UTILIZING ANNUAL WIND SPEED DATA AS A REPRESENTATIVE OF LOCATIONS AND SIZING OUTSTANDING WIND TURBINE OF OPTIMUM POWER DUTY TO RUN A CERTAIN LOAD AROUND THE GLOBE M.A. Alghoul a,b,c *, A.A. Aljaafar d , S.W. Lim d , W.J. Hee d , K. Sopian a a Solar Energy Research Institute, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia b Energy and Building Research Center, Kuwait Institute for Scientific Research, Safat 13109, Kuwait c Center of Research Excellence in Renewable Energy (CoRe-RE), Research Institute, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran 31261, Saudi Arabia d The School of Applied Physics, Faculty of Science and Technology, Universiti Kebangsaan Malaysia *Corresponding author e-mail: [email protected]ABSTRACT: Electricity generated by wind is fast becoming one of the most affordable and cheapest forms of energy. Literature on sizing wind power systems are limited to specific wind speed data and load profile for designated locations. Also, case studies employed different brands and power capacities of wind turbines and batteries. Due to these huge discrepancies, it is very hard to generalize the outcomes for other locations. Generalizing the outcome to the world instead of a specific location is a more practical measure for industries, customers, and researchers. In this study, there is no pre-selection of locations; instead, a range of annual wind speeds (3- 12 m/s) were used as inputs to be representative of so many locations around the globe. The aim of this study is to size an outstanding wind turbine power for a certain load profile using only annual wind speed data instead of the regular approach of using monthly wind speed data for specific locations. The load profile was selected for many small- scale applications (8kW) with an operating load of 10 hrs. Seven types of wind turbines with different power sizes were implemented: [SW Whisper 500 (3kW), BWC Excel-R (7.5kW), BWC Excel-S (10kW), PGE 20-25 (25kW), Fuhrlander FL30/13 (30kW), PGE 11-35 (35kW), and the Entegrity
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UTILIZING ANNUAL WIND SPEED DATA AS A REPRESENTATIVE OF LOCATIONS AND SIZING OUTSTANDING WIND TURBINE OF OPTIMUM POWER DUTY TO RUN A CERTAIN
LOAD AROUND THE GLOBE
M.A. Alghoul a,b,c*, A.A. Aljaafar d, S.W. Lim d, W.J. Hee d , K. Sopian a
a Solar Energy Research Institute, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysiab Energy and Building Research Center, Kuwait Institute for Scientific Research, Safat 13109, Kuwait
c Center of Research Excellence in Renewable Energy (CoRe-RE), Research Institute, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran 31261, Saudi Arabia
d The School of Applied Physics, Faculty of Science and Technology, Universiti Kebangsaan Malaysia
a representative of locations is accurate to predict the outstanding wind turbine of optimum power duty to
run 8kW load around the globe.
Keywords: certain load profile, annual wind speed data (3-12m/s) as representative of locations, different wind turbines size, HOMER simulation tool, evaluation parameters of techno-economic feasibility, outstanding wind turbine of optimum power duty, validation test
Contents outlines1. INTRODUCTION2. MATERIALS AND METHOD
2.1 Research Design 2.2 Materials2.3 HOMER Simulation Tool
3. RESULTS AND DISCUSSION 3.1 determining the outstanding wind turbine power 3.1.1 Effect of wind speed and wind turbine sizes on number of turbines 3.1.2 Effect of wind speed and wind turbine sizes on number of batteries 3.1.3 Effect of wind speed and wind turbine sizes on battery lifetime 3.1.4 Effect of wind speed and wind turbine sizes on initial cost 3.1.5 Effect of wind speed and wind turbine sizes on O&M cost, total NPC and COE 3.1.6 The Outstanding wind turbine size powering under a certain load around the Globe
3.2 Effect of Different Hub Heights of the Outstanding Wind Turbine (PGE 20-25) on optimizing the Parameters of Techno-economic feasibility 3.2.1 Effect of hub height on number of turbines 3.2.2 Effect of hub height on number of batteries 3.2.3 Effect of hub height on battery lifetime 3.2.4 Effect of hub height on initial cost 3.2.5 Effect of hub height on O&M cost, total NPC and COE 3.3 Validation of the Outstanding Wind Turbine at Dammam City, Saudi Arabia 3.3.1 Effect of different wind turbine sizes on number of turbines 3.3.2 Effect of different wind turbine sizes on number of batteries 3.3.3 Effect of different wind turbine sizes on battery lifetime 3.3.4 Effect of different wind turbine sizes on cost of energy3.3.5 Determining Wind Turbine Size at other Different Tested Locations Using their Monthly Wind Speed Data4. CONCLUSION
1. INTRODUCTIONWind is a renewable source of energy that is freely available, clean, and economical inexpensive (no fuel
costs and no price risk). Due to the promising prospect of renewable energy, many researches have been
carried out by researchers and companies to evaluate the feasibility of harnessing renewable energy at
their respective locations. Some of them have adopted HOMER software (Hybrid Optimization Model for
Electric Renewables) as their simulation tool in assisting them in their respective researches. HOMER
was developed at National Renewable Energy Laboratory (NREL) and can be considered as global
standard for preliminary techno-economic analysis of sustainable micro-grid systems such as remote
power, island utilities and micro-grids [1].
Table 1(A, B) shown below summarizes the R&D aspects covered by previous researchers studies on
components sizing and techno-economic feasibility of wind power systems done by HOMER simulation
tool.
Table 1-A: Summary of previous studies on components sizing and techno-economic feasibility of wind power systems done by HOMER simulation tool
References Location Wind Speed Wind power system type
Load profile
(kWh/day)
Application
Ashourian et al. 2013[1]
Tioman Island,
Malaysia
Annual(3.18 m/s)
Standalone: Hybrid PV-
Wind-Diesel-Battery-Fuel
Cell
496 30 chalets
Sadeghi et al. (2012)[2]
Baladeh city, North of Iran
Monthly (3.0 – 4.0
m/s)
Grid-connected
NA Residential buildings
Aagreh & Al-Ghzawi
(2013) [3]
Ajloun city,Jordan
Annual4.57 m/s @10 m height
6.76 m/s@ 60 m height
Grid-connected:
Hybrid Wind-PV-Battery
97 Small hotel
Goodbody et al. (2012)[4]
Ireland NA Grid-connected:
Hybrid Wind-PV-Diesel-
Hydro-Biomass-Battery
12-25 Apartment, households and retail unit
Adaramola (2012)[5]
Ondo State, Nigeria
Annual(3.26 m/s)
Standalone: Hybrid Wind-
PV-Diesel-Battery
25 Households
Osorio et al. (2012)[6]
Madrid (Cuatro
Vientos), Burgos, Alto do Rodicio and Punta
Candelaria, Spain
Annual Madrid (CuatroVientos)
(3.92 m/s)Burgos –(5.08 m/s)
Alto do
Grid-connected:
Hybrid Wind-Diesel-Battery
Oct-March (63)Apr-
Sept(62)
Dairy cattle farms
Rodicio – (6.18 m/s)
PuntaCandelaria – (7.38 m/s)
Fahmy et al. (2012)[7]
Hurghada, Egypt
Annual (6.93 m/s)
Standalone: Hybrid Wind-PV-Battery-
Fuel Cell
60 Small Scale Brackish Reverse Osmosis
Desalination Unit and a Tourism Motel
Moniruzzaman & Hasan (2012)[8]
Bandarban, Bangladesh
Annual (4.12 m/s)
Grid-connected:
Hybrid Wind-PV-Diesel-
Battery
35 Household/small restaurant
Sadrul Islam et al. (2012)
[9]
St. Martin Island,
Bangladesh
Annual(4.71 m/s)
Standalone: Hybrid Wind-
PV-Diesel-Battery
78 Households and shops
Badawe et al. (2012) [10]
Mulligan, Labrador, Canada.
Annual(6.26 m/s)
Standalone: Hybrid Wind-
PV-Diesel-Battery
79 Microwave repeaters (telecommunication)
Abulqasem et al. (2011)[11]
Mrair-Gabis Village, Libya
Annual(3.7-4.4 m/s)
Standalone: Hybrid Wind-
PV-Diesel-Battery
86 Small Scale Seawater Reverse OsmosisDesalination Unit
Chowdhury & Oo (2012)
[12]
Australia Annual(2.13–6.11
m/s)
Grid-connected:PV-Wind-
Battery system
1000 Electricity generation
Ibrahim et al. (2010)[13]
Kuala Terengganu,
Malaysia
Monthly (3.16 m/s)
Standalone:PV-Wind-
Diesel-Battery-Fuel Cell system
20 Residential building
Thakur et al. (2012)[14]
Jabalpur Engineering
College, Jabalpur,
India
Monthly(4.0 m/s)
Standalone:PV-Wind-
Diesel-battery system
2000 College
Fantidis et al. (2012)[15]
Plaka, Greece Annual(6.44 m/s)
Standalone:Hybrid PV-
wind, Wind-diesel
15.15-15.4 60 household electricity
Nandi & Ghosh (2010)
[16]
Chittagong, Bangladesh
Monthly(3.0- 5.0m/s)
Standalone:PV-Wind-
Battery system
160 Households
Niazi et al. (2014)[17]
Nok kundi &Ormara, Pakistan
Monthly (4.1- 5.5) &
(2.2- 5.1)
Grid-connected:
Hybrid Wind-
13.1 small houses
m/s PV-DieselShaahid
(2007)[18]Dhahran
(East-Coast, KSA)
Monthly (3.3- 5.6m/s)
Grid-connected:
Hybrid Wind-Diesel-Battery
351 100 of typical residential buildings
Amini (2010) [19]
Ghardaia and Djanet, Algeria
NA PV systems, Wind
generators and Batteries
NA Rural Health Clinics
Amin et al. (2014)[20]
St. Martin Island,
Bangladesh
Monthly (3.6- 6.5m/s)
Standalone: Hybrid Wind-
PV-Diesel-Battery
1421 650 households.
Soe & Zheng (2014) [21]
Wetkaik village,
Myanmar
Annual(3.7 m/s)
Grid-connected:
Hybrid Wind-Diesel-Battery
1167 850 households
Diab et al. 2015[22]
(Alexandria, Qena and Aswan), Egypt
Monthly(4.8- 6.2m/s)(4.3- 5.6m/s)(4.3- 5.5m/s)
Standalone: hybrid
PV/wind/diesel /battery
19,906 tourist village
Table 1-B: Continued summary of previous studies on components sizing and techno-economic feasibility of wind power systems done by HOMER simulation toolReferences Turbines spec. & no.
Niazi et al. (2014) - Bergey Excel BWXL- 1kW at 25m height- 2 turbine
NA Nok kundi:- NPC of $15,872- COE $0.087/kWhOrmara:- NPC of $14,508- COE $0.078/kWh
Shaahid (2007) NA NA - COE -$0.070/kWh
Amini (2010) NA - 10 batteries - Initial $14,600- NPC of $17,323
Amin et al. (2014) - Fuhrlander AG(FL30)- 1.5kW at 19m height- 3 turbines
Beacon Smart Energy 25
- 500 batteries
- Initial $2,140,000- NPC of $3,991,487- COE $0.602/kWh
Soe & Zheng (2014)
- PGE 20-25- 25kW at 25m height- (8-12) turbines
- Trojan L16P- 500 batteries
- COE 0.257 $/kWh
Diab et al. (2015) NA NA - COE 0.172 $/kWh- COE 0.182 $/kWh- COE 0.179 $/kWh
In this section, an overview discussion will be performed based on table 1 (A, B). Throughout the
literature survey, the case studies that are done covered locations from different continents such as Asia,
Africa, North America, Australia and Europe. The annual average wind speeds ranged from 2.13 to 7.38
m/s. However, all of the annual wind speeds are below 10m/s. There are many simulation studies
covering stand-alone or grid-connected system. Some researchers tended to design a hybrid wind power
system such as PV-Wind power system or PV-Wind-Diesel power system due to the limited wind
resource at their sites and therefore, other power systems are needed in order to sustain the designed load.
From Table 1 also, the studied load profiles are ranging from 12kWh/day to 2MWh/day. The applications
of the case studies covered residential buildings, small business premises, desalination units and college
building. Moreover, different case studies employed different models of wind turbine with different
power capacity. The wind turbines’ capacity ranges from 1 to 100 kW depending on the applications.
Besides, different brands of batteries are also used for different case studies. For the costs aspect, there is
no general trend that can be seen from the case studies; different turbines and batteries (brand, type &
power) will definitely affect the techno-economic aspects of wind turbine system. Also, many researchers
who conducted studies on wind power system focused on case studies related to their specific wind speed
and load data as shown from the literature summary in table 1.
So far, there is no research that generalizes the outstanding wind turbine size for a certain load to be
applicable many locations around the globe. Also, scientific investigation regarding how the different
wind turbine sizes and wind turbine hub heights can affect the wind power system is still limited and not
discussed extensively using the parameters of the techno-economic feasibility. This research intends to
uncover the effect of different annual wind speed, different wind turbines sizes, as well as different hub
heights on the values of the techno-economic feasibility parameters of wind power system. Also in this
study, the analysis will reveal the possibility of using annual wind speed data as a sufficient data to
predict the outstanding wind turbine size that can power a certain load at locations within annual wind
speed range (3-12 m/s) which is more practical for researchers and customers to learn lessons. There are
no pre-selections of locations in this research; instead, a range of annual wind speed (3-12 m/s) is taken as
inputs into HOMER to represent so many locations around the planet and to generate output.
2. MATERIALS AND METHOD
2.1 RESEARCH DESIGN
Figure 1: Block diagram of the study evolution methodology
The studied load is chosen to be a common load for many small-scale applications as learned from
literature summarized in Table 1. 8 kW load is assumed to operate for 10 hours from 8 o’clock in the
morning till 6 o’clock in the evening. For each hour, the load profile is assumed a full load as shown in
Figure 2. On average, the small scale unit will consume 80 kWh of electricity per day.
Objectives
Simulation results: Techno-economic
feasibility parameters of wind turbine power
system
Input data/ components/ design scope
- To size the outstanding wind turbine power fit around the globe for a certain load using annual wind speed data range (3-12) m/s instead of actual monthly wind speed data of the respective locations. - To validate the outstanding wind turbine using actual monthly wind speed data of some locations. The implemented hub height is the minimum for each turbine.- To determine the effect of the other hub heights of the outstanding wind turbine on the values of techno-economic parameters.
1. No. of turbine2. No. of batteries3. Battery lifetime4. Initial capital cost ($)5. Operating cost ($/yr.)6. Total NPC ($)7. COE ($/kWh)
- Primary load (8kW)- Batteries type (Trojan L16P) specifications- Converter cost /kW- (7) Types of wind turbines and their specifications - hub heights of the implemented wind turbines - Capacity shortage (0%) is assumed- Annual wind speed range (3-12) m/s as input data to present any location around the globe - Actual monthly wind speed data for some locations i.e. Dammam city as case studies for validation purposes
Figure 2: 8kW load profile of a small scale wind power system operating 10 hour daily
In this work, the real monthly wind speed data that can describe respective location will not be used as
input data in the base line of wind speed. Instead of that, only the annual wind speed will be used as input
data, and will be directly keyed-in the sensitivity values window of HOMER simulation tool within the
range (3-12) m/s, as shown in Figure 3. The annual wind speed values falls within 3-12 m/s is taken as
inputs to represent any location around the planet. Wind speeds of 1 and 2 m/s are disregarded in this
study, as most of the wind turbines have a minimum cut-in speed of around 3 m/s. Also, the maximum
annual wind speed value used in this study is 12 m/s which reflect extreme exceptional case under the
index value of wind resource.
Figure 3: Annual wind speed data keyed-in the software to represent any location around the globe
Because the analysis depended on the annual wind speed data only, the obtained results are regarded as a
preliminary indicator for techno-economic feasibility, where the actual feasibility is logically higher than
the obtained results. Generally, under this stage of analysis, the results of techno-economic parameters are
adequate and appropriate for comparison purposes to predict the outstanding size of wind turbine that can
power (8kW) load around the globe.
For the validation step of the proposed sizing approach, real monthly wind speed data will be directly
keyed-in in the base line of wind speed. Figure 4 illustrates the monthly wind speed data of Dammam city
as a case study to predict the outstanding wind turbine size among the seven wind turbines. If the results
of this case predict the same wind turbine size to power the load, it can be concluded that proposed sizing
approach is fit to predict the outstanding wind turbine around the globe for a certain load profile.
Figure 4: monthly wind speed input data for Dammam city
However, to confirm the accuracy of the proposed sizing approach under different annual wind speed, the
validation will test another two locations besides Dammam city location. The monthly wind speed data
(m/s) for the three tested locations is shown in table 2.
Table 2: The real monthly wind speed data (m/s) for the three tested locations
Month Dammam / Saudi [23]
Ras Monief / Jordan [24]
kokhanok, Alaska / USA [25]
January 4.16 6.56 7.73
February 4.72 6.76 10.01
March 4.72 7.17 8.53
April 4.72 6.17 7.22
May 5 5.85 7.09
June 5.27 6.49 7.84
July 4.72 6.95 7.03
August 3.88 6.22 6.68
September 3.88 5.49 7.33
October 3.61 4.83 6.86
November 4.16 6.46 8.46
December 4.16 6.14 9.32
Annual 4.41 6.3 7.84
2.2 Materials
The wind turbines were selected from the list of wind turbines available in the HOMER software. The
most important criteria in selecting these turbines are its expected suitability vis-à-vis the load demand.
After series of evaluations, seven types of wind turbine with different sizes were selected to be simulated
in this study. These seven wind turbines are: SW Whisper 500, BWC Excel-R, BWC Excel-S, PGE 20-
25, Fuhrlander FL 30/13, PGE 11-35, and Entegrity EW15. For each wind turbine, there are
recommended hub heights by the manufacturer; hence, the simulation will include the possible hub height
of the selected turbines. The rated power of the wind turbines varies from 3 kW to 50 kW. The Capital
Cost of each of the wind turbines is taken from the company’s website or latest journals publications that
used the wind turbine. As for the Replacement Cost and Operation & Maintenance (O&M) Cost, the
values are estimated to be 85% and 2.5% of the Capital Cost, respectively. The specifications of each
From a financial perspective, the higher hub heights were minimally influential vis-à-vis the costs of the
wind power system at an annual wind speed range of 4-12 m/s.
Although the hub height minimally influences the techno-economic aspects of wind power systems, it
was found that wind power systems with higher hub heights are somehow preferable at low annual wind
speeds. However, if we consider the cost of the infrastructure for the wind turbine to stand higher hub
heights, it will increase the total cost of the wind power project (cost of wind power system and cost of
infrastructure for higher hub heights).
Overall, the effects of hub heights on the techno-economic aspects of wind power system can be regarded
to not be as important as annual wind speeds and wind turbine types. Similar to the first part of the
analysis, the effects of wind speed and types of wind turbine were more obvious than that in hub heights.
Finally, as the wind speed increases, lesser equipment (turbines, batteries, etc.) is needed by the system,
which reduces the initial, operation, and maintenance (O&M) costs, and consequently the total NPC cost.
3.3 Validation of the Outstanding Wind Turbine at Dammam City, Saudi Arabia
This round of analysis aims to determine the wind turbine size that can power a certain load using real
monthly wind speed data for testing and validation purposes. The tested location is Dammam city. The
determined wind turbine size will be compared with the outstanding wind turbine size determined by the
proposed sizing approach. If the wind turbine size is found same, this confirms that the proposed sizing
approach is accurate to predict the outstanding wind turbine size at any location around the globe.
The analysis in this section will be performed under the following assumptions:
1. Location of the study is Dammam city.
2. Studied load is 8 kW.
3. Real monthly wind speed data obtained from weather2 website.
4. Seven wind turbines of different sizes are used.
5. Minimum hub height for each wind turbine is implemented.
6. Implemented annual capacity shortage: (0%)
3.3.1 Effect of Different Wind Turbine Size on Number of Turbines
SW500
(3kW)
BWC-S(10
kW)
BWC-R(7.5kW
)
PGE 11
-35(35
kW)
EW15(50kW
)
FL30/
13(30k
W)
PGE 20-2
5(25kW
)
02468
10121416182022
22
1412
7
4 42
Wind Turbine Type
No. o
f Tur
bine
s
Figure 22: Number of turbines versus wind turbine sizes for Dammam city
Figure 22 illustrates the number of turbines vs. different wind turbine types at Dammam city. A turbine’s
size, with a minimum quantity of turbines, is most crucial when designing the wind power system.
Therefore, in this round of evaluation, PGE 20-25 (25kW) with 2 turbines was determined to be
outstanding.
3.3.2 Effect of Different Wind Turbine Size on Number of Batteries
The wind power system that utilized the minimum number of batteries is regarded as outperformed. From
Figure (23), 3 wind turbines required the minimum number of batteries below 100 to power the load. The
number of batteries was 86, 82, and 95 for PGE 20-25 (25kW), Fuhrlander FL30/13 (30kW) and PGE 11-
35 (35kW), respectively. Therefore, Fuhrlander FL30/13 (30kW) is regarded as an outstanding turbine in
terms of the minimum amount of batteries. However, the difference in the number of batteries between
Fuhrlander FL30/13 (30kW) and PGE 20-25 (25kW) was only 4 batteries. So, PGE 20-25 (25kW) could
be regarded as superior compared to other turbines.
BWC-S(10
kW)
SW500
(3kW)
EW15(50k
W)
BWC-R(7.5kW
)
PGE 11
-35(35
kW)
PGE 20
-25(25
kW)
FL30/
13(30k
W)
70
90
110
130
150
170
190193 192 192
186
9586 82
Wind Turbine Type
No. o
f Bat
teries
Figure 23: Number of batteries versus wind turbine sizes for Dammam city
3.3.3 Effect of Different Wind Turbine Size on Battery Lifetime
Batteries with a standard lifetime remain the best choice. All wind turbines showed standard lifetime of up to 10 years for their battery bank system, as shown in Figure 24.
SW50
0(3kW
)
BWC-R
(7.5k
W)
BWC-S(
10 kW
)
PGE 20
-25(25
kW)
FL30
/13(30
kW)
PGE 11
-35(35
kW)
EW15
(50kW
)0123456789
10
Wind Turbine Type
Bat
tery
Life
time
(yrs
)
Figure 5.24: Battery lifetime versus wind turbine sizes for Dammam city
3.3.4 Effect of Different Wind Turbine Sizes on Cost of Energy
From Figure (25), PGE 11-35 (35 kW) shows the extreme highest levelized cost of energy compared to
other turbines, while PGE 20-25 (25 kW) shows the lowest levelized cost of energy. So, PGE 20-25
(25kW) was determined to be absolutely superior in terms of levelized cost of energy.
PGE 11-35
(35kW
)
EW15
(50kW
)
BWC-S
(10 kW
)
BWC-R
(7.5k
W)
FL30/13
(30kW
)
SW50
0(3kW
)
PGE 20-25
(25kW
)0
0.30.60.91.21.51.82.12.42.7
33.3 3.052
2.859
2.106
1.6221.336
1.138
0.724
Wind Turbine Type
CO
E($
/kW
h)
Figure 25: COE versus wind turbine sizes for Dammam city
3.3.5 Determining Wind Turbine Size at other Different Tested Locations Using their Monthly Wind Speed Data
Based on Figures 22, 23, 24, and 25, it can be seen that the wind power system that has the minimum
amount of wind turbines, where the number of batteries approaches the minimum, with the standard
battery lifetime and lowest levelized cost of energy being PGE 20-25 (25 kW). As a result of this, it was
concluded that the outstanding wind turbine was the PGE 20-25 (25 kW) for powering 8 kW load at a
Dammam city.
Dammam city as a tested location with real monthly wind speed data, the recommended wind turbine
amongst the (7) wind turbines is again PGE 20-25 (25kW). The same analyses are performed for another
two locations using their real monthly data. The outstanding turbine size based on techno-economic
feasibility parameters for three tested locations is shown in table 11. The results of the analyses confirmed
that the outstanding wind turbine size is still again PGE 20-25 (25kW). This confirms that the shortcut
sizing approach adopted in section 3.1 is accurate to determine the outstanding wind turbine that is
confirmed using real monthly data at the tested locations. So, the proposed sizing approach is fit to predict
the outstanding wind turbine at any location around the globe.
Table11: The outstanding turbine size based on techno-economic feasibility parameters for three tested
locations
Case studies with real monthly
wind speed data
Wind turbine
sizes with min
No. of turbines
Wind turbine sizes
with min No. of
batteries
Wind turbine
sizes with max
Battery lifetime
Wind turbine
size with min COE
Optimum turbine size at the tested
locations using
monthly wind speed
data
Outstanding turbine size predicted by annual wind speed data
Dammam/Saudi
25kW@30kW: 82@25kW: 86
All sizes 25kW 25kW
25kWRas Monief/
Jordan25kW, 30kW 50kW
25kW All sizes 25kW 25kW
kokhanok, Alaska/ USA
25kW, 30kW 35kW, 50kW
25kW All sizes 25kW 25kW
4. CONCLUSION
This study proposed a shortcut sizing approach that predicts the outstanding wind turbine size to power a
certain load 8 kW at so many locations around the globe within annual wind speed range (3-12 m/s). For
this purpose, seven wind turbine types with different power were selected. The selection was based on the
expected appropriateness of the power capacity to power an 8 kW load, on top of the initial cost of each
wind turbine. The outstanding wind turbine size is determined based on the results of techno-economic
feasibility parameters. The results of the techno-economic feasibility parameters are obtained using
HOMER simulation tool. The seven turbines performances versus the annual wind speed range are
compared at each parameter. The results showed that the outstanding size of wind turbine is PGE 20-25
(25 kW). For further confirmation, validation test is performed at three locations using their monthly wind
speed data to predict the optimum wind turbine size. The validation results showed that optimum wind
turbine size is PGE 20-25 (25 kW) at the three tested locations. Moreover, the results showed that the
outstanding wind turbine size and annual wind speed did impose more impacts on the values of techno-
economic feasibility parameters compared to higher hub heights. Finally it can be concluded that the
proposed sizing approach utilizing annual wind speed data (3-12m/s) a representative of locations is
accurate to predict the outstanding wind turbine of optimum power duty to run 8kW load around the
globe.
REFERENCES
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[2] Sadeghi, M., Gholizadeh, B., Gilanipour, J. & Khaliliaqdam, N. Economicanalysis of using of wind energy, Case study: Baladeh city, North of Iran. International Journal of Agriculture and Crop Sciences 2012; 4(11):666-73.
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