W I N D R E S O U R C E A S S E S S M E N T

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1 W I N D R E S O U R C E A S S E S S M E N T Annual Energy Production Project: Hundhammer_WS_Express Layout: Layout1 Customer: WindSim WindSim AS Fjordgaten 15, N Tønsberg, Norway phone.: info@windsim.com Page of

2 1. Executive Summary This report provides the wind resource assessment and the Annual Energy Production (AEP) for the project: Hundhammer_WS_Express. These data have been derived from numerical flow modeling which has been weighted against the local wind climate. The local wind climate is either obtained from on-site measurements or from reanalysis datasets, in any case it is representing the long-term average wind climate at the site and it is referred to as a climatology object. The following climatology objects have been provided: Climatology name Climatology1 Table 1.1 File name Representative period Hundhammer_83m 27/04/ :56-30/04/ :36 Key climatology object characteristics Measurement height (m) Average wind speed(m/s) The above climatology objects have been used in the estimation of the gross AEP, the full load hours and capacity factor. The average wind speed for the wind farm is the average of the wind speeds at all turbine positions at hub height without wake effects. No losses of any kind have been included, except the wake losses which has been applied in the estimation of "AEP with wake losses". Turbine Type Hub Height (m) No. of turbines Capacity (MW) Gross AEP (GWh/y) Average wind speed (m/s) Wake losses (%) AEP with wake losses (GWh/y) Full load hours (hours) Capacity factor(%) Table 1.2 Key wind farm and production characteristics 1.1 Disclaimer This report represents, to the best of our knowledge, the state-of-art within wind resource assessment methods and annual energy production estimation. Efforts have been made to secure reliable results, although customer supplied input data is out of our control. WindSim AS affirms that the software has been tested with due care. However, this software, like all software products, may show deficiencies during use, which have escaped detection. WindSim AS cannot in any way be held responsible neither to the use of the findings in this report nor for any direct or indirect losses arising from such use or from errors of any kind in the report content. Page 2 of 29

3 Table of Contents 1. Executive Summary 1.1. Disclaimer Table of Contents 2. Preamble 3. Local wind climate 4. Wind farm layout and turbines 5. Numerical simulations 5.1. Digital Terrain Model D model setup 5.3. Simulations 6. Annual Energy Production AEP 6.1. Wind resource map 6.2. Energy production estimation Appendix 1 Methodology Appendix 2 - Online digital terrain models Page 3 of 29

4 2. Preamble This report provides the wind resource assessment and the Annual Energy Production (AEP) for the wind farm project: Hundhammer_WS_Express. These data have been derived using WindSim Express; a CFD (Computational Fluid Dynamics) based micrositing software operated in three simple steps. First, name the project. Second, position the turbines and introduce the local wind climate through so called climatology objects. WindSim Express automatically downloads a terrain model of the area of interest. Finally, the resolution of the numerical model is set in accordance with the available computer resources. WindSim Express can be run on a local computer or on a remote server, so called Cloud Computing; visit This report is automatically generated after the execution of the above described three steps. For a more details description of the methodology see Appendix 1. Figure 2.1 Definition sketch of WindSim Express procedure, 1) Name the project, 2) introduce turbines and local wind climate, 3) set resolution and run wind field simulations Page 4 of 29

5 3. Local wind climate The average wind condition at the site is used in calibration of the wind resources and in the annual energy production estimation. A wind climatology is presented as a wind rose, giving the average wind speed distribution divided in velocity intervals (bins) and wind directions (sectors). The original wind speed data are divided in 50 bins, whereas in the graphical wind rose presentation all occurrences of wind speeds above 16 m/s have been accumulated in the bin above 16 m/s. Incoming wind directions are divided in 36 sectors, where the first sector is centered around north. The frequency distribution has been fitted to a Weibull distribution. Page 5 of 29

6 3.1 Climatology: Hundhammer_83m File name Hundhammer_83m Period, # records 27/04/ :56-30/04/ : Position: easting, northing, z (agl) Average wind speed, Weibull k, A Table 3.1 Climatology characteristics including average wind speed (m/s) for all sectors, Weibull shape (k) and scale (A) parameters for all sectors Figure 3.1 Wind rose (left) and frequency distribution with Weibull fitting (right) for all sectors Average wind speed (m/s) Frequency (%) Weibull shape, k Weibull scale, A Average wind speed (m/s) Frequency (%) Weibull shape, k Weibull scale, A Average wind speed (m/s) Frequency (%) Weibull shape, k Weibull scale, A Table 3.1a Average wind speed, frequency and Weibull shape (k) and scale (A) parameters versus sectors Page 6 of 29

7 4. Wind farm layout and turbines The wind farm consists of 19 turbines. The layout is presented in figure 4.1 and the turbine positions are given in table 4.1. The technical specifications of the wind turbines proposed to be used in the wind farm project are given in table 4.2. Figure 4.1 Wind farm layout; triangles represent turbines, dots represent climatologies Turbine name Turbine type Hub height Easting Northing z Turbine Turbine Turbine Turbine Turbine Turbine Turbine Turbine Turbine Turbine Turbine Turbine Turbine Page 7 of 29

8 Turbine Turbine Turbine Turbine Turbine Turbine Table 4.1 Turbine type Turbine names, types and positions mode0_hubheight100_105.2db( Wind Speed (m/s) Power (kw) Thrust coefficient Rated wind speed (m/s) Cut-in wind speed (m/s) Cut-off wind speed (m/s) Diameter (m) Air density (kg/m3) kg/m Table 5.2 Turbine characteristics with power coefficient and thrust coefficient for air density of Page 8 of 29

9 5. Numerical simulations A numerical wind database is established by simulations CFD. The numerical wind database is used to transfer the wind conditions from the measurement point to the wind turbine hub positions. This chapter describes how the numerical model is set up, simulated and validated. 5.1 Digital Terrain Model A digital terrain model containing elevation and roughness data has been established for the area given in table 5.1 and figure 5.1. The coordinate system is UTM, Zone: 33, Datum: WGS_84, which is the coordinate system referred to whenever coordinates are used in this report. Note that the underlying datasets for elevation and roughness might have different resolution. The following online sources have been used for elevation: ASTER GDEM Worldwide Elevation Data (1.5-arc-second Resolution) and for roughness: CORINE Land Cover Europe (100 m Resolution), see appendix 2 for details. Min (m) Max (m) Extension (m) Resolution Terrain Data (m) Easting (m) Northing (m) Table 5.1 Coordinates, extensions and resolution of the digital terrain model referring to coordinate system: UTM, Zone: 33, Datum: WGS_84 Figure 5.1 Terrain elevation (m) (left) and roughness (m) (right) The complexity at the site depends on the changes in elevation and roughness. The complexity in elevation is visualized by the inclination angles, which are derived quantities expressing the first order derivatives of the elevation. Page 9 of 29

10 Figure 5.2 Terrain inclination (deg) (left) and logarithmic roughness (m) (right) Page 10 of 29

11 5.2 3D model setup The elevation and roughness data defined above is used to define the ground level of a three-dimensional domain divided in cells with a variable horizontal and vertical resolution. The grid is generated and optimized from the digital terrain model. Horizontally the grid is refined around the wind farm with expansions towards the outer borders. Vertically the resolution is uniform from ground level to the top of the rotor followed by an expansion towards the top border, as seen in figure 5.3. Figure 5.3 Horizontal grid resolution (left) and schematic view of the vertical grid resolution (right) Easting Northing z Total Grid spacing (m) Variable - Number of cells Table 5.2 Grid spacing and number of cells The grid extends 947 (m) above the point in the terrain with the highest elevation. The grid is refined towards the ground. The left and right columns display a schematic view of the distribution at the position with maximum and minimum elevation respectively. The nodes, where results from the simulations are available, are situated in the cell centers indicated by dots z-dist. max (m) z-dist. min (m) Table Simulations Distribution of the first 10 nodes in z-direction, relative to the ground, at the position with maximum and minimum elevation The digital model represents the computational domain where the Reynolds averaged Navier Stokes equations have been numerically solved. In total 36 simulations have been performed in order to have a 3D wind field for every 10 degree sector. The simulation time and the number of iteration to reach a converged Page 11 of 29

12 solution for each sector is given in table 5.5. The column "Status" should display a "C" indicating that the numerical procedure has converged, which means that the found solution actually is a solution of our specified problem. If the solution procedure don't find a solution the "Status" will display a "D" for divergence, or a "-" indicating that the solution procedure reach the maximum set number of iterations before a converged solution was found. Height of boundary layer (m) Speed above boundary layer (m/s) 10.0 Boundary condition at the top Potential temperature Turbulence model Solver fix pres. No Standard Maximum iterations 500 Table 5.4 Solver settings Sectors Simulation time Iterations Status Sectors Simulation time Iterations Status :09: C :31: C :39: C :33: C :54: C :02: C :44: C :00: C :09: C :45: C :53: C :50: C :39: C :52: C :13: C :46: C :23: C :44: C :00: C :48: C :56: C :49: C :44: C :51: C :33: C :13: C :29: C :50: C :19: :18: :50: C :44: C :21: :56: C :18: :22: Table 5.5 Simulation time, number of iterations and convergence status GCV Page 12 of 29

13 The convergence of the wind field simulations is evaluated by inspection of the spot and residual values for the velocity components (U1,V1,W1), the turbulent kinetic energy (KE) and its dissipation rate (EP). All variables are scaled according to the minimum and maximum values obtained during the simulation. The simulation stops automatically when the solution falls below a certain convergence criteria and then the solution is said to be converged. Figure 5.1 Residuals (left) and spot values (right) for sector 000 Figure 5.2 Residuals (left) and spot values (right) for sector 010 Figure 5.3 Residuals (left) and spot values (right) for sector 020 Figure 5.4 Residuals (left) and spot values (right) for sector 030 Page 13 of 29

14 Figure 5.5 Residuals (left) and spot values (right) for sector 040 Figure 5.6 Residuals (left) and spot values (right) for sector 050 Figure 5.7 Residuals (left) and spot values (right) for sector 060 Figure 5.8 Residuals (left) and spot values (right) for sector 070 Page 14 of 29

15 Figure 5.9 Residuals (left) and spot values (right) for sector 080 Figure 5.10 Residuals (left) and spot values (right) for sector 090 Figure 5.11 Residuals (left) and spot values (right) for sector 100 Figure 5.12 Residuals (left) and spot values (right) for sector 110 Page 15 of 29

16 Figure 5.13 Residuals (left) and spot values (right) for sector 120 Figure 5.14 Residuals (left) and spot values (right) for sector 130 Figure 5.15 Residuals (left) and spot values (right) for sector 140 Figure 5.16 Residuals (left) and spot values (right) for sector 150 Page 16 of 29

17 Figure 5.17 Residuals (left) and spot values (right) for sector 160 Figure 5.18 Residuals (left) and spot values (right) for sector 170 Figure 5.19 Residuals (left) and spot values (right) for sector 180 Figure 5.20 Residuals (left) and spot values (right) for sector 190 Page 17 of 29

18 Figure 5.21 Residuals (left) and spot values (right) for sector 200 Figure 5.22 Residuals (left) and spot values (right) for sector 210 Figure 5.23 Residuals (left) and spot values (right) for sector 220 Figure 5.24 Residuals (left) and spot values (right) for sector 230 Page 18 of 29

19 Figure 5.25 Residuals (left) and spot values (right) for sector 240 Figure 5.26 Residuals (left) and spot values (right) for sector 250 Figure 5.27 Residuals (left) and spot values (right) for sector 260 Figure 5.28 Residuals (left) and spot values (right) for sector 270 Page 19 of 29

20 Figure 5.29 Residuals (left) and spot values (right) for sector 280 Figure 5.30 Residuals (left) and spot values (right) for sector 290 Figure 5.31 Residuals (left) and spot values (right) for sector 300 Figure 5.32 Residuals (left) and spot values (right) for sector 310 Page 20 of 29

21 Figure 5.33 Residuals (left) and spot values (right) for sector 320 Figure 5.34 Residuals (left) and spot values (right) for sector 330 Figure 5.35 Residuals (left) and spot values (right) for sector 340 Figure 5.36 Residuals (left) and spot values (right) for sector 350 Page 21 of 29

22 6. Annual Energy Production - AEP The wind resource map and the annual energy production have been calculated based upon the long term on-site wind conditions and CFD results. 6.1 Wind resource map The wind resource map is used to identify the high wind speed area based on the average wind speed. The wind resource map is established by weighting the CFD results against the expected average conditions given as input. If several climatologies are available the wind resource map is based on them all, by weighting based on the inverse radial distance to each climatology. Figure 6.1 The wind resource map with average wind speed (m/s) at a hub height of 80 meters. Triangle: wind turbine, Dot: climatology Page 22 of 29

23 6.2 Energy production estimation The gross energy production is the energy production of the wind farm calculated by predicted free stream wind speed distribution at the hub height of each turbine location and the turbine s power curve provided by manufacturers. The free stream wind speed distribution is obtained by the WindSim flow model and the long term on-site wind conditions. Wind turbines extract energy from the wind. The wind speed downstream from the wind turbine is therefore reduced. As the flow proceeds further, the wake is spreading and recovers towards free stream conditions. The wake effect is calculated by the WindSim wake model. Then the potential energy production is obtained by taken into account of the wake losses, using the Jensen wake model [Katic, I., Højstrup, J., Jensen, N.O. "A Simple Model for Cluster Efficiency" EWEC Proceedings, 7-9 October 1986, Rome, Italy]. Turbine Type Hub Height (m) No. of turbines Capacity (MW) Gross AEP (GWh/y) Average wind speed (m/s) Wake losses (%) AEP with wake losses (GWh/y) Full load hours (hours) Capacity factor(%) mode0_hubheigh t100_105.2db(a) Table 6.1 Key wind farm and production characteristics Climatology name Gross AEP (GWh/y) AEP with wake losses (GWh/y) Wake losses (GWh/y) Wake losses (%) Climatology Table 6.2 Energy production in GWh/y based on climatology represented by the frequency distribution Turbine name Turbine1 Turbine2 Turbine3 Turbine4 Turbine5 Turbine6 Turbine7 Turbine8 Turbine9 Turbine10 Turbine11 Turbine12 Turbine13 Turbine type Air density (kg/m3) Average wind speed (m/s) Gross AEP (GWh/y) Wake Losses (%) AEP with wake losses (GWh/y) Full load hours (hours) Page 23 of 29

24 Turbine Turbine Turbine Turbine Turbine Turbine Table 6.3 Annual energy production per turbine Page 24 of 29

25 Appendix 1 - Methodology Numerical flow modeling, based on Computational Fluid Dynamics (CFD), is used to transfer the wind conditions from a measurement point to the wind turbine positions at hub height. These chapters describe how a digital terrain model is used to establish the 3D numerical flow model. How the wind fields are simulated within the 3D numerical flow model, and finally how the numerical results are scaled against the local wind climate giving the wind resource map and estimates of the Annual Energy Production, AEP. The local wind climate can be obtained from on-site measurements or alternatively from reanalysis datasets, which uses observational data and a Numerical Weather Prediction (NWP) model over an extended historical period. Figure 1.1 The Wind Resource Assessment procedure including the AEP estimation consists of the following steps: Digital terrain model - 3D numerical flow model - Wind field simulations - Local wind climate - Wind resource map - Annual Energy Production, AEP. The following site specific analyses are presented: Location of the site Expected average wind conditions at the site Wind farm configuration Used input terrain data Selected configuration of the model Modeled wind resource Energy yield assessment Modeling is needed in order to transfer the measured wind distribution at mast position to any other position within the wind farm, and thereby obtain a high resolution three dimensional wind resource map. The local wind field is affected by the terrain. This information is given to the software through a digital terrain model which includes terrain height and roughness data. Publicly available terrain data are used to establish a domain where the RANS (Reynolds Averaged Navier Stokes) equations are numerically solved. 36 RANS simulations are performed in order to have a 3D wind field for every wind direction. The expected average wind conditions at the site are used to scale the simulations and obtain the wind resource in the 3D domain. Wind measurements at different height/position are used also as a means to quality check the results from the CFD simulations in terms of horizontal/vertical extrapolation of wind speed and turbulence. The gross energy production is the energy production of the wind farm calculated by predicted free stream wind speed distribution at hub height of each turbine location and the turbine s power curve provided by the manufacturer. The power curve is individually corrected based on the average air density at every Page 25 of 29

26 turbine site. The free stream wind speed distribution is obtained by the WindSim flow model and long term on-site wind conditions. The power curve is adjusted by the difference of the predicted long term annual onsite air density and the air density stated in the manufacture s power curve. Wind turbines extract energy from the wind. The wind speed downstream from the wind turbine is therefore reduced. As the flow proceeds further, the wake is spreading and recovers towards free stream conditions. The wake effect is calculated by an analytical wake model and then the potential annual energy production is obtained taking into account the wake losses. The overview of the three processes is shown in Figure 1.2. Figure 1.2 Flow chart for AEP assessment using WindSim Express. Page 26 of 29

27 Appendix 2 - Online digital terrain models Various online digital terrain and roughness models are available. The coverage varies from local to global and the resolution varies from 500 to 30 m. ASTER GDEM Worldwide Elevation data set (1.5 Arc-Second Resolution) SRTM Worldwide Elevation Data (3 Arc-Second Resolution) United States Elevation Data (NED) (10 or 30 m Resolution) Roughness is based upon Corine Land Cover Europe (100 m resolution) VCF Tree Cover Worldwide 2005 (500 m Resolution) NLCD 2006 (US National Land Cover Database (30 m Resolution) Land Cover Roughness length (m) Continuous urban fabric 2.0 Discontinuous urban fabric 1.0 Industrial or commercial units 0.8 Road and rail networks and associated land 0.3 Port areas 0.8 Airports 0.03 Mineral extraction sites 0.1 Dump sites 0.1 Construction sites 0.1 Green urban areas 0.3 Sport and leisure facilities 0.5 Non-irrigated arable land 0.01 Permanently irrigated land 0.01 Rice fields 0.01 Vineyards 0.25 Fruit trees and berry plantations 0.25 Olive groves 0.25 Pastures 0.03 Annual crops associated with permanent crops 0.05 Complex cultivation patterns 0.05 Land principally occupied by agriculture 0.1 Agro, forestry areas 0.8 Broad, leaved forest 0.8 Coniferous forest 0.8 Page 27 of 29

28 Mixed forest 0.8 Natural grasslands 0.03 Moors and heathland 0.02 Sclerophyllous vegetation 0.02 Transitional woodland, shrub 0.5 Beaches, dunes, sands Bare rocks Sparsely vegetated areas 0.2 Burnt areas 0.1 Glaciers and perpetual snow Inland marshes 0.1 Peat bogs 0.03 Salt marshes 0.01 Salines 0.01 Intertidal flats 0.01 Water courses 0.01 Water Bodies Coastal lagoons 0.01 Estuaries 0.01 Sea and ocean Table A2.1 Corine Land Cover Europe Land Cover Roughness length (m) No Data Value, Alaska Zones Only Open Water 0.03 Perennial Ice/Snow Developed, Open Space 0.4 Developed, Low Intensity 0.6 Developed, Medium Intensity 1 Developed, High Intensity 1.5 Barren Land (Rock/Sand/Clay) 0.1 Unconsolidated Shore 0.05 Deciduous Forest 1.4 Evergreen Forest 1.4 Mixed Forest 1.4 Dwarf Scrub 0.05 Shrub/Scrub 1 Grassland/Herbaceous 0.05 Grassland/Herbaceous 0.05 Lichens 0.2 Moss 0.2 Page 28 of 29

29 Pasture/Hay 0.03 Cultivated Crops 0.01 Woody Wetlands 1 Palustrine Forested Wetland 1 Palustrine Scrub/Shrub Wetland 1 Estuarine Forested Wetland 1 Estuarine Scrub/Shrub Wetland 1 Emergent Herbaceous Wetlands 0.1 Table A2.2 NLCD 2006 Page 29 of 29