Bremangerlandet, Bremanger, Sogn Og Fjordane Wind Analysis, Production Estimate and Climatic Conditions at Bremangerlandet Wind Farm

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Bremangerlandet, Bremanger, Sogn og Fjordane Wind analysis, production estimate and climatic conditions at Bremangerlandet wind farm

Report number: KVT/RKTR/2011/R045

KVT/RKTR/2011/R045

Content

1 INTRODUCTION ...... 3 2 METHODS AND INPUT DATA ...... 4 2.1 WIND MEASUREMENTS 4 2.2 MODEL DATA 5 2.3 REFERENCE DATA 5 2.4 TERRAIN AND ROUGHNESS DATA 5 2.5 LONG TERM EXTRAPOLATION OF THE WRF DATA 6 2.6 WASP MODEL 6 3 WIND CONDITIONS ...... 8 3.1 WIND CONDITIONS 8 3.2 TURBULENCE AND VERTICAL WIND 9 3.3 ICING 9 4 WIND MAP AND ENERGY YIELD ...... 10 4.1 WIND MAP 10 4.2 ENERGY YIELD AND LOSSES 11 5 UNCERTAINTY ESTIMATES ...... 14 6 EXTREME WIND CONDITIONS ...... 15 6.1 EXTREME WIND ANALYSIS AT SVINØY LIGHTHOUSE 15 6.2 TRANSFERRING EXTREME WIND SPEEDS FROM SVINØY TO KRÅKENES 16 6.3 TRANSFERRING FROM EXTREME WIND SPEEDS FROM KRÅKENES TO BREMANGER 17 7 OTHER ENVIRONMENTAL CONDITIONS ...... 19 7.1 TEMPERATURE 19 7.2 AIR DENSITY 20 8 SUMMARY ...... 22 9 REFERENCES ...... 23 APPENDIX A: WRF DESCRIPTION ...... 24 APPENDIX B: WINDPRO PRINTOUTS ...... 25

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1 Introduction

This report gives an estimate of the long-term wind speed conditions and the energy production for the planned wind farm at Bremangerlandet. Extreme wind, density and temperature conditions are also considered. The wind analysis is based on WRF simulations and measurements in a telecommunication mast. An overview of the wind farm area, WRF points and the telecommunication mast is given in Figure 1-1.

Turbulence and icing in the wind farm area are also considered.

Figure 1-1 Overview of the WRF points and the telecommunication mast in the Bremangerlandet wind farm.

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2 Methods and input data

2.1 Wind measurements

Wind measurements have been carried out from January 2008 at a telecommunication mast at Bremangerlandet, marked as 6401 Bremangerlandet in Figure 1-1. The permanent telecommunication mast has the two anemometers, one heated, and one wind vane mounted as shown in Figure 2-1. Table 2-1 has an overview of the main parameters of the sensors. The antenna system in the mast will influence at the measurements at all directions.

Figure 2-1 Met mast at Bremangerlandet.

Table 2-1 Overview of sensors Sensor Type Height [m] Comments Wind speed NRG #40 41.5 Boom direction 125° Wind speed NRG HAE ICE III 40.5 Boom direction 125° Wind dir NRG HVE Ice III 39.5 Boom direction 125° Temp NRG 110S 33.5

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2.2 Model data

The wind analysis is based on the meso-scale model WRF (Weather Research and Forecast model). Kjeller Vindteknikk AS has calculated a wind map for Norway for the Norwegian Water and Energy Directorate(Byrkjedal 2009). A short description of the model is given in the Appendix. This wind map is based on one year of data from a simulation done by the meso-scale model WRF with a horizontal resolution of 1 km x 1km. As the terrain at Bremangerlandet is rather complex four WRF points are used in the analysis. The Figure 1-1 shows the four WRF point used in the analysis. The WindPRO (EMD 2008) /WAsP model(WAsP Manual 1993) are applied to calculate a wind map of the wind farm area in Bremangerlandet.

2.3 Reference data

A simulation done with the same meso-scale model (as for the 1kmx1km simulation) was done with a horizontal resolution of 5kmx5km from 2000 to December 2010 to correct the one year simulation for long term variations. The 5kmx5km simulation domain is shown in Figure A1. Our experience with use of WRF data as reference data set has shown good consistency and correlation with measured data.

Information of the data sets used in the analysis is seen in Table 2-2.

Table 2-2 Details of the weather input data used in the production analysis.

Position Site name (WGS84, UTM zone 32) WRF_8001 N6865819,E292680 WRF_8002 N6865904,E291662 WRF_8011 N6867005,E290727 WRF_8012 N6867090,E289708 WRF_6001 N6869286,E291794

2.4 Terrain and roughness data

The WAsP/WindPRO model uses height contours, roughness values of the surface and wind statistics as input parameters. Local terrain data at Bremangerlandet are given in 20 meter contours. The roughness classification is done manually based on background maps and aerial photos. The roughness lengths applied to WindPRO/WAsP for different surface types are presented in Table 2-3. The roughness classification follows standard roughness values from the WAsP manual (WAsP Manual 1993).

Table 2-3 Roughness lengths for different surface types Surface type Roughness length [m] Water 0.0002 Open area 0.03 Mixed village and agricultural area 0.2 Forest 0.5

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2.5 Long term extrapolation of the WRF data

The long term extrapolation of the measurement data are carried out by a method called the Sector-Bin method. Documentation of the method can be found in (Harstveit 2004). A Sector Bin method uses partial coefficients placed in a 13x13 matrix according to the closest 30° direction, including calm. Each cell in the matrix is then defined by the ratio of the wind speed measured in the corresponding sectors at the reference station and site. The partial coefficients are integrated for each sector at the site to yield a weighted transformation matrix mapping the reference stations values to site.

The final uncertainty of the long-term estimate depends on the length of the time series, the standard deviation of annual mean values, the homogeneity of the reference series, the model uncertainty and the correlation between the reference and measured data.

2.6 WAsP model

The WindPRO/WAsP models are applied to calculate a wind map of the wind farm area in Bremangerlandet and the energy output of the wind farm. The WAsP model is used as a tool for the horizontal extrapolation of the measurements. This is a linearized micro-scale flow model further described by (Bowen og Mortensen 1996) and the WAsP manual (1993).

Linearized models are unable to predict flow separation, and this is a problem in complex terrain. To correct for the expected nonlinear influence of the flow field an empirical method called ΔRIX correction has been developed. This method uses the RIX factor as a measure of the complexity of a location. RIX is short for Ruggedness Index, and is a measure of how large part of the surrounding area that has a steepness above a given threshold. WindPro suggest a RIX radius of 3.5km. An analysis based on experimental results described by (Berge, et al. 2006) has studied several RIX radiuses. The best practice from this paper is to define the RIX number as the fraction of the terrain within a radius of 2km with a slope exceeding 30%, corresponding to 16.7º. The velocity correction in a point is made based on the difference, ΔRIX, from the RIX- value of the measurement location according to the formula:

,where UWAsP∆RIXcorr is the ∆RIX-corrected wind speed of the WAsP estimated wind speed, UWAsP .

Generally the uncertainty of the horizontal extrapolation is smallest if the ΔRIX level is small, and increasing with increasing ΔRIX level. For high RIX values the model is operating outside its physical limits.

Bremangerlandet is a very complex area and the RIX calculation can be around the physical limits of the WAsP model. Several RIX radiuses from 0.5km to 3.5km are therefore tested. Maximum RIX values are rather high for all RIX radiuses tested. KVT has therefore chosen to present two wind maps based on RIX radiuses of WindPRO and (Berge, et al. 2006).

An important feature of the WAsP model is the capability of generalizing the measured observation at a local site into a regional wind climate – the so-called wind atlas. Once a wind atlas is established the local wind conditions at any site where the wind atlas is valid, can be calculated. We have now taken the advantage of this approach in order to map the WRF- calculations into the local height contours and surface roughness. The following procedure has been applied:

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- WAsP is set up with the WRF-terrain and roughness. The annual average wind speed at 80 m height above ground level is extracted from WRF at 4 different points in the wind farm (see Figure 1-1).

- 4 wind atlases from WAsP are then derived based on each of these 4 data points.

- The wind atlases, local topography (20 meter equidistance) and roughness values (Arial photos) are used to calculate 4 wind maps in the area of the wind farm with a horizontal resolution of 50 m in WAsP.

- The 4 wind maps are ΔRIX corrected and weighted based on the distance to the WRF points and a final wind map for the entire wind farm is presented in Section 4.1.

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3 Wind conditions

3.1 Wind conditions

The heated anemometer at 40.5 meter is compared to WRF data in the measuring period. The measurements contain several periods of missing data. The correlation between WRF and the measurements are listed in Table 3-1, from around 0.9 for southerly winds to 0.2 for north- north westerly winds. The directional scaling factor between the measurements and the WRF data shows large variation for each sector due to influenced measurements by the mast. KVT has therefore chosen to use WRF data and not the measurements in the calculation of the wind map at Bremangerlandet wind farm. The long term corrected WRF wind rose at 80 m.a.g.l. is shown in Figure 3-1. As this is derived from a model the wind rose in the farm may differ from this.

Table 3-1 Sectorial correlation between measurements and WRF data Sector 1 2 3 4 5 6 7 8 9 10 11 12 Correlation 0.68 0.60 0.56 0.27 0.62 0.74 0.90 0.89 0.88 0.66 0.24 0.62

Figure 3-1 WRF wind rose at Bremangerlandet

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3.2 Turbulence and vertical wind

The terrain at Bremangerlandet is complex, particularly in the southern and northern parts. High turbulence and strong vertical winds can be expected in large parts of the wind farm area. The measurements in the telecommunication mast have low quality due to mast influence and they are not usable for turbulence calculations. It is recommended to use a CFD model, for example WindSIM (http://www.windsim.com) to further examine the turbulence. To have a reliable result from WindSIM it is essential to have measurements from one or more met masts with reliable measurements in the farm area.

3.3 Icing

Measurements of wind speed and direction with heated sensors are performed in the telecommunication mast at Bremangerlandet. Normally icing events are found by comparing heated and non-heated instruments. In the telecommunication mast the only wind vane mounted is heated and can therefore not be used to analyze icing. The availability of the non- heated sensor is also low. By studying the heated measurements large amount of offset values of the anemometer is found. These incidents are expected to be icing events or sheltering from the telecommunication mast. Figure 3-2 shows a icing event on the instruments in the telecommunication mast.

The area of Bremangerlandet wind farm has large variation in the elevation above sea level, from around 300m to 650 meters. These large variations leads to large variation in the icing at Bremangerlandet wind farm. The highest altitudes are expected to be within the cloud base for larger periods and the largest amount of icing episodes is expected at these heights. The maximum icing values found in the icing map for Norway (Byrkjedal 2009) is 500-1000 hours per year with icing >10g/hour. It is therefore recommended to use one met mast with heated sensors in a measurement campaign at a higher altitude in the wind farm.

Figure 3-2 Icing event on instruments in the telecommunication mast

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4 Wind map and Energy Yield

4.1 Wind map

The wind map at Bremangerlandet is calculated by utilizing wind data from four WRF points, shown in Figure 1-1, and the methodology described in Section 2.6. As described in Section 2.6 the WAsP model uses a so called RIX-correction for areas with complex terrain, such as Bremangerlandet. The RIX values found at Bremangerlandet are very high and the uncertainty in the wind map calculations is therefore high. KVT has chosen to present two wind maps, one were the RIX correction is based on a RIX radius of 3500m and one with a RIX radius of 2000m, due to the high uncertainty. The wind maps are presented in Figure 4-1 and Figure 4-2. Wind speed of 11-11.5m/s is found in the northern parts of the wind farm in both wind maps. The northern part of the wind farm has some very steep area further north. These areas can cause speed-up, but according to the wind rose in Figure 3-1 the wind direction are rarely compared to the main wind direction from south south-southwest. Large speed-up effects in the northern part of the wind farm are not expected and for these areas the wind speed is expected to be over predicted.

Figure 4-1 Wind map at 80 m.a.g.l. at Bremangerlandet wind farm utilizing a RIX radius of 3500m.

Considerable differences are found between the two maps in Figure 4-1 and Figure 4-2 in the other areas of the wind farm. Comparing the wind maps the wind speed can be underestimated

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in some of the southern areas of the wind farm. This illustrates the uncertainty of the ΔRIX correction routine. It must be noted that more wind measurements in at least two locations are necessary to reduce the uncertainties significantly. KVT has chosen to calculate the energy production in the wind farm based on the wind map with RIX radius of 3.5 km.

Figure 4-2 Wind map at 80 m.a.g.l. at Bremangerlandet wind farm utilizing a RIX radius of 2000m.

4.2 Energy Yield and losses

The energy calculations are based on the wind map at 80 meter with a RIX radius of 3500 m. Two turbine layouts are received from Vestavind Kraft AS and no modification of the turbine positions is done by KVT. The energy calculations are performed for these two layouts at 80 meter height. The first layout consists of 29 Enercon E82 3.0MW turbines and the second consists of 26 Siemens SWT-101-3.0MW turbines. The turbine layouts are presented with the wind map at 80 m.a.g.l. in Figure 4-3 and Figure 4-4. As can be seen from the layouts some of the turbines are located in the areas with very high wind in the north of the wind farm. If measurements verify the calculated wind map, the wind conditions at the site might be too high for an IEC class I turbine. The calculated wind speed is most likely too high in the northern part of the wind farm which can reduce energy production of these turbines.

High turbulence and strong vertical winds can be expected in large part of the wind farm area.

The energy calculations are performed with WindPRO 2.7 and are presented in Table 4-1. For Bremangerlandet wind farm the losses are increased from standard 10% to 15% due to the expected high turbulence and icing in the in the wind farm. To quantify these losses measurements in the wind farm area is needed.

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Table 4-1 Key results for the energy production for two layouts at Bremangerlandet Turbine Installed capacity Energy production Wake losses [MW] [MW/h] [%] Enercon E82 3MW 87 235 4.6 Siemens SWT 101 3MW 78 256 4.1

Figure 4-3 Wind map at 80 m.a.g.l. with 29 Enercon E82 3.0MW wind turbines

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Figure 4-4 Wind map at 80 m.a.g.l. with 26 Siemens SWT-101-3.0MW wind turbines

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5 Uncertainty estimates

The wind conditions at Bremangerlandet are calculated with the WAsP model based on data from the wind map for Norway (Byrkjedal 2009). Due to the very complex terrain in and around the wind farm WAsP is close to or above its physical limits.

In the wind map for Norway (Byrkjedal 2009) an uncertainty of 10 % was found in the wind map by comparing WRF wind data to measurements from several meteorological masts. However in areas with complex terrain and high RIX values, higher uncertainties between measurements and WRF data were found.

By downscaling the wind map with WAsP another uncertainty of horizontal extrapolation is introduced, and for the complex terrain in Bremangerlandet this uncertainty is expected to be large. By the use of different RIX radius of 2km and 3.5 km an uncertainty of about 5 % in wind speed is seen between the final wind maps (Figure 4-1 and Figure 4-2). The uncertainty of the wind map is therefore assumed to 20 %, but for the northern parts of the wind farm with very high wind speeds the uncertainty can be even as large as 25%.

No detailed uncertainty of the energy production is estimated in this analysis due to the high uncertainty in the wind map. The mean wind speed at Bremangerlandet is high, and the energy sensitivity is expected to be low. Low energy sensitivity indicates that the energy production is less sensitive to changes in the mean wind speed.

To reduce the uncertainty in the wind level and to examine the turbulence level measurements are needed in different parts of the wind farm.

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6 Extreme Wind Conditions

To calculate the extreme wind conditions at Bremanger, comparison to data from Kråkenes lighthouse seems to be the most suitable. However, Kråkenes has too short series of reliable anemometer data to be used directly as a reference station. Therefore, the Kråkenes reference data has to be extrapolated from another reference station. Svinøy lighouse seems to be most suitable. There are problems using Svinøy directly to predict wind conditions on Bremanger due to the large variations in the wind conditions around the Stad corner.

6.1 Extreme Wind Analysis at Svinøy lighthouse

Svinøy is chosen due to good quality since 1974 for extreme wind speed purposes, while Kråkenes has poor data before 1995 and also some missing episodes after 1995. The station therefore is used as a help-station for further transformation of the Svinøy data to the Bremanger site. The method used to calculate the extreme wind conditions at the reference station is the Gumbel method (Gumbel 1958) with the Lieblein method for optimizing the curve to the observations. This method is recommended by the IEC standard (IEC-61400-1 2003). The series of yearly extremes for the period 1974/75 – 2009/10 is used in the analysis, with each wind year defined from September to August. Yearly maximum wind data from 8 directions were taken from paper registration before 1999, and from e-klima after the automatization in 1999. All data is quality checked at Kjeller Vindteknikk. The Svinøy station is also operated before 1974, but the data were not found to have sufficient quality and is not used in this analysis.

The Gumbel - distribution, also named the Fisher-Tippet Type I distribution, can be written as P(v>V) = 1 - exp(-exp(y)) = 1 - exp(-exp(a1V-a2)) where a1 and a2 represent two parameters to be optimized. The maximum 10 min wind speeds were used as input data, and wind speeds of return periods 2 - 100 years were calculated.

Figure 6-1 shows a Gumbel-Lieblein plot of data from the Svinøy lighthouse. The wind speed, U is plotted versus Y, where v=u2 is used as input parameter to the Gumbel - distribution. The corresponding return period and probability of non – exceedance are given on the y-axis. The curve is concave due to the use of V=U2 as the transformed parameter in the Gumbel – distribution.

The Lieblein method is used for fitting the curve to the observed yearly extremes. The method gives less weight to the highest and lowest values, and thus is less sensitive to outliers like error values or very seldom storms occurring in the observation series, than other methods (method of moments, least square or maximum likelihood). The New Year Day storm 1.1.1992 was such an outlier, with return period of several hundred years (46 m/s in Figure 6-1).

Table 6-1 Extreme values at different sectors at Svinøy

N NE E SE S SW W NW Omni Sector coefficient 0.78 0.75 0.60 0.64 0.81 1.00 0.92 0.77 1.00

50-yr Wind 31.1 30.3 24.0 25.8 32.7 40.0 36.8 31.0 40.2

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Figure 6-1 Gumbel - Lieblein plot of the Svinøy lighthouse for maximum 10 min wind speed

6.2 Transferring extreme wind speeds from Svinøy to Kråkenes

Sectorial transfer coefficients from Svinøy are calculated to achieve 50 year wind at Kråkenes based on Table 6-1. Table 6-2 illustrates the strong wind at soutwesterly sector at the lighthouse. In Table 6-2 the sector extremes at Kråkenes is related to the wind direction at Svinøy.

Table 6-2 Extreme values at different sectors at Kråkenes N NE E SE S SW W NW Omni

Sector coefficient from Svinøy 1.15 1.08 0.92 0.97 1.36 1.22 1.11 1.04

50-yr Wind at Kråkenes 35.8 34.9 22.0 25.0 44.6 48.7 40.7 32.3 49.2

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6.3 Transferring from extreme wind speeds from Kråkenes to Bremanger

A usual way of transferring extremes from a reference station to a project site is to create transfer coefficients between the stations. In this analysis, the highest values of the storms with a minimum separation of 3 days were used. Data for the period from 31.08.2008 to 09.02.2011 for the station 6401, Bremanger, is used for the analysis. Only data where records were available for both stations were used. For each sector sorted by direction at the reference station, the storm maximum of the 10 minute wind speed at both stations and the gusts at the project station, where individually sorted. The 5 highest values in each sector were used to calculate transfer coefficients.

Data from the ice-free sensor mounted at the telecommunication mast, 40 m above ground are used. All data which is slowed down to zero due to ice are removed. The data are influenced by the tower, and the calculations therefore represent the measuring point only.

The sector extremes at the site are given using the direction at the reference station. In this case there are some deviations from the site direction, and a post discussion is needed at the site if directional extremes are important for the project. This is not done here. The omni- directional wind speed is not influenced. To produce this omni directional extreme value (extreme value without sectorial condition) at the sites, we assume independent wind extremes in each sector. Then the probability of exceedance of the 50 year value in each sector contributes to the omni directional probability through a condition probability, and in sum:

(1) where Di refer to sector i.

We thus find the omni directional 50 year wind value by summing up the directional extremes by an iteration procedure, given P(U>U50)=0.02. The equation used is:

(2)

based on Rayleigh – distributed parent data and Gumbel distribution is used to calculate each p|Di-value.

The probabilities given each sector are given in the last row in Table 6-3 and Table 6-4.

Table 6-3 Sector analysis for 10 m mean wind speed [m/s] at station 6401 Bremanger, 40 magl. N NE E SE S SW W NW Omni

50-yr Wind speed, Kråk. (Ref) 35.8 32.7 22.1 25.0 44.5 48.8 40.8 32.2 49.2

Mean 5up 10min_Ref 22.4 21.7 15.3 14.1 32.3 31.7 24.0 20.5

Mean 5up 10min_Site 25.8 22.9 22.9 30.2 35.7 31.0 21.6 21.1

U10min_Site/U10min_Ref 1.15 1.05 1.50 2.14 1.11 0.98 0.90 1.03

50 yr 10 min_Site 41.1 34.4 33.1 53.6 49.3 47.8 36.8 33.2 54.3

P 0.000 0.000 0.000 0.016 0.003 0.002 0.000 0.000 0.02

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It is clearly seen that the omni directional wind speed here is higher than the highest sector extreme, reflecting that the chance of exceedance of a given wind speed is reduced if we restrict to a single sector. If groups of sectors are relevant, however, the group extreme wind speed may be higher than the highest of the group members.

Table 6-4 Sector analysis for 3 sec gust speed [m/s] at station 6401 Bremanger, 40 magl. N NE E SE S SW W NW Omni 50-yr Wind speed, Kråk. (Ref) 35.8 32.7 22.1 25.0 44.5 48.8 40.8 32.2 49.2

Mean 5up 10min_Ref 22.4 21.7 15.3 14.1 32.3 31.7 24.0 20.5

Mean 5up 3sec gust_Site 39.0 31.2 29.8 37.2 46.8 39.7 34.1 33.0

U3sec gust_Site/U10min_Ref 1.74 1.44 1.95 2.63 1.45 1.25 1.42 1.61

50 yr gust_Site 62.1 47.0 43.1 65.9 64.5 61.2 58.1 51.9 68.8

P 0.003 0.000 0.000 0.009 0.006 0.002 0.001 0.000 0.020

It should be stressed that this extreme value is valid for the anemometer position in the telecommunication mast, and the representativeness for a freely exposed point at the same site is not known. The very high transfer coefficient for SE sector, however, also have physical basis in more shielding at Kråkenes and more speed-up at Bremanger.

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7 Other Environmental Conditions

7.1 Temperature

A temperature analysis has been performed for Bremangerlandet. The measurement period is from Feb 2008 to Feb 2011. The measurements are carried out at 33m height in the telecommunication mast. The mean of the measured temperature is 3.2°C. This result can be biased by periods without measurements. The lowest and highest temperature observed in the measurement period is -12.3°C and 25.9°C respectively.

For the long term estimation of the temperature conditions at Bremangerlandet, data collected at Kråkenes in the period from 1993-2011 has been used. The lowest temperature that has occurred at Kråkenes is -12.3°C

Simultaneous temperature data from Bremangerlandet and Kråkenes have been compared to get a statistical relation between the two stations. The regression analysis is done for the 3 complete years following Feb 2008. As shown in Figure 7-1, the correlation between the two stations is good, calculated to be 0.96. Scaling factors calculated by the regression analysis are used for estimation of the expected temperature conditions at Bremangerlandet. The distribution of the expected temperatures at Bremangerlandet is shown in Figure 7-2.

Figure 7-1 Temperature at Bremangerlandet plotted against Kråkenes at concurrent times (2008-2011)

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Figure 7-2 Probability density of the expected temperatures [°C] at Bremangerlandet

The calculated expected monthly average temperatures at Bremangerlandet are given in Table 7-1, and the expected numbers of days when the temperature is below –10°C and –20°C are specified in Table 7-2. The difference between the averages in the two tables can be explained by periods with missing data at Kråkenes.

Table 7-1 Expected monthly mean temperature at Bremangerlandet [°C] 33 m.a.g.l. Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Year -1.4 -2.4 -1.7 0.9 3.6 6.2 9.0 9.8 8.2 4.1 1.1 -1.0 3.0

Table 7-2 Statistical temperature conditions at Bremangerlandet calculated for the year 1993-2011 [Days/Year] [Days/Year] [Days/Year] Mean [°C] Min [°C] Max [°C] T<-20°C T<-10°C T<0°C 2.9 -15.6 22.1 0.0 0.8 117.8

7.2 Air Density

The air density at height at 80 m.a.g.l. at Bremangerlandet is calculated by using the ideal gas equation and the hydrostatic equation and observations of temperature and pressure from Kråkenes (75 m.a.s.l.). The equations are given below, the ideal gas law to the left and the hydrostatic equation to the right.

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and

Here, pressure ( ) is a function of density ( ) and temperature ( ). The gas constant of dry air ( ) is 287 J/(K kg) and the gravitational constant ( ) is 9.81 m/s2. For vertical distribution standard atmosphere is assumed, together with climatic temperature gradients of -0.006°C/m. The telecommunication mast at Bremangerlandet has an elevation of 630 a.s.l. The monthly expected mean air densities at Bremangerlandet at height 80 m.a.g.l. are given in Table 7-3, and the annual mean air density is calculated to be 1.177 kg/m3.

Table 7-3 Expected monthly mean air density at Bremangerlandet [kg/m3] (height 80 m.a.g.l.) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Year 1.189 1.195 1.194 1.188 1.180 1.168 1.155 1.153 1.159 1.170 1.180 1.190 1.177

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8 Summary

The wind conditions at Bremangerlandet are calculated using WAsP calculations based on WRF data. Bremangerlandet wind farm is located in a very complex area and the uncertainties in the WAsP model is expected to be large. The wind map shows good wind resources in the entire wind farm, but the wind speed in the northern region of the wind farm is expected to be overestimated. The uncertainty of the wind map is assumed to 20 %, but for the northern parts of the wind farm with very high wind speeds the uncertainty can be even as large as 25%.

The complex terrain in and around the wind farm are likely to produce high turbulence levels and vertical winds. The high turbulence level is expected to be a main challenge for Bremangerlandet wind farm. Turbulence levels have not been calculated since the quality of the measurements is very low.

High frequency icing is seen from measurements in the telecommunication mast and in the icing map of Norway (Byrkjedal 2009).

The extreme wind is found to be high for the measurements in the telecommunication mast at Bremangerlandet. Due to the mast influence on the measurements the extreme wind may not be representative for the wind farm.

More measurements at different locations within the wind farm area are necessary to reduce the uncertainty in the wind conditions. Measurements will also be needed to evaluate the icing and turbulence level in the wind farm.

Production estimates are calculated based on the estimated wind map and two turbine layouts. The layouts consist of 26 Siemens SWT-101-3.0MW turbines and 29 Enercon E-82 3.MW. The key result of the energy production is summarized in Table 8-1.

Table 8-1 Key results for the energy production with 15% losses for two layouts at Bremangerlandet Turbine Installed capacity[MW] Energy production [MWh/year] Wake losses[%] Enercon E82 3MW 87 235 4.6 Siemens SWT 101 3MW 78 256 4.1

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9 References

Berge, E, F Nyhammer, L Tallhaug, og O Jacobsen. «An evaluation of the WAsP model at a coastal mountainous site in Norway.» Wind Energy, 9, 2006: 131-140.

Bowen, A. J., og N. G. Mortensen. «Exploring the limits of WAsP the wind atlas analysis and application program.» Proceedings of EWEC. Gothenburg, Sweden, 1996.

Byrkjedal, Ø. Åkervik, E. Vindkart for Norge. 9/2009: NVE, 2009.

EMD. WindPRO 2.6 User Guide. 1. Edition. EMD International AS, 2008.

Gumbel, E. Statistics of extremes. Columbia University Press, 1958.

Harstveit, K. “Estimating long-term wind distribution from short-term data set using a reference station.” European Community Wind Energy Conference, EWEC. London, 2004. 87-90.

IEC-61400-1. Wind turbines - Part 1: Design requirements. IEC, 2003.

WAsP Manual. «WAsP Users Guide Vol 2: Wind Analysis and Application Program (WAsP).» 1993.

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Appendix A: WRF description

The Weather Research and Forecast (WRF) model is a state-of-the-art meso-scale numerical weather prediction system, aiming at both operational forecasting and atmospheric research needs. A description of the modelling system can be found at the home page http://www.wrfmodel.org/. The model version used in this work is v3.01 described in Skamarock et al. 20081. Details about the modelling structure, numerical routines and physical packages available can be found in for example Klemp et al. (2000)2 and Michalakes et al. (2001)3. The development of the WRF-model is supported by a strong scientific and administrative community in U.S.A. The number of users is large and it is growing rapidly. In addition the code is accessible for the public.

The most important input data are geographical data and metrological data. The geographical data is from National Oceanic and Atmospheric Administration (NOAA). The data includes topography, surface data, albedo and vegetation. These parameters have high influence for the wind speed in the layers close to the ground. Global meteorological data with 1 degree resolution, available from the National Centres for Environmental Protection (NCEP) with 6 hours interval, is used as boundary data for the model. The data originates from the Final Global Data Assimilation System (FNL). FNL is an operational assimilation model that incorporates all available observation data globally, and uses this data to create a global analysis dataset, or a snapshot of the atmosphere, four times every day. The assimilation model incorporates data from several thousand ground based observation stations, vertical profiles from radiosondes, aircrafts, and satellites.

See http://wwwt.emc.ncep.noaa.gov/gmb/para/parabout.html for further description of the data. The global data analysis is based on observational data for the time-frames 00, 06, 12 and 18 UTC. The model set up used for this analysis is shown in Figure A-1. The outer squares have grid cells of 15km*15km, while the inner domain has 5km*5km grid cells. The time series used for this analysis is from the inner domain.

Figure A-1: Model set up for the WRF simulation. The inner square shows the region with grid cells equal to 5km*5km.

1 Skamarock WC, Klemp JB, Dudhia J, Gill DO, Barker DM, Duda MG, Huang X-Y, Wang W. and Powers JG, 2008: A Description of the Advanced Research WRF Version 3, NCAR Technical Note NCAR/TN-475+STR, Boulder, June 2008 2 Klemp JB., Skamarock WC. and Dudhia J., 2000: Conservative split-explicit time integration methods for the compressible non-hydrostatic equations (http://www.wrf-model.org/) 3 Michalakes J., Chen S., Dudhia J., Hart L., Klemp J., Middlecoff J., and Skamarock W., 2001: Development of a Next Generation Regional Weather Research and Forecast Model. Developments in Teracomputing: Proceedings of the Ninth ECMWF Workshop on the Use of High Performance Computing in Meteorology. Eds. Walter Zwieflhofer and Norbert Kreitz. World Scientific, Singapore.

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Appendix B: WindPRO printouts

25/25 WindPRO version 2.7.486 Jan 2011 Project: Printed/Page Bremangerlandet Steinfjellet og Blåfjellet 12.05.2011 18:50 / 1 Licensed user: Kjeller Vindteknikk AS Gunnar Randres vei 12 NO-2007 Kjeller (+47) 480 50 480 Reiar Kravik / [email protected] Calculated: 12.05.2011 18:41/2.7.486 PARK - Main Result Calculation: Enercon E82 3.0MW Wake Model N.O. Jensen (RISØ/EMD)

Calculation Settings Air density calculation mode Individual per WTG Result for WTG at hub altitude 1.179 kg/m³ to 1.215 kg/m³ Air density relative to standard 96.5 % Hub altitude above sea level (asl) 357.5 m to 658.4 m Annual mean temperature at hub alt. 3.0 °C to 5.0 °C Pressure at WTGs 934.6 hPa to 969.9 hPa

Wake Model Parameters Wake Decay Constant 0.075 Open farmland

Wake calculation settings Angle [°] Wind speed [m/s] start end step start end step 0.5 360.0 1.0 0.5 30.5 1.0

Scale 1:75 000 New WTG Resource file(s) L:\...\01 Bremanger\Analyser_2011_ASK\11_WAsP\Wind_maps\Andre_vindkart\Windmap_Bremangerlandet_n3500rix_80_weight_exp_1.wrg

Calculated Annual Energy for Wind Farm Specific results¤) WTG combination Result Result-15.0% GROSS (no loss) Park Capacity Mean WTG Full load Mean wind speed PARK Free WTGs efficiency factor result hours @hub height [MWh/y] [MWh] [MWh/y] [%] [%] [MWh/y] [Hours/year] [m/s] Wind farm 276 324.7 234 876.0 289 783.1 95.4 30.8 8 099.2 2 700 9.2 ¤) Based on Result-15.0% Calculated Annual Energy for each of 29 new WTGs with total 87.0 MW rated power WTG type Power curve Annual Energy Park Terrain Valid Manufact. Type-generator Power, Rotor Hub Creator Name Result Result-15.0% Efficiency Mean rated diameter height wind speed [kW] [m] [m] [MWh] [MWh] [%] [m/s] 1 A Yes ENERCON E-82 E3-3 000 3 000 82.0 80.0 USER Level 0 - calculated - Rev 2.0 - 11/2009 8 458.5 7 190 89.6 8.64 2 A Yes ENERCON E-82 E3-3 000 3 000 82.0 80.0 USER Level 0 - calculated - Rev 2.0 - 11/2009 9 097.0 7 732 96.1 8.58 3 A Yes ENERCON E-82 E3-3 000 3 000 82.0 80.0 USER Level 0 - calculated - Rev 2.0 - 11/2009 10 519.1 8 941 95.3 9.85 4 A Yes ENERCON E-82 E3-3 000 3 000 82.0 80.0 USER Level 0 - calculated - Rev 2.0 - 11/2009 9 428.2 8 014 95.1 8.91 5 A Yes ENERCON E-82 E3-3 000 3 000 82.0 80.0 USER Level 0 - calculated - Rev 2.0 - 11/2009 9 967.3 8 472 95.2 9.39 6 A Yes ENERCON E-82 E3-3 000 3 000 82.0 80.0 USER Level 0 - calculated - Rev 2.0 - 11/2009 11 075.4 9 414 95.1 11.00 7 A Yes ENERCON E-82 E3-3 000 3 000 82.0 80.0 USER Level 0 - calculated - Rev 2.0 - 11/2009 10 875.5 9 244 95.4 10.71 8 A Yes ENERCON E-82 E3-3 000 3 000 82.0 80.0 USER Level 0 - calculated - Rev 2.0 - 11/2009 11 185.5 9 508 96.2 10.83 9 A Yes ENERCON E-82 E3-3 000 3 000 82.0 80.0 USER Level 0 - calculated - Rev 2.0 - 11/2009 10 247.7 8 711 95.6 9.71 10 A Yes ENERCON E-82 E3-3 000 3 000 82.0 80.0 USER Level 0 - calculated - Rev 2.0 - 11/2009 9 932.8 8 443 95.2 9.49 11 A Yes ENERCON E-82 E3-3 000 3 000 82.0 80.0 USER Level 0 - calculated - Rev 2.0 - 11/2009 7 923.9 6 735 89.8 8.21 12 A Yes ENERCON E-82 E3-3 000 3 000 82.0 80.0 USER Level 0 - calculated - Rev 2.0 - 11/2009 8 179.3 6 952 92.3 8.25 13 A Yes ENERCON E-82 E3-3 000 3 000 82.0 80.0 USER Level 0 - calculated - Rev 2.0 - 11/2009 9 039.9 7 684 96.3 8.63 14 A Yes ENERCON E-82 E3-3 000 3 000 82.0 80.0 USER Level 0 - calculated - Rev 2.0 - 11/2009 8 786.5 7 468 98.1 8.30 15 A Yes ENERCON E-82 E3-3 000 3 000 82.0 80.0 USER Level 0 - calculated - Rev 2.0 - 11/2009 8 818.9 7 496 95.8 8.53 16 A Yes ENERCON E-82 E3-3 000 3 000 82.0 80.0 USER Level 0 - calculated - Rev 2.0 - 11/2009 10 552.0 8 969 96.2 10.17 17 A Yes ENERCON E-82 E3-3 000 3 000 82.0 80.0 USER Level 0 - calculated - Rev 2.0 - 11/2009 11 364.0 9 659 97.0 11.21 18 A Yes ENERCON E-82 E3-3 000 3 000 82.0 80.0 USER Level 0 - calculated - Rev 2.0 - 11/2009 8 811.2 7 489 95.2 8.41 19 A Yes ENERCON E-82 E3-3 000 3 000 82.0 80.0 USER Level 0 - calculated - Rev 2.0 - 11/2009 8 514.8 7 238 95.6 8.15 20 A Yes ENERCON E-82 E3-3 000 3 000 82.0 80.0 USER Level 0 - calculated - Rev 2.0 - 11/2009 8 699.3 7 394 94.6 8.34 21 A Yes ENERCON E-82 E3-3 000 3 000 82.0 80.0 USER Level 0 - calculated - Rev 2.0 - 11/2009 8 850.5 7 523 93.8 8.50 22 A Yes ENERCON E-82 E3-3 000 3 000 82.0 80.0 USER Level 0 - calculated - Rev 2.0 - 11/2009 9 328.0 7 929 97.2 8.66 23 A Yes ENERCON E-82 E3-3 000 3 000 82.0 80.0 USER Level 0 - calculated - Rev 2.0 - 11/2009 9 283.1 7 891 96.9 8.62 24 A Yes ENERCON E-82 E3-3 000 3 000 82.0 80.0 USER Level 0 - calculated - Rev 2.0 - 11/2009 9 459.6 8 041 97.4 8.70 25 A Yes ENERCON E-82 E3-3 000 3 000 82.0 80.0 USER Level 0 - calculated - Rev 2.0 - 11/2009 10 864.8 9 235 95.9 10.43 26 A Yes ENERCON E-82 E3-3 000 3 000 82.0 80.0 USER Level 0 - calculated - Rev 2.0 - 11/2009 9 093.5 7 730 95.0 8.80 27 A Yes ENERCON E-82 E3-3 000 3 000 82.0 80.0 USER Level 0 - calculated - Rev 2.0 - 11/2009 9 948.0 8 456 96.2 9.35 28 A Yes ENERCON E-82 E3-3 000 3 000 82.0 80.0 USER Level 0 - calculated - Rev 2.0 - 11/2009 8 921.7 7 583 93.4 8.76 29 A Yes ENERCON E-82 E3-3 000 3 000 82.0 80.0 USER Level 0 - calculated - Rev 2.0 - 11/2009 9 099.1 7 734 98.7 8.49

WindPRO is developed by EMD International A/S, Niels Jernesvej 10, DK-9220 Aalborg Ø, Tlf. +45 96 35 44 44, Fax +45 96 35 44 46, e-mail: [email protected] WindPRO version 2.7.486 Jan 2011 Project: Printed/Page Bremangerlandet Steinfjellet og Blåfjellet 12.05.2011 18:50 / 2 Licensed user: Kjeller Vindteknikk AS Gunnar Randres vei 12 NO-2007 Kjeller (+47) 480 50 480 Reiar Kravik / [email protected] Calculated: 12.05.2011 18:41/2.7.486 PARK - Main Result Calculation: Enercon E82 3.0MW WTG siting UTM WGS84 Zone: 32 East North Z Row data/Description UTM WGS84 Zone: 32 [m] 1 New 290 316 6 867 517 478.7 ENERCON E-82 E3 3000 82.0 !O! hub: 80.0 m (234.1) 2 New 289 430 6 867 950 480.0 ENERCON E-82 E3 3000 82.0 !O! hub: 80.0 m (234.2) 3 New 289 614 6 868 303 480.0 ENERCON E-82 E3 3000 82.0 !O! hub: 80.0 m (234.3) 4 New 289 848 6 868 111 440.1 ENERCON E-82 E3 3000 82.0 !O! hub: 80.0 m (234.4) 5 New 290 152 6 867 984 440.0 ENERCON E-82 E3 3000 82.0 !O! hub: 80.0 m (234.5) 6 New 290 442 6 867 962 480.0 ENERCON E-82 E3 3000 82.0 !O! hub: 80.0 m (234.6) 7 New 290 716 6 867 860 460.0 ENERCON E-82 E3 3000 82.0 !O! hub: 80.0 m (234.7) 8 New 290 985 6 867 813 480.0 ENERCON E-82 E3 3000 82.0 !O! hub: 80.0 m (234.8) 9 New 291 234 6 867 681 405.1 ENERCON E-82 E3 3000 82.0 !O! hub: 80.0 m (234.9) 10 New 291 493 6 867 534 380.0 ENERCON E-82 E3 3000 82.0 !O! hub: 80.0 m (234.10) 11 New 290 087 6 867 248 500.0 ENERCON E-82 E3 3000 82.0 !O! hub: 80.0 m (234.11) 12 New 289 852 6 867 036 534.9 ENERCON E-82 E3 3000 82.0 !O! hub: 80.0 m (234.12) 13 New 289 541 6 866 854 578.4 ENERCON E-82 E3 3000 82.0 !O! hub: 80.0 m (234.13) 14 New 289 444 6 866 070 540.0 ENERCON E-82 E3 3000 82.0 !O! hub: 80.0 m (234.15) 15 New 289 788 6 866 168 545.2 ENERCON E-82 E3 3000 82.0 !O! hub: 80.0 m (234.16) 16 New 291 773 6 867 486 395.0 ENERCON E-82 E3 3000 82.0 !O! hub: 80.0 m (234.17) 17 New 292 217 6 867 520 370.6 ENERCON E-82 E3 3000 82.0 !O! hub: 80.0 m (234.18) 18 New 292 986 6 866 268 277.5 ENERCON E-82 E3 3000 82.0 !O! hub: 80.0 m (234.19) 19 New 292 140 6 866 791 320.0 ENERCON E-82 E3 3000 82.0 !O! hub: 80.0 m (234.20) 20 New 292 380 6 866 609 320.0 ENERCON E-82 E3 3000 82.0 !O! hub: 80.0 m (234.21) 21 New 292 649 6 866 347 336.2 ENERCON E-82 E3 3000 82.0 !O! hub: 80.0 m (234.22) 22 New 292 133 6 865 970 376.2 ENERCON E-82 E3 3000 82.0 !O! hub: 80.0 m (234.23) 23 New 292 407 6 865 769 360.0 ENERCON E-82 E3 3000 82.0 !O! hub: 80.0 m (234.24) 24 New 292 686 6 865 627 343.3 ENERCON E-82 E3 3000 82.0 !O! hub: 80.0 m (234.25) 25 New 292 501 6 867 473 340.0 ENERCON E-82 E3 3000 82.0 !O! hub: 80.0 m (260) 26 New 290 098 6 866 357 560.0 ENERCON E-82 E3 3000 82.0 !O! hub: 80.0 m (261) 27 New 291 072 6 866 731 479.0 ENERCON E-82 E3 3000 82.0 !O! hub: 80.0 m (262) 28 New 291 316 6 866 904 420.0 ENERCON E-82 E3 3000 82.0 !O! hub: 80.0 m (263) 29 New 288 965 6 867 037 551.0 ENERCON E-82 E3 3000 82.0 !O! hub: 80.0 m (267)

Calculation Settings Air density calculation mode Individual per WTG Result for WTG at hub altitude 1.179 kg/m³ to 1.217 kg/m³ Air density relative to standard 98.7 % Hub altitude above sea level (asl) 340.0 m to 658.2 m Annual mean temperature at hub alt. 3.0 °C to 5.1 °C Pressure at WTGs 934.6 hPa to 972.0 hPa

Wake Model Parameters Wake Decay Constant 0.075 Open farmland

Wake calculation settings Angle [°] Wind speed [m/s] start end step start end step 0.5 360.0 1.0 0.5 30.5 1.0

Scale 1:75 000 New WTG Resource file(s) L:\...\01 Bremanger\Analyser_2011_ASK\11_WAsP\Wind_maps\Andre_vindkart\Windmap_Bremangerlandet_n3500rix_80_weight_exp_1.wrg

Calculated Annual Energy for Wind Farm Specific results¤) WTG combination Result Result-15.0% GROSS (no loss) Park Capacity Mean WTG Full load Mean wind speed PARK Free WTGs efficiency factor result hours @hub height [MWh/y] [MWh] [MWh/y] [%] [%] [MWh/y] [Hours/year] [m/s] Wind farm 300 760.5 255 646.5 313 467.3 95.9 37.4 9 832.6 3 278 9.1 ¤) Based on Result-15.0% Calculated Annual Energy for each of 26 new WTGs with total 78.0 MW rated power WTG type Power curve Annual Energy Park Terrain Valid Manufact. Type-generator Power, Rotor Hub Creator Name Result Result-15.0% Efficiency Mean wind rated diameter height speed [kW] [m] [m] [MWh] [MWh] [%] [m/s] 1 A Yes Siemens SWT-3.0-101-3 000 3 000 101.0 80.0 USER SWT-3.0-101 10 807.1 9 186 95.9 8.48 2 A Yes Siemens SWT-3.0-101-3 000 3 000 101.0 80.0 USER SWT-3.0-101 10 727.7 9 119 97.8 8.23 3 A Yes Siemens SWT-3.0-101-3 000 3 000 101.0 80.0 USER SWT-3.0-101 11 072.6 9 412 95.9 8.64 4 A Yes Siemens SWT-3.0-101-3 000 3 000 101.0 80.0 USER SWT-3.0-101 10 122.5 8 604 92.4 8.23 5 A Yes Siemens SWT-3.0-101-3 000 3 000 101.0 80.0 USER SWT-3.0-101 10 386.4 8 828 91.5 8.47 6 A Yes Siemens SWT-3.0-101-3 000 3 000 101.0 80.0 USER SWT-3.0-101 11 301.7 9 606 96.6 8.63 7 A Yes Siemens SWT-3.0-101-3 000 3 000 101.0 80.0 USER SWT-3.0-101 12 585.1 10 697 95.8 9.74 8 A Yes Siemens SWT-3.0-101-3 000 3 000 101.0 80.0 USER SWT-3.0-101 11 690.8 9 937 95.7 9.07 9 A Yes Siemens SWT-3.0-101-3 000 3 000 101.0 80.0 USER SWT-3.0-101 13 157.4 11 184 96.1 10.92 10 A Yes Siemens SWT-3.0-101-3 000 3 000 101.0 80.0 USER SWT-3.0-101 12 855.6 10 927 96.6 10.47 11 A Yes Siemens SWT-3.0-101-3 000 3 000 101.0 80.0 USER SWT-3.0-101 13 254.3 11 266 96.8 10.73 12 A Yes Siemens SWT-3.0-101-3 000 3 000 101.0 80.0 USER SWT-3.0-101 12 468.2 10 598 96.1 10.15 13 A Yes Siemens SWT-3.0-101-3 000 3 000 101.0 80.0 USER SWT-3.0-101 10 680.4 9 078 94.1 8.55 14 A Yes Siemens SWT-3.0-101-3 000 3 000 101.0 80.0 USER SWT-3.0-101 11 809.6 10 038 96.3 9.17 15 A Yes Siemens SWT-3.0-101-3 000 3 000 101.0 80.0 USER SWT-3.0-101 10 772.8 9 157 95.0 8.36 16 A Yes Siemens SWT-3.0-101-3 000 3 000 101.0 80.0 USER SWT-3.0-101 10 553.1 8 970 95.6 8.18 17 A Yes Siemens SWT-3.0-101-3 000 3 000 101.0 80.0 USER SWT-3.0-101 11 450.0 9 733 96.9 8.73 18 A Yes Siemens SWT-3.0-101-3 000 3 000 101.0 80.0 USER SWT-3.0-101 10 886.6 9 254 94.7 8.47 19 A Yes Siemens SWT-3.0-101-3 000 3 000 101.0 80.0 USER SWT-3.0-101 11 363.0 9 659 97.3 8.60 20 A Yes Siemens SWT-3.0-101-3 000 3 000 101.0 80.0 USER SWT-3.0-101 11 376.6 9 670 96.8 8.65 21 A Yes Siemens SWT-3.0-101-3 000 3 000 101.0 80.0 USER SWT-3.0-101 11 127.3 9 458 95.4 8.77 22 A Yes Siemens SWT-3.0-101-3 000 3 000 101.0 80.0 USER SWT-3.0-101 13 272.2 11 281 97.3 11.05 23 A Yes Siemens SWT-3.0-101-3 000 3 000 101.0 80.0 USER SWT-3.0-101 12 296.4 10 452 96.0 9.74 24 A Yes Siemens SWT-3.0-101-3 000 3 000 101.0 80.0 USER SWT-3.0-101 10 940.0 9 299 98.6 8.31 25 A Yes Siemens SWT-3.0-101-3 000 3 000 101.0 80.0 USER SWT-3.0-101 11 107.2 9 441 96.6 8.66

To be continued on next page... WindPRO is developed by EMD International A/S, Niels Jernesvej 10, DK-9220 Aalborg Ø, Tlf. +45 96 35 44 44, Fax +45 96 35 44 46, e-mail: [email protected] WindPRO version 2.7.486 Jan 2011 Project: Printed/Page Bremangerlandet Steinfjellet og Blåfjellet 12.05.2011 18:50 / 2 Licensed user: Kjeller Vindteknikk AS Gunnar Randres vei 12 NO-2007 Kjeller (+47) 480 50 480 Reiar Kravik / [email protected] Calculated: 12.05.2011 18:40/2.7.486 PARK - Main Result Calculation: Siemens SWT-101-3.0MW

...continued from previous page WTG type Power curve Annual Energy Park Terrain Valid Manufact. Type-generator Power, Rotor Hub Creator Name Result Result-15.0% Efficiency Mean wind rated diameter height speed [kW] [m] [m] [MWh] [MWh] [%] [m/s] 26 A Yes Siemens SWT-3.0-101-3 000 3 000 101.0 80.0 USER SWT-3.0-101 12 695.9 10 791 96.1 10.11

WTG siting UTM WGS84 Zone: 32 East North Z Row data/Description UTM WGS84 Zone: 32 [m] 1 New 289 783 6 866 192 545.5 Siemens SWT-3.0-101 3000 101.0 !O! hub: 80.0 m (177.1) 2 New 289 428 6 866 103 540.0 Siemens SWT-3.0-101 3000 101.0 !O! hub: 80.0 m (177.2) 3 New 289 561 6 866 904 578.2 Siemens SWT-3.0-101 3000 101.0 !O! hub: 80.0 m (177.6) 4 New 289 868 6 867 161 527.7 Siemens SWT-3.0-101 3000 101.0 !O! hub: 80.0 m (177.7) 5 New 290 154 6 867 414 500.0 Siemens SWT-3.0-101 3000 101.0 !O! hub: 80.0 m (177.8) 6 New 289 446 6 867 956 480.0 Siemens SWT-3.0-101 3000 101.0 !O! hub: 80.0 m (177.9) 7 New 289 718 6 868 268 474.4 Siemens SWT-3.0-101 3000 101.0 !O! hub: 80.0 m (177.10) 8 New 290 091 6 867 967 440.0 Siemens SWT-3.0-101 3000 101.0 !O! hub: 80.0 m (177.11) 9 New 290 431 6 867 953 480.0 Siemens SWT-3.0-101 3000 101.0 !O! hub: 80.0 m (177.12) 10 New 290 738 6 867 835 455.0 Siemens SWT-3.0-101 3000 101.0 !O! hub: 80.0 m (177.13) 11 New 291 074 6 867 787 477.3 Siemens SWT-3.0-101 3000 101.0 !O! hub: 80.0 m (177.14) 12 New 291 726 6 867 510 391.7 Siemens SWT-3.0-101 3000 101.0 !O! hub: 80.0 m (177.15) 13 New 291 388 6 866 990 401.5 Siemens SWT-3.0-101 3000 101.0 !O! hub: 80.0 m (177.18) 14 New 291 098 6 866 791 467.2 Siemens SWT-3.0-101 3000 101.0 !O! hub: 80.0 m (177.19) 15 New 292 451 6 866 594 320.0 Siemens SWT-3.0-101 3000 101.0 !O! hub: 80.0 m (177.21) 16 New 292 147 6 866 766 320.0 Siemens SWT-3.0-101 3000 101.0 !O! hub: 80.0 m (177.22) 17 New 292 124 6 865 943 379.2 Siemens SWT-3.0-101 3000 101.0 !O! hub: 80.0 m (177.23) 18 New 292 751 6 866 385 306.6 Siemens SWT-3.0-101 3000 101.0 !O! hub: 80.0 m (177.24) 19 New 292 755 6 865 600 336.8 Siemens SWT-3.0-101 3000 101.0 !O! hub: 80.0 m (177.26) 20 New 292 445 6 865 767 360.0 Siemens SWT-3.0-101 3000 101.0 !O! hub: 80.0 m (177.27) 21 New 290 132 6 866 364 554.7 Siemens SWT-3.0-101 3000 101.0 !O! hub: 80.0 m (225) 22 New 292 205 6 867 488 374.1 Siemens SWT-3.0-101 3000 101.0 !O! hub: 80.0 m (232) 23 New 291 413 6 867 656 377.3 Siemens SWT-3.0-101 3000 101.0 !O! hub: 80.0 m (233) 24 New 288 943 6 866 987 547.4 Siemens SWT-3.0-101 3000 101.0 !O! hub: 80.0 m (264) 25 New 293 165 6 866 289 260.0 Siemens SWT-3.0-101 3000 101.0 !O! hub: 80.0 m (265) 26 New 292 569 6 867 422 324.1 Siemens SWT-3.0-101 3000 101.0 !O! hub: 80.0 m (266)

WindPRO is developed by EMD International A/S, Niels Jernesvej 10, DK-9220 Aalborg Ø, Tlf. +45 96 35 44 44, Fax +45 96 35 44 46, e-mail: [email protected]