A MATLAB Estimation of FUTO Solar PV Potential Using

Transcription

AASCIT Journal of Energy
2015; 2(3): 16-28
Published online April 30,2015 (http://www.aascit.org/journal/energy)
A MATLAB Estimation of FUTO
Solar PV Potential Using Hottel and
Liu Jordan Clear Sky Radiation
Model
F. N. Ugwoke1, K. O. Chilakpu2, G. N. Ezeh3, I. E. Achumba3,
K. C. Okafor3
1
Keywords
MATLAB,
Solar Radiation,
Average Peak,
Photovoltaic Module,
Daily Variations,
Energy Resources
Received: March 29, 2015
Revised: April 14, 2015
Accepted: April 15, 2015
Dept. of Computer Science, Michael Okpara University of Agriculture, Umudike, Umuahia,
Nigeria, Nigeria
2
Dept. of Agricultural Engineering, Federal University of Technology, Owerri, Imo State, Nigeria
3
Dept. of Electrical Electronic Engineering, Federal University of Technology, Owerri, Imo State,
Nigeria
Email address
[email protected] (F. N. Ugwoke), [email protected] (K. O. Chilakpu),
[email protected] (G. N. Ezeh), [email protected] (I. E. Achumba),
[email protected] (K. C. Okafor)
Citation
F. N. Ugwoke, K. O. Chilakpu, G. N. Ezeh, I. E. Achumba, K. C. Okafor. A MATLAB Estimation
of FUTO Solar PV Potential Using Hottel and Liu Jordan Clear Sky Radiation Model. AASCIT
Journal of Energy. Vol. 2, No. 3, 2015, pp. 16-28.
Abstract
The erratic supply of power as well as the depletion of fossil fuel on a worldwide basis
has necessitated the use of alternative energy resources to meet up present day demand.
There are various alternative energy resources amongst which solar energy is the most
promising as it is readily available and the most reliable. This work used a MATLAB
program to estimate the hourly solar radiation for 8hours (0900 – 1700 hours) daily in
Federal University of Technology, Owerri (FUTO) for the year 2014.This is based on the
Hottel and Liu-Jordan clear sky radiation model. Daily Variations of hourly output power
for every month were computed. Also, daily energy outputs and monthly energy outputs
were determined. The chosen efficiency of photovoltaic module is 22%. Based on the
analysis of clear sky solar radiation data and the daily variations, it was observed that
daily peak output occurred between the hours of 1100 – 1300. It was observed that the
month of March had the highest monthly output energy with a value of 40899.6237
Watts/Mtr2-Hr. December had the lowest output energy with a value of 35510.4480
Watts/Mtr2-Hr. A 99501 m2 area was determined to generate 20MW based on the average
peak output power. An average yearly energy outputof456766.6570Wh equivalent to
1644.36 MJ per square meter (1kWh = 3600 kJ) was obtained. This was based on the
analysis of all the monthly output energy values obtained.
1. Introduction
Energy is required for a wide range of applications such as transportation, industrial
applications, agricultural applications, domestic applications and office applications, [1].
The availability and accessibility of sufficient amount of energy can accelerate
individual’s and nation’s development [1]. It could be inferred that the two main drivers
for increase in the energy demand includes: growth in the world’s population and technoeconomic growth of the countries, particularly developing countries [2]. Increase in the
above factors implies that the energy demands will increase proportionally. In the
developing countries with abundant supply of affordable solar energy sources, there is
17
F. N. Ugwoke et al.: A MATLAB Estimation of FUTO Solar PV Potential Using Hottel and Liu Jordan Clear
Sky Radiation Model
need to use appropriate techniques to carry out an estimation
analysis. This will help to ascertain the feasibility of power
generation in a given environment. A brief discussion on
solar energy prospects in Nigeria is presented next.
1.1. Prospects of Solar Energy Utilization
Nigeria
Solar energy is the most promising of all the renewable
energy sources in view of its apparent limitless potential [3].
The sun is the most readily and widely available renewable
energy source capable of meeting the energy needs of whole
world. It can provide more power than any fossil fuel on the
planet. The sun radiates its energy at the rate of about 3.8 x
1023 kW per second. Nigeria is one of the tropical countries
of the world which lies approximately between 4o and 13o
with landmass of 9.24 x 105 km2 enjoys an average daily
sunshine of 6.25 hours, ranging between about 3.5 hours at
the coastal areas and 9.0 hours at the far northern boundary.
Nigeria receives about 4.851x 1012 kWh of energy per day
from the sun [3]. Based on the land area of 924 x 103 km2 for
the country and an average of 5.535kWh/m2/day, Nigeria has
an average of 1.804 x 1015 kWh of incident solar energy
annually. This annual solar energy insolation value is about
27 times the nation’s total conventional energy resources in
energy units [3]. In other words, about 3.7% of the national
land area is needed to be utilized in order to collect an
amount of solar energy equal to the nation’s conventional
energy reserve [3].
Figure 1. Map of Nigeria depicting solar radiation, (Source: www.solargis.info)
Figure 2. Solar farm power plant in ELDI, Awka, 2014
AASCIT Journal of Energy 2015; 2(3): 16-28
From Figure 1, it can be deduced that the northern part of
Nigeria receives the highest amount of solar energy in the
excess of 1800kWh/m2 while the lowest value (800kWh/m2)
is experienced in the Niger-delta. Solar electricity may be
used for power supply to remote villages and locations not
connected to the national grid. It may also be used to
generate power for feeding into the national grid. Other areas
of application of solar electricity include low and medium
power application such as: water pumping, village
electrification, local power supply for homes and businesses
(see figure 2), traffic lighting and lighting of road signs, etc.
1.2. Constraints to Solar Utilization in
Nigeria
With all the obvious factors in favour of Nigeria being a
country with abundant solar energy utilization prospects, the
country has not experienced much advancement in this area.
This is due to the fact that there are some factors that hinder
the progress of solar technology utilization. Some of these
factors are:
i. High Cost Constraints: Solar panels in Nigeria are
imported. The cost of importation and the cost of installation
are very high. Solar technology has high upfront costs and
low payback time. This is a major factor militating against its
development in Nigeria.
ii. Investor-Friendly Policies: The country as at present
lacks policies that would attract investors in the field of solar
technology. The independent power producers in Nigeria are
either non-functional or are still concerned with producing
power via conventional energy methods.
iii. Low Technological know-How: As stated previously,
the technical know-how is still lacking in Nigeria. Some
other components are manufactured locally but most are
imported.
This research was carried out with a view of presenting
renewable energy (in this case, solar energy) as a viable
means of maximizing and increasing the efficiency of power
generation and supply. This research used the Federal
University of Technology, Owerri (FUTO) Nigeria as the
research testbed. Besides the base station estimation of PV
data, there is need to use a computational approach in
harnessing solar energy for the production of reliable and
sustainable energy. An adaptation of related mathematical
methods for estimation of solar radiation as well as carrying
out analysis results for photovoltaic power generation
capacity in FUTO will facilitate renewable energy research in
tertiary institutions.
1.3. Research Contributions
The main research contribution is to estimate the
photovoltaic power potential of FUTO using the Hottel’s
clear sky radiation models. This work will demonstrate that
solar photovoltaic plants can serve as a means of improving
power supply and sustainability. The work will increase
awareness on the potentials of renewable energy sources that
18
abound in Nigeria using FUTO as a case study.
2. Related Works
Several works on tilted surface radiation models were
studied such as the isotropic model proposed by Liu and
Jordan [4], anisotropic model by HDKR [5], Klucher [6], and
Reindl et al [7], [8]. In these works, the predicted model
results were based on the input data of sources concerned
while using relevant datasets for comparative studies.
According to [9], solar radiation data is described in terms of
the total solar radiation, which is the summation of beam plus
diffuse and ground reflected radiation. Most of the total
radiation is measured on horizontal surfaces by local
meteorological stations. It can be observed through satellites.
But meteorological stations provide more perfect estimates
since it holds the site specific characteristics. Besides, the
solar conversion systems are tilted towards the sun in order to
maximize the amount of solar radiation incident on PV
module surface. The availability of recorded on tilted
surfaces is very rare, therefore, the tilted surface radiation in
most cases is calculated from horizontal surface by means of
empirical models [10].
The work in [11], examined the performance of tilted
surface solar radiation models for selection of estimated
amount of solar radiation. The model results were evaluated
on the basis of one-sample statistical test. In [12] an attempt
was made to use clear sky radiation for predicting the
average global solar radiation. Various regression analyses
were applied to analyse and validate their results. This work
leveraged existing works in [4],[11] and [12] to carry out its
investigation in FUTO environment for possible solar plant
deployment.
3. Methodology
3.1. Test Bed Description
FUTO have several departments and faculties that needs
power supply consistently, but to estimate the solar
photovoltaic power generation potential in FUTO, the
amount of solar radiation received by FUTO was determined
for possible power generation. FUTO has latitude of 5.463
and an altitude of 90.91 m. The daily variations, daily energy
output, average monthly energy and yearly energy outputs
were found out and related graphs were plotted showing the
variations in outputs in different seasons and times. Also,
information on the peak energy output for different days of
the month were used to calculate the average monthly peak
output for a year and variations in monthly energy peak for
the year was plotted. The average annual energy peak is also
calculated and is used to estimate the potential of solar
photovoltaic power generation and the area for a 20MW solar
photovoltaic power generation scheme to be located in the
FUTO.
MATLAB scripting was used as a unique way of
19
Sky Radiation Model
determining solar radiation since other similar works made
use of information from sources such as meteorological
stations, and geographical Information Systems. These
sources can only offer limited measured data. Hence, Hottel
and Liu-Jordan clear sky radiation model [4] was used in the
determination of solar irradiance. The model was used due to
the lack of readily available meteorological information on
solar radiation for the area at the time of the study. Beside,
the adapted models are close and offer accurate alternatives
to actual measurement data. A program for the model was
written in MATLAB and the radiation values were
determined. In this case, the model was used to estimate the
hourly solar radiation from 0900 to 1700 hours daily from
January to December, 2014 in FUTO Bakassi Utility
substation. The other reasons for choosing Hottel and LiuJordan Clear Sky Radiation model include:
It is simple to use and does not involve rigorous
calculations.
Data necessary for the application of the model such as
zenith angle and altitude are readily available.
It provides a means of taking climate type into
consideration.
It does not require ambiguous atmospheric data which
are not readily available in this location unlike some
other models.
It also provides a great degree of accuracy.
One very important point to be noted in the application of
this model is that it does not consider the effects of clouds
and non-clear skies. Thus, clear skies were assumed for the
estimation of the solar photovoltaic potential.
noon. It varies seasonally due to tilt of the earth on its
axis and its rotation around the sun.
The Solar Constant (Gsc): This is the energy from the
sun per unit time received on a surface perpendicular to
the direction of propagation of radiation at mean earthsun distance outside the atmosphere [5]. Various
experiments delivered various results but the World
Radiation Center (WRC) has adopted a value of
1367W/m2 with an uncertainty order of 1% [5].
According to [12], considering FUTO environment let the
atmospheric transmittance, Ʈb for beam radiation be given by
Equ 1:
Ʈb =
∗
+
exp
(1)
= 0.4327 – 0.00821 (6 – A)2
∗=
(2)
0.5055 + 0.00595 (6.5 – A)2
(3)
k* = 0.2711 + 0.01858 (2.5 – A)2
(4)
A in the Equ 2 to 4, is the altitude in kilometers (km). The
constants ,
and k for standard atmosphere with 23km
visibility can be found by multiplying ∗ , ∗ and k*, which are
given for altitudes less than 2.5km by Equ2 to 4 with the
correction factors for the various climate types given in table 1
Table 1. Correction Factors For The various Climate Types
Climate Type
Tropical
Midlatitude Summer
Subarctic Summer
Mid-latitude Winter
R0
0.95
0.97
0.99
1.03
R1
0.98
0.99
0.99
1.01
Rk
1.02
1.02
1.01
1.00
3.2. Estimation of Clear Sky Solar Radiation
In context, various terms used in the application of Hottel
and Liu-Jordan models are described below:
Beam Radiation (Gcb): The solar radiation received from
the sun without having been scattered by atmosphere. It
is also called direct radiation.
Diffuse Radiation (Gcd): This is radiation received from
the sun which has been scattered by atmospheric
particles, clouds or reflected off some surfaces.
Total Radiation (GT): This is the sum total of the beam
(direct) and diffuse radiation. It is sometimes called
global radiation.
Irradiance: The rate at which solar radiation is incident
on a particular surface per unit area. It is measured in
Watts/Mtr2.
Latitude (Φ): The angular position north or south of the
equator.
Zenith Angle (θZ): The angle between the vertical and
line to the sun, that is, the angle of beam radiation on a
horizontal surface.
Hour Angle (H):It is the angular displacement of the sun
east or west of the local meridian due to rotation of the
earth on its axis through 150 per hour. It is positive in the
afternoons and negative in the mornings.
Declination (δ): The angular position of the sun at solar
Using FUTO environment as a tropical climate type (as
shown in table 1),
=
∗
=
∗ ×R
× R0
(5)
(6)
1
k = k* × Rk
(7)
Now, θz is the zenith angle and Cos θzis given by:
Cos θz = Sin Φ Sin δ + Cos Φ Cos δ Cos H
(8)
Where:
Φ is the latitude of the area, δ is the declination angle and
H is the hour angle.
δ and H are given by:
Declination angle, δ = 23.45 Sin
(n– 81)
Hour angle, H = 150 × (time – 12 noon)
(9)
(10)
The clear sky beam normal radiation denoted by Gcnb, is
then given as:
Gcnb= Gon× Ʈb
(11)
Gon is the extraterrestrial radiation incident on a plane
normal to the radiation on the nth day of the year and is given
by:
Gon = Gsc(1.000110 + 0.034221 cosB + 0.001280 sin B
+0.000719 cos2B + 0.000077 sin 2B)
(12)
Where n is the day of the year, Gsc is the solar constant (=
1367W/m2) and B is given by
B = (n – 1) 360/365
(13)
From this, the clear sky beam horizontal radiation,
Gcb(W/m2) is given by Equ (14)
Gcb = Gon× Ʈb × Cos θz
(14)
The relationship between transmission coefficients for
beam and diffuse radiation as given by Liu and Jordan is
given byEqu (15).
Ʈd = 0.271 – 0.294Ʈb
(15)
Whereζd is the transmission coefficient for diffuse
radiation.
From this, the clear-sky diffuse radiation, Gcd (W/m2) can
be calculated by the Equ (16)
Gcd = Gon × Ʈd × Cosθz
(16)
Finally, the clear-sky total solar radiation on a horizontal
plane GT (W/m2) is calculated by summing up the clear-sky
horizontal beam radiation and the clear-sky diffuse radiation.
This is given by Equ (17)
GT = Gcb + Gcd
(17)
3.3. MATLAB M-File Computation
Manual calculations for hourly radiation for the hours of
20
0900 to 1700 daily for the whole year can be very strenuous.
To overcome this problem, the model derivations (Equ 1 to
Equ 17) was written as an M-File program in MATLAB. It’s
gives the value of solar radiation for every day of the year
ranging from 0900 hours to 1700 hours.
Thus, the solar radiation values were derived via
MATLAB. They were also tabulated hourly for the whole
number of days in the month and were multiplied by the
efficiency chosen to derive the output power for each input
value. The flowchart for the Hottel and Liu-Jordan model
written as a MATLAB program is shown in Figure 3.
The program and flowchart in figure 3 was used to
estimate solar radiation at hourly intervals ranging from 0900
hours to 1700 hours, everyday for the year 2014.Based on
Equ 2 to Equ 7, the values of ,
and k were represented
by a0, a1and k respectively with ∗ , ∗ and k*computed at an
altitude (A) of 0.09091km.
However, tables of results for the first days of every month
were captured. Total and average radiation taking hourly
intervals from 0900 to 1700 hours daily for the 12 months
were also determined. A sample of the tables from the month
of January to December is shown in from tables 2 to 13.
MATLAB implementations of the results computed by the
program are provided in Fig 3.
In analyzing the results obtained, daily variations for all
the months are investigated and subsequently, the daily and
monthly outputs for all the months were estimated. The daily
variations are shown in tables 2 to 13.The average solar
radiation in watts on an area of one square meter over a
period of one hour is calculated as shown in the third column
of the table. The daily energy output is the total of all the
values gotten hourly for each day and the monthly energy
output is calculated by multiplying the daily energy output by
the number of days in the month.
Table 2. Daily Variations of Solar Radiation for January, 2014
TIME (Hours)
0900 – 1000
1000 – 1100
1100 – 1200
1200 – 1300
1300 – 1400
1400 – 1500
1500 – 1600
1600 – 1700
AVERAGE OUTPUT
POWER (Watts/Mtr2)
144.9499
176.4090
192.8601
192.8601
176.4090
144.9499
101.5575
51.9593
AVERAGE HOURLY OUTPUT
ENERGY(Watts/Mtr2-Hr)
144.9499
176.4090
192.8601
192.8601
176.4090
144.9499
101.5575
51.9593
DAILY ENERGY
OUTPUT (Watts/Mtr2-Hr)
M0NTHLY ENERGY
OUTPUT(Watts/Mtr2-Hr)
1181.9548
36640.5988
Table 3. Daily Variations of Solar Radiation for February, 2014
TIME (Hours)
0900 – 1000
1000 – 1100
1100 – 1200
1200 – 1300
1300 – 1400
1400 – 1500
1500 – 1600
1600 – 1700
AVERAGE OUTPUT
POWER (Watts/Mtr2)
154.7838
187.4777
204.5527
204.5527
187.4777
154.7838
109.5877
57.5140
154.7838
187.4777
204.5527
204.5527
187.4777
154.7838
109.5877
57.5140
DAILY ENERGY OUTPUT
(Watts/Mtr2-Hr)
M0NTHLY ENERGY
1260.7301
36561.1729
21
Sky Radiation Model
Table 4. Daily Variations of Solar Radiation for March, 2014
TIME (Hours)
0900 – 1000
1000 – 1100
1100 – 1200
1200 – 1300
1300 – 1400
1400 – 1500
1500 – 1600
1600 – 1700
AVERAGE OUTPUT
POWER (Watts/Mtr2)
162.1654
195.3337
212.6459
212.6459
195.3337
162.1654
116.1921
62.8606
162.1654
195.3337
212.6459
212.6459
195.3337
162.1654
116.1921
62.8606
DAILY ENERGY OUTPUT
(Watts/Mtr2-Hr)
M0NTHLY ENERGY
1319.3427
40899.6237
Table 5. Daily Variations of Solar Radiation for April, 2014
TIME (Hours)
0900 – 1000
1000 – 1100
1100 – 1200
1200 – 1300
1300 – 1400
1400 – 1500
1500 – 1600
1600 – 1700
AVERAGE OUTPUT
POWER (Watts/Mtr2)
161.4296
193.5768
210.3509
210.3509
193.5768
161.4296
116.8312
64.9120
AVERAGE HOURLY
OUTPUTENERGY(Watts/Mtr2-Hr)
161.4296
193.5768
210.3509
210.3509
193.5768
161.4296
116.8312
64.9120
DAILYENERGY OUTPUT
(Watts/Mtr2-Hr)
M0NTHLY ENERGY
1312.4578
39373.7340
Table 6. Daily Variations of Solar Radiation for May, 2014
TIME (Hours)
0900 – 1000
1000 – 1100
1100 – 1200
1200 – 1300
1300 – 1400
1400 – 1500
1500 – 1600
1600 – 1700
AVERAGE OUTPUT
POWER (Watts/Mtr2)
154.9012
185.1948
201.0221
201.0221
185.1948
155.5569
112.8212
63.8401
154.9012
185.1948
201.0221
201.0221
185.1948
155.5569
112.8212
63.8401
DAILYENERGY OUTPUT
(Watts/Mtr2-Hr)
M0NTHLY ENERGY
1259.5466
39045.9446
Table 7. Daily Variations of Solar Radiation for June, 2014
TIME (Hours)
0900 – 1000
1000 – 1100
1100 – 1200
1200 – 1300
1300 – 1400
1400 – 1500
1500 – 1600
1600 – 1700
AVERAGE OUTPUT
POWER (Watts/Mtr2)
150.1519
179.3431
194.5804
194.5804
179.3431
150.1519
109.544
65.5456
150.1519
179.3431
194.5804
194.5804
179.3431
150.1519
109.5440
65.5456
DAILY ENERGY
M0NTHLY ENERGY
1223.2404
36697.2120
Table 8. Daily Variations of Solar Radiation for July, 2014
TIME (Hours)
0900 – 1000
1000 – 1100
1100 – 1200
1200 – 1300
1300 – 1400
1400 – 1500
1500 – 1600
1600 – 1700
AVERAGE OUTPUT
POWER (Watts/Mtr2)
151.9929
181.6499
197.1285
197.1285
181.6499
151.9929
110.8626
62.9604
151.9929
181.6499
197.1285
197.1285
181.6499
151.9929
110.8626
62.9604
DAILY ENERGY
M0NTHLY ENERGY
1235.3656
38296.3336
22
Table 9. Daily Variations of Solar Radiation for August, 2014
TIME (Hours)
0900 – 1000
1000 – 1100
1100 – 1200
1200 – 1300
1300 – 1400
1400 – 1500
1500 – 1600
1600 – 1700
AVERAGE OUTPUT
POWER (Watts/Mtr2)
157.8944
189.1110
205.4617
205.4617
189.1110
157.8944
114.5518
64.0454
157.8944
189.1110
205.4617
205.4617
189.1110
157.8944
114.5518
64.0454
DAILY ENERGY
M0NTHLY ENERGY
1283.5315
39789.4757
Table 10. Daily Variations of Solar Radiation for September, 2014
TIME(Hours)
0900 – 1000
1000 – 1100
1100 – 1200
1200 – 1300
1300 – 1400
1400 – 1500
1500 – 1600
1600 – 1700
AVERAGE OUTPUT
POWER (Watts/Mtr2)
160.4166
192.9408
209.9142
209.9142
192.9408
160.4166
115.4638
67.1604
POWER(Watts/Mtr2-Hr)
160.4166
192.9408
209.9142
209.9142
192.9408
160.4166
115.4638
67.1604
DAILY ENERGY
M0NTHLY ENERGY
1309.1675
39275.0264
Table 11. Daily Variations of Solar Radiation for October, 2014
TIME (Hours)
0900 – 1000
1000 – 1100
1100 – 1200
1200 – 1300
1300 – 1400
1400 – 1500
1500 – 1600
1600 – 1700
AVERAGE OUTPUT
POWER (Watts/Mtr2)
155.1697
185.5635
204.5871
204.5871
185.5635
155.1697
110.2352
58.3411
155.1697
185.5635
204.5871
204.5871
185.5635
155.1697
110.2352
58.3411
DAILY ENERGY
M0NTHLY ENERGY
1259.2169
39035.7239
Table 12. Daily Variations of Solar Radiation for November, 2014
TIME (Hours)
0900 – 1000
1000 – 1100
1100 – 1200
1200 – 1300
1300 – 1400
1400 – 1500
1500 – 1600
1600 – 1700
AVERAGE OUTPUT
POWER (Watts/Mtr2)
145.8716
177.3822
191.0176
191.0176
177.3822
145.8716
102.4374
102.4374
145.8716
177.3822
191.0176
191.0176
177.3822
145.8716
102.4374
52.7014
DAILY ENERGY
M0NTHLY ENERGY
1183.6816
35510.4480
Table 13. Daily Variations of Solar Radiation for December, 2014
TIME (Hours)
0900 – 1000
1000 – 1100
1100 – 1200
1200 – 1300
1300 – 1400
1400 – 1500
1500 – 1600
1600 – 1700
AVERAGE OUTPUT
POWER (Watts/Mtr2)
140.8895
171.9118
187.9171
187.9171
171.9118
140.8894
98.3774
49.9072
140.8895
171.9118
187.9171
187.9171
171.9118
140.8894
98.3774
49.9072
DAILY ENERGY OUTPUT
(Watts/Mtr2-Hr)
M0NTHLY ENERGY
1149.7214
35641.3630
23
Sky Radiation Model
Fig. 3. MATLAB Algorithm for Estimating Hourly Total Radiation Using Hottel and Liu-Jordan Model
The average monthly output energy is calculated by adding all the monthly outputs and dividing by the number of months.
Finally, average yearly output energy is estimated by multiplying the average monthly output by the number of months in the
year. This is shown in table 14.
24
Table 14. Daily and Monthly Energy output, Avg Monthly and Yearly Energy Output for Jan to Dec, 2014
MONTHS
DAILY SOLAR ENERGY
MONTHLY ENERGY
JANUARY
FEBRUARY
MARCH
APRIL
MAY
JUNE
JULY
AUGUST
SEPTEMBER
OCTOBER
NOVEMBER
DECEMBER
1181.9548
1260.7301
1319.3427
1312.4578
1259.5466
1223.2404
1235.3656
1283.5315
1309.1675
1259.2169
1183.6816
1149.7214
36640.5988
36561.1729
40899.6237
39373.7340
39045.9446
36697.2120
38296.3336
39789.4765
39275.0250
39035.7239
35641.3634
35510.4480
From the table shown previously, the average monthly
energy output is estimated as 38063.8881 Watts/Mtr2-Hr and
thus the average yearly energy output is given as
AVERAGE MONTHLY ENERGY
AVERAGE YEARLY
ENERGY OUTPUT
(Watts/Mtr2-Hr)
38063.8881
456766.6570
456766.6570 Watts/Mtr2-Hr. The energy output is an
indicator of how much electrical energy can be generated.
Note that 1kWh = 3600kJ. Thus,
Average yearly energy output = 456766.6570 ×3600000 = 1644359965.2 J = 1644.36MJ
1000
The peak values for the different months are shown in
table 15 and the average peak value is used to estimate the
area of the solar photovoltaic power generating plant by
multiplying with the desired plant capacity.
Table 15. Peak Variations, Average Peak Output and Proposed Capacity of the Plant.
MONTH
PEAK OUTPUT
(Wp/M2)
JANUARY
192.8601
FEBRUARY
204.5527
MARCH
212.6459
APRIL
210.3509
MAY
201.0221
JUNE
194.5804
JULY
197.1285
AUGUST
205.4617
SEPTEMBER
209.9142
OCTOBER
204.5871
NOVEMBER
191.0176
DECEMBER
187.9171
AVERAGE PEAK OUTPUT
(Wp/M2)
DESIRED PLANT
CAPACITY(MWp)
ESTIMATEDPV
ARRAY AREA (M2)
201.0032
20MW
99501
Based on estimations and the solar radiation potential of
201.0032 Wp/m2, a typical 20MW plant would occupy an
area of 99501m2.
4. Analysis and Discussion of Results
4.1. Analysis of Solar Radiation Data
In this section, the tables1 to 15 were analyzed and
discussed. Graphs were plotted considering time of peak
output. The graphs showing the various daily variations are
plotted as shown in fig 4 to 15 with the average hourly output
power on the vertical axis and the corresponding time range
on the horizontal axis. The values plotted in the graphs are
provided in tables 1 – 12.The solar photovoltaic power
potential in FUTO is estimated from data derived using the
Hottel and Liu-Jordan clear sky radiation model as depicted
in fig3. The effects of cloudy weather and non-clear skies are
not included in the model application and estimation of the
plant capacity.
It can be easily seen from the graphs that the daily highest
power output occurs within the time interval of 1100 – 1300
hours. This is due to the fact that the sun is almost
perpendicular to the surface of the earth at noon. Solar
radiation at midday is very high and consequently the output
from the solar photovoltaic plant will be higher for this time
interval than at other times. Thus, daily peak output from the
proposed plant will occur at midday. The graphs have a
parabolic shape with the lowest solar radiation occurring in
time interval 1600 – 1700 hours. This is because the day is
25
Sky Radiation Model
coming to an end and the sun begins to set. Hence, output
from the solar plant within that time interval would be low.
From the graphs in Fig 16-18, it can be seen that the
highest energy output from the plant was recorded in the
month of March with the value of 40899.6237Watts/Mtr2-Hr.
This is because March is at the boundary between the rainy
season and the dry season and is almost totally devoid of
cloudy weather and non-clear skies.
Hence, solar radiation is high in March. There is also a dip
in May through July corresponding with the rainy season and
cloudy weather experienced in those months. In August and
Fig 4. Graph Showing Daily Variations for Jan, 2014
Fig 6. Graph Showing Daily Variations for April 2014
Fig 8. Graph Showing Daily Variations for June 2014
September, there is a rise in solar radiation output due to the
break in the rainy season experienced in these months and the
corresponding rise in solar radiation intensity. The Lowest
output from the plant was recorded in the month of
December with the value of 35510.4480Watts/Mtr2-Hras can
be easily seen from the afore-mentioned graphs
corresponding to. Actual data from past works on solar
radiation in Owerri attests to the fact that the lowest radiation
occurs in December and the highest in March. Hence, it is
expected that more power will be produced in March and less
power in December.
Fig 5. Graph Showing Daily Variations for Feb.2014
Fig 7. Graph Showing Daily Variations for March 2014
Fig 9. Graph Showing Daily Variations for May 2014
Fig 10. Graph Showing Daily Variations for July 2014
Fig 11. Graph Showing Daily Variations for Aug, 2014
Fig 12. Graph Showing Daily Variations for Sept, 2014
Fig 13. Graph Showing Daily Variations for Oct, 2014
Fig 14. Graph Showing Daily Variations for Nov, 2014
Fig 15. Graph Showing Daily Variations for Dec, 2014
The corresponding graphs for the daily and monthly outputs are shown in fig 16 to18
Fig 16. Avg Daily Energy Outputs for Jan to Dec, 2014
Fig 17. Monthly Energy Outputs for Jan to Dec, 2014
26
27
Sky Radiation Model
Fig 18. Graph Showing Peak Output Power Variations for January to December, 2014
4.2. Research Implications
With clear skies, solar PV plant productivity is showed to
offer satisfactory results. Also, with the monthly peaks
considered, the average peak energy output is calculated and
the possible plant capacity estimate is made. The best output
from a photovoltaic power plant is obtained when the
weather is clear and solar intensity is high. The higher the
intensity of solar radiation, the higher the amount of power
produced. The maximum peak output power was recorded in
the month of March with the value of 212.645Wp/m2 while
the least value was recorded in the month of December with
the value of 187.917 Wp/m2.
Actual measurements of meteorological data, their analysis
and subsequent estimation of solar potential from the analysis
may provide results that will vary with the results provided
here by some percentage. This is due to the fact that actual
measurements record the data under real and actual
conditions as opposed to data used here which considered
only clear-sky conditions. The methodology used in this
analysis has been used by some other authors and engineers
and is satisfactory for estimating the solar photovoltaic
potential and plant capacity for an arbitrarily chosen area.
1300. This work demonstrates that solar photovoltaic plants
can serve as a means of improving power supply and
sustainability within tertiary institutions. With such
estimations, an increase awareness on the potential of
renewable energy sources that abound in Nigeria using
FUTO as a case study will be achieved.
Moreover, in a power challenged environment such as
FUTO, solar power substitute offers a cost effective solution
compared with solar chimney [13] and Space power solar
systems [13]. However, the effects of environmental
constraints such as power output drop caused by fall in
irradiation as well as material reliability could still affect
solar PV optimality. With the right tandem of PV array,
optimal storage network and tightly coupled switching
system, output stability will be achieved for the radiation
datasets. This will form basis for a future work. The focus
will be to develop an efficient Hybrid grid connected PV
system that will significantly facilitate downtime in case of
PV failure.
References
[1]
Chetan Singh Solanki, “Solar Photovoltaic, fundamentals,
technologies and applications, 2nd Edition, PHI learning, New
Delhi, 2014.
[2]
Energy Information Administration,- International Energy
Journal, 2005.
[3]
O. Awogbemi and C.A. Komolafe, “Potential for sustainable
renewable energy development in Nigeria” Pacific Journal of
Science and Technology, vol. 12, no. 1, pp. 161-169, May
2011.
[4]
Liu, B.Y.H. and R.C. Jordan, 1963. The Long-term Spain.
Energy Conversion and Management, Average Performance of
Flat-Plate Solar Energy 46(13): 2075-2092.
[5]
Duffie, J.A. and W.A. Beckman, 2006. Solar Engineering of
Thermal Processes. 3rd ed. John Wiley and Sons, Inc. New
York, USA, pp: 1-928
[6]
Klucher, T.M., 1979. Evaluation of Models to Predict
Insolation on Tilted Surfaces. Solar Energy, Evaluation in the
North Mediterranean Belt Area.23(2): 111-114.
5. Conclusion
This research has presented a computational approach to
solar PV estimation of Federal University of techonology
Owerri. Using FUTO environment as a tropical climate type,
Hottel and Liu-Jordan Clear Sky Radiation Model was
leveraged for the PV estimation. MATLAB M-file was used
to perform the computation. This work used a MATLAB
program to estimate the hourly solar radiation for 8 hours
(0900 – 1700 hours) daily in Federal University of
Technology, Owerri (FUTO) for the year 2014. Daily
Variations of hourly output power for every month were
computed. Also, daily energy outputs and monthly energy
outputs were determined. Based on the analysis of clear sky
solar radiation data and the daily variations, it was observed
that daily peak output occurred between the hours of 1100 –
[7]
Reindl, D.T., W.A. Beckman and J.A. Duffie, 1990. Predicting
Hourly Solar Irradiations on Inclined Diffuse Fraction
Correlations. Solar Energy, 45: 1.
[8]
Reindl, D.T., W.A. Beckman and J.A. Duffie, 1990.
Evaluation of Hourly Tilted Surface Radiation. Solar Energy,
45: 9.
[9]
Okundamiya, M.S. and A. Nzeako, 2011. Empirical Model for
Estimating Global Solar Radiation on Horizontal Surfaces for
Selected Cities in theSix Geopolitical Zones in Nigeria.
Journal of Control Science and Engineering, pp: 1-7.
[10] Jakhrani, A.Q., A.K. Othman, A.R.H. Rigit and S.R. Samo,
2011. Comparison of Solar Photovoltaic Module Temperature
Models. World Applied Sciences Journal (Special Issue of
Food and Environment), 14: 1-8.
28
[11] A. Q. Jakhrani, S. R. Samo, A. R. H. Rigit and S. A. Kamboh,
“Selection of Models for Calculation of Incident Solar
Radiation on Tilted Surfaces”, World Applied Sciences
Journal
22
(9):
Pp.1334-1343,
2013.DOI:
10.5829/idosi.wasj.2013.22.09.316.
[12] M. Maroof Khan* and M. Jamil Ahmad, “Estimation of global
solar radiation using clear sky radiation in Yemen”, Journal of
Engineering Science and Technology Review 5 (2) (2012) 1219.
[13] Okafor, K.C, Onwusuru I.M, I. C. Okoro, “Solar Satellite: A
Green Energy Infrastructure for Power Challenged
Environments, a Case for Solar Cell I-V Behavior African
Journal of Computing & ICT, IEEE, Vol 6. No. 5, December
2013.Pp.69-80

A MATLAB Estimation of FUTO Solar PV Potential Using

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