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