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Design And Simulation Of A PV System With Battery Storage Using Bidirectional
DC-DC Converter Using Matlab Simulink
Article in International Journal of Scientific & Technology Research · July 2017
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INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 6, ISSUE 07, JULY 2017
ISSN 2277-8616
Design And Simulation Of A PV System With
Battery Storage Using Bidirectional DC-DC
Converter Using Matlab Simulink
Mirza Mursalin Iqbal, Kafiul Islam
Abstract: PV (Photovoltaic) systems are one of the most renowned renewable, green and clean sources of energy where power is generated from
sunlight converting into electricity by the use of PV solar cells. Unlike fossil fuels, solar energy has great environmental advantages as they have no
harmful emissions during power generation. In this paper, a PV system with battery storage using bidirectional DC-DC converter has been designed and
simulated on MATLAB Simulink. The simulation outcomes verify the PV system‘s performance under standard testing conditions.
Index Terms: Bidirectional converters, Battery, Inverter, Matlab, Photovoltaic, Renewable Energy, Simulink
————————————————————
1. Introduction:
Renewable energy sources offer clean production of
electrical power using sunlight, wind, biomass, tidal waves
etc. Renewable energy generation have grown greatly due
to the concerns of climate change and the increase in oil
prices. The growth in renewable energy has been very
consistent in the last two decades. Not only the increasing
concerns regarding climate change and the soaring of oil
prices but also the great support by renewable energy
legislation and incentives with a close to 150 billion US
Dollars in 2007 have brought this alternative source of
electrical power generation to the big picture[1].
Photovoltaic (PV) systems are one of the most popular
renewable energy sources. It is an interesting energy source
as it is not only renewable but also inexhaustible and nonpolluting unlike the conventional fossil fuels such as coal, oil
and gas. These unique features have made power
generation through Photovoltaic sources one of the most
popular renewable energy sources in last decade [2].
Photovoltaics convert sunlight into electrical energy using
photoelectric effect. Sun's radiation is converted directly into
usable electricity by the photovoltaic systems. Photovoltaic
(PV) systems are made of photovoltaic modules which are
semiconductor devices that convert the incident solar
radiation directly into electrical energy. PV power depends
greatly on solar irradiation and temperature. As a result the
power generated by solar systems are not constant. Apart
from this clean conversion of solar energy into electrical
energy, one thing which is holding back the photovoltaic
systems is their lack of reliability. Depending on different
solar irradiation levels and temperatures, their production
rate varies. Therefore by adding a supplemental source of
power, Solar Power‘s reliability can be greatly increased
where this supplement energy source will work as a backup
energy source. Whenever the load demand will not be fully
met by the primary solar energy source it will be supported
by the backup energy source. And on the other hand when
the demand will be less than the generation, the primary
solar source will energize the backup source. The main
aim of this work is to model and analyze a photovoltaic
system coupled with battery energy storage systems using
bidirectional DC-DC converters.
2. Modelling of PV array
Photovoltaic devices are nonlinear devices. Their
parameters are sunlight and temperature dependent.
Sunlight is converted into electricity by photovoltaic cells.
Photovoltaic arrays consist of parallel and series of PV
modules. In order to form the panels or modules cells are
grouped together. Not only a DC load can be fed by the
voltage and current produced at the terminals of a PV but
they can also be connected to an inverter to produce
alternating current. Photovoltaic cell models have been
used for the description of photovoltaic cell behaviors for
researchers and professionals for a long time. The Single
diode circuit model is among the most common models
which are used to predict energy production in PV cells [35].
Fig 1: Solar Cell Model using single diode along with series
and shunt resistances
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The model of a PV cell can be defined using the following
equations:
IPV=NPISC – NSID [exp{q(VPV+IPVRS)/ NSAkT} - 1] – VPV +
(IPVRS)/RP
where,
k= the Boltzmann constant (1.38 *10-23) JK-1
q= the electronic charge (1.602 * 10-19)
T= the cell temperature (K)
A= the diode ideality factor
RS= the series resistance (Ω)
RP= the shunt resistance (Ω)
NS= the number of cells connected in series
NP= the number of cells connected in parallel
ISC=the photocurrent in (A)
T= Module operating temperature in Kelvin
IPV= the output current of the photovoltaic cell
ID= the diode saturation current
Fig.3: P-V characteristics of a Photovoltaic cell
Incorporation of series resistance and shunt resistances
provide accurate modelling opportunity of the PV cell as Rs
corresponds to the internal losses due to current flow and
Rp corresponds to the leakage current to the ground.
Incorporation of series module (cells) Ns increases the
output voltage of photovoltaic array and incorporation of the
parallel module Np increases the output current of the
photovoltaic array. [6-7] Manufacturers of PV modules
provide reference values for specified operating condition
such as STC (Standard Test Conditions) for which the
irradiance is 1000 Wm-2 and the cell temperature is 25◦C.
Practical operating conditions are mostly different from the
desired standard conditions, mismatch effects can also
affect the real values of these mean parameters. The
Simulink implementation of this photovoltaic model is
shown in figure2. The Simulation was carried out for
different level of irradiances and also for different
temperature levels. Irradiation level was varied from 250
Wm-2 to 1000 Wm-2 and the resultant P-V and I-V curves
can be seen in Fig.3 and Fig.4 respectively.
Fig.4: I-V characteristics of a Photovoltaic cell
Temperature was varied from 25◦C to 75◦C and the resultant
P-V and I-V curves can be seen in Fig.5 and Fig.6
respectively.
Fig.5: P-V characteristics of a Photovoltaic cell
Fig 2: Block diagram of a Photovoltaic Model
Fig.6: I-V characteristics of a Photovoltaic cell
The Simulation results obtained from the Fig.3,4,5,6 exhibit
that the voltage variation with the change of irradiation is
very little whereas with the increase in temperature the
voltage decreases .Typically the voltage will decrease by
2.3mV per ◦C per cell [8].It can also be seen that each curve
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has an operating point for a certain operating voltage at
which the module produces the maximum power. This point
is known as the Maximum Power Point(MPP). The aim is to
operate the photovoltaic system always at this maximum
point to extract maximum power from the module. It can
also be observed that at different level of solar irradiations
the open circuit voltages are almost the same and at
different level of temperatures the short circuit currents are
almost the same. This in turns illustrates that at different
level of solar irradiations, the voltage at which maximum
power point located is almost the same. But at different
level of temperatures, the maximum power point is located
at various operating voltages which are far from each other.
This maximum power point varies at every instance and to
have an efficient system it is necessary to track this
maximum point at every instance of operation
ISSN 2277-8616
The Maximum Power Point Tracker (MPPT) is a control
device which is used to track the constantly changing MPP.
This control device or controller consists of two main parts,
a micro-controller to track the MPP and a converter to
convert the generated voltage to a desired level for the
load. To track the MPP an algorithm is being run on the
micro-controller. Lots of different algorithms are being used
to track the MPP [11], although all the algorithms do not
work properly in quick variations such as fast changing
levels of irradiance or during the partial shading of the solar
panel [12]. But it is very important for the system to have an
algorithm which can provide accurate control signals even
during the fast changing levels of irradiance or the partial
shading of the solar panel. The efficiency of the algorithm is
therefore very important.
3. Maximum Power Point Tracking
The maximum power (MP) is obtained when the solar panel
is being operated at the voltage where the global maximum
of the P-V characteristic lies. It shows that for one specific
operating point, the maximum power output can be
obtained from the solar panel. This point in the P-V
characteristic curve is called the Maximum Power Point
(MPP). This point lies always on the knee of the I-V curve of
the solar panel. In summary it can be concluded that on the
I-V curve of the solar panel there is a point called
MPP(Maximum power point) which always occurs on the
knee of the curve where the generated PV power is
maximized. From Fig.7 and Fig.8 it can be seen that the
maximum power point indicated by a red dot occurs on the
knee of the I-V curve. MPP position is constantly being
changed. This MPP changes with the change of the
irradiation and temperature [9]. The irradiation and
temperature are dynamic in nature, therefore the MPP
tracking algorithm has to be working practically in real time
by updating the duty cycle constantly and thereby keeping
the speed and accuracy of tracking [10].
Fig. 7: I-V characteristics curve
Fig.8: P-V characteristics curve
Fig.9: MPPT schematic block diagram
The algorithm is executed by the MPPT controller to find
the MPP. The measured output voltage and current of the
solar panel are inputs of the controller. The algorithm
performs its calculations depending on these inputs. The
controller produces an output which is the adjusted duty
cycle of the PWM. It drives the DC-DC converter‘s
switching device. For every different operating point the
controller produces a different duty cycle.
3.1 MPPT Algorithms
To obtain the maximum power from the solar panels, an
efficient tracker algorithm is required for the MPPT. The
tracker algorithm‘s task is to track the maximum power
point of the solar panel as accurately as possible. The
algorithm also has to be fast and reliable as well. Hill
Climbing Algorithms, Fuzzy Logic Control, the Current
Sweep method, Perturb and Observe, Incremental
Conductance algorithm [12] etc. are a few state of the art
algorithms. Incremental Conductance (InC) algorithm has
been used to track the maximum power point of the solar
panel. InC provides the optimal duty cycle to generate the
PWM signals for the switching devices of the DC-DC
converter stage.
3.1.1 Incremental Conductance
The Incremental Conductance algorithm is an improvement
to the Perturb & Observe Algorithm. This algorithm ensures
higher accuracy and efficiency specially under varying
atmospheric conditions. In spite of these advantages there
are few drawbacks of this algorithm such as higher
response time and it is also not economic for small scale
PV plants [13]. The maximum power point is being tracked
by the Incremental Conductance algorithm by means of
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comparing the module‘s instantaneous I-V characteristics
and it‘s incremental conductances (dI/dV). This algorithm
can determine the distance to the MPP and thereby stop
the perturbation and tracking procedure after it has reached
the MPP [14]. The flowchart of the Incremental
Conductance algorithm can be found in Fig.10. At
maximum power point the slope of P-V curve is equal to
zero[15].The
following
equations
show
these
characteristics:
𝑑𝑃
=0
𝑑𝑉
ISSN 2277-8616
3.2 Simulation using Incremental Conductance
algorithm
The maximum power point is being tracked by the
Incremental Conductance algorithm by means of comparing
the module‘s instantaneous I-V characteristics and its
incremental conductances (dI/dV). This algorithm can
determine the distance to the MPP and thereby stop the
perturbation and tracking procedure after it has reached the
MPP [14].The Simulink model of the Photovoltaic system
with MPPT control can be seen in fig.11
, it can be further rewritten as:
𝑑𝑃 𝑑(𝑉. 𝐼)
𝑑𝐼
𝑑𝑉
𝑑𝐼
=
=𝑉
+𝐼
=𝑉
+𝐼 =0
𝑑𝑉
𝑑𝑉
𝑑𝑉
𝑑𝑉
𝑑𝑉
Furthermore,
𝑑𝐼
𝐼
=−
𝑑𝑉
𝑉
In conclusion for Incremental Conductance algorithm,
= − , at MPP
>−
, left of MPP
<−
, right of MPP
Fig.11 Simulink Model of PV with MPPT controller based on
Incremental Conductance Algorithm The Simulation results
can be seen in fig.12
Using above three equations the next operating point is
chosen.
Fig.12: PV output power with MPPT controller based on
Incremental Conductance Algorithm
Fig.13: PV output voltage and boosted voltage
It can be seen from fig.12 that the maximum power point is
being tracked by the MPPT controller based on Incremental
Conductance algorithm with a high speed. At t = 2.2s the
level of irradiation was increased from 750 to 850 Wm−2
and the algorithm also responded quickly and reached
maximum power. The steady state ripples are also very little
but there is still room for improvement to reduce the amount
of the steady state ripples.
Fig.10: Flowchart of the Incremental Conductance
algorithm
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4. PV System with Battery Storage using
Bidirectional DC-DC Converter
Bidirectional DC–DC converters are used to perform the
process of power transfer between two dc sources in either
direction. They are widely used in various applications. A
bidirectional DC-DC converter is an important part of
standalone solar Photovoltaic systems for interfacing the
battery storage system. The circuit is operated in such a
way that one switch, one coupled inductor and three diodes
are used for step-up operation to boost the voltage of the
battery to match the high voltage dc bus. The other switch,
remaining diode and simple inductor are used for step down
operation to charge the battery from the surplus PV energy.
The high efficiency of the converter is achieved by
optimizing components used for each step. The
bidirectional DC-DC converter with high power rate plays a
key role in power storage system, while it converts DC
voltage or DC current for the power storage battery. The
Bidirectional DC-DC converter operates either as a buck or
as boost converter at any instance. It works as a buck
converter for charging the battery whereas it operates as a
boost converter [18-20] while the battery discharges power
to the load.
ISSN 2277-8616
as a buck converter for charging the battery through the
switching actions performed by the switch S3. On the other
hand it‘s operation as a boost converter is dictated by the
switching actions of the switch S2 [15], [16].
4.1.1 Operating Modes
The Photovoltaic system with Battery storage shown in
Fig.5.1 has four different operating modes based the
amount of power supplied by the PV panels which depends
on the irradiance and temperature [17].
Mode 1 : The first operating mode is triggered when the
power generated by the PV system is less than the power
demanded by the load which can be a simple resistive load
or three phase load or grid (i.e. PPV < Pload) and the battery
system is also deeply discharged then the whole system is
shut down.
Mode 2: The second operating mode is activated when
PPV < Pload but the battery is charged and is also able to
provide power. At this point of operation the battery
provides backup power along with the PV power as long as
the battery is not fully discharged. The PV panels power the
load as much as possible with the MPPT algorithm enabled.
Whereas the battery provides complementary power
through operating in boost mode of operation of the
bidirectional buck/boost converter.
Mode 3: When PPV > Pload and the battery is not in a fully
charged state then this mode is activated. During this mode
of operation the PV panels not only supply power to the
load under maximum power point enabled control but also
the excess power produced by the PV panels are used to
charge the battery. During this mode the battery is charged
through the buck mode of operation of the bidirectional
buck/boost converter.
Mode 4: In this mode of operation PPV > Pload and the
battery is also fully charged. During this mode the PV
panels supply power to the load under maximum power
point enabled control and it is also ensured that the
batteries remain in fully charged state through constant
voltage charging so that the battery does not have any kind
of self-discharge. These operating modes have been
illustrated using the flowchart in fig.15
4.1.2 Simulation and Results
The PV System with Battery Storage using Bidirectional
DC-DC converter was simulated using Matlab Simulink to
observe the output under various modes of operations. The
simulation model can be seen in fig.16.
Fig.14: Circuit diagram of Photovoltaic system with Battery
storage using bidirectional DC-DC converter.
From fig.14 it can be seen that the PV voltage source has
immediately next to it a boost converter stage powered by
MPPT controller which will step up the PV voltage to the
desired DC bus voltage extracting maximum power from
the PV system at every instance of operation. It is then
followed by couple of IGBTs and a battery acting as a
secondary source. The Bidirectional DC-DC converter
operation is carried out through these two IGBTs which are
controlled by two different controllers. One controller
provides the control signal for Boost operation and the other
provides the control signal for Buck operation. It operates
Fig.16: Simulink model of Photovoltaic system with Battery
storage using Bidirectional DC-Dc converter
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Simulation results shown in figures 17, 18, 19, 20, 21, 22
illustrate the charging and discharging operation modes of
the battery power PV system.
ISSN 2277-8616
The simulation results in fig.17, 18 and 19 show the
discharging characteristics of the battery storage system.
The Bidirectional controller operates as a boost converter.
During this mode of operation the battery current is high.
From fig.5.4 it can be seen that at t=1.5s the battery starts
to discharge so at this point of operation there appears a
high transient current which stabilizes shortly after the
transient period and discharging continues. Also at the start
of discharging the battery voltage drops significantly but
again reaches stability within a very short period of time.
Fig.19: PV output power and DC Bus power during
Discharging
On the other hand during the charging mode the
bidirectional converter operates as a buck converter and
the charging characteristics of the battery storage system
can be seen in fig.20,21and 22. From fig.20, it can be seen
that at t=1.5s the battery enters into charging mode.
Immediately after the transient period the battery current is
stabilized.
Fig.15: Flowchart of different operating modes of
Photovoltaic system with Battery storage.
Fig. 20: Battery voltage and current characteristic during
charging.
Fig.17: Battery voltage and current characteristic during
Discharging
Here the battery current is negative since the high voltage
source (i.e. DC Bus) is feeding current into the battery.
During charging, VPV and VDCbus show stable characteristics
but the DC bus voltage takes some extra time to be
restored to its nominal value which can be seen in fig.22.
PV output power can be seen in fig.21. Table 1 shows the
list of important parameters used during the simulation.
Fig.18: PV output voltage and DC Bus voltage during
Discharging
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220 V
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5. Conclusion
An MPPT controlled PV system with battery energy storage
system using bidirectional DC-DC converter has been
successfully modelled and simulated in this research work.
A boost converter stage that steps up the PV output voltage
was also successfully modelled and simulated. Closed loop
control was achieved using PI controller. Incremental
Conductance algorithm has been applied to the MPPT
controller. PI control algorithm for the Bidirectional converter
has also been modelled and simulated. Finally the overall
model has been successfully simulated in Matlab Simulink
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