B 1_LH 1.8% 0.0% 5.8%
to reproduce wind flow at one location (modelled site)., using B 2_LH -3.3% -5.0% 0.2%
the wind data collected at the other location (Reference met
mast site). Afterwards, the average value of the wind speed
Table 9: Site B. Wind speed deviation. Windsim model
obtained at the modelled site is compared with the theoretical
Reference met mast
average value of wind speed calculated from the measured data.
The difference that it can be seen in the WAsP model when the B 1_HH B 1_LH B 2_LH

Modelled
same mast is used as Reference met mast and as Modelled site B 1_HH 0.0% -10.2% -6.8%
is due to the Weibull-fit. B 1_LH 11.3% 0.0% 3.8%
B 2_LH 7.4% -3.7% 0.0%
Site A:
For this type of semi-complex terrain, as in its estimation of
The following tables the deviation of average wind speed wind shear, WASP provides in this case more suitable results.
respect to the theoretical calculated value from measurements The constant roughness of that site avoid the uncertainties met
for each model. in the previous less complex terrain Site A. An error within a
range of 1% to 3,5% is reached with WASP, while an error of
Table 6: Site A. Wind speed deviation. WAsP model +/- 11% is obtained with Windsim between Mast B1 and B2 at
Reference met mast their highest height.
A 1_HH A 1_LH A 2_LH It can seen also that in the case of a series of data presenting a
Modelled

better Weibull distribution adjustment, WAsP generate a much

A 1_HH 3.3% 17.7% 16.8%
model of wind flows.
A 1_LH -8.8% 3.6% 5.2% As for the vertical extrapolation, the low size of the map can be
A 2_LH -9.1% 5.0% 3.3% explain the difficulties met by Windsim to simulate the
horizontal evolution of wind flows.
Table 7: Site A. Wind speed deviation. Windsim model As for site A, in the case of a semi-complex terrain with not any
Reference met mast important changes of roughness, the cross-checking comparison
has shown that the linear model would preferable in this case.
A 1_HH A 1_LH A 2_LH
Modelled

A 1_HH 0.0% 11.4% -0.3% Site C:

A 1_LH -9.9% 0.0% -10.4%
A 2_LH 11.9% 0.8% 0.0% The following tables the deviation of average wind speed
respect to the theoretical calculated value from measurements
As expected following results previously presented in the for each model.
vertical extrapolation study, avoiding wind shear extrapolation,
it can be expected to obtain with both model an error about 5% Reference met mast
in this case. We can see surprisingly that, in the Windsim
C 1_HH C 1_LH C 2_LH
Modelled

simulation, when the lower height (10 m) of each site is used as

Reference, the value obtained at the same height (10 m) at the C 1_HH -0.2% 0.8% 10.1%
other location is very close to the theoretical value (-0,3% and + C 1_LH 1.7% 0.7% 14.9%
0,8%). However, when a vertical extrapolation is required this C 2_LH -10.0% -8.0% -1.9%
model produce much less satisfying results. Table 10: Site C. Wind speed deviation. WAsP model
WASP seems to be more accurate modelling this type of terrain.
However, the absence of roughness changes information
Reference met mast
generate an serious increscent of uncertainties. As orography is
not a determinant parameter the eventual difference in C 1_HH C 1_LH C 2_LH
Modelled

roughness between Reference met mast site and modeled site is C 1_HH 0.0% -3.7% 7.4%
not taken into account and might affect significantly the value C 1_LH 3.8% 0.0% 11.3%
of the average wind speed obtained, especially in the case of C 2_LH -6.8% -10.2% 0.0%
site A2, which measurement height is 10 m. This tendency Table 11: Site C. Wind speed deviation. WAsP model
increases in the case of WAsP model.
Moreover, in the case of site A, located very close to sea, As expected for a such a complex site, important differences are
atmospheric instability is another parameter which could have produced by both models. Results improves when the highest
generated inaccuracy for both models. At site A, the cross- mast is used as reference for the calculations, as it reduces
checking comparison has shown that the linear model would approximations due to vertical extrapolation. It can be seen that,
preferable in this case. in each case, the best results are obtained when the highest
height of site C1 is used as Reference met mast. However, the
Site B: incapacity of WAsP to reproduce the vertical evolution of wind
flows increases the error when Reference met mast site and
modeled met mast presents different heights.
Windsim model is more able to reproduce the changes in wind of wind flows. Nevertheless, in the case of smaller slopes or
directions generated by the orography and consequently elevation changes, this model has a slight tendency to
reproduce the difference of wind rose between met mast C1 and overestimate the wind shear and thus remains conservative.
C2, which influence strongly the value of the average wind
speed. Nonetheless, it is necessary to consider that in the case of
a terrain presenting a very high level of complexity, the CFD 7. Acknowledgement
model results need to be balance by measurements as it is not
completely reliable. This work is being carried out with funding from the Spanish
Ministerio de Ciencia y Tecnologia (projects: DPI2003–09731
The figure 11 resumes the difference between the theoretical and FIT-120000-2004-182) and the Cai-Europa Program.
measured wind speed at one site (met mast) from another met
mast. Results are presented in function of met mast height (LH: 8. References
Lower height and HH: Higher height) from the lowest to
highest complexity of terrain. [1] RISO WAsP web site, . http://www.wasp.dk.

[2] VECTOR AS Windsim, http://www.windsim.com.

10

C2_LH 9 9.1% 7.0% [3] P. Moreno, M. Romero and A. N. Gravdhal, “Wind

1.7% 0.8% 1.9%
flow over Complex Terrain: Application of Linear and
C1_LH 8 3.3%
10.0% 0.7%
2.2%
10.1%
CFD Models”, METEOTEST, Bern, Switzerland;
10.8%
VECTOR AS, Tonsberg, Norway. 1998.
C1_HH7 13.7%
0.2% 8.0% 14.9%
Modelled met mast site

B2_LH 6 3.8% 3.7% [4] A.J. Bowen and N.G Mortensen, WAsP Prediction
1.8% 1.7% 0.2%
Errors Due to Site Orography” , Riso National
B1_LH 5
3.3%
6.8%
4.0%
7.4%
Laboratory, December 2004, pp 28-29, pp 34-35.
10.2%
B1_HH 4 11.3%
0.2% 5.0% 5.8% [5] B. Schaffner and A. N. Gravdhal, “Wind modelling
A2_LH 3 11.9% 0.8% in Mountains: Intercomparison and Validation of
5.0% 3.3%
9.1%
Models”, ECOTECNIA, Barcelona, Spain; VECTOR AS,
A1_LH 2
8.8%
9.9%
3.6% 5.2%
10.4%
Tonsberg, Norway. 2003.
11.4%
A1_HH 1 0.3%
3.3% 16.8% [6] P.S. Jackson, J.C.R. Hunt, Turbulent wind flow over a low
17.7%
hill, Quart. J. R. Met. Soc. 101 (1975) 929-955.
0
A1_H H A1_LH A 2_LH B1_H H B1_LH B2_LH C1_HH C1_LH C2_LH
0 1 2 3 4 5 6 7 8 9 10
Refe rence M e t M ast
[7] AWS TrueWind, LLC “Estimation of the Long-
% Over estimation WAsP
% Under estimation WAsP
% Over estimation Windsim
% Under estimation Windsim
Term Average Wind Speed and Energy Production at the
Proposed. East Mountain Wind Project Site”.
Figure 11: Site A, B and C WAsP and Windsim cross-checking
results [8] I. Troen and E.L. Petersen, “European Wind Atlas”,
Riso National Laboratory, 1989. pp 389.
6. Conclusions [9] R. Derickson, M. McDiarmid, B. Cochran and J.A.
Peterka, “Wind Energy Explained, Theory, Design and
In a very flat terrain, both models seem to be very sensitive to
Application”,. ”, WILEY, University of Massachusetts,
roughness changes, not considered in this project. Moreover, in
2002, pp. 50-78, pp 370-409.
the case of site A, located very close to sea, atmospheric
stability is another parameter which could have generated
inaccuracy for both models. However, the cross-checking
comparison has shown that the linear model would preferable in
this case. The linear WAsP shows an high dependency to
roughness modelling in the case of very flat terrain, with a
almost null influence of orography.

In a semi-complex terrain, presenting gentle hills and constant

roughness, the CFD model has a tendency to underestimate the
wind shear, producing a “too vertical” wind profile. Both
models can be used in this case, requiring however important
corrections in function of measured data. Moreover, Windsim
shows an high dependency to the dimensions of the calculation
domain, giving some unrealistic values in the case of too small
area, such as Site B.

In a very complex terrain configuration (site C), the CFD model

Windsim tends to reproduce more accurately all effects of
orography and simple roughness who acts significantly on wind
shear. Change in wind direction over the complete site would be
better taken into account by a CFD model which would made
that model more suitable for that type of terrain. For site
presenting high RIX coefficient, the Windsim model seems to
produce in a better way both horizontal and vertical evolution

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