SINERGI Vol. 22, No.1, February 2018: 1-6
DOAJ:doaj.org/toc/2460-1217
DOI:doi.org/10.22441/sinergi.2018.1.001

PHOTOVOLTAIC MODELING METHODS BASED ON MATLAB
SIMULINK IMPLEMENTATION
Azriyenni Azhari Zakri, Nurhalim, Dohardo P.H. Simanulang, Ihwallibi Tribowo
Study Program of Electrical Engineering, Faculty of Engineering, University of Riau, Pekanbaru
Campus of Bina Widya, Simpang Baru, Tampan, Pekanbaru, Riau 28293 Indonesia
Email: azriyenni@eng.unri.ac.id
Abstract -- This paper presents photovoltaic system as a stand-alone electric power plant in the
renewable energy development. To maximize these stand-alone generators, it is necessary to design
photovoltaic modeling to produce energy and maximum power. The problems that exist in the design
of PV systems are PV configuration, battery size, and the maximum power system. Therefore, this
research will be proposed modeling Matlab/Simulink based PV system. The contribution of this research
can provide various characteristics of the photovoltaic system with a capacity of 100 Wp. This modeling
is designed using Matlab/Simulink software. The data generated from this simulation will provide a good
reference for designing the stand-alone generators in the future.
Keywords: Renewable energy; Photovoltaic; Electric System; Temperature System
INTRODUCTION
Electrical technology for the Renewable
Energy (RE) uses solar energy source as an
alternative source of reliable energy. Some of the
problems arising in the photovoltaic system (PV)
among others: prediction of PV configuration and
battery size, energy consumption prediction,
maximum power system and performance analysis
of PV system. The PV system modeling is an initial
phase to be considered, such as size, identification
and simulation applications. In some literature,
models have been proposed for modeling of
different components on the stand-alone PV. Some
methods are based on software simulation namely
by using the program such as; PSpice, Matlab
Simulink and Labview (Wahid and Hambali, 2015;
Ramenah et al., 2014; Mizuki et al., 2014).
This article will introduce a model of solar
radiation based on weather forecasting system. It
produces short-term photovoltaic output predictions
from the forecasting information from the
meteorological department. Based on the output
results of solar radiation and distribution network,
then an algorithm can be generated. The prediction
model of produced photovoltaic power can predict
the output and algorithm in computational speed
(Bastidas-Rodríguez et al., 2018; Mai et al., 2017;
Lin, 2012).
Meng et al. (2014) have proposed to utilize
solar power plant (photovoltaic) on a micro grid
system with DC voltage source. The battery
charging and discharging system is connected to
the traction system through a DC bus. For tracking
the maximum power point is adopted on the solar
power plant. Consequently, if applied for
locomotives on working condition and regenerative
braking produce a more stable voltage.
Akos Baldauf (2015) has developed an RE

source for housing by using the photovoltaic
system as a smart home design (Adriansyah and
Dani, 2014). For controlling the production of
electrical power to the grid system is using Demand
Side Management (DSM) technique. This
technique is applied for the purpose to reduce
customer costs as well as power losses on the grid.
A scheduling algorithm is applied by using
consumer historical data.
Darbali-Zamora and Ortiz-Rivera (2016)
have also developed a new alternative energy that
is environmentally friendly as a source of solar
panel power plant. This solar panel needs a power
management approach that is able to extract
maximum power. The Maximum Power Point
Tracking (MPPT) is a technique used to gain
maximum power from one or more solar panels.
This technique allows the sun to operate on voltage
and current at the optimum value. MPPT algorithm
is used in combination with DC voltage to DC. This
converter is able to convert DC voltage into different
regulated DC voltage. The optimum ratio of the
converter allows the solar panels to operate
optimally.
The main objective of this research is to
design the modeling and simulation of PV system
as the power supply. This PV system model will be
operated on the condition of solar radiation and PV
temperature variable. This is to analyze the effect
of solar absorption process and charging to the
battery. This modeling and simulation will be
implemented by using Matlab/Simulink.
LITERATURE REVIEW
Solar energy in the form of electromagnetic
radiation that is emitted to the earth in the form of
sunlight consisting of photons or solar energy
particles, which are converted into electrical

A. A. Zakri et al., Photovoltaic Modeling Methods based on MATLAB

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SINERGI Vol. 22, No.1, February 2018: 1-6

energy. The solar energy getting at the surface of
the earth is called a global solar radiation that is
measured by power density at the surface of the
receiving area. The average value of the earth's
atmospheric solar radiation is 1,353 W/m2 stated
as a solar constant.
The solar radiation intensity is affected by the
cycle time of the earth's rotation. Weather condition
comprises cloud quality and quantity, the season
change and latitude position. The solar radiation
intensity in Indonesia runs for 4 - 5 hours per day
(Tsuji and Matsumoto, 2014).
Photovoltaic (FV)
The PV technology is designed to capture
solar energy in order to produce higher PV output
voltage comparing to battery voltage. The PV
output voltage can be adjusted by using a Solar
Charge Controller (SCC) that is functioning as a
voltage regulator to the battery. The initial design
that can be done for the distribution of electrical
energy is to determine the amount of energy
required. In this research will be simulated solar cell
based on the Matlab/Simulink with Solar 100 Wp
(Watt-Peak) capacity. At that capacity will be
observed the influence of solar radiation,
temperature, PV voltage, PV current and battery
charging process. In order to keep the battery
security, then it is necessary to observe the voltage
when charging the battery. SCC will operate on DC
voltage and can stabilize the voltage released from
the PV at the time of charging the battery.
Battery
The battery is storing the appropriate
electrical energy used for PV. If the battery
capacity is 100 Ah with a voltage of 12 Vdc, the
battery efficiency is about 80%. The battery
charging time is for 12 - 16 hours. The energy unit
(Watt-hour) is converted to Ah so that the battery
capacity can be calculated by using Formula (1).
Ah = Wh / Vb

(1)

The unit of the day to store and distribute energy to
the load is determined one day, so the battery only
stores the energy and distributes it on the same
day. The rate of Deep of Discharge (DOD) on the
battery is 80%. The calculation of required battery
capacity is shown in Formula (2).

2

𝐶𝜕 =

𝐴𝐻 𝑥 𝑑
𝐷𝑂𝐷

(2)

Inverter
An Inverter is an electrical device having the
ability to convert DC voltage into AC voltage with
an adjustable frequency value. An ideal inverter is
when the incoming DC voltage is free of ripple and
the voltage coming out of the inverter is a pure
sinusoidal wave (AC).
Most of the inverters used are Pulse Width
Modulation (PWM) Inverters, using the concept of
PWM switching. PWM Inverter is a process of
changing the wave signal with the principle of
setting the size of the wave pulse width. PWM
technique is likened to a manipulation technique in
processing wave signal by using switching
principle. Therefore, the setting of the wave signal
is on and off. One phase PWM inverters can be
realized with bipolar switching and unipolar
switching. Bipolar switching is a switching
condition that experiences a condition of positive
and negative voltage pulses. While the unipolar
switching can be defined as the condition of the
switching, which has a positive, negative, and zero
voltage condition.
METHOD
This research is carried out by designing
photovoltaic modeling as electric power plant by
using Matlab/Simulink. Fig. 1 shows the solar cell
absorption process in the photovoltaic system part
modeling by Matlab/Simulink. A capacity of
simulated PV can be selected (in this model 100
Wp). In Fig. 1, it has also been simulated to show
the value of solar radiation and PV temperature to
see the prediction of PV output voltage and battery
charging process.
Fig. 2 is a battery charging process model
based on Matlab/Simulink. In this Fig. 2 will show
the simulation at the time of solar radiation and PV
temperature. The voltage and current output of the
PV at a capacity of 100 Wp affect the battery
charging process. This modeling is simulated
comprehensively with solar radiation variations of
100 W/m2 up to 1000 W/m2.

A. A. Zakri et al., Photovoltaic Modeling Methods based on MATLAB

ISSN: 1410-2331

Figure 1. Photovoltaic Modeling

Figure 2. Battery charging modeling
This research applies the simulation by using
Simulink methods. Then the simulation is to
generate data that can be a reference to the design
of a stand-alone PV power plant. The following
section will present the results and analysis of this
study.
RESULT AND DISCUSSION
The result of simulation from photovoltaic
system modeling by using Matlab/Simulink
methods is shown in Table 1. The condition of
State of Charge (SOC) at the time of solar
absorption is in the range of 12% up to 84%.
Analysis of the simulation result of this
photovoltaic generator modeling is presented in
Fig. 3 – Fig. 7.

Fig. 3 shows the simulation result on the
absorption of solar radiation (Ir) condition to the PV
panel with a range of solar radiation from 100
W/m2 up to 1000 W/m2. The measured
temperature in the PV panel is obtained from 32°C
up to 52°C. Then, the voltage of PV (VPV) is
obtained at amount 18 VDC up to 21 VDC. It shows
that the voltage produced by the PV is higher than
the battery voltage (12 VDC).
Fig. 4 shows the simulation result of the
photovoltaic current (IPV) condition is at the range
of 0.9 A up to 5.3 A. Such condition is read when
the photovoltaic temperature (TPV) starts from
32°C up to 52°C.

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Table 1. Simulation Result of Photovoltaic System
Ir (W/m2)
500
700
200
300
900
500
1000
100
400
600
800
700
300
100
600
400
600
400
500
700
400

TFV (C)
32
35
36
42
35
45
44
46
48
42
45
47
44
47
47
50
46
47
52
52
49

T (C)
31,4
32,3
32,8
32,9
33,0
33,4
33,9
34,2
35,1
35,2
35,8
36,0
36,2
36,6
37,0
37,2
37,5
37,6
37,7
38,0
38,3

VFV (V)
21,9
21,2
20,0
19,9
21,6
20,2
21,0
18,3
19,7
20,6
20,6
20,3
19,7
18,3
20,2
19,5
20,3
19,8
19,6
19,9
19,6

IFV (A)
0,9
1,5
2,4
2,7
1,9
5,0
4,2
5,3
4,8
4,5
4,2
4,6
4,7
4,9
3,8
5,0
4,9
0,8
4,8
4,6
4,9

Vb (V)
12,41
12,48
12,77
12,64
12,60
13,04
13,08
13,21
12,39
13,31
13,47
13,22
13,42
13,66
13,50
13,81
14,27
12,89
14,00
13,96
13,01

Ib (A)
5,0
5,2
2,2
5,2
5,0
1,0
5,2
0,7
0,8
5,7
5,1
3,6
5,2
0,9
0,5
0,5
3,7
2,2
5,0
2,8
5,0

SOC (%)
16
19
12
28
21
31
38
44
13
53
63
35
55
68
49
72
59
52
77
55
84

Figure 3. Graphic of solar radiation absorption

Figure 4. Graphic of PV Current to Regulator
Fig. 5 shows the simulation result of battery
charging condition with battery voltage (V b) in the
range of 12.4 VDC up to 14 VDC. While the battery
current (Ib) is at the range of 0.5 A up to 5.7 A. The
incoming voltage to the battery has been
stabilized by the regulator, then it is nearly to the
nominal value of the battery (12 VDC).

4

Fig. 6 shows the highest error percentage
analysis of 2.4%. It indicates that the condition at
the time of battery charging, the product is nearly
expected. And then at the time of the battery
charging, the SOC position is in the range of 12%
up to 84%.

A. A. Zakri et al., Photovoltaic Modeling Methods based on MATLAB

ISSN: 1410-2331

Figure 5. Graphic of battery charging

Figure 6. Graphic of Photovoltaic Current Error Percentage to SCC

Figure 7. Graphic of Photovoltaic System Error Percentage to Battery
Fig. 7 shows error percentage analysis for
the photovoltaic process system to reach the
battery. The parameters shown in the graph are
VPV, IPV, SOC, Vb, and Ib. For the voltage (VPV), the
smallest error percentage rate is 0.0001% and the
highest error percentage rate is 2.4%.
This PV modeling has been flexibly
designed and simulated with photovoltaic (Wp)
capacity as required. Therefore, the simulation
result can provide predictions in the design of the
prototype. Simulation and analysis result is
expected to provide characteristics of PV system

with 100 Wp capacity. Some of the characteristics
produced are; characteristics of solar radiation
absorption system, PV characteristics toward the
regulator and characteristics of battery charging.
The photovoltaic system can be a reference for the
design of the prototype with the capacity as we
desire in the future.
CONCLUSION
The result of this modeling design is a
Photovoltaic system with a capacity of 100Wp as
an energy source by using Matlab/Simulink

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methods. The capacity of this stand-alone
generator can be planned in accordance with what
we desire, therefore it makes us easy to determine
the electricity needs of consumers. Simulation is
also carried out to generate prediction data of
stand-alone electric power plant. The simulation
result and the analysis to result have provided
various characteristics of solar radiation
absorption system, PV characteristics toward the
Regulator and characteristics of battery charging.
The photovoltaic system can be a reference for the
design of the prototype with the capacity as we
desire in the future.
Nomenclature
SOC = State of Charge
DOD = Deep of Discharge
SCC
= Solar Charge Controller
VAC
= Voltage Alternating Current
VDC
= Voltage Direct Current
Ah
= Ampere Hour
FV
= Photovoltaic
Ir
= Solar Radiation
Tfv
= Photovoltaic Temperature
Ifv
= Photovoltaic Current
Vfv
= Photovoltaic Voltage
Vb
= Battery Voltage
Ib
= Battery Current
Wh
= Watt per Hour
C
= Battery Capacity required
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A. A. Zakri et al., Photovoltaic Modeling Methods based on MATLAB