Upload
others
View
1
Download
2
Embed Size (px)
Citation preview
International Journal of Applied Environmental Sciences
ISSN 0973-6077 Volume 13, Number 3 (2018), pp. 287-307
© Research India Publications
http://www.ripublication.com
Effects of Climate Characteristics on Wind Power
Potential and Economic Evaluation in Salamis
Region, Northern Cyprus
Youssef Kassem1,2, Hüseyin Gökçekuş1, Hüseyin Çamur2
1,2 Department of Civil Engineering, Civil and Environmental Engineering Faculty, Near East University, Nicosia, via Mersin 10, Turkey.
2 Department of Mechanical Engineering, Engineering Faculty, Near East University, Nicosia, via Mersin 10, Turkey.
Abstract
Eight-year meteorological data observed at Salamis region, Northern Cyprus,
from 2009 to 2016 at 10m height, are studied to analyze the wind
characteristics and wind energy potential by the assistance of Weibull
distribution model. Therefore, the annual values of the Weibull parameters k
and c are 9.008 and 3.127m/s, respectively, and the power density
is16.724W/m2. Power law model is used to calculate the yearly and monthly
mean wind speed at various heights. Based on the result, it can be concluded
that the wind power density value in the region is considerable and can be
exploited using small-scale wind turbines. Additionally, the economic analysis
of 10 wind turbines model was estimated using present value cost method
(PVC). Based on the results, the highest annual capacity factor was 51.1%
using Finn Wind Tuule C 200 wind turbine, whereas, the minimum was found
as 11.7% using the Enercon E53 wind turbine. Moreover, the lowest and
highest values of annual electricity cost are obtained in the region as
0.00011$/kWh and 0.082 $/kWh using Enercon E82 and EolSenegal 500,
respectively. Furthermore, wavelet transform of the time series is applied to
study the relationship between wind speed, air temperature, and humidity.
Keyword: Cross wavelet transform; economic evaluation; Salamis; Weibull
distribution model; wind potential; wavelet transform; wavelet coherence
288 Youssef Kassem, Hüseyin Gökçekuş, Hüseyin Çamur
INTRODUCTION
Climate variability and wind speed are completely interconnected. Although much
attention is given to the potential effects of climate change on surface temperatures,
humidity, and precipitation, there has been a comparatively minor discussion or
analysis of changes in wind speeds.
Wind energy is one of the most efficient renewable energy sources available but it
also requires a very detailed analysis of the wind characteristics of the selected region
[1]. Global wind speed is used to evaluate power generation of wind power plant over
a lifetime of the project [2]. Wind potential is a critical factor for the viability of wind
power generation, which is mainly affected by the cost of wind power generation in a
particular location. Many studies on wind characteristics and wind power potentials
over an extended period of time have been conducted worldwide recently [3–7].
Weibull statistical distribution is a common method for analyzing wind speed
measurements and determining wind energy potential [6, 8, 9]. Weibull probability
density function can be used to forecast wind speed, wind density and wind energy
potential [6].
In recent years, the growth of population and other factors in Northern Cyprus have
led to an increase in energy source demand such as fossil fuels. The environmental
problems associated with the use of fossil fuels have necessitated the development of
alternative energy sources such as wind energy for electricity generation. Therefore,
the need for a new clean source of energy that increases electricity production from a
renewable source is crucial.
The objectives of the study are: (1) to investigate the wind characteristics in Salamis
region, Northern Cyprus using Weibull function, (2) study the economic evaluation of
selected site using the method of present value of costs (PVC) and (3) examine the
climate characteristics (humidity, temperature and wind speed) of the region using
wavelet transform analysis to show the relationship between power density, economic
evolution and climate characteristics. The data used are collected from Meteorological
Department for eight-year period between January 2009 and December 2016.
Furthermore, power law model is used to calculate the yearly and monthly mean wind
speed at various heights.
METHODOLOGY
Weibull distribution function
In order to characterize the wind speed of any region, the model distribution function
is required. The two-parameter Weibull distribution function is widely used to
characterize the wind speed and to estimate wind energy potential in a region.
Weibull distribution of wind speed is represented as a probability distribution function
(PDF) and cumulative distribution function (CDF). Table 1 presents the probability
density function (PDF) and cumulative distribution function (CDF) of two parameters
Weibull distribution function. The method used for probability density function and
cumulative distribution function is the maximum likelihood method (MLM) was used.
Effects of Climate Characteristics on Wind Power Potential and Economic Evaluation… 289
Table 1. Weibull distribution function for wind characteristic
Distribution function Equation References
PDF 𝑃𝐷𝐹 = (
𝑘
𝑐) (𝑣
𝑐)𝑘−1
𝑒−(𝑣𝑐)𝑘
[10-14]
CPF 𝐶𝑃𝐹 = 1 − 𝑒𝑥𝑝 [−(
𝑣
𝑐)𝑘
]
Parameters
Weibull shape parameter (k) 𝑘 = (
∑ 𝑣𝑖𝑘𝑙𝑛(𝑣𝑖)
𝑛𝑖=1
∑ 𝑣𝑖𝑘𝑛
𝑖=1
−∑ 𝑙𝑛(𝑣𝑖)𝑛𝑖=1
𝑛)
[10-14]
Weibull scale (c) [m/s]
𝑐 = (1
𝑛∑𝑣𝑖
𝑘
𝑛
𝑖=1
)
1𝑘
𝒗 wind speed value
The power of the wind (𝑃𝑣) that flows at speed v through a blade sweep area A
increases with the cube of its velocity and is given by [15]
𝑃𝑣 =1
2𝜌𝐴�̅�3 (1)
where �̅� is velocity in m/s, A is swept area in m2 and ρ is the density of air.
Monthly or annual wind power density (𝑃𝑤) per unit area of a site based on a Weibull
probability density function can be expressed as follows [15]:
𝑃𝑤 =1
2𝜌𝑐3Γ (1 +
3
𝑘) (2)
Where ρ = air density at the site. The air density is calculated using the following
expression [16]
𝜌 =353.049
𝑇𝑒(−0.034
𝑧𝑇) (3)
Where z is the elevation and T is the temperature at a considered site in [K].
Wind speed data extrapolation
For any wind project, it is very important to estimate the wind speed at the wind
turbine hub height. Therefore, The power law method is most commonly used to
estimate the wind speeds at various heights [17, 18]. It is expressed as:
𝑣
𝑣10= (
𝑧
𝑧10)𝛼
(4)
290 Youssef Kassem, Hüseyin Gökçekuş, Hüseyin Çamur
where v is the wind speed at the wind turbine hub height z, 𝑣10 is the wind speed at
original height 𝑧10, and α is the surface roughness coefficient, which is depends on the
characteristics of the region [6]. The value of α can be obtained from the following
expressions [19, 20]
𝛼 =0.37 − 0.088𝑙𝑛(𝑣10)
1 − 0.088𝑙𝑛(𝑧10 10⁄ ) (5)
In most cases, the accurate value of the surface roughness coefficient is not readily
available or ascertained. Therefore, the Weibull parameters with height can be
estimated using Eqs. (6) and (7) according to Belabes et al. [4].
𝑐(𝑧) = c0 (𝑧
𝑧10)𝑛
(6)
𝑘(𝑧) =k0 [1 − 0.088ln (
𝑧1010)]
[1 − 0.088ln (𝑧1010)]
(7)
where c0 and k0 are the scale and shape factors, respectively, at the measurement
height 𝑧10, while z is the hub height. The exponent n is defined as [4]:
𝑛 =[0.37 − 0.088𝑙𝑛(c0)]
1 − 0.088ln (𝑧10)
(8)
Energy output of wind turbines
Power curve of the wind turbines and wind speed characteristics are the mean
parameters to estimate the wind energy of the turbine, Ewt, [21]. The total power
output of the wind turbine can be expressed by Eq. (9)
𝐸𝑤𝑡 =∑𝑃𝑤𝑡(𝑖)
𝑛
𝑖=1
𝑡 (9)
where t is the number of hours in the period under consideration.
The power curve of wind turbines can be approximated with a parabolic law as given
by [22]
𝑃𝑤𝑡(𝑖) =
{
𝑃𝑟
𝑣𝑖2 − 𝑣𝑐𝑖
2
𝑣𝑟2 − 𝑣𝑐𝑖2 (𝑣𝑐𝑖 ≤ 𝑣𝑖 ≤ 𝑣𝑟)
1
2𝜌𝐴𝐶𝑝𝑣𝑟
2 (𝑣𝑟 ≤ 𝑣𝑖 ≤ 𝑣𝑐𝑜)
0 (𝑣𝑖 ≤ 𝑣𝑐𝑖 𝑎𝑛𝑑 𝑣𝑖 ≥ 𝑣𝑐𝑜)
(10)
where 𝑣𝑖 is the vector of possible wind speed at a given site, 𝑃𝑤𝑡(𝑖) is the vector of
corresponding wind turbine output power (W), 𝑃𝑟 is the rated power of the turbine
(W), 𝑣𝑐𝑖 is the cut-in wind speed (m/s), 𝑣𝑟 is the rated wind speed (m/s) and 𝑣𝑐𝑜 is the
Effects of Climate Characteristics on Wind Power Potential and Economic Evaluation… 291
cut-out wind speed (m/s) of the wind turbine. 𝐶𝑝 is the coefficient of performance of
the turbine, it is a function of the tip speed ratio and the pitch angle. The coefficient of
performance is considered to be constant for the entire range of wind speed [23] and
can be calculated as
𝐶𝑝 = 2𝑃𝑟
𝜌𝐴𝑣𝑟3 (11)
The capacity factor (CF) of a wind turbine is the fraction of the total energy generated
by the wind turbine over a period of time to its potential output if it had operated at a
rated capacity during entire time. The capacity factor of a wind turbine based on the
local wind regime of a given site can be estimated as
𝐶𝐹 =𝐸𝑤𝑡𝑃𝑟 . 𝑡
(12)
Economic analysis of wind turbines
The main parameters that govern wind power costs [24, 25] are
1. Capital costs, including wind turbines, foundations, road construction and grid
connection
2. Operation and maintenance costs.
3. Electricity produced, which depends on region and wind turbine
characteristics.
4. Discount rate and economic lifetime of the investment.
These factors may vary from country to country and region to region [25]. Table 2
tabulates the cost of wind turbine based on the rated power of the turbine [24, 25].
Table 2. Cost of wind turbines based on the rated power [25]
Wind turbine size [kW] Specific cost [US$/kW] Average specific cost
[US$/kW]
10 to 20 2200 to 2900 2550
20 to 200 1500 to 2300 1900
200 > 1000 to 1600 1300
Several methods have been used to estimate the wind energy cost such as PVC
methods [26]. The present value of costs (PVC) is given in [26] as the following
equation:
𝑃𝑉𝐶 = [𝐼 + 𝐶𝑜𝑚𝑟 (1 + 𝑖
𝑟 − 𝑖) × [1 − (
1 + 𝑖
1 + 𝑟)𝑛
] − 𝑆 (1 + 𝑖
1 + 𝑟)𝑛
] (13)
292 Youssef Kassem, Hüseyin Gökçekuş, Hüseyin Çamur
where, r is the discount rate, i is the inflation rate, n is the machine life as designed by
the manufacturer, the 𝐶𝑜𝑚𝑟 is the cost of operation and maintenance, I is the
investment summation of turbine price and other initial costs, including provisions for
civil work, land, infra-structure, installation and grid integration and S is the scrap
value of the turbine price and civil work (see Table 3).
Table 3. Parameters of PVC
Parameter Value Parameter Value
r [%] 8 I [%] 68
i [%] 6 S [%] 10
n [year] 20 𝑪𝒐𝒎𝒓 [%] 7
The cost per kW h of electricity generated (UCE) can be determined by the following
expression [26]:
𝑈𝐶𝐸 =𝑃𝑉𝐶
𝑡 × 𝑃𝑟 × 𝐶𝑓 (14)
Measurement data
Cyprus is situated at latitude 35° North and longitude 33° East, surrounded by the
Eastern Mediterranean Sea. The surface winds over Cyprus are controlled by local
surface effects, such as the temperature contrast between land and open sea (land and
sea breezes), the differential heating of land (anabatic and catabatic winds) and the
constraints imposed by topography.
Air temperature, humidity and wind speed values at 10 m heights were measured
using thermometers, hygrometers and anemometers, respectively. Data were
measured continuously during the period from 2009 to 2016. The detailed geographic
information of the selected site is illustrated in Figure 1 and Table 4.
Table 4. Details of each station used in the analysis
Site Coordinates Height
[m]
Period Characteristics
of the site Latitude
[°N]
Longitude
[°E]
Salamis 35º07'52.2" 33º55'33.42" 10 2009-2016 coastal
Effects of Climate Characteristics on Wind Power Potential and Economic Evaluation… 293
Figure 1. Location of the site used in this study
RESULTS AND DISCUSSIONS
Wind speed variation at 10 m height
Mean Hourly wind speed
The mean hourly wind speeds at different seasons: winter (December, January and
February), spring (March, April and May), summer (June, July and August) and
autumn (September, October and November) are presented in Figure 2. In winter, a
great deviation can be identified between the morning and the afternoon periods. A
steady wind speed is observed before 8am and increases sharply to reach the
maximum speed at 2pm. Also, the wind speed varies between 2 and 6 m/s. During the
spring, the daily variation in Salamis has a maximum which occurs at noon time (12
pm) and a minimum which occurs between 4 and 5 am. In summer, the maximum
mean daily variation speed values are recorded between 10 and 11am. In addition, the
mean wind speeds range between 0.5 and 7 m/s during spring and summer seasons.
Moreover, it is noticed that during autumn, the minimum and maximum wind speed
are obtained at morning time which occurs at 8 and 12am, respectively. In general,
the mean wind speed increases sharply after 6 am and reaches the peak-point around
12pm.
294 Youssef Kassem, Hüseyin Gökçekuş, Hüseyin Çamur
Figure 2. Mean hourly wind speed (2009-2016)
Mean monthly wind speed
Mean monthly wind speed during the studied period from 2009 to 2016 is shown in
Figure 3. The highest monthly mean wind speed of 5.5 m/s occurred in February
2010, while the lowest mean wind speed of 2.1 m/s occurred in September 2015.
Generally, it is found that the mean monthly wind speed in the period from 2009 to
2016 was in the range of 2.1 to 5.5 m/s.
Effects of Climate Characteristics on Wind Power Potential and Economic Evaluation… 295
Figure 3. Monthly mean wind speeds (2009-2016)
Mean yearly wind speeds
Mean yearly wind speeds for different seasons of the year 2009-2016 are plotted in
Figure 4. It is observed that the highest values of mean seasonal wind speed of the
area occurred during winter and autumn seasons. During these seasons, the wind
speed values are ranged between 2.24 and 3.74 m/s. Furthermore, it is noticed that the
minimum value of wind speeds occurred during summer.
Figure 4. Seasonal and annual wind speeds (2009-2016)
Average air density of the site
The air temperature values were collected during the investigated period (Figure 5).
Assuming that air is an ideal gas, average yearly air density values were calculated
using Eq. (7). The mean monthly air density values are shown in Figure 6. Also, mean
0.0
1.0
2.0
3.0
4.0
5.0
6.0M
ean
mo
nth
ly w
ind
sp
ee
d [
m/s
]
2009
2010
2011
2012
2013
2014
2015
2016
Average
2.0
2.5
3.0
3.5
4.0
2009 2010 2011 2012 2013 2014 2015 2016
Me
an m
on
thly
win
d s
pe
ed
[m
/s]
Winter
Spring
Summer
Autumn
Annual
296 Youssef Kassem, Hüseyin Gökçekuş, Hüseyin Çamur
yearly air density values for the years 2009-2016 are tabulated in Table 5. The
average density values for 10 m were assumed to be constant for the other heights as
there will be no significant difference.
Figure 5. Average monthly air temperature in Salamis (2009–2016)
Figure 6. Monthly variation in air density (2009–2016)
0
5
10
15
20
25
30
35
Air
te
mp
era
ture
[℃] 2009
2010
2011
2012
2013
2014
2015
2016
1.14
1.15
1.16
1.17
1.18
1.19
1.20
1.21
1.22
1.23
1.24
De
nsi
ty [
kg/m
^3]
2009
2010
2011
2012
2013
2014
2015
2016
Effects of Climate Characteristics on Wind Power Potential and Economic Evaluation… 297
Table 5. Mean yearly air density values (2009-2016) for Salamis
Year Average
Temperature [K]
Humidity
[%]
Average density
[kg/m3]
2009 295.81 68.2 1.192
2010 296.38 69.2 1.190
2011 295.52 68.0 1.193
2012 295.69 68.4 1.193
2013 295.78 66.2 1.192
2014 296.03 68.8 1.191
2015 295.67 67.9 1.193
2016 296.08 65.6 1.191
Frequency distribution of wind speeds at 10m height
The most important part of the measured wind speed data is its characteristics. An
evaluation of the data is needed to understand its characteristics. The characteristics
can be evaluated from the hourly wind speed, the daily wind speed, the monthly wind
speed, the annual wind speed, the wind speed distribution function, the mean wind
power and the energy density. The parameter estimates, shape (k) and scale (c), for
Weibull function have been performed using the maximum likelihood method are
tabulated in Table 6. Moreover, Figure 7 shows the Weibull distribution function,
described by its PDF and CPF, versus the mean wind speed, for data collected on an
annual basis from 2009 to 2016, based on parameters calculated using the maximum
likelihood method.
Table 6. Weibull parameters at height 10m
Parameters 2009 2010 2011 2012 2013 R2
k 12.50 8.03 5.82 4.63 3.75
0.987
c [m/s] 2.83 2.84 2.85 3.13 3.26
Mean [m/s] 2.77 2.62 2.62 2.80 2.92
WPD [W/m2] 12.59 12.49 12.52 16.95 19.73
Actual mean [m/s] 2.72 2.69 2.65 2.87 2.96
Observed WPD
[W/m2] 12.36 11.88 11.41 14.45 15.92
298 Youssef Kassem, Hüseyin Gökçekuş, Hüseyin Çamur
Parameters 2014 2015 2016 Whole R2
k 7.692 5.316 4.555 9.008
0.987
c [m/s] 2.874 2.978 2.872 3.127
Mean [m/s] 2.701 2.734 2.560 3.079
WPD [W/m2] 12.909 14.397 13.078 16.724
Actual mean [m/s] 2.700 2.741 2.644 2.965
Observed WPD
[W/m2] 12.056 12.612 11.320 15.962
Figure 7. Wind speed probability frequency at height 10m
Shear profile and surface roughness of Salamis
The best wind speed for installing the wind turbine is 6.7 m/s and more, but it should
be mentioned that it is not safe to install wind turbines in areas with wind speed of
more than 11 m/s, because of the possibility of damage to the system [6]. The
exponent α is the roughness coefficient which depends on land surface characteristic
[6]. The value of α is between the range of 0.05 and 0.5 [6]. The surface roughness
values (α) determined by using Eq.(9) for different years are shown in Table 7.
In this paper, annual mean wind speeds have been calculated using the value of α at
the various heights. The variation in the wind speed with height above ground is
called the wind shear profile. Figure 8 presents the vertical wind shear profile at six
studied sites for various heights.
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
0
0.2
0.4
0.6
0.8
1
1.2
2 2.5 3 3.5 4O
bse
rve
d m
ean
win
d s
pe
ed
[m
/s]
PD
F/C
DF
Mean wind speed [m/s]
Observed PDF CDF
Effects of Climate Characteristics on Wind Power Potential and Economic Evaluation… 299
Table 7. Roughness values for different years (2009-2016)
Year Mean [m/s] at 10m height roughness coefficient
2009 2.722 0.282
2010 2.690 0.283
2011 2.651 0.284
2012 2.868 0.277
2013 2.962 0.274
2014 2.700 0.283
2015 2.741 0.281
2016 2.644 0.284
Whole 2.965 0.274
Figure 8. Vertical wind shear profile at Salamis
Table 8 tabulates the resultant parameters of Weibull at various heights. As shown,
the maximum observed power density values are 131.81 W/m2 at 130m height, while
the minimum power was observed at 20m for a value of 28.24 W/m2. The wind
energy generation potential of a site is classified according to average power density
values given in [6]. Hence, this site can be considered as of power class 1, poor, [6],
which indicates poor wind energy potential. Commercial wind turbines with high
capacities (MWs) are not suitable to be used in this area. However, the available wind
energy potential of the area can be exploited using small scale wind turbines.
0102030405060708090
100110120130
2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0
He
igh
t ab
ove
th
e g
rou
nd
[m
]
Mean wind speed [m/s]
300 Youssef Kassem, Hüseyin Gökçekuş, Hüseyin Çamur
Table 8. Weibull parameters and power density at various heights for year 2009-2016
Parameters 20 30 50 70 80 90 130 R2
k 8.362 8.362 8.362 8.362 8.362 8.362 8.362
0.983 c [m/s] 3.515 4.018 4.52 4.957 5.142 5.311 5.875
Mean [m/s] 3.32 3.71 4.27 4.68 4.86 5.02 5.55
WPD [W/m2] 23.75 35.48 50.51 66.62 74.36 81.94 110.91
Actual mean [m/s] 3.59 4.01 4.61 5.06 5.25 5.42 5.99
Observed WPD [W/m2] 28.24 39.43 60.03 79.19 88.39 97.39 131.81
Economic analysis of electricity generation potential
The characteristic properties of the three selected wind turbines are presented in Table
9. In this study, the annual output energy and the capacity factor of large and small
different wind turbines for the studied site were calculated using Eqs. (15) and (16).
Table 9. Characteristics of the selected wind energy turbines
Characteristics Aircon10 EolSenegal
500
Finn Wind
Tuule C
200
P10-20 EWT
DW
Hub height [m] 12 18 27 36.6 50
Rated power [kW] 10 0.5 3 20 250
Cut in speed [m/s] 2.5 2 1.9 2.5 2.5
Rated speed [m/s] 11 9 10 10 10
Cut out speed
[m/s] 32 12 - 25 25
Characteristics Enercon
E33 P-15-50
DW61-900
kW
Enercon
E53
Enercon
E82
Hub height [m] 50 50 61 70 130
Rated power [kW] 330 50 900 800 2000
Cut in speed [m/s] 2.5 2.5 2.5 2.5 2.5
Rated speed [m/s] 13 10 11.5 13 12.5
Cut out speed
[m/s] 25 25 25 25 25
Effects of Climate Characteristics on Wind Power Potential and Economic Evaluation… 301
Table 10 is tabulated with the annual energy power (AEP) and capacity factors (CF)
of the wind turbines. It can be noted that the highest capacity factor is obtained in the
site with EolSenegal 500. This may be attributed to the rated wind speed of 9m/s and
time generation of 22h, which is lower than the other turbine models. In addition,
Enercon E82 wind turbine model produces the highest annual energy compared to
other models and it has the lowest capacity factor of 13%. This may be attributed to
the high turbine hub height and time generation of 10h. In general the capacity factor
is greater for the wind turbines for which the nominal speed is lower. This remark was
observed for both the large wind turbines and for the small wind turbines.
Moreover, Table 10 shows the cost of unit energy per kW h based on the PVC
method. This cost is computed using Eqs. (17) and (18). The lowest and highest
values during the studied period (2009-2016) of electricity cost are obtained in site as
0.00011$/kWh and 0.082 $/kWh using Enercon E82 and EolSenegal 500,
respectively.
Based on the capacity factor, it can be concluded that the EolSenegal 500 turbine is
cost-effective for the Salamis region and could be strongly recommended for
installation.
Table 10. Electricity production and financial indices at site
Turbine 2009 2010 2011 2012 2013 2014 2015 2016
Aircon10 Generation time [h] 10.0 9.0 9.0 11.0 17.0 10.0 10.0 10.0
AEP [MWh] 47.7 36.2 46.7 117.5 212.9 58.4 85.0 59.6
CF% 13.3 11.2 14.4 29.7 34.8 16.2 23.6 16.6
Average cost analysis [$/kWh] 3.6E-02 4.7E-02 3.6E-02 1.4E-02 8.0E-03 2.9E-02 2.0E-02 2.9E-02
EolSenegal 500 Generation time [h] 22.0 22.0 22.0 22.0 24.0 22.0 24.0 22.0
AEP [MWh] 21.4 20.5 19.9 28.7 36.0 21.3 25.8 20.3
CF% 53.9 51.7 50.2 72.4 83.3 53.7 59.8 51.1
Average cost analysis [$/kWh] 8.0E-02 8.3E-02 8.6E-02 5.9E-02 4.7E-02 8.0E-02 6.6E-02 8.4E-02
Finn Wind
Tuule C 200
Generation time [h] 24.0 24.0 24.0 24.0 24.0 24.0 24.0 24.0
AEP [MWh] 124.3 119.7 116.5 162.4 185.0 123.8 136.5 118.5
CF% 47.9 46.2 44.9 62.7 71.4 47.8 52.6 45.7
Average cost analysis [$/kWh] 1.4E-02 1.4E-02 1.5E-02 1.0E-02 9.2E-03 1.4E-02 1.2E-02 1.4E-02
P10-20 Generation time [h] 10.0 9.0 9.0 11.0 17.0 10.0 10.0 10.0
AEP [MWh] 116.8 88.6 114.2 284.3 521.3 142.9 193.9 145.9
CF% 16.2 13.7 17.6 35.9 42.6 19.9 26.9 20.3
Average cost analysis [$/kWh] 1.5E-02 1.9E-02 1.5E-02 6.0E-03 3.3E-03 1.2E-02 8.8E-03 1.2E-02
302 Youssef Kassem, Hüseyin Gökçekuş, Hüseyin Çamur
Turbine 2009 2010 2011 2012 2013 2014 2015 2016
EWT DW Generation time [h] 10.0 9.0 9.0 11.0 17.0 10.0 10.0 10.0
AEP [MWh] 1460.1 1108.1 1427.9 3594.4 6515.9 1786.6 2600.6 1823.8
CF% 16.2 13.7 17.6 36.3 42.6 19.9 28.9 20.3
Average cost analysis [$/kWh] 6.7E-04 8.9E-04 6.9E-04 2.7E-04 1.5E-04 5.5E-04 3.8E-04 5.4E-04
Enercon E33 Generation time [h] 10.0 9.0 9.0 11.0 17.0 10.0 10.0 10.0
AEP [MWh] 1927.4 1462.7 1884.9 4744.5 8601.0 2358.3 3432.8 2407.4
CF% 16.2 13.7 17.6 36.3 42.6 19.9 28.9 20.3
Average cost analysis [$/kWh] 5.1E-04 6.7E-04 5.2E-04 2.1E-04 1.1E-04 4.2E-04 2.9E-04 4.1E-04
P-15-50 Generation time [h] 10.0 9.0 9.0 11.0 17.0 10.0 10.0 10.0
AEP [MWh] 292.0 221.6 285.6 718.9 1303.2 275.7 471.8 237.6
CF% 16.2 13.7 17.6 36.3 42.6 15.3 26.2 13.2
Average cost analysis [$/kWh] 4.9E-03 6.5E-03 5.0E-03 2.0E-03 1.1E-03 5.2E-03 3.0E-03 6.0E-03
DW61-900 kW Generation time [h] 10.0 9.0 9.0 11.0 17.0 10.0 10.0 10.0
AEP [MWh] 3911.1 2968.2 3700.3 9627.7 17453.4 4785.4 6965.9 4885.2
CF% 12.1 10.2 12.7 27.0 31.7 14.8 21.5 15.1
Average cost analysis [$/kWh] 2.5E-04 3.3E-04 2.7E-04 1.0E-04 5.6E-05 2.1E-04 1.4E-04 2.0E-04
Enercon E53 Generation time [h] 10.0 9.0 9.0 11.0 17.0 10.0 10.0 10.0
AEP [MWh] 2691.5 2042.6 2632.1 6278.5 12011.0 3293.2 4793.8 3361.8
CF% 9.3 7.9 10.2 19.8 24.5 11.4 16.6 11.7
Average cost analysis [$/kWh] 3.6E-04 4.8E-04 3.7E-04 1.6E-04 8.2E-05 3.0E-04 2.0E-04 2.9E-04
Enercon E82 Generation time [h] 10.0 9.0 9.0 11.0 17.0 10.0 10.0 10.0
AEP [MWh] 7300.7 5540.6 7139.6 17971.8 32579.7 8697.6 12119.8 9119.0
CF% 10.1 8.6 11.0 22.7 26.6 12.1 16.8 12.7
Average cost analysis [$/kWh] 1.3E-04 1.8E-04 1.4E-04 5.5E-05 3.0E-05 1.1E-04 8.1E-05 1.1E-04
Time frequency space using Cross wavelet transform (XWT) and Wavelet
Coherence (WTC)
The wavelet transform analysis method has been widely used in the study of climate
characteristics of different areas to investigate the meteorological variations of
variables such as temperature and wind speed [27].
Effects of Climate Characteristics on Wind Power Potential and Economic Evaluation… 303
Figures 9-11 show the cross and coherence wavelet spectrum for time series of wind
speed, temperature and humidity, respectively.
It is obvious from Figure 9 that there is a high zone for the period between 8 and 16
months, implying that the two series have significant correlation for the annual period
at which the phase arrows mostly point northwest.
Figure 9. Wavelet spectrum for time series of average wind speed and temperature
Figure 10 shows the XWT and CWT for wind speed and humidity temperature (the
colour bar, scale, is the same as in Figure 9). It can be seen that the two series have
significant correlation during the annual period. However, most of the arrows point to
the west, implying that they are in phase. The change in wind speed during the year
coincides with that in wind potential power cost and electricity demand cost.
Figure 10. Wavelet spectrum for time series of average monthly wind speed and
humidity
304 Youssef Kassem, Hüseyin Gökçekuş, Hüseyin Çamur
Figure 11 shows the XWT and CWT for wind speed and solar radiation. The two
series have higher correlation at the annual period with the phase arrows pointing
northwest and west.
Figure 11. Wavelet spectrum for time series of average monthly temperature and
humidity
Moreover, it is found that the coherence spectrum presents a higher zone at some
areas, similar to the case of the cross wavelet spectrum for the same data pair.
CONCLUSIONS
In the contents of this study; wind speed and wind direction data from 2009 to 2016
have been analyzed for the Salamis region in Northern Cyprus. Wind speed frequency
distributions, mean wind speed, and four distribution function parameters have been
calculated. Observed values were compared with the calculated Weibull probability
distribution results. Wind direction trends were analyzed and summarized in the form
of a wind rose graph. Also, in this study, wind turbine performance assessment and
economic analysis of selected wind turbines were examined. Moreover, a
performance study of all the wind turbines was achieved in all sites by determining
the capacity factor and the cost of unit energy (UCE) per kW h based on the PVC
method. Analysis has yielded the following conclusions;
The annual mean wind speeds for Salamis region at 10 m height were found to
be t in the range of 2 – 4 m/s.
The wind data of Salamis showed that the maximum monthly wind speed
occurs in the winter while summer season has the lowest mean wind speed. In
addition, the highest mean wind speed value with 3.74 m/s is determined in
the winter season (in 2013), while the lowest value is in the summer season (in
2016) with 2.24 m/s.
Effects of Climate Characteristics on Wind Power Potential and Economic Evaluation… 305
From the results, it can be said that Salamis has poor wind characteristics.
This is shown by the low hourly, monthly and yearly mean wind speed for the
whole year.
Among all analyzed wind turbine, the highest capacity factor was found in
2013, which was 83.3% using Finn Wind Tuule C 200 wind turbine. Whereas,
the minimum is found in 2010 as 7.9% using the Enercon E53 wind turbine.
The lowest and highest values of electricity cost during the studied period
(2009-2016) are obtained in site as 0.00011$/kWh and 0.082 $/kWh using
Enercon E82 and EolSenegal 500, respectively
Wind speed, temperature and humidity are near anti-phase, implying that wind
energy and air temperature can act complementary to each other with regards
to electricity generation in the area under study.
ACKNOWLEDGMENTS
The authors would like to thank the Faculty of Engineering especially the Civil
Engineering Department and Mechanical Engineering Department of the Near East
University for their support and encouragement.
REFERENCES
[1] Baniyounes, A. M. (2017). Renewable energy potential in Jordan.
International Journal of Applied Engineering Research, 12(19), 8323-8331
[2] Azad, A., Rasul, M., Islam, R., & Shishir, I. R. (2015). Analysis of Wind
Energy Prospect for Power Generation by Three Weibull Distribution
Methods. Energy Procedia, 75, 722-727. doi:10.1016/j.egypro.2015.07.499
[3] Solyali, D., Altunç, M., Tolun, S., & Aslan, Z. (2016). Wind resource
assessment of Northern Cyprus. Renewable and Sustainable Energy Reviews,
55, 180-187. doi:10.1016/j.rser.2015.10.123
[4] Belabes, B., Youcefi, A., Guerri, O., Djamai, M., & Kaabeche, A. (2015).
Evaluation of wind energy potential and estimation of cost using wind energy
turbines for electricity generation in north of Algeria. Renewable and
Sustainable Energy Reviews, 51, 1245-1255. doi:10.1016/j.rser.2015.07.043
[5] Balamurugan, P., Muthukannan, P., & Subramanian, R. (2017). Stability
enhancement in grid integrated real time wind energy conversion system with
compensating devices. International Journal of Applied Engineering
Research, 12(14), 4571-457
[6] Ozay, C., & Celiktas, M. S. (2016). Statistical analysis of wind speed using
two-parameter Weibull distribution in Alaçatı region. Energy Conversion and
Management, 121, 49-54. doi:10.1016/j.enconman.2016.05.026
306 Youssef Kassem, Hüseyin Gökçekuş, Hüseyin Çamur
[7] Mostafaeipour, A. (2013). Economic evaluation of small wind turbine
utilization in Kerman, Iran. Energy Conversion and Management, 73, 214-
225. doi:10.1016/j.enconman.2013.04.018
[8] Soulouknga, M., Doka, S., N., N., & T. (2018). Analysis of wind speed data
and wind energy potential in Faya-Largeau, Chad, using Weibull distribution.
Renewable Energy, 121, 1-8. doi:10.1016/j.renene.2018.01.002
[9] Parajuli, A. (2016). A Statistical Analysis of Wind Speed and Power Density
Based on Weibull and Rayleigh Models of Jumla, Nepal. Energy and Power
Engineering, 08(07), 271-282. doi:10.4236/epe.2016.87026
[10] Hennessey, J. P. (1977). Some Aspects of Wind Power Statistics. Journal of Applied Meteorology, 16(2), 119-128. doi:10.1175/1520-
0450(1977)016<0119:saowps>2.0.co;2
[11] Chang, T. P. (2011). Estimation of wind energy potential using different
probability density functions. Applied Energy, 88(5), 1848-1856.
doi:10.1016/j.apenergy.2010.11.010
[12] Shu, Z., Li, Q., & Chan, P. (2015). Statistical analysis of wind characteristics
and wind energy potential in Hong Kong. Energy Conversion and Management, 101, 644-657. doi:10.1016/j.enconman.2015.05.070
[13] Justus, C. G., Hargraves, W. R., Mikhail, A., & Graber, D. (1978). Methods
for Estimating Wind Speed Frequency Distributions. Journal of Applied Meteorology, 17(3), 350-353. doi:10.1175/1520-
0450(1978)017<0350:mfewsf>2.0.co;2
[14] Ouarda, T., Charron, C., Shin, J., Marpu, P., Al-Mandoos, A., Al-
Tamimi, M., … Al Hosary, T. (2015). Probability distributions of wind speed
in the UAE. Energy Conversion and Management, 93, 414-434.
doi:10.1016/j.enconman.2015.01.036
[15] Chang, T. P. (2011). Performance comparison of six numerical methods in
estimating Weibull parameters for wind energy application. Applied Energy, 88(1), 272-282. doi:10.1016/j.apenergy.2010.06.018
[16] Sathyajith, M. (2014). Wind Energy Fundamentals, Resource Analysis and
Economics. Berlin: Springer Berlin.
[17] Kaabeche, A., Belhamel, M., & Ibtiouen, R. (2011). Techno-economic
valuation and optimization of integrated photovoltaic/wind energy conversion
system. Solar Energy, 85(10), 2407-2420. doi:10.1016/j.solener.2011.06.032
[18] Ucar, A., & Balo, F. (2008). A Seasonal Analysis of Wind Turbine
Characteristics and Wind Power Potential in Manisa, Turkey. International Journal of Green Energy, 5(6), 466-479. doi:10.1080/15435070802498101
[19] Irwanto, M., Gomesh, N., Mamat, M., & Yusoff, Y. (2014). Assessment of
wind power generation potential in Perlis, Malaysia. Renewable and
Effects of Climate Characteristics on Wind Power Potential and Economic Evaluation… 307
Sustainable Energy Reviews, 38, 296-308. doi:10.1016/j.rser.2014.05.075
[20] Mostafaeipour, A. (2010). Feasibility study of harnessing wind energy for
turbine installation in province of Yazd in Iran. Renewable and Sustainable Energy Reviews, 14(1), 93-111. doi:10.1016/j.rser.2009.05.009
[21] Gökçek, M., & Genç, M. S. (2009). Evaluation of electricity generation and
energy cost of wind energy conversion systems (WECSs) in Central Turkey.
Applied Energy, 86(12), 2731-2739. doi:10.1016/j.apenergy.2009.03.025
[22] Pallabazzer, R. (2003). Parametric analysis of wind siting efficiency. Journal
of Wind Engineering and Industrial Aerodynamics, 91(11), 1329-1352.
doi:10.1016/j.jweia.2003.08.002
[23] Nouni, M., Mullick, S., & Kandpal, T. (2007). Techno-economics of small
wind electric generator projects for decentralized power supply in India.
Energy Policy, 35(4), 2491-2506. doi:10.1016/j.enpol.2006.08.011
[24] Gölçek, M., Erdem, H. H., & Bayülken, A. (2007). A Techno-Economical
Evaluation for Installation of Suitable Wind Energy Plants in Western
Marmara, Turkey. Energy Exploration & Exploitation, 25(6), 407-427.
doi:10.1260/014459807783791791
[25] Gökçek, M., & Genç, M. S. (2009). Evaluation of electricity generation and
energy cost of wind energy conversion systems (WECSs) in Central
Turkey. Applied Energy, 86(12), 2731-2739.
doi:10.1016/j.apenergy.2009.03.025
[26] Adaramola, M., Paul, S., & Oyedepo, S. (2011). Assessment of electricity
generation and energy cost of wind energy conversion systems in north-
central Nigeria. Energy Conversion and Management, 52(12), 3363-3368.
doi:10.1016/j.enconman.2011.07.007
[27] Chang, T., Liu, F., Ko, H., & Huang, M. (2017). Oscillation characteristic
study of wind speed, global solar radiation and air temperature using wavelet
analysis. Applied Energy, 190, 650-657. doi:10.1016/j.apenergy.2016.12.149