Microsoft Word - ETASR_V12_N6_pp9551-9559
Engineering, Technology & Applied Science Research Vol. 12, No. 6, 2022, 9551-9559 9551
www.etasr.com Kassem et al.: Wind Power Generation Scenarios in Lebanon
Wind Power Generation Scenarios in Lebanon
Youssef Kassem
Department of Mechanical Engineering
Engineering Faculty
Near East University
Nicosia, Cyprus
yousseuf.kassem@neu.edu.tr
Huseyin Camur
Department of Mechanical Engineering
Engineering Faculty
Near East University
Nicosia, Cyprus
huseyin.camur@neu.edu.tr
Hüseyin Gokcekus
Department of Civil Engineering
Civil and Environmental Engineering Faculty
Near East University
Nicosia, Cyprus
huseyin.gokcekus@neu.edu.tr
Abdalla Hamada Abdelnaby Abdelnaby
Department of Mechanical Engineering
Engineering Faculty
Near East University
Nicosia, Cyprus
20213582@std.neu.edu.tr
Received: 13 August 2022 | Revised: 10 September 2022 and 16 September 2022 | Accepted: 18 September 2022
Abstract-Renewable energy in terms of solar and wind energy
can be an essential part of Lebanon's strategies to add new
capacity, increase energy security, address environmental
concerns, and resolve the electricity crisis. In this regard, there is
an urgent need to develop road maps in order to reduce the effect
of global warming and enhance sustainable technological
development for generating clean power in the country.
Therefore, the present paper evaluates Lebanon's wind energy
generation potential as an alternative solution to supply
electricity to households in various locations distributed over
Lebanon. In the present study, the measured data are used to
evaluate the wind energy potential in Lebanon and to find
suitable locations to install wind farms in the country.
Accordingly, the results demonstrated that Ain ed Dabaa is the
most suitable location for the installation of a wind farm.
Moreover, the study aims to develop a wind energy cost analysis
techno-economic model for eight conventional wind turbines and
a Barber wind turbine, which was found to be very competitive.
Consequently, this study showed that the implementation of a
wind turbine could provide clean, economical, and continuous
production of electricity in countries that suffer from daily
blackouts.
Keywords-Lebanon; wind potential; conventional wind
turbines; Barber wind turbine; techno-economic model
I. INTRODUCTION
Energy demand growth, global warming, climate change,
and increasing consumption of fossil fuels has led to the
transition from conventional fuels to renewable energy
resources [1-3]. Moreover, the use of fossil fuels in the
generation of electricity contributes significantly to Greenhouse
Gas (GHG) emissions, which further contributes to climate
change [4, 5]. According to [6], utilizing renewable energy as
an alternative energy source will help reduce environmental
problems, essentially GHG emissions and air pollution. The
use of renewable energy, including wind and solar energy, is
rapidly increasing because it is economically viable and has
limited environmental impact [7]. During the recent years,
many studies have evaluated wind and solar energy potential as
clean power sources for electricity generation in various
countries. For instance, authors in [8] analyzed the performance
of a 5kW rooftop photovoltaic (PV) system in Northern India.
The results indicated that the annual average capacity
utilization factor was 16.39%. Authors in [9] evaluated the
wind potential in Taza and Dakhla cities, Morocco using the
Weibull distribution function. The results demonstrated that the
value of wind power density is 435.96W/m
2
and 122.91W/m
2
in Dakhla and Taza respectively. Authors in [10] estimated the
potential of rooftop PV electricity in Lethbridge, Canada using
LiDAR data and ArcGIS. The results showed that the
developed system has a huge potential to offset the energy
demand of the city. Authors in [11] assessed the performance
of the Aventa AV-7 wind turbine for electricity generation in
Medina. It was found that the annual generation of energy from
the selected turbine is 8648kWh.
A. Electricity Situation in Lebanon
Lebanon is located on the Eastern edge of the
Mediterranean Sea, between 33.8547°N latitude and 35.8623°E
longitude. At present, fossil fuels (97%) and hydropower (3%)
are the main sources of electrical energy in Lebanon.
Generally, the production capacity of electricity reaches
3600MW, while the actual production capacity currently does
not exceed 2000MW. Currently, Lebanon has 7 thermal power
plants, 6 hydroelectric plants, and 2 power ships operating to
generate electricity. The electricity consumption has increased
due to the growth of the population and the continuing demand
for new large and small appliances as shown in Figure 1 [12].
Besides, only 70% of total generated power covered the energy
needs in the country as shown in Figure 2.
Corresponding author: Youssef Kassem.
Engineering, Technology & Applied Science Research Vol. 12, No. 6, 2022, 9551-9559 9552
www.etasr.com Kassem et al.: Wind Power Generation Scenarios in Lebanon
Fig. 1. Electrical power demand distribution [12].
Fig. 2. Electrical power demand distribution [13].
The electricity crisis is one of the most important issues
affecting the daily lives of citizens, shop owners, and small
businesses in Lebanon for years. The electricity crisis in
Lebanon is not new and the electricity sector has suffered from
decades of mismanagement, weak policies, and the absence of
proper planning. The country has been suffering from a severe
shortage of energy due to the dilapidation of old power stations
that were accompanied by sabotage operations during the past
years. As a result, duration of power cuts for citizens increased
to more than 20 hours a day. For this reason, citizens rely on
domestic power generators or small home generators, both of
them adding great financial burdens. According to [14, 15],
private generators are utilized in all the country to meet the
energy demand, and they are considered the third main source
of production of electricity.
B. The Renewable Energy Situation in Lebanon
According to [14, 16], the power sector in the country
contributes about 58% of total CO2 emissions, of which 25%
come from private generators. Consequently, utilizing
renewable energy would reduce the consumption of fossil fuels
and could be a clean source for electricity generation in the
country. Authors in [17] concluded that wind power systems
could be alternative sources to fulfill the electrical power
required and reduce the CO2 emissions. Moreover, according to
[17], standalone wind turbines are privately utilized for the
generation of electricity for homes and small private
companies. Approximately 2.06% of wind power is currently
used for electric power generation in Lebanon [18]. Authors in
[19] found that wind energy could be utilized in the country for
electricity generation during the night. Authors in [20]
concluded that the utilization of small-scale wind systems
could be used for the generation of electricity from wind
energy in Beirut, Sidon, and Tripoli. Authors in [21] concluded
that wind turbine systems could be installed to complement the
main grid with electric power during the peak hours.
Numerous researchers have investigated the potential of
renewable energy, particularly wind energy in different
locations in Lebanon [16, 19-25]. For instance, authors in [25]
investigated the wind potential in 9 locations in Lebanon. The
author concluded that a small-scale wind system could be more
profitably used for these areas. Authors in [23] studied the
characteristics of wind energy in five locations in Lebanon.
Their results showed that the wind energy system can be an
alternative solution and more cost-effective than conventional
power sources. Authors in [24] assessed the wind energy
potential in 8 locations in the Northern Lebanon. The results
showed that wind power system can be utilized to reduce the
energy shortage in the country. According to [16, 19-25], it can
be concluded that:
The utilization of wind power systems in Lebanon is
limited.
Renewable energy systems can solve the electricity crisis
and reduce CO2 emissions.
Only a few studies investigated the wind turbine
performance for generating electricity in Lebanon.
According to the literature review, no studies evaluated the
potential and variability assessment of wind energy over
Lebanon considering various climate conditions.
C. The Importance of the Current Study
Globally, wind energy and its associated technologies are
essential to support electricity consumption and supply power
in order to mitigate the electricity crisis. Besides, as an ongoing
study on the assessment of renewable energy systems,
primarily wind and solar energy in different locations, the
present study aims to investigate the wind potential in various
locations distributed over Lebanon. To achieve this, measured
data for the period 2010-2017 were collected from
meteorological agencies. Consequently, the Weibull
distribution function was used to evaluate the characteristics of
wind speed in the selected locations. The distribution
parameters were estimated using the maximum likelihood
method. The power density was calculated and was used to
evaluate the potential of wind energy in each location.
Additionally, the Power-Law exponent method was utilized to
estimate the wind speed at various hub heights. Based on the
literature, wind power is low and unsustainable in many
regions in Lebanon. Consequently, this study aims to evaluate
the techno-economic performance of the Ferris wheel-based
Barber wind turbine, which is a new wind turbine technology,
at low wind speed locations. Furthermore, the performance of
the selected wind turbine is compared with conventional wind
turbines under the same economic conditions.
0
10
20
30
40
50
A
g
ri
c
u
lt
u
re
a
n
d
F
is
h
e
ry
T
e
rt
ia
ry
In
d
u
s
tr
ia
l
R
e
si
d
e
n
ti
a
l
E
le
c
tr
ic
p
o
w
e
r
d
e
m
a
n
d
d
is
tr
ib
u
ti
o
n
[
%
]
-500
500
1500
2500
3500
2011 2012 2013 2014 2015
D
e
m
a
n
d
[
M
W
]
Peak Power Demand Peak Power Generation
Difficiency
Engineering, Technology & Applied Science Research Vol. 12, No. 6, 2022, 9551-9559 9553
www.etasr.com Kassem et al.: Wind Power Generation Scenarios in Lebanon
II. MATERIALS AND METHODS
The proposed methodology aims to assess the wind energy
potential in 12 locations in Lebanon in order to identify suitable
locations for future wind power systems. Then, the economic
viability of wind systems is evaluated. Figure 3 illustrates the
proposed methodology used in this study.
A. Study Area and Dataset
This examination was undertaken to identify the best
location for installing wind power systems among the chosen
locations in Lebanon. Table I summarizes the geographic
information of the selected locations.
TABLE I. THE SELECTED LOCATIONS
Location Latitude [°N] Longitude [°E] Elevation [m]
Younine 34.0776 36.2750 1198
Birket Aarous 34.2911 36.1456 2766
Ain ed Dabaa 34.4431 35.8992 296
Mqaybleh 34.6460 36.3577 330
Ras Ouadi Ed Darje 34.2533 36.5775 1555
Kfardebian 34.0017 35.8349 1670
Qaraoun 33.5669 35.7193 913
Khartoum 33.4079 35.3751 305
Iskandarounah 33.1550 35.1685 53
Beirut 33.8938 35.5018 40
Khiam 33.3294 35.6148 697
Hekr El Dahri 34.6306 36.0237 10
B. Wind Speed Distribution
The correct determination of the probability distribution of
wind speed is a prominent factor in evaluating the wind energy
potential in a region. Thus, knowing the wind speed
distribution at a specific location is necessary for determining
its potential. A 2-parameter Weibull distribution is commonly
utilized to study the characteristics of wind speed. The Weibull
Probability Density Function (PDF)���� and the cumulative
distribution function ���� expressions are [26]:
���� = ��
��
��
��� �− ��
� � (1)
���� = 1 − ��� �− ��
� � (2)
where � is the mean wind speed (m/s), � is the shape
parameter, and c is the scale parameter of the Weibull
distribution.
To determine the distribution parameter, the Maximum
Likelihood (ML) method is used to determine k and c [27]:
� = �∑ ��� ����� ��� ∑ ����� − ∑ �������� � ��
(3)
� = �
� ∑ ����
�⁄ (4)
Following the Weibull distribution, (5) and (6) are utilized
to estimate the average wind speed and the standard deviation
of the wind speed respectively.
� = �Γ �1 +
�
(5)
# = $�% &Γ �1 + %�
− Γ% �1 +
�
' (6)
C. Wind Power Density
To assess the potential of wind energy, (7) is utilized to
determine the Wind Power Density (WPD) at a specific
location:
�()
* =
% +�,Γ �1 + ,�
(7)
The average value of WPD can be estimated by [21]:
() =
% +�, (8) () =
% +�,���� (9) (.) =
% +�̅ , (10)
where P is wind power density (W), 0. is the mean wind power
density (W), A is the swept area (m
2
), ρ is the air density
(kg/m
3
), ���� is the PDF, and � 1 is the mean wind speed (m/s).
D. Wind Speed Data Extrapolation at Different Hub Heights
Generally, wind speed measurements are carried out at a
height of 10m above the ground. To obtain energy from wind
turbines, it is necessary to estimate wind speeds at various hub
heights using (11) [28]:
���2 = � 33�2
2.5672.2889��:�2��72.2889��;�2 �2⁄ � (11)
where � is the wind speed at the wind turbine hub height z, �
<
is the wind speed at the original height =
<.
E. Energy Output of a Wind Turbine
The produced energy by the wind turbine >?@A can be
estimated by [28]: >?@A = ∑ PCDE F��G
(12)
where F is time and 0?@A is determined by:
0?@A =
⎩⎪⎨
⎪⎧ 0 when � < � �(R �S���S�� ��R� + � (R�R���S�� � � � when � � ≤ � ≤ �U 0U when �U ≤ � ≤ � ? 0 when � > � ?
(13)
where 0U is the rated power of the wind turbine, � � the
turbine’s cut-in speed, � ? turbine’s cut-off speed, and �U is its
rated wind speed.
The average power generation of the turbine can be
expressed as [29]:
0?@A = 0U WXYZ[��:S�S
�\�XYZ[��:RS
�\�:RS
���:S�S
� − ��� ]− ��S^
� _` (14)
The Capacity Factor (CF) is estimated [29, 30]:
a� = b^cdefg<(R (15)
Engineering, Technology & Applied Science Research Vol. 12, No. 6, 2022, 9551-9559 9554
www.etasr.com Kassem et al.: Wind Power Generation Scenarios in Lebanon
Fig. 3. Flowchart of the proposed methodology.
In this study, various wind turbines assumed the load
profile for all months of the year. Nine wind turbines (Table II)
with various characteristics including the Capital Cost of
acquisition (CC) and O&M Cost (OMC) are selected as shown
in Tables II-III. Generally, Conventional Wind Turbines
(CWTs) are designed to generate electricity at high wind
speeds. The cut-in speed and rated wind speed for these
turbines are within the range of 3-5m/s and 12-15m/s
respectively [30, 31]. CWTs are heavy and expensive to buy,
install, and maintain. Therefore, the performance of the Ferris
Wheel Wind Turbine (FWWT) is compared with the selected
CWTs due to its advantages, such as its light design, lower
weight to power ratios, etc. [32].
TABLE II. SELECTED WIND TURBINES
Model No. Wind turbine model Manufacture
Model#1 Enercon E5 Enercon GmbH
Model#2 Enercon E44 Enercon GmbH
Model#3 EWT DW61 Emergya Wind Technologies B.V.
Model#4 GE SLE 1.5 Winergy/Eickhoff/Bosch.
Model#5 AN Bonus 1 MW/54 Siemens Wind Power A/S
Model#6 DEWIND-62-91.5 m DeWind
Model#7 Neg-Micon NEG Micon A/S
Model#8 Vestas-V66 Vestas Wind Systems A/S
Model#9 Barber wind turbine BarberWind Turbines
TABLE III. CHARACTERISTICS AND SPECIFICATIONS
Model
No.
HH
[m]
hi
[kW]
jkl
[m/s]
ji
[m/s]
jkm
[m/s]
CC
[USD]
OMC
[USD/y]
Model#1 76 800 3 13 25 1750000 51250
Model#2 55 900 3 16.5 34 2337500 51250
Model#3 69 900 2.5 10 25 1918770 57158
Model#4 85 1500 3 14 25 3375000 57158
Model#5 50 1000 3 15 25 863530 25043
Model#6 91.5 1000 2.5 11.5 23 1124758 32618
Model#7 70 1000 4 14 20 1162460 33711
Model#8 67 1650 4 16 25 1768000 51272
Model#9 70 800 3 9.6 20 1400000 42000
F. Economic Viability of a Wind System
The wind energy farm’s viability is dependent on its ability
to generate energy at low operating cost. In this paper, the
Levelized Cost Of Electricity (LCOE) is utilized to calculate
the Electricity Generated Cost (EGC) of the wind turbine [32]:
nao> = p ���q�����q���7�efg<r���&�st)�5uv ' w [1 + u^x��X &1 − �
yX
y�
�'\ (16)
Engineering, Technology & Applied Science Research Vol. 12, No. 6, 2022, 9551-9559 9555
www.etasr.com Kassem et al.: Wind Power Generation Scenarios in Lebanon
where +=1.23kg/m3 is air's density, z is the swept area (m2), aZ
is the Betz limit power coefficient (theoretical value aZ =0.59), � is the escalation rate of operation and maintenance, { is the
interest rate, | is the useful lifetime of the turbine in years, and a? is the operation and maintenance costs during the first
year.
Additionally, (17) is used to determine the simple payback
period (SPP) [32]:
SPP = ~efg<r���&�st)�5uv'(� (17)
where � is the installed capital cost of the wind turbine plus the
costs of civil works and 0X is the price of electricity ($/kWh).
III. RESULTS AND DISCUSSION
A. Characteristics of the Wind Speed at 10m Height
The statistical description of monthly wind speed includes
mean value, Standard Deviation (SD), Coefficient of Variation
(CV), Minimum (Min.), Maximum (Max.), Kurtosis (K), and
Skewness (S) as summarized in Table IV for all selected
locations. According to the findings, it is noticed that the
maximum mean monthly wind speed of 4.90m/s is recorded in
Ain ed Dabaa, while the minimum of 2.81 is recorded in
Khiam and Khartoum. The monthly wind speeds for Ain ed
Dabaa and Khartoum are illustrated in Figure 4. It is observed
that the maximum value of monthly wind speed of 5.82m/s is
recorded in January. The highest value of 3.03m/s for
Khartoum is recorded in June as shown in Figure 4. It can be
seen that the CV values are moderately low, ranging from
6.52% to 17.25%. Additionally, all S values for most of the
selected locations are negative, indicating that all distributions
are left skewed.
Fig. 4. Mean monthly wind speed at a height of 10m.
TABLE IV. STATISTICAL ESTIMATORS OF MONTHLY WIND SPEED
Variable Younine Birket Aarous Ain ed Dabaa Mqaybleh Ras Ouadi Ed Darje Khiam
Mean 2.90 3.01 4.90 2.91 3.19 2.81
SD 0.45 0.31 0.53 0.30 0.45 0.18
CV 15.37 10.14 10.79 10.14 14.05 6.52
Min. 2.32 2.44 4.00 2.36 2.54 2.47
Max. 3.51 3.45 5.82 3.34 4.03 3.03
S 0.03 -0.56 -0.15 -0.56 0.52 -0.54
K -1.46 -0.26 -0.41 -0.26 -0.19 -0.81
Variable Kfardebian Qaraoun Khartoum Iskandarounah Beirut Hekr El Dahri
Mean 3.48 2.86 2.81 3.32 3.48 2.91
SD 0.41 0.19 0.18 0.57 0.41 0.30
CV 11.66 6.52 6.52 17.25 11.66 10.14
Min. 3.04 2.51 2.47 2.72 3.04 2.36
Max. 4.22 3.08 3.03 4.27 4.22 3.34
S 0.82 -0.54 -0.54 0.42 0.82 -0.56
K -0.75 -0.81 -0.81 -1.46 -0.75 -0.26
B. Determination of Weibull Parameters
The Weibull distribution parameters for the selected
locations using monthly wind speed data were determined
using the ML approach. The calculated shape k and scale c
parameters of all selected locations at 10m height are shown in
Figure 5. The value of k ranged from 5.99 to 15.45 with an
average value of 10.49. The annual value of c range was 2.88-
5.04m/s with an average value of 3.32m/s. Moreover, the
average wind speed and its SD were calculated using from (5)
and (6) and are shown in Figure 5. It is observed that the mean
wind speed and SD are within the range of 2.78-4.8m/s and
0.22-0.63m/s respectively. Moreover, the PDF indicates the
frequency of different levels of speed. It can be utilized to
estimate which level of wind speed is prevalent in the location.
The wind speed where the distribution curve peaks are the most
frequently observed wind speed for the location. Figure 6
illustrates the PDF of all selected locations.
C. Wind Power Density
To evaluate the wind potential in the selected locations, the
annual WPD is computed from (7). The value of WPD for each
location is tabulated in Figure 7. It is found that the value of
WPD ranged from 13.18W/m
2
to 67.44W/m
2
with an average
value of 21.03W/m
2
. Based on these values, the wind energy
generation potential of these locations is classified as class 1
(Poor) as shown in Table V. Therefore, small-scale wind
turbines are suitable to be used in the selected regions for
exploiting the available wind energy potential. Furthermore, it
can be concluded that high-capacity wind turbines (MWs) with
a height of 90m and above can be suitable for gathering the
wind energy potential in the selected locations. This is
investigated using the power-law method, i.e., the collected
data at 10m height is synthesized to the 90m height at which
most of the 1MW or above capacity wind turbine height is.
Engineering, Technology & Applied Science Research Vol. 12, No. 6, 2022, 9551-9559 9556
www.etasr.com Kassem et al.: Wind Power Generation Scenarios in Lebanon
Fig. 5. Weibull parameters for all selected locations at a height of 10m.
Fig. 6. The probability density function for all selected locations.
TABLE I. WIND POWER CLASSIFICATION AT 10m
HEIGHT
Power class h1[W/m2]
1 (Poor) ≤100
2 (Marginal) ≤150
3 (Moderate) ≤200
4 (Good) ≤250
5 (Excellent) ≤300
6 (Excellent) ≤400
7 (Excellent) ≤1000
Fig. 7. The annual value of wind power density for all selected locations.
D. Techno-Economic Model
As mentioned above, 9 wind turbines with various
characteristics were selected and the wind speed at various hub
heights was calculated using (11). For instance, Figure 8
illustrates the monthly variation of wind speed at various hub
heights for 3 selected locations. It can be seen that the value of
wind speed increases with the increasing hub height of the
wind turbine.
Fig. 8. Monthly average wind speed for 3 selected locations at various hub
heights.
Engineering, Technology & Applied Science Research Vol. 12, No. 6, 2022, 9551-9559 9557
www.etasr.com Kassem et al.: Wind Power Generation Scenarios in Lebanon
Fig. 9. The estimated results in terms of AEP, CF, EGC, and SPP.
To investigate the performance of wind energy, 9 wind
turbines with various rated powers were considered. The aim is
to select the turbine that best matches the wind regime at the
selected locations. The electricity cost per kWh in Lebanon, the
annual interest rate, the capital cost of acquisition, operation,
and maintenance costs of wind turbines, and the life of the
wind turbines are required for economic viability. These data
were obtained from previous studies, trading economics, and
global petrol prices. The Annual Energy Produced (AEP) and
CF for the selected turbines were determined by (12) and (15)
respectively. EGC and SPP were determined by (16) and (17).
Figure 9 illustrates the estimated results in terms of AEP, CF,
EGC, and SPP for all selected locations. It is found that the
APE values ranged from 339.55MWh to 5017.35MWh with an
average value of 1241.875MWh. The maximum and minimum
values of APE are recorded in Ain ed Dabaa and Khartoum for
a hub height of 91.5m (Model#6) and 55m (Model#2)
respectively. The highest and lowest CF values are 59.23%
(Ain ed Dabaa) and 2.79% (Khiam) respectively with an
average value of 14.28%. Furthermore, the EGC values are
within the range of 0.033-0.761USD/kWh with an average
value of 0.287USD/kWh. Furthermore, the shortest payback
period of 1.54 years is observed at Ain ed Dabaa for a hub
height of 91.5m (Model#6).
Other researchers who analyzed the performance of a wind
farm system in terms of CF, SPP, and EGC support these
observations [30, 33-39]. For instance, authors in [35] found
CF values within the range of 31.1-49% and 37.3-56.6% for
50m and 75m hub-height turbines. Authors in [30] found that
the BWT 61m–800kW wind turbine has a payback period of
1.9-27.3 years and its EGC values were within the range of
0.04-0.43USD/kWh. In [36], it was found that the payback
period for different wind farms with a capacity of 100MW
ranged from 6.34 to 19.9 years. Authors in [33] found that the
values of CF and EGC produced by various wind turbines were
within the range of 32-38% and 0.255-0.306 USD/kWh
respectively. Authors in [37] found that the CF values varied
from 6.8% to 47.6% using various wind turbines with various
characteristics. According to the finding of [34], the CF values
of different wind turbines are estimated to be within the range
of 22.9-50.6%. It should be noted that when the value of SPP
exceeds the assumed lifetime of the wind turbine, it means that
installing the wind turbines in locations is not economically
viable for energy production for the selected wind turbine
model. Moreover, according to [30, 31, 38], the SPP value for
small and medium-scale wind turbines is within the range of 5-
12 years. Therefore, more than 50% of the selected cities fall
within the range of SPP. Based on these results, it can be
concluded that:
The results demonstrate the competitiveness of BWTs for
operation compared to CWTs, especially at locations with
low wind conditions.
Although Model#6 has a lower EGC in all selected
locations, Model#9 has a robust design and a wider range of
applications for all classes of wind resources. Thus,
model#9 would have a lower cost in the long run.
FWWT technology is a strong candidate to increase the
availability of economic, green, and sustainable energy in
Lebanon.
The EGC values obtained from the current study are
compared with the current electricity prices in Lebanon in
Engineering, Technology & Applied Science Research Vol. 12, No. 6, 2022, 9551-9559 9558
www.etasr.com Kassem et al.: Wind Power Generation Scenarios in Lebanon
Figure 10. It is clear that the price of the electricity generated
by wind turbine systems is less than the one from conventional
systems. It can be concluded that the developed systems ensure
the economic feasibility of the project for all locations. Wind
energy will be able to solve the problem of a chronic lack of
electricity and reduce the electricity bills in Lebanon.
Fig. 10. Monthly average wind speed for some selected locations and
various hub heights.
IV. CONCLUSIONS
Lebanon is experiencing a serious water and electricity
crisis. The growing challenges faced by the population and
energy sectors are outpacing the government's efforts to
provide high-quality, affordable, and accessible electricity. In
this context, utilizing wind energy is considered an alternative
solution to supply electricity to households, reduce the effect of
global warming, and enhance sustainable technological
development. Therefore, there is an urgent need to develop
road maps for the exploitation of renewable energy sources.
In this regard, the techno-economic model for the
assessment of wind energy in selected locations in Lebanon is
presented in this research. In this study, the techno-economic
performance of new technology of wind turbine, the Barber
wind turbine (Model#9), is compared to conventional wind
turbines under the same economic conditions.
The results showed that the Barber wind turbine (Model#9)
is very competitive to 8 known commercial wind turbines for
low wind speed conditions. Consequently, the current study
may encourage stakeholders in the renewable energy sector to
provide support mechanisms for the adoption of large-scale and
small-scale wind systems in the country.
REFERENCES
[1] H. Fang, J. Li, and W. Song, "Sustainable site selection for photovoltaic
power plant: An integrated approach based on prospect theory," Energy
Conversion and Management, vol. 174, pp. 755–768, Oct. 2018,
https://doi.org/10.1016/j.enconman.2018.08.092.
[2] S. N. Shorabeh, M. K. Firozjaei, O. Nematollahi, H. K. Firozjaei, and M.
Jelokhani-Niaraki, "A risk-based multi-criteria spatial decision analysis
for solar power plant site selection in different climates: A case study in
Iran," Renewable Energy, vol. 143, pp. 958–973, Dec. 2019,
https://doi.org/10.1016/j.renene.2019.05.063.
[3] E. Tercan, A. Eymen, T. Urfalı, and B. O. Saracoglu, "A sustainable
framework for spatial planning of photovoltaic solar farms using GIS
and multi-criteria assessment approach in Central Anatolia, Turkey,"
Land Use Policy, vol. 102, Mar. 2021, Art. no. 105272, https://doi.org/
10.1016/j.landusepol.2020.105272.
[4] B. Memon, M. H. Baloch, A. H. Memon, S. H. Qazi, R. Haider, and D.
Ishak, "Assessment of Wind Power Potential Based on Raleigh
Distribution Model: An Experimental Investigation for Coastal Zone,"
Engineering, Technology & Applied Science Research, vol. 9, no. 1, pp.
3721–3725, Feb. 2019, https://doi.org/10.48084/etasr.2381.
[5] Y. Kassem, H. Gokcekus, and H. S. A. Lagili, "A Techno-Economic
Viability Analysis of the Two-Axis Tracking Grid-Connected
Photovoltaic Power System for 25 Selected Coastal Mediterranean
Cities," Engineering, Technology & Applied Science Research, vol. 11,
no. 4, pp. 7508–7514, Aug. 2021, https://doi.org/10.48084/etasr.4251.
[6] F. Elmahmoudi, O. E. K. Abra, A. Raihani, O. Serrar, and L. Bahatti,
"Elaboration of a Wind Energy Potential Map in Morocco using GIS and
Analytic Hierarchy Process," Engineering, Technology & Applied
Science Research, vol. 10, no. 4, pp. 6068–6075, Aug. 2020,
https://doi.org/10.48084/etasr.3692.
[7] A. G. Olabi and M. A. Abdelkareem, "Renewable energy and climate
change," Renewable and Sustainable Energy Reviews, vol. 158, Apr.
2022, Art. no. 112111, https://doi.org/10.1016/j.rser.2022.112111.
[8] S. K. Yadav and U. Bajpai, "Performance evaluation of a rooftop solar
photovoltaic power plant in Northern India," Energy for Sustainable
Development, vol. 43, pp. 130–138, Apr. 2018, https://doi.org/
10.1016/j.esd.2018.01.006.
[9] El Khchine, M. Sriti, and N. E. El Kadri Elyamani, "Evaluation of wind
energy potential and trends in Morocco," Heliyon, vol. 5, no. 6, Jun.
2019, Art. no. e01830, https://doi.org/10.1016/j.heliyon.2019.e01830.
[10] F. Mansouri Kouhestani, J. Byrne, D. Johnson, L. Spencer, P.
Hazendonk, and B. Brown, "Evaluating solar energy technical and
economic potential on rooftops in an urban setting: the city of
Lethbridge, Canada," International Journal of Energy and
Environmental Engineering, vol. 10, no. 1, pp. 13–32, Mar. 2019,
https://doi.org/10.1007/s40095-018-0289-1.
[11] K. S. AlQdah, R. Alahmdi, A. Alansari, A. Almoghamisi, M.
Abualkhair, and M. Awais, "Potential of wind energy in Medina, Saudi
Arabia based on Weibull distribution parameters," Wind Engineering,
vol. 45, no. 6, pp. 1652–1661, Dec. 2021, https://doi.org/10.1177/
0309524X211027356.
[12] R. B. Chahine, "Energy Strategy to Supply Electric Demands in
Politically Unstable Developing Countries (Lebanon Case Study)," M.S.
thesis, University of Strathclyde, Glasgow, UK, 2018.
[13] Physical Infrastructure - Electricity Sector Overview. Libanon: Council
for Development and Reconstruction, 2016.
[14] N. Wehbe, "Optimization of Lebanon’s power generation scenarios to
meet the electricity demand by 2030," The Electricity Journal, vol. 33,
no. 5, Jun. 2020, Art. no. 106764, https://doi.org/10.1016/j.tej.2020.
106764.
[15] R. A. Farhat et al., "A Multi-attribute Assessment of Electricity Supply
Options in Lebanon," in Food-Energy-Water Nexus Resilience and
Sustainable Development: Decision-Making Methods, Planning, and
Trade-Off Analysis, S. Asadi and B. Mohammadi-Ivatloo, Eds. New
York, NY, USA: Springer, 2020, pp. 1–27.
[16] Y. Kassem, H. Gokcekus, and W. Janbein, "Predictive model and
assessment of the potential for wind and solar power in Rayak region,
Lebanon," Modeling Earth Systems and Environment, vol. 7, no. 3, pp.
1475–1502, Sep. 2021, https://doi.org/10.1007/s40808-020-00866-y.
[17] E. Kinab and M. Elkhoury, "Renewable energy use in Lebanon: Barriers
and solutions," Renewable and Sustainable Energy Reviews, vol. 16, no.
7, pp. 4422–4431, Sep. 2012, https://doi.org/10.1016/j.rser.2012.04.030.
[18] H. L. Moore and H. Collins, "Decentralised renewable energy and
prosperity for Lebanon," Energy Policy, vol. 137, Feb. 2020, Art. no.
111102, https://doi.org/10.1016/j.enpol.2019.111102.
[19] G. Al Zohbi, P. Hendrick, and P. Bouillard, "Wind characteristics and
wind energy potential analysis in five sites in Lebanon," International
Journal of Hydrogen Energy, vol. 40, no. 44, pp. 15311–15319, Nov.
2015, https://doi.org/10.1016/j.ijhydene.2015.04.115.
[20] Y. Kassem, H. Gokcekus, and M. Zeitoun, "Modeling of techno-
economic assessment on wind energy potential at three selected coastal
regions in Lebanon," Modeling Earth Systems and Environment, vol. 5,
Engineering, Technology & Applied Science Research Vol. 12, No. 6, 2022, 9551-9559 9559
www.etasr.com Kassem et al.: Wind Power Generation Scenarios in Lebanon
no. 3, pp. 1037–1049, Sep. 2019, https://doi.org/10.1007/s40808-019-
00589-9.
[21] M. Elkhoury, Z. Nakad, and S. Shatila, "The Assessment of Wind Power
for Electricity Generation in Lebanon," Energy Sources, Part A:
Recovery, Utilization, and Environmental Effects, vol. 32, no. 13, pp.
1236–1247, Apr. 2010, https://doi.org/10.1080/15567030802706754.
[22] A. El-Ali, N. Moubayed, and R. Outbib, "Comparison between solar and
wind energy in Lebanon," in 9th International Conference on Electrical
Power Quality and Utilisation, Barcelona, Spain, Oct. 2007, pp. 1–5,
https://doi.org/10.1109/EPQU.2007.4424155.
[23] G. A. Zohbi, P. Hendrick, and P. Bouillard, "Assessment of wind energy
potential in Lebanon," Research in Marine Sciences, vol. 3, no. 4, pp.
401–414, 2018.
[24] H. Gokcekus, Y. Kassem, and M. A. Hassan, "Evaluation of Wind
Potential at Eight Selected Locations in Northern Lebanon Using Open
Source Data," International Journal of Applied Engineering Research,
vol. 14, no. 11, pp. 2789–2794, 2019.
[25] Y. Kassem, H. Camur, M. A. H. A. Abdalla, B. D. Erdem, and A. M. R.
Al-ani, "Evaluation of wind energy potential for different regions in
Lebanon based on NASA wind speed database," IOP Conference Series:
Earth and Environmental Science, vol. 926, no. 1, Aug. 2021, Art. no.
012093, https://doi.org/10.1088/1755-1315/926/1/012093.
[26] A. Serban, L. S. Paraschiv, and S. Paraschiv, "Assessment of wind
energy potential based on Weibull and Rayleigh distribution models,"
Energy Reports, vol. 6, pp. 250–267, Nov. 2020, https://doi.org/
10.1016/j.egyr.2020.08.048.
[27] J. A. Guarienti, A. Kaufmann Almeida, A. Menegati Neto, A. R. de
Oliveira Ferreira, J. P. Ottonelli, and I. Kaufmann de Almeida,
"Performance analysis of numerical methods for determining Weibull
distribution parameters applied to wind speed in Mato Grosso do Sul,
Brazil," Sustainable Energy Technologies and Assessments, vol. 42,
Dec. 2020, Art. no. 100854, https://doi.org/10.1016/j.seta.2020.100854.
[28] M. M. Alayat, Y. Kassem, and H. Çamur, "Assessment of Wind Energy
Potential as a Power Generation Source: A Case Study of Eight Selected
Locations in Northern Cyprus," Energies, vol. 11, no. 10, Oct. 2018, Art.
no. 2697, https://doi.org/10.3390/en11102697.
[29] L. Bilir, M. Imir, Y. Devrim, and A. Albostan, "An investigation on
wind energy potential and small scale wind turbine performance at İncek
region – Ankara, Turkey," Energy Conversion and Management, vol.
103, pp. 910–923, Oct. 2015, https://doi.org/10.1016/j.enconman.
2015.07.017.
[30] S. Rehman, M. M. Alam, L. M. Alhems, and M. M. Rafique,
"Horizontal Axis Wind Turbine Blade Design Methodologies for
Efficiency Enhancement—A Review," Energies, vol. 11, no. 3, Mar.
2018, Art. no. 506, https://doi.org/10.3390/en11030506.
[31] "Size specifications of common industrial wind turbines," American
Wind Energy Association. http://www.aweo.org/windmodels.html.
[32] K. A. Adeyeye, N. Ijumba, and J. S. Colton, "A Techno-Economic
Model for Wind Energy Costs Analysis for Low Wind Speed Areas,"
Processes, vol. 9, no. 8, Aug. 2021, Art. no. 1463, https://doi.org/
10.3390/pr9081463.
[33] A. Ucar and F. Balo, "Investigation of wind characteristics and
assessment of wind-generation potentiality in Uludağ-Bursa, Turkey,"
Applied Energy, vol. 86, no. 3, pp. 333–339, Mar. 2009,
https://doi.org/10.1016/j.apenergy.2008.05.001.
[34] J. W. Tester, E. M. Drake, M. J. Driscoll, M. W. Golay, and W. A.
Peters, Sustainable Energy, second edition: Choosing Among Options.
Cambridge, MA, USA: MIT Press, 2012.
[35] A. Allouhi et al., "Evaluation of wind energy potential in Morocco’s
coastal regions," Renewable and Sustainable Energy Reviews, vol. 72,
pp. 311–324, May 2017, https://doi.org/10.1016/j.rser.2017.01.047.
[36] M. A. Alsaad, "Wind energy potential in selected areas in Jordan,"
Energy Conversion and Management, vol. 65, pp. 704–708, Jan. 2013,
https://doi.org/10.1016/j.enconman.2011.12.037.
[37] H. D. Ammari, S. S. Al-Rwashdeh, and M. I. Al-Najideen, "Evaluation
of wind energy potential and electricity generation at five locations in
Jordan," Sustainable Cities and Society, vol. 15, pp. 135–143, Jul. 2015,
https://doi.org/10.1016/j.scs.2014.11.005.
[38] N. Boccard, "Capacity factor of wind power realized values vs.
estimates," Energy Policy, vol. 37, no. 7, pp. 2679–2688, Jul. 2009,
https://doi.org/10.1016/j.enpol.2009.02.046.
[39] "Future of Wind: Deployment, investment, technology, grid integration
and socio-economic aspects," PERISCOPE. https://periscope-
network.eu/analyst/future-wind-deployment-investment-technology-
grid-integration-and-socio-economic-aspects.
[40] T. R. Ayodele, A. A. Jimoh, J. L. Munda, and J. T. Agee, "Viability and
economic analysis of wind energy resource for power generation in
Johannesburg, South Africa," International Journal of Sustainable
Energy, vol. 33, no. 2, pp. 284–303, Mar. 2014, https://doi.org/
10.1080/14786451.2012.762777.