COMPARISON BETWEEN WIND AND SOLAR ELECTRIC ENERGY
POTENTIAL IN EDO STATE; A CASE STUDY OF BENIN CITY.
Yisah Y.A.1, Abdulraheem T.2, Anaza S.O.3 & Imadu O.S.4
Corresponding Author : mytajuddeen@gmail.com
Power Equipment and Electrical Machinery Development Institute1,2,3,4,
ABSTRACT
For obvious reasons, nations of the world are shifting attention to renewable energy means of
generating electricity as against the conventional use of fossil fuel. This study presents a
comparison between solar and wind electric energy potential in Edo State, Nigeria with Benin
City as a case study. Wind data was collected from the Nigerian Meteorological Agency
(NIMET), Benin City and solar data from National Aeronautics and Space Administration
(NASA) website. The results show that the lowest average monthly solar electric energy density
of 20.35kWh/m2 obtained is far greater than the highest average monthly wind electric energy
density of 4.95kWh/m2. Hence, the deployment solar electric energy can be said to economically
feasible but the same cannot be said for wind electric energy.
KEYWORD : wind electric energy, solar electric energy, wind data, solar data,
INTRODUCTION
The usefulness of electric power in our daily lives cannot be overemphasized. More than half of
Nigerians have no stable electricity despite the fact that power is an integral component of the
overall development of any nation. Provision of electricity is largely supplemented by the use of
electricity generators powered by diesel or petrol. Over 70% of businesses in Nigeria have
private electricity generators, leading to high production cost (Vincent-Akpu, 2012). The
growth of medium scale enterprises is one of the indices used for measuring the growth of an
economy. Most of these enterprises primarily need electricity to operate. Hence, the need to
look into other means of generating electricity other than the conventional use of fossil fuel.
The use of renewable energy sources presents a solution to this problem. (Abdulraheem &
Igbinovia, 2018).
Renewable energy can be defined as energy gotten from sources that are naturally recreated.
Renewable energy sources include solar, wind, hydro, biomass and geothermal. Apart from the
fact that these sources can be replenished, they are also free from harmful emissions of green
house gases (Oluseyi, 2010). Wind and solar energy are one the renewable energy that is yet to
be given attention and fully harnessed in Nigeria. The country is endowed with the abundance
of renewable energy sources. Adopting renewable energy resources for electricity production
and other energy needs has become a notable objective globally. In recent, efforts have been
made by the federal government of Nigeria to build wind turbines. For instance, 10MW is
currently under construction in Lambarimi, Rimi local government area of Katsina. (Idris et al,
2012)
METHODOLOGY
Wind speed data for seven years (2009-2015) was obtained from the Nigerian Meteorological
Agency (NIMET) located in Benin City measured at a height of 10m. Also, solar irradiation for
22 years was sourced from National Aeronautics and Space Administration (NASA) .
RESULTS AND DISCUSSION
3.1 Wind speed obtained from NIMET
Table 1: Monthly wind speed (m/s) for Benin City from 2009-2015 (Abdulraheem & Igbinovia,
2018).
Month
Wind speed (m/s)
2009
2010
2011
2012
2013
2014
2015
Average
Jan.
XX
1.03
1.08
1.09
1.14
1.18
1.41
1.16
Feb.
XX
1.32
1.44
1.20
1.33
1.10
1.35
1.29
Mar.
XX
1.10
1.40
1.41
1.40
1.21
1.05
1.26
Apr.
XX
1.32
1.12
1.32
1.18
1.18
0.78
1.15
May
XX
1.14
1.16
1.13
1.16
1.04
1.13
1.13
Jun.
0.76
1.10
1.10
1.12
1.16
1.05
1.01
1.04
Jul.
1.28
1.14
1.18
1.20
1.20
1.09
1.18
1.18
Aug.
1.55
1.22
1.37
1.58
1.45
1.33
1.29
1.40
Sep.
1.21
1.15
1.23
1.10
XX
1.11
1.16
1.16
Oct.
0.96
0.71
0.90
0.80
1.07
0.90
0.84
0.88
Nov.
0.80
0.54
0.81
0.83
0.94
0.79
0.77
0.78
Dec.
0.80
0.74
1.07
0.75
1.02
0.73
0.85
0.85
Average
1.11
3.2 Wind speed at varying heights.
Wind speed var.The wind speed was measured at an altitude of 10m
.The average monthly wind speed measured at a height of 10 m above the ground was used as
reference to compute the wind speed at heights of 30 m and 50 m.
V = Vref [
𝐻 ∝
]
𝐻𝑟𝑒𝑓
Where
V= calculated wind speed
Vref = measured wind speed
𝐻 = height at which wind speed is calculated
𝐻𝑟𝑒𝑓 = height at which wind is measured
∝= friction coefficient or Hellman exponent
Friction coefficient of 0.4 was used because the wind data were obtained from stations located
in a city area with high rise building.
For example the average monthly wind speed for Benin City in January at the heights of 30 m
and 50 m can be calculated as follows.
At a height 30 m;
V = 1.16 [
30 𝟎.𝟒
]
10
V = 1.16 [
50 𝟎.𝟒
]
10
V = 1.80 m/s
At a height of 50 m
V = 2.21 m/s
The rest of the wind speed at heights of 30 m and 50 m was calculated in the same way as
presented in Table 2. This shows that the wind speed increases with height.
Table 2: calculated average monthly wind
speed at varying height for Benin City.
MONTHS
Average wind speed(m/s)
10m
30m
50m
Jan.
1.16
1.80
2.21
Feb.
1.29
2.00
2.46
Mar.
1.26
1.96
2.40
Apr.
1.15
1.79
2.19
May
1.13
1.75
2.15
Jun.
1.04
1.61
1.98
Jul.
1.18
1.83
2.25
Aug.
1.40
2.17
2.67
Sep.
1.16
1.80
2.21
Oct.
0.88
1.37
1.68
Nov.
0.78
1.21
1.49
Dec.
0.85
1.32
1.62
3.3
Wind Electric Energy
Wind electric energy (kWh) =
𝑤𝑖𝑛𝑑 𝑒𝑙𝑒𝑐𝑡𝑟𝑖𝑐 𝑝𝑜𝑤𝑒𝑟(𝑤) 𝑋 𝑡𝑖𝑚𝑒(ℎ)
1000
Wind electric power = 2𝜌𝐴𝑣 3 Cp
1
Where,
𝜌 = air density in kg/m3 (taking as 1.225kg/m3)
V = wind speed in m/s
Cp= power coefficient
Time = 30 days
Time = 30 x 24 hours
Time =720 hours
Therefore wind electric energy =
𝜌𝐴𝑣 3 Cp x 720
2 𝑋 1000
Therefore wind electric energy = 0.36𝜌𝐴𝑣3 Cp
While wind electric energy density = 0.36𝜌𝑣3 Cp
The power co- efficient Cp used is 0.59, since that is the maximum fraction of electric power that
can be extracted from wind. For instance the wind electric energy density for the month of
January at the height of 50 m can be calculated as follows:
Average wind speed for January at height of 50 m is 2.21 m/s
Wind electric energy density = 0.36 x 1.225 x (2.21)3 x 0.59
= 2.809 kWh/m2
Using a rotor blade of diameter 5m ,
Swept area A = ∏ R2
Where R= radius of the rotor blade
Wind electric energy = 2.809 x (3.142 x 2.52)
Wind electric energy = 55.16 kWh
For a rotor blade diameter of 10m
Wind electric energy = 2.809 x (3.142 x 52)
Wind electric energy = 220.65 kWh
Table 3: Estimated Wind Electric Energy for Benin City
Month
Wind speed at
50 m (m/s)
Electric
Density(kWh/m2)
Energy
Electric Energy for
diameter(kWh)
5m
5m and 10m rotor
10m
Jan.
2.21
2.809
55.16
220.65
Feb.
2.46
3.873
76.06
304.22
Mar.
2.40
3.597
70.64
282.54
Apr.
2.19
2.733
53.67
214.68
May
2.15
2.586
50.78
203.13
Jun.
1.98
2.020
39.67
158.67
Jul.
2.25
2.964
58.21
232.82
Aug.
2.67
4.953
97.27
389.06
Sep.
2.21
2.809
55.16
220.65
Oct.
1.68
1.234
24.23
96.93
Nov.
1.49
0.861
16.91
67.63
Dec.
1.62
1.106
21.72
86.88
Annual
electric
31.545
energy density
Annual
electric energy
619.48
Annual
2477.86
electric energy
It can be observed from table 3 that the energy generated increases with increase in the size of
the rotor diameter.
3.4 Solar data obtained from NASA
Table 4: Average monthly insolation incident on a horizontal surface for Benin City
month Insolation
Insolation
Per day
(kWh/m2/day) Per month
(kWh/m2/month)
Jan.
5.43
162.90
Feb.
5.49
164.70
Mar.
5.35
160.50
Apr.
5.05
151.50
May
4.75
142,50
Jun.
4.23
126.90
Jul.
3.57
107.10
Aug.
3.57
107.10
Sep.
3.82
114.60
Oct
4.38
131.40
Nov.
4.99
149.70
Dec.
5.24
157.20
It is not all the solar insolation that is converted to electricity by the solar panel. Just a little
percentage is converted to electricity. The amount of electricity produced depends on the
conversion efficiency of the solar panel. The maximum conversion efficiency of solar panel
available commercially is 19% (Harthmut, 2012). The solar electric energy can be calculated as
follows:
Solar electric energy density = solar insolation x conversion efficiency of the panel
For instance, for the month of January;
Solar electric energy density = 162.90 x 0.19
Solar electric energy density = 30.95 kWh/m2
Solar electric energy density for other months were calculated in the same way as shown in
Table 5.
Table 5: Solar electric energy density for Benin City
Month Solar electric energy
density for Edo State
kWh /m2
Jan.
30.95
Feb.
31.29
Mar.
30.50
Apr.
28.79
May
27.08
Jun.
24.11
Jul.
20.35
Aug.
20.35
Sep.
21.77
Oct
24.97
Nov.
28.44
Dec.
26.51
3.5 : Comparison between wind and solar electric Energy in Benin City.
Table 6: Comparison between Wind and solar electric energy densities for Benin City
Month
Wind electric energy
Solar electric energy
density
density
(kWh /m2)
(kWh /m2)
Jan.
2.809
30.95
Feb.
3.873
31.29
Mar.
3.597
30.50
Apr.
2.733
28.79
May
2.586
27.08
Jun.
2.020
24.11
Jul.
2.964
20.35
Aug.
4.953
20.35
Sep.
2.809
21.77
Oct
1.234
24.97
Nov.
0.861
28.44
Dec.
1.106
26.51
Table 6 shows that the lowest average monthly solar electric energy density of 20.35 kWh/m2 is
far greater than the highest average monthly wind electric energy density of 4.95 kWh/m 2. This
implies that for renewable energy installation in Benin City, Solar is better.
CONCLUSION
A comparison between wind and solar electric energy potential has been presented. From
the result obtained and analysis made, it can be established that wind electric energy
potential in Benin City is far below that of solar. Solar energy should therefore be
exploited to help reduce the problem of inadequate power.
REFERENCES
Abdulraheem T. & Igbinovia S.O. (2018) “Estimation of wind energy potential of Benin City,
Nigeria using data from two weather stations” International Journal of Research, Volume
05 Issue 12.
Idris, N. A., Lamin, H. S., Ladan, M. J. & Yusuf, B. H.(2012) “Nigeria’s Wind Energy
Potentials: the Path to a Diversified Electricity Generation-Mix” International Journal of
Modern Engineering Research, vol.2. No.4, 2012.
Oluseyi, O. A (2010) “The Potential for Wind Energy in Nigeria” Wind Engineering vol.34,
No 3.
Vincent-Akpu, I. (2012) “Renewable energy potentials in Nigeria” University of Stirling,
http://www.nsong.org on 3rd April, 2016