Ingenieur Vol. 75 ingenieur July 2018-FA - Page 39

a different heights to determine the wind shear profile at the sites. The Weibull Distribution Function (Figure 9) is plotted to estimate the wind speed frequency distribution for measured wind data. Lastly, the wind rose results (Figure 10) are plotted to determine the frequency of wind direction. Figure 9: Weibull distribution plot (Source: UMT) (iii) Micrositing The smallest scale, or third stage, of wind resource assessment is micrositing. Its main objective is to quantify the small-scale variability of the wind resource over the terrain of interest. Ultimately, micrositing is used to position one or more wind turbines on a parcel of land to maximize the overall energy output of the wind plant. The measured wind data are utilised in the flow modelling using the WAsP flow model to predict the wind speed at the selected site as shown in Figure 11. Table 2 presents the mean values for wind speed and Weibull parameters. The best mean value for wind speed to develop a wind energy farm is 5 m/s and above. Table 2: Mean values of wind speed and Weibull parameters Figure 10: Wind Rose (Source: UMT) Height Mean value (m) of wind speed, (m/s) 70 5.90 50 5.40 35 4.80 10 2.90 Weibull shape parameter, k 2.22 2.15 2.06 1.87 Scale parameter, c (m/s) 6.70 6.10 5.40 3.30 Techno-economic Assessment Figure 11: Micrositing Map (Source: UMT) A Techno-economic Assessment (TEA) is a cost benefit analysis to determine the feasibility of a specific project. One of metrics in TEA is the levelized cost of energy (LCOE). The LCOE is the net present value of the unit-cost of electricity over the lifetime of a wind turbine system. It is often taken as an average price that the wind turbine must receive to break even over its lifetime. The typical parameters that are required to calculate the LCOE are Annual Energy Production (AEP), the capital cost of the wind turbine, maintenance 37