Renewable energy sources offer a clean production of

electrical power from sunlight, wind, biomass, tidal waves

etc.

Renewable energy generation has 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 increase 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 [15].

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 non

polluting 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 the last decade [3].

Photovoltaic convert sunlight into electrical energy using

photoelectric effect. Sun's radiation is converted directly

into usable electricity by photovoltaic systems. Photovoltaic

(PV) systems are made of photovoltaic modules which are

semiconductor devices that convert the solar radiation

directly into electrical energy [3]. The power generated

using solar energy is stored in batteries during the sunshine

hours and is consumed during night.

A lot of research has been done to improve the efficiency of

the PV modules. A number of methods to track the

maximum power point of a PV module have been proposed

to overcome the efficiency limitation [10][17]. The use of

the newest power control mechanisms called the Maximum

Power Point Tracking (MPPT) algorithms leads to increase

the efficiency of solar module operation and is effective in

the field of utilization of renewable sources of energy.

MPPT algorithm controls the power converters to

continuously detect the instantaneous maximum power

working point of the PV array [2][4][8].

Perturb and observe (P&O) methods are widely applied as

an MPPT controller due to their simplicity and easy

implementation.

The P&O methods involves a perturbation in the operating

voltage of the PV array, Incremental conductance (IC)

method, which is based on the fact that the slope of the PV

array power versus voltage curve is zero at the MPP has

been proposed to improve the tracking accuracy and

dynamic performance under rapidly varying conditions

[6][7], and Fuzzy logic MPPT method which doesn't need

the knowledge about model of the system, Inputs of the

fuzzy logic controller are the error of the system and the

change of error[5][9][13] .This control is better suited for

non-linear systems. An improved MPPT algorithm for PV

sources was proposed to reduce the involved tracking time

where a dc-dc boost converter was used to track the MPP

and was brought out that tracking performance depends

PV System with Battery Storage Using Bidirectional DC-DC Converter

ACHWAK ALAZRAG, L. SBITA

Process laboratory, Energetics, Environment and electrical systems, LR18ES34, National Engineering School

of Gabes, University of Gabes, TUNISIA

Abstract: — With the increase in demand for generating power using renewable energy sources, energy storage

and interfacing the energy storage device with the load has become a major challenge. Energy storage using

batteries is most suitable for renewable energy sources such as solar, wind etc. A bi-directional DC-DC

converter provides the required bidirectional power flow for battery charging and discharging mode. The duty

cycle of the converter controls charging and discharging based on the state of charge of the battery and direction

of the current. In this paper, a nonisolated bi-directional DC-DC converter is designed and simulated for energy

storage in the battery and interfacing it with the DC grid. The power extracted from the solar panel during the

daytime is used to charge the batteries through the DC-DC converter operating in buck mode and when solar

power is unavailable, the battery discharges to supply power to DC load through the converter operating in

boost mode. Solar arrays connected through a DC bus to a load. Due to the instantaneous changes of solar

irradiance and temperature, maximum power point tracking (MPPT) is integrated in the inverter control. The

technique of maximum power point tracking (MPPT) is used in photovoltaic systems to extract the maximum

power. The most popular MPPT techniques are reviewed and studied, such as: Perturb and Observe, Increment

of Conductance and control based on fuzzy logic (LF). The simulation is done in matlab/simulink and results

are presented.

Keywords: — photovoltaic system, Boost converter, bidirectional DC-DC converters, MPPT.

Received: September 19, 2022. Revised: February 16, 2023. Accepted: March 15, 2023. Published: May 4, 2023.

1. Introduction

International Journal of Electrical Engineering and Computer Science

DOI: 10.37394/232027.2023.5.3

Achwak Alazrag, L. Sbita

E-ISSN: 2769-2507

11

Volume 5, 2023

upon the tracking algorithm. The overall performances of

the PV system depends on the type of the DC-DC converter

used and the algorithm used for tracking the MPPT both of

this parameter plays an important role in increasing the

performance of the PV array [1][11].The main objective of

this work is to model and analyze a photovoltaic system

incorporating battery energy storage systems based on

bidirectional DC-DC converters comparative study between

the three performed algorithms which is incremental

conductance algorithm , perturbs and observe algorithm and

fuzzy logic. The boost converter used to compare in this

study.

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 [3].

The equation relation of the output current and PV module

p ph d sh

I I I I  

(1)

And :

d p s p

V V R I

(2)

Photo-current of the module :

.[exp( ) 1]

p s p p s p

p ph s

T sh

V R I V R I

I I I VR



   

(3)

ph

I

:photo-current,

cc

I

: Short circuit current of the cell under the standard

conditions reference (

ref

E

and

ref

T

),

E

: Sunshine received by the cell (

2

/Wm

),

ref

E

: Reference sunshine,

icc

K

: Current short-circuit-temperature coefficient (A/°C),

..

Bj

T

n K T

Vq



: Thermodynamic potential

s

I

: Inverse current saturation of the diode,

q : Charge of an electronn,

B

K

: Constant of Boltezmann,

j

T

: temperature of the junction(°C),

n: Ideal factor of the solar cell.

pv

I

: Output current of the photovoltaic cell,

pv

V

: Output voltage of the photovoltaic cell.

Incorporation of series resistance and shunt resistances

provide accurate modeling opportunity of the PV cell as

s

R

corresponds to the internal losses due to current flow and

p

R

corresponds to the leakage current to the ground.

Incorporation of series module (cells)

s

n

increases the

output voltage of photovoltaic array and incorporation of the

parallel module

p

n

increases the output current of the

photovoltaic array. Manufacturers of PV modules provide

reference values for specified operating condition such as

STC (Standard Test Conditions) for which the irradiance is

1000

2

/Wm

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 levels

of irradiances and also for different temperature levels.

Irradiation level was varied from 0

2

/Wm

to

1000

2

/Wm

and the resultant P-V and I-V curves can be

seen in Figure 3 and 4 .

Figure 1 : Integration of battery energie storage system

to solar PV panel

Figure 2 : Equivalent model of real cell

Figure 3 : PV generator

2. PV array

International Journal of Electrical Engineering and Computer Science

DOI: 10.37394/232027.2023.5.3

Achwak Alazrag, L. Sbita

E-ISSN: 2769-2507

12

Volume 5, 2023

Ns

-Voltage are added

-Current remains constant

Np

-Current are added

-Voltageremains constant

The PV generator can be characterized by current / voltage

curve, often called "characteristic I = f (V) and P = (V)".

The following figure ensures the extraction of the optimum

power from the photovoltaic generator.

Or the equations relation of the PV generator;

pvg phg dg shg

I I I I  

(7)

.[exp( ) 1]

dg

dg sg

Tg

V

II V



(8)

With the diode generator voltage is :

dg pvg sg pvg

V V R I

(9)

.

Tg s T

V n V

(10)

Or

.

phg p ph

V n V

(11)

The overall current delivered by the PV generator is :

.

.[exp( ) 1] . p s p

d

pvg p ph s

T sh

V R I

V

I n I I VR



   

(12)

It is clear that the relationship between the output current

and the voltage of the generator nonlinear because of the

exponential. the result of simulating the generator current as

a function of its voltage is a single representation where the

output current is a constant during a well defined interval of

the generator until it reaches a point where it begins to

decrease. The figure shows Variation of the current and the

power of the generator according to the voltage under

different irradiations and at temperature equal to 25 ° C.

The Simulation results obtained from the Figures 3 and 4

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 .It can also be seen that each curve 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 at this maximum point to extract

maximum power from the module. It can also be observed

that at different levels of solar irradiation the open circuit

voltages are almost the same and at different levels of

temperatures the short circuit currents are almost the same.

This in turn illustrates that at different levels of solar

irradiation, the voltage at which maximum power point is

located is almost the same. But at different levels 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.

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. This MPP

changes with the change of the irradiation and temperature

[4]. 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 [8].

Figure 4 : Characteristic I = f (V) and P = (V).

Figure 5 : Variation of the current and the power of the

generator according to the voltage under different irradiations

and at temperature equal to 25 ° C

3. Maximum Power Point Tracking

International Journal of Electrical Engineering and Computer Science

DOI: 10.37394/232027.2023.5.3

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E-ISSN: 2769-2507

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Volume 5, 2023

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.

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.There are

several principles of operation of MPPT

algorithms more or less successful based on the properties

of the PV array.

And Table 1 summarizes the main specifications of the

various and famous MPPT algorithms previously presented.

Was evaluated and compared these algorithms in terms of

complexity, precision, speed and technical knowledge of PV

panel settings.

TABLE1 : Technical compare of MPPT

MPPT

P&O

InC

LF

Sensors use

1voltage

1current

1voltage

1current

Identification pv

panel

parameters

Not

necessary

Not

necessary

Yes

necessary

Complexity

low

medium

high

Number of

iterations

45

48

27

Speed of

convergence

medium

very fast

Precision

95%

98%

99%

In this work, we interested t study the three famous

algorithm :

The principle of this control algorithm is to generate

disturbances by reducing or increasing the duty cycle and

observe the effect on the power output of the PV generator

[6][7].

The P&O method operates periodically incrementing

or decrementing the output terminal voltage of the PV

and comparing the power obtained in the current cycle

with the power of the previous cycle. If the voltage varies

and the power increases, the control system changes the

operating point in that direction, otherwise change the

operating point in the opposite direction. Once the

direction for the change of current is known, the current

is varied at a constant rate. This rate is a parameter that

should be adjusted to allow the balance between faster

response with less fluctuation in steady state. The

flowchart of this algorithm is presented in Figure 7.

A modified version is obtained when the steps are changed

according to the distance of the MPP, resulting in higher

efficiency. A frequent trouble in P&O methods is that the

output terminal voltage of the PV is perturbed every

MPPT cycle even when the MPP is reached, resulting in

loss of power.

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 economical for small scale PV plants

[6]. 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 flowchart of the Incremental Conductance

algorithm can be found in Figure 8. At maximum power

point the slope of the P-V curve is equal to zero[7]. The

following equations show these characteristics:

0

dP

dV 

(12)

( . ) 0

dP d V I dI dV dI

V I V

dV dV dV dV dV

    

(13)

Furthermore :

dI I

dV V



(14)

start

Mesure I(k), V(k)

ΔP=I (k)*V(k)

ΔP=P(k)-P(k-1)

ΔP> 0

V(k)-V(k-1)> 0

Increase V

P(k-1)=P(k)

V(k-1)=V(k)

No

Yes

Decrease V

Increase V

NO

Yes

V(k)-V(k-1)> 0

Figure 7 : P&O Algorithm

Figure 6 : MPPT schematic block diagram

3.3HUWXUEDQG2EVHUYH32$OJRULWKP

3.,QFUHPHQWDO&RQGXFWDQFH,&$OJRULWKP

International Journal of Electrical Engineering and Computer Science

DOI: 10.37394/232027.2023.5.3

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E-ISSN: 2769-2507

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Volume 5, 2023

In conclusion for Incremental Conductance algorithm,

, at MPP

, left of MPP

, right of MPP

dI I

dV V

dI I

dV V

dI I

dV V





Conventional methods of tracking the optimal point of

operation have shown their limits to sudden changes of

weather and the load connected to the panel, several

methods have emerged to try to alleviate these shortcomings

and improve the operation of these generators. The approach

of Artificial Intelligence in the case of fuzzy logic is

implemented to improve control performance and the

pursuit of maximum power point by simulation and

modeling of a controller based on fuzzy logic [5][13]. The

advent of microcontrollers has enabled the spread of fuzzy

control in the pursuit of optimal points during the last

decade[9].

The fuzzy controller has the following three blocks:

Fuzzification of input variables by using the trapezoidal and

triangular functions, then these variables fuzzification

inference or are compared with predefined packages to

determine the appropriate response. And finally the

defuzzification to convert the subset fuzzification into

values using the centric defuzzification. The five linguistic

variables used are: NB (Negative Big), NS (Negative

Small), ZE (Zero Approximately), PS (Positive Small), PB

(Positive Big) [14][15]. The two FLC input variables are the

error E and change of error CE at sampled times k defined

by [13]: The operation of this algorithm is done in three

blocks:

fuzzification, inference and defuzzification (figure 9).

Where P(k) is the instantaneous power of the photovoltaic

generator. The input

()k



shows if the load operation point

at the instant k is located on the left or on the right of the

maximum power point on the PV characteristic, while the

input

()k

t





expresses the moving direction of this point.

Table 2 : Fuzzy logic rules

t





PB

PS

ZE

NS

NB

PB

ZE

NB

PS

ZE

NS

ZE

NS

ZE

PS

NS

PS

NS

ZE

NB

PB

PS

PB

ZE

start

Mesure I(k), V(k)

ΔI=I (k)-I(k-1)

ΔV=V(k)-V(k-1)

ΔV=0

 

ΔV>0

Increase V

P(k-1)=P(k)

V(k-1)=V(k)

Yes

No

Yes



 +

 

Decrease V

Increase V

NO

Yes

Figure 8 : Incremental Conductance Algorithm

Figure 9

: Step of Fuzzy logic

3.)X]]\ORJLF

International Journal of Electrical Engineering and Computer Science

DOI: 10.37394/232027.2023.5.3

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E-ISSN: 2769-2507

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Volume 5, 2023

888888888888888888888888888888888888888888888888

88888888888888888888888888////////////////////////////////////////

///////////////////////////////////////////////////////////////////////////////////////

//

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

the 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

[12] while the battery discharges power to the load.

From figure 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 a 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 as a buck

converter for charging the battery through the switching

actions performed by the switch S3. On the other hand its

operation as a boost converter is dictated by the switching

actions of the switch S2 .

For the proper functioning of the hybrid energy system, the

storage system plays a crucial role, it allows for continuity

of service and better quality of energy supplied. We recall

some electrical parameters used to characterize a battery,

these are:

• Nominal capacitor (Qn): This is the maximum number of

ampere-hours (Ah) that can be extracted from the battery,

for given discharge conditions.

• The state of charge “SOC” (State Of Charge): This is the

ratio between the capacity at time q(t) and the nominal

capacity Qn, is :

()

( ) , with (0 1)

qt

SOC t SOC

Qn

  

(15)

If SOC=1 the battery is totally charged and if SOC=0 the

battery is totally discharged.

• The charging cycle (or discharging): This is the parameter

which reﬂects the relationship between the nominal capacity

of a battery and the current at which it is charged (or

discharged). It is expressed in hours.

•Cycle life : This is the number of charge/discharge cycles

that the battery can sustain before losing 20% of its nominal

capacity.

By analyzing the ﬁgure above, we can see the presence of

three speciﬁc points on the characteristic (Q-V): these three

points are: the full load voltage (E0), the voltage

corresponding to the end of the exponential zone ( Eexp)

and the corresponding voltage at the end of the nominal

zone (En).

The charge and discharge equations are given as follows :

*Discharge :

*

B 0 t exp(t)

t

Q

E = E - Ri - K (i +i )+E

Q-i

(15)

*Charge :

*

B 0 t exp(t)

tt

QQ

E = E - Ri - K i -K i +E

Q-i Q-i

(15)

With :

exp(t) exp(t)

E = B i(t) .(-E ( ))Asel t

Figure 12 shows the discharge characteristic of the storage

system used and the evolution of its voltage for different

discharge currents.

Figure 10 : Diagram of Photovoltaic system with Battery

storage using bidirectional DC-DC converter

figure 11 : Battery discharge curve

Figure 12 : Battery voltage characteristic for different

discharge currents.

4. Battery Storage

International Journal of Electrical Engineering and Computer Science

DOI: 10.37394/232027.2023.5.3

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Volume 5, 2023

The bidirectional DC DC converter is a combination of

boost and buck converters. Such a converter is used to

charge and discharge the battery.

The boost mode is applied for the discharging procedure of

the battery storage. Figure shows the circuit of the boost

mode operation of the converter, where the direction of the

inductor current is from the lower voltage side to the

higherK1 voltage side . The averaged large signal inductor

current,

L

i

, and the DC-bus output voltage,

Vdc

, in a

continuous conduction mode (CCM) of operation can be

found using the equations below.

is closed

10

K0

and is open

11

K1

.

B

di

Ldt B dc

VV

(14)

The buck mode is applied for the charging process of the

battery storage. Figure presents the circuit of the buck

mode operation converter. In contrast to the buck mode

operation, the inductor current flows from the

higher voltage side to the lower voltage side . The

averaged large signal inductor current,

L

i

, and the output

battery voltage,

B

V

, are calculated by the equations below,

and describe the buck-mode operation in a CCM of the

converter.

is open

10

K1

and is closed

11

K0

B

di

Ldt B

V

(15)

By analyzing these two configurations, we can conclude that

the relationships between the input quantities

( , )

BB

Vi

and

the output quantities

( , )

dc B m

Vi



of the converter are given by

the system of equations below

11

(1 )

BB dc

B m B

di

L V V u

dt

i I u



  









(16)

Due to the intermittent and fluctuating character of hybrid

systems (pv and wind), the voltage at the DC bus will be

disturbed and fluctuating. This is why the DC bus voltage

must be kept constant at its reference. In this case, the value

of this voltage

dc

V

must be well chosen for proper operation

of the PV system connected to the grid.

The capacitor at the input of the inverter has two essential

tasks:

a) In steady state, it keeps the DC bus voltage constant with

low oscillations.

b) it serves as an energy storage element to compensate for

the difference in actual power between the load and the

source during transient periods.

Figure 13 shows the DC bus voltage regulation loop to

generate the reference power. The DC bus control generates

the fluctuating power in the DC bus capacitor, subtracted

from the power at the output of the inverter, giving us the

reference active power that must be fed into the grid. A

dynamic reference of reactive power, allows us for small

powers to impose a zero reactive power.

The DC power is :

.

dc dc dc

P I V

(17)

Then :

2

1.

2dc

dc dc dV

PC

dt



(17)

dc

C

: the DC bus capacitor.

So :

Figure 13 : Circuit of the DC–DC bidirectional converter

figure 14 : Quadratique control of DC bus

4.1 Boost Mode

4.2 Buck mode operation

4.3 Quadratic DC bus control

International Journal of Electrical Engineering and Computer Science

DOI: 10.37394/232027.2023.5.3

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E-ISSN: 2769-2507

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Volume 5, 2023

22

( ) ( 1) ( ( ) ( )

dc dc pv

dc

Te

V n V n P n P n

C



   





(17)

Te : Sampling period

The control strategy is divided into two blocks the first is for

the calculation and the second is reserved for the control.

All simulations and results, of the previous algorithms

studied, were performed under the same condition to ensure

that the circuit comparison can be determined accurately.

Input, output, voltage, current, and power are the primary

comparison to consider. The complexity and simplicity of

the circuit was determined on the basis of the literature.

Convergence speed, required hardware and efficiency range.

Table 3 takes an illumination of 1000 and a temperature of

25 as the initial value.

Table 3: Electrical characteristic of PV panel

Size

Value

Open circuit voltage Voc (V)

36.3

Voltage at maximum power point Vmp(V)

29

Short-circuit current Isc (A)

7.84

Current at maximum power point Imp (A)

7.35

Diode saturation current Is (A)

2.9259 e-10

Shunt resistance Rp (ohms)

313.3991

Series resistance Rs (ohms)

0.39383

Maximum power point tracking

213.15

Series-connected modules per string Ns

1

Parallel strings Np

5

The irradiation equals

2

1000 /Wm

and the battery current is

around -20 A. The state of charge (SOC) is increasing so it

means the battery is charging and PV side power is 1000W.

The Bus voltage is around 48 V.

When the irradiation is decreased

2

700 /Wm

the battery

current is decreased around -10A. The moment of the

changing irradiation the system is unstable and then a few

seconds later the system returns stable.

Decreasing the irradiation of

2

400 /Wm

the battery current

is zero.

We can say the battery is disable.

I want to decrease irradiation to

2

0/Wm

and the battery

current is around 15 A. The state of charge (SOC) is

decreasing so it means the battery is discharging and PV

side power is

2

0/Wm

. Then the PV power is disabled.

When the irradiation is equal

2

400 /Wm

the SOC is stable

and the current battery is zero. So the battery is disable.

When we increase the irradiation to

2

600 /Wm

the battery is

charging because the SOC is increasing and the battery

current is negative. PV power is increasing.

Then the bus voltage is constant.

Figure 15 : Characteristic of pv with battery based on Perturb & Observe algorithm

5. Simulation and result

International Journal of Electrical Engineering and Computer Science

DOI: 10.37394/232027.2023.5.3

Achwak Alazrag, L. Sbita

E-ISSN: 2769-2507

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Volume 5, 2023

Figure 16: Characteristic of pv with battery based on incremental Conductance algorithm

Figure 15 : Characteristic of pv with battery based on Fuzzy logic algorithm

International Journal of Electrical Engineering and Computer Science

DOI: 10.37394/232027.2023.5.3

Achwak Alazrag, L. Sbita

E-ISSN: 2769-2507

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Volume 5, 2023

Moreover, to evaluate the performance the proposed MPPT,

the PV is exposed to different levels of irradiance that is

changed randomly and rapidly (although normal solar

irradiance does not abruptly, but this would happen in

partially shaded PV systems. According to the obtained

results presented in Figures 15,16 and 17 the MPPT

algorithm tracks the values of maximum power. In each

case, the power extracted from the PV is well controlled.

The results prove that the convergence speed is relatively

high.

To evaluate the performance of the PV with load, a

comparison between the three methods of MPPT with

battery storage. PV maximum

power and that computed from the algorithm is carried out

for different values of solar irradiance and the results are

plotted . Moreover, the corresponding tracking efficiencies

of the proposed MPPT under different irradiance levels are

computed and presented in the figures of the simulation

part..

According to the obtained results, the tracking efficiency is

not less than 95 %.

Therefore, the proposed method guarantees good tracking

efficiency under different operating conditions.

Discussion:

* The P&O algorithm is a classic and simple algorithm. In

general, this algorithm depends strongly on the initial

conditions and it oscillates around the optimal value. The

major drawback of this algorithm is its poor behavior

following a sudden change in illumination (clouds).

* The INC algorithm appears to be an improvement of the

P&O algorithm. Indeed, it behaves better during a rapid

change of meteorological conditions. However, this is a

more complex algorithm than the previous one. Algorithms

based on measuring a fraction of open circuit voltage or a

fraction of short circuit current are very simple and easy to

implement. The major drawback is the loss of energy and

the stopping of power transfer when measuring the

quantities Voc and Isc. To overcome this problem, a pilot

cell of the same type as the panel cells is used. In addition,

determining the optimum value of the parameter k is very

difficult. Therefore, these methods seem just an

approximation and they do not have enough precision and as

a result the system does not always perform at the optimum

point.

* The fuzzy logic algorithm is a robust and efficient

algorithm. Indeed, this algorithm works at the optimal point

without oscillations. In addition, it is characterized by good

behavior in transient state. However, the implementation of

this type of algorithm is more complex than traditional

algorithms. In addition, the efficiency of this algorithm

depends heavily on the inference table. The following table

summarizes the main specifications of the various MPPT

algorithms previously studied. We evaluated and compared

these algorithms in terms of technical knowledge of PV

panel parameters, complexity, speed and precision.

Also the battery characterized is changed with the variation

of irradiation and the change of three MPPT algorithms .

We can conclude that both the boost converter and the

battery are affected by the mppt algorithm.

This work describes the main elements of the PV system.

Then, we recalled the principle of three most popular MPPT

algorithms. Finally, we ended with a simulation of the

different algorithms. The simulation results show that the

INC algorithm performs better than the P&O and the fuzzy

logic based control shows good behavior and better

performance compared to the P&O, INC. These algorithms

improve the dynamics and steady state performance of the

photovoltaic system as well as it improves the efficiency of

the dc-dc converter system.

At the end of this work, several direct perspectives are

announced and the following points are quoted by way of

illustration :

network.

PV system. -tolerant control algorithms.

[1] A.Pradeep Kumar Yadav, S. G. (2012). Comparison of

MPPT Algorithms for DC-DC Converters Based PV

Systems . International Journal of Advanced Research in

Electrical, Electronics and Instrumentation Engineering .

[2] Cylia TIGRINE,Ouerdia Ait Ouali.(2018/2019). Etude et

simulation des techniques MPPT d’un système

photovoltaïque. République Algérienne Démocratique et

Populaire Ministère de l’Enseignement Supérieur et de la

Recherche Scientifique UniversitéA.MIRA-BEJAIA.

[3] Djamila Rekioua, T. R. (2015). Control of a Grid

Connected Photovoltaic System. 4th International

Conference on Renewable Energy Research and

Applications. Palermo, Italy.

[4] Hanen Abbes, H. A. (2013). Etude comparative de cinq

algorithmes de commande MPPT pour un système

photovoltaïque . Conférence Internationale des Energies

Renouvelables (CIER’13) . Sousse Tunisie.

[5] Unal Yilmaza, A. K. (2018). PV system fuzzy logic

MPPT method and PI control as a charge controller.

Elsevier , 994-1001.

[6] William Christopher, D. 1. (2013). Comparative Study

of P\&O and InC MPPT Algorithms . American Journal of

Engineering Research (AJER) , 402-408 .

[7] F. Ansari ,A. K. Jha, Maximum power point tracking

using perturbation and observation as well as incremental

conductance algorithm international journal of research in

engineering \& applied sciences, issn: 2294-3905, PP 19-

30,2011.

[8] B. S, Thansoe, N. A, R. G, K. A.S., and L. C. J., "The

Study and Evaluation of Maximum Power Point Tracking

6. Conclusion

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DOI: 10.37394/232027.2023.5.3

Achwak Alazrag, L. Sbita

E-ISSN: 2769-2507

20

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Contribution of Individual Authors to the

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Policy)

The authors equally contributed in the present

research, at all stages from the formulation of the

problem to the final findings and solution.

Sources of Funding for Research Presented in a

Scientific Article or Scientific Article Itself

No funding was received for conducting this study.

Conflict of Interest

The authors have no conflicts of interest to declare

that are relevant to the content of this article.

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International Journal of Electrical Engineering and Computer Science

DOI: 10.37394/232027.2023.5.3

Achwak Alazrag, L. Sbita

E-ISSN: 2769-2507

21

Volume 5, 2023