Design methodology of smart photovoltaic plant

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 11, No. 6, December 2021, pp. 4718~4730
ISSN: 2088-8708, DOI: 10.11591/ijece.v11i6.pp4718-4730  4718
Journal homepage: http://ijece.iaescore.com
Design methodology of smart photovoltaic plant
Boutlilis Fatima1
, Chouitek Mama2
, Bekkouche Benaissa3
1,3
Department of Electrical Engineering, Signals and Systems Laboratory Research, Faculty of Science and Technology,
Abdelhamid Ibn Badis University, Mostaganem, Algeria
2
Institute of Maintenance and Industrial Security of University A.B of Oran, Algeria
Article Info ABSTRACT
Article history:
Received May 17, 2020
Revised May 21, 2021
Accepted Jun 14, 2021
In this article, we present a new methodology to design an intelligent
photovoltaic power plant connected to an electrical grid with storage to
supply the laying hen rearing centers. This study requires a very competent
design methodology in order to optimize the production and consumption of
electrical energy. Our contribution consists in proposing a robust
dimensioning synthesis elaborated according to a data flow chart. To achieve
this objective, the photovoltaic system was first designed using a
deterministic method, then the software "Homer" was used to check the
feasibility of the design. Then, controllers (fuzzy logic) were used to
optimize the energy produced and consumed. The power produced by the
photovoltaic generator (GPV) is optimized by two fuzzy controllers: one to
extract the maximum energy and another to control the batteries. The energy
consumed by the load is optimized by a fuzzy controller that regulates the
internal climate of the livestock buildings. The proposed control strategies
are developed and implemented using MATLAB/Simulink.
Keywords:
Design
Fuzzy logic
Internal climate livestock
buildings
Photovoltaic
Storage
This is an open access article under the CC BY-SA license.
Corresponding Author:
Boutlilis Fatima
Department of Electrical Engineering
Faculty of Science and Technology
Abdelhamid Ibn Badis University
Mostaganem, Algeria
Email: boutlifatima@gmail.com
1. INTRODUCTION
To solve the global warming potential and depletion of fossil fuels, the Paris agreement was adopted
through the 21st
conference held in Paris, France in December 2015. In response to this, the Algerian
government plans to launch several projects by 2020, with a total capacity of 800 MW. During the period
2021-2030, PV plants of 200 MW per year are expected to be built [1].
The production of electricity with a photovoltaic system connected to the grid is of great interest to
countries with high solar potential. However, the design of such a system must be taken correctly in order to
optimize the collection of radiation by the photovoltaic cells and increase the efficiency of the system. The
use of smart technology can improve the operation of these plants. Therefore, it helps to improve the quality
of service of the electricity network, which has been insufficient in recent years, due to the great demand on
electricity. In summary, this work concerns the design and optimization of the operation of a smart
photovoltaic generator used to supply an industrial campus for laying hens, located in western Algeria. These
sites were created in the 1990s by the Algerian government to improve poultry production [2]. It contains
four centers, two for the preparation (breeding) of the laying hen and two laying centers. Each center is made
up of 10 livestock buildings with an area of 80x12 m2
, i.e. 40 buildings for the entire site. Each building is
equipped with an electrical control cabinet. To better undertake this study, a survey was launched on the

Int J Elec & Comp Eng ISSN: 2088-8708 
Design methodology of smart photovoltaic plant (Boutlilis Fatima)
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requirements of the company, the conditions of the site to design the photovoltaic system and to understand
the techniques of raising laying hens. However, we concluded that such a project requires a very competent
design methodology in order to optimize the production and consumption of electrical energy and thus
reduce investment costs. Therefore, we have proposed a robust dimensioning of the photovoltaic system as
shown in the flowchart in Figure 1. It contains three steps. First for the dimensioning we used a deterministic
approach based on the worst month of the year [3]. To validate the design results and determine the optimal
configuration, the Homer design software [4] was integrated in the second phase of this work. Indeed, the
interpretation of the HOMER results allowed us to validate the sizing steps carried out previously and to have
an optimal configuration of the system in order to ensure the best compromise between technical feasibility
and economic profitability. The third design phase was devoted to the optimization of the energy produced
and consumed.
However, to optimize the energy produced, we have proposed the use of a fuzzy intelligent regulator
to extract the maximum MPPT power [5] and, another fuzzy regulator to charge and discharge the batteries
because this device is very sensitive in these installations. In addition, the building must create an
environment favorable to the breeding of poultry, that is to say meeting their physiological needs. These
requirements are determined by temperature, air speed and humidity [6], [7]. It therefore influences the
profile of electrical energy consumption and the equipment operating service. To optimize the energy
consumed, we used intelligent temperature and humidity controllers. These controllers are very effective and
robust in maintaining the internal climate according to the standards required for raising chickens. The
MATLAB/Simulink software was used for the simulation of all the intelligent control procedures [8].
2. RESEARCH METHOD
2.1. Design methodology
In general, the design methodology of a photovoltaic system is based on standard approaches
[9]-[11]. However, each designer has to design his own algorithm that depends on the nature of the system to
be sized, the functional relationships between the system components, the external influences and the
technology used. Our design methodology is summarized in the flowchart shown in Figure 1.
Figure 1. The flowchart of our methodology

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Int J Elec & Comp Eng, Vol. 11, No. 6, December 2021 : 4718 - 4730
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This structure includes the following main phases:
First phase: classic dimensioning;
Second phase: verification of the feasibility of the design with the "Homer" design software;
Third phase:
a. Optimization of the production of the photovoltaic system;
b. Optimization of electrical energy consumption by intelligent control of the interior climate of the
livestock building
2.2. Details of the various phases
2.2.1. First phase: classic sizing
a. Presentation of the site and evaluation of the solar resource
Geographical coordinates of the breeding site Latitude: 35 degrees 12 minutes north, Longitude: 1
degree 15 minutes East, Time: GMT +1: 00. The ideal orientation of a photovoltaic panel obeys the rule of
orienting it towards the equator. Since latitude of our installation is (35°), the inclination will be
α=35+10=45°.
b. Meteorological data
For this study, technical data is collected from the NASA global satellite [11]. It should be noted
that the average monthly solar radiation in the region exceeded 7.14 kWh/m2
/day in June and decreased to
2.25 kWh/m²/day in December with an average of 4.72 kWh/m2
/day. Therefore, the most unfavourable
month was taken into account in the design procedure [12], [13].
c. The determination of the user's electrical energy needs
However, the electrical energy E consumed by the breeding center is calculated by (1).
Et = ∑ Piti
n
i=1 (1)
With: Pi= electrical power of a device expressed in Watt (W),
ti= duration of use of this device in hours per day (h/d)
The peak power of the panels of two buildings is obtained by:
Ezone = Ne ∗ Pc ∗ CP (2)
Ezone (kWh/d)= the energy consumed
Ne=2.25 h: the period of solar irradiation of the worst month (December)
Cp=0.6: the equivalent coefficient of losses inherent in the whole process of energy conversion.
d. Dimensioning of electrical energy storage
The objective of the sizing of the Battery Park is to have a good yield, a long life and a low
investment cost [14]. In general, the main characteristics of an accumulator are:
− The voltage at the terminals of an accumulator element;
− The capacity provided by the manufacturers for a given discharge regime in hours (C20 or C100);
− The discharge depth Pd which represents the maximum discharge threshold.
The calculation of the usable and nominal capacity:
CU: Useful capacity of the battery in Wh,
Nja: Number of days of autonomy without solar input (02 Days)
Ezone: Daily consumption of the zone in Wh/day
Rt: Temperature coefficient
The calculation of nominal capacity Cnom is given by
Cnom =
Nja∗Ezone
Pd x Rt
(3)
e. Define the type of system to install
The chosen configuration includes a photovoltaic generator with MPPT controller, a storage system
with battery regulators, DC/DC converters, DC/AC converters, load and power grid as shown in Figure 2,
[15], [16].

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Figure 2. Scheme of the proposed photovoltaic system
2.2.2. Second phase: verification of design feasibility with "homer" design software
The designed system is introduced into the software by specifying the options of technology
availability, component cost and resource availability. HOMER is a simulation and technical and economic
optimization software for hybrid renewable energy systems. The method used by Homer gives us the
reliability of the system by loss of power supply probability (LPSP) which is calculated for each
configuration and the optimal configuration chosen is based on the minimum life cycle cost (LCC). The
architecture of the photovoltaic power plant using the Homer interface is illustrated in Figure 3. Therefore,
our main objective is to evaluate the feasibility of the studied PV system [17], [18].
Figure 3. Architecture of the PV plant
a. How to use [18]
Place the site on the geographic map of the software to identify the coordinates. Assemble the
components and choose their technical characteristics. Introduce or import the average consumption of the
load per hour for each month of the year.
2.2.3. Third phase: Optimization of the energy produced and consumed by intelligent controllers
In short, fuzzy theory allows "the rigorous modeling and processing of imprecise, uncertain and
subjective information". It allows the approximation of non-linear functions. It is therefore a theory that is
perfectly adapted to the optimization problem we are dealing with in this work [19], [20].
a. Optimizing the GPV production by smart regulation
In this phase, we propose two fuzzy regulators. An intelligent MPPT controller will be used to
extract the maximum power produced by the photovoltaic generator. Another fuzzy regulator will be used to
control the charge and discharge of the battery. At the end, a check of the GPV generator operation will be
performed to confirm the optimization phase of the energy production. The simulation is performed with
MATLAB/Simulink [21], [22]. Figure 4 shows the proposed global system model.

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Figure 4. The proposed overall system model
1) Fuzzy MPPT controller
To determine the efficiency and transient response times of the fuzzy MPPT controller. The latter
has been compared to a widely used conventional P&O controller [23], [24]. To implement the fuzzy
controller, we need two inputs {(
𝑑𝑃
𝑑𝑉
) and (
𝑑2𝑃
𝑑𝑉2)}. The output is the change in the duty cycle ∆d of the DC/DC
converter. This value is used to determine the VMPPT value at any time. Figure 5 shows the configuration of
the selected fuzzy controller with these three phases.
Figure 5. The proposed MPPT control using fuzzy logic
After obtaining the value of the output ∆d at time k we calculate the new duty cycle and apply it to
the DC/DC converter:
𝑑 (𝑘) = 𝑑 (𝑘 − 1) + ∆𝑑 (𝑘)
Each fuzzy inputs and output variables are divided into five fuzzy sets, viz. negative big (NB),
negative small (NS), zero (Z), positive small (PS), and positive big (PB). Figures 6 and 7 show its
membership functions of fuzzy input and output variables, respectively. The control rules for this FLC
controller is shown in Table 1.
(a) (b)
Figure 6. Membership functions of fuzzy input: (a)
𝑑𝑃
𝑑𝑉
, (b)
𝑑2𝑃
𝑑𝑉2

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Figure 7. Membership functions of fuzzy output
Table 1. Rule base for the fuzzy ouput variable
∆d
𝑑2
𝑃
𝑑𝑉2
NB NS ZE PS PB
dP/dV
NB ZE ZE PB PB PB
NS ZE ZE PS PS PS
ZE PS ZE ZE ZE NS
PS NS NS NS ZE ZE
PB NB NB NB ZE ZE
2) Fuzzy charger battery
For the control of the battery charge and discharge regulator, a fuzzy model of Takagi-Sugeno [25],
[26] has been chosen, which presents an efficient technique. The input is the battery voltage (Vb) and the
outputs are the signals K1 and K2. The signal (K1) is the command of switch 1 between the GPV and the
battery [0,1] and the signal (K2) is the command of switch 2 between the battery and the load [0,1] as shown
in Figure 8(a) and 8(b) [13].
(a) (b)
Figure 8. Fuzzy charger battery; (a) schema of charging and discharging the battery, (b) the fuzzy battery
regulator model
b. Verification of the operation of the GPV generator
After confirming the robustness of the two previous controllers, we tested the operation of the GPV.
1) Optimization of the consumption of electrical energy
When the internal climate of the building is unstable, this has a great influence on the life of the hens
with consequences on the consumption of electrical energy. However, to stabilize the internal climate and
reduce the operating time of the equipment, thus saving energy [27], [28]. We equipped the building with two
fuzzy logic controllers, temperature and humidity. The temperature controller model has two inputs, a
temperature error (Tref-Ti) and external temperature T0. Thus, it contains two outputs, to control heating and
ventilation as shown in Figure 9(a). The humidity controller model has two inputs, humidity error (Href-Hi)
and outdoor humidity H0, as well as an output for controlling the humidifier as shown in Figure 9(b).

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(a) (b)
Figure 9. Fuzzy logic controllers (a) The temperature controller, (b) The humidity controller
3. RESULTS
3.1. The results of the first phase
The results of the calculations of the first phase of the photovoltaic installation are summarized in
Table 2. The results obtained show that the generator works correctly to meet the load demanded by the
campus, because we based our calculations on the sunshine of the most devourable month.
Table 2. The dimensions of the photovoltaic power plant designed
Parameters Dimensions
Ezone (containing two buildings) 105.15 kWh
The peak power of the panels of zone 77.88 kW
Installed power (kWp) 79.3 kW
Panel power in STD (Wc) 305 W
Total number of panels 260
Number of panels per branch/string 5/52
Parallel branches (string) 26x2
Panel efficiency under STD (%) 18.1
The DC/DC converters 100 kW,300 V, 500 V
Energie storage Cnom 323538.46 Wh
Total number of batteries (1 KWh, 12 V) 336 unités
DC bus voltage 500 V,
Total number of series/parallel batteries 42/8
The DC rated inverter 100 kW,800 V, 210 A
The AC rated inverter 100 kW,400 V, 146 A, 50 Hz
Rendement max. 96,40%
3.2. Résults of Second phase: verification of the feasibility of the design with the "Homer" design
software
3.2.1. Global solar energy
Figure 10 obtained by the Homer software shows the significant influence of the inclination of the
panels (40% additional energy for the unfavorable months). Thus, in spite of the distinctions between seasons,
the annual energy produced by the inclined panels remains uniformly distributed. On the contrary, without
tilting the overall solar energy is very important, but with an uneven annual distribution.
Figure 10. Annual distribution of global solar energy and energy produced by the inclined plane

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3.2.2. The power flow during the summer and winter
The comparison of the production of the designed photovoltaic field with the actual consumption of
the buildings during the summer solstice (June 21) and the winter solstice (December 21) obtained by Homer
in Figure 11 shows that the energy demand is entirely provided by the photovoltaic generators and storage
system. It is clear that in the absence of lighting, the load is met by the storage. But from sunrise onwards, the
energy flow changes direction. Therefore, during the day, the increasing photovoltaic energy production can
only satisfy the demand of the building and the charge of the batteries. Therefore, the company cannot sell
excess energy to the power grid until the batteries are fully charged. Figures 11(a) and 11(b) show the
electrical power produced by the GPV, the amount of excess electrical power sold to the grid, the charge
consumption in KW and the charge/discharge of the batteries in KW.
(a) (b)
Figure 11. Electrical power produced by the GPV; (a) The power flow on a winter,
(b) The power flow on a summer
3.2.3. Energy storage
The Figures 12(a) and 12(b) show that the availability of energy stored by the battery fully covers
the company's consumption during the night and during climatic disturbances. Indeed, this result gives us a
good indicator of storage sizing, and it confirms the calculation of the capacity realized in the first phase.
(a) (b)
Figure 12. The charge and discharge of the batteries; (a) The state of charge and discharge of the batteries for
one year, (b) The charge and discharge cycle of batteries for one day
3.2.4. Evaluation
According to the histogram presented in Figure 11, the breeding center will make an additional
profit each month through the sale of excess electricity to the network. We see that the energy injected into
the electrical network varies according to the season and weather conditions. Furthermore, it should be noted
that the total annual sales made by the company is 6,969 kWh, approximately 24.93% for an annual
production of 27,959 kWh. The company consumes a total of 20,990 kWh which represents 75.07%. The
load profile illustrated in Figures 13(a) and 13(b) correctly reflects the energy consumption of the farm. It is
very important during temperate times, due to the air-conditioning of buildings, and it is less important

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during moderate seasons. Indeed, the results of the HOMER software allowed us to validate the sizing steps
previously carried out and to have an optimal configuration of the system to ensure the best compromise of
technical feasibility and economic profitability. In general, this study highlights the existence of a strong
coupling between the components of the photovoltaic system: solar potential, structure, storage, converters,
energy management and the load. Therefore, this also justifies a systematic approach to design in which
energy optimization of all subsystems is necessary.
(a) (b)
Figure 13. Evaluation; (a) the energy benefit, (b) annual load
3.3. Results of the third phase
3.3.1. Optimization of the production of GPV by intelligent regulation
a. MPPT Controller tests
The graphs in Figure 14 illustrate the input and output signals of the fuzzy controller compared to
the conventional P&O controller (disturbance and observation). These graphs show additional gains, power
(GPV) and voltage when using the fuzzy controller, especially for transient operation, in constant weather
conditions. The results show the effectiveness of the fuzzy controller. It indicates a good indication on the
functioning of the designed GPV. Transient response times are very acceptable because the maximum power
is reached in 0.1 s. Once again, to validate our method, we tested our controller for different irradiation and
temperature values Figure 14(c).
The simulation results obtained in Figure 14(c) show again the efficiency and robustness of the
fuzzy controller to extract the maximum power from the photovoltaic generator, whatever the irradiation
variations (79.04 kW for 1000 W/m² and 60 kW for 800 W/m²). These results are acceptable in terms of
system stability. By comparing these results, we can see that the performances of the fuzzy regulation are
better than those of the "P&O" regulation. Therefore, it is proven that the fuzzy controller has better
performance, fast response time, very low steady state error and robustness to different variations in
atmospheric conditions.
b. Results of Fuzzy charger battery test
When the irradiation changes, the voltage supplied to the load with the fuzzy controller varies in a
range between 503.8 V and 504.6 V and reaches a value of 589.5 when the battery operates without
regulation as shown in Figure 15. It can be concluded from this result that the influence of the voltage
regulator load is maximal because it maintains the voltage in the range that protects the battery.
c. The GPV operating
On the basis of the results of the two regulators, we performed an analysis of the operation by
simulating the voltage and power delivered to the load under constant irradiation and temperature conditions
(1000 W/m2
, 25°) by the optimized GPV. The purpose of this simulation is to test the robustness of the fuzzy
regulators and to ensure the production of the optimal system.
The results shown in Figures 16(a) and 16(b) are very acceptable. In stable mode, the frequency of
50 HZ is respected; the effective amplitude is 380 V between phases and an effective current of 120 A. The
waveforms are perfectly sinusoidal after adjusting the connection filter. The transient active power reaches
the value of 170 kW, which is the sum of two powers (PV generator+battery) before the intervention of the
battery controller. The same behavior is observed with the effective current value of 260 A. This confirms the
efficiency and superiority of the selected fuzzy controller, as well as optimal operation of the GPV.

4727
(a) (b)
irradiation GPV power under variable
irradiation
GPV power under variable
temperature
(c)
Figure 14. MPPT controller tests; (a) GPV voltage fuzzy MPPT and P&O, (b) the power of GPV Fuzzy
MPPT and P&O, (c) simulation results of (boost) with fuzzy MPPT for different irradiation and temperature
values
(a) (b) (c)
Figure 15. Simulation results (fuzzy battery charger); (a) irradiation, (b) battery voltage with fuzzy
control battery, (c) voltage regulation without control
(a) (b)
Figure 16. Simulation results; (a) current at load output, (b) power at load output

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3.3.2. Results of optimization of electrical energy consumption by intelligent control of the interior
climate of the livestock building
a. External disturbances in temperature and humidity
The Figure 17 shows the curves of two external disturbances in temperature and humidity
respectively. These disturbances can be explained by the error between the reference and the controlled
temperature value (Tref-Ti) as well as by the humidity curve (Href-Hi). We chose a step of 500 s
(≈9 minutes), because the temperature and humidity variations Ti and Hi are actually quite slow. Thus at
1000 s for an outdoor temperature and humidity of 19.2 °C and 77%, the errors with reference to 25 °C and
60% are 2.4 °C for temperature and 5% for humidity. So, these results show that the internal climate is very
well stabilized with the two fuzzy controllers.
b. The temperature and humidity inside the barn
The Figure 18 shows the temperature and humidity results inside the barn. We find that, without a
regulator, the response would not only be slow, but also too high, which would have a harmful effect on the
hens (health and nutrition), consequently on the weight of the eggs and on the yield of the laying in general.
Regarding the two parameters, the effect of our regulator is fairly rapid and responds correctly to the set
point. Finally, we deduced by these two tests shown in Figure 17 and Figure 18 that these regulators
significantly improve the system by reducing the equipment operating time. Consequently, we have noticed a
remarkable optimization of the energy consumed by the building.
External humidity External temperature
Figure 17. Inputs of the fuzzy controllers
Figure 18. Internal temperature and humidity (controlled)

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4. CONCLUSION
In this work, we presented a methodology for the design of an intelligent photovoltaic power plant
connected to the grid with storage to feed a chicken breeding company. This approach facilitates the
integration of renewable energies in agriculture and reduces the consumption of fossil fuels. These
photovoltaic systems make a significant contribution to relieving the burden on the national electricity grid
and provide a better service to farmers. In addition to helping to reduce the effects of carbon dioxide, the
intelligent power plant also improves the quality of service (no disruption to the power grid). It will benefit
from 24.93% of the sale of surplus energy to the national grid. This is mainly due to the optimization of
energy production and consumption by fuzzy controllers.
REFERENCES
[1] Z. Arthouros, “Report on the Global Status of Renewable Energy 2016,” (in French). [Online]. Available:
https://www.ren21.net/wp-content/uploads/2019/05/REN21_GSR2016_KeyFindings_fr_09.pdf
[2] C. Chaulet, “A strategic choice, the choice of agronomic technologies: Example of animal production,” (in French),
The notebooks of the CREAD, vol. 1, no. 7, pp. 115-172, 1986. [Online]. Available:
https://www.asjp.cerist.dz/en/article/22270
[3] A. Maheri, “A critical evaluation of deterministic methods in size optimisation of reliable and cost effective
standalone Hybrid renewable energy systems,” Reliability Engineering & System Safety, vol. 130. pp. 159–174,
2014, doi: 10.1016/j.ress.2014.05.008.
[4] S. Jorge and D. Vilar, “General introduction to the HOMER software for the design of renewable energy projects”,
(in French), CEREEC Achada Santo Antonio B.P. 288, Praia, June 2013. [Online]. Available:
http://www.ecreee.org/sites/default/files/event-att/02_simulation.pdf
[5] Y. E. A. Eldahab, N. H. Saad, and A. Zekry, “Enhancing the tracking techniques for the global maximum power
point under partial shading conditions,”Renewable and Sustainable Energy Reviews, vol. 73, pp.1173–1183, 2017,
doi: 10.1016/j.rser.2017.02.029.
[6] N. Bouzidi et al., “Salmonella contamination of laying-hen flocks in two regions of Algeria,” Food Research
International, vol. 45, no. 2, pp. 897-904, 2012.
[7] Y. Kasmi, “Influence of the breeding mode of laying hens on the quality of eggs,” (in French), memory of magister
supported in 2016/2017, Institute of Veterinary Sciences and Agricultural Sciences University Batna 1. [Online].
Available: http://theses.univ-batna.dz › doc_download
[8] Mathworks, “Simscape Power Systems Reference (Simscape Components),” 2016. [Online]. Available:
http://www.mathworks .com/help/releases/R2016a/pdf doc/physmod/sps/sps ref.pdf
[9] Ministry of energy and mines, “National New and Renewable Energy Program adopted by the Government,” (in
French), in Feb. 2015 and revised in May 2019. [Online]. Available: https://www.energy.gov.dz/
[10] B. Hotz-Har, “PV Power Plants Sizing and Project Implementation Guide for Engineering Offices,” (in French),
PACER, 1996.
[11] D. Abbes, “Contribution to the sizing and optimization of wind-photovoltaic hybrid systems with batteries for
autonomous residential housing,” (in French), Electrical engineering thesis. Poitiers: University of Poitiers, 2012.
[Online]. Available: http://theses.univ-poitiers.fr
[12] “Power Data Access Viewer,” NASA. [Online]. Available: https://power.larc.nasa.gov/data-access-viewer/
[13] M. Q. Taha, “Advantages and recent advances of smart energy grid,” Bulletin of Electrical Engineering and
Informatics (BEEI), vol. 9, no. 5, pp. 1739–1746, Oct. 2020, doi: 10.11591/eei.v9i5.2358.
[14] L. Bridier, “Modeling and optimization of a storage system coupled with intermittent renewable electricity
production,” (in French), Electrical energy, University of Reunion Island, French, 2016.
[15] M. A. Hamouda et al., “Study and Realization of an intelligent system of electrical energy supply of a house in an
urban environment with injection in the network,” (in French), Review of renewable energies, vol. 14 no. 2,
pp. 187–202, 2011.
[16] N. A.Qarabsh, S. S. Sabry, and H. A. Qarabash, “Smart grid in the context of industry 4.0: an overview of
communications technologies and challenges,” Indonesian Journal of Electrical Engineering and Computer
Science (IJEECS), vol. 18, no. 2, pp. 656–665, 2020, doi: 10.11591/ijeecs.v18.i2.pp656-665.
[17] A. Kaabeche and R. Ibtiouen, “Techno-economic optimization of hybrid photovoltaic/wind/diesel/battery
generation in a stand-alone power system,” Solar Energy, vol. 103, pp. 171–182, 2014, doi:
10.1016/j.solener.2014.02.017.
[18] N. Agarwal, A. Kumar, and Varun, “Optimization of grid independent hybrid PV–diesel–battery system for power
generation in remote villages of Uttar Pradesh, India,” Energy for Sustainable Development, vol. 17, no. 3,
pp. 210–219, 2013, doi: 10.1016/j.esd.2013.02.002.
[19] A. Bajpai and V.S. Kushwah, “Importance of Fuzzy Logic and Application Areas in Engineering,” International
Journal of Recent Technology and Engineering, vol. 7, no. 6, pp. 1467–1471, Mar. 2019.
[20] A. Ziouh and A. Abbou, “Fuzzy-Super Twisting Sliding Mode MPPT Control for Three-Phase Grid-Connected
PV,” International Journal of Renewable Energy Research (IJRER), vol. 8, no. 4, pp. 1812–1823, 2018.
[21] Z. Assia and C. Rachid, “Study of the influence of different parameters on a photovoltaic module,” (in French),
University Constantine 1 Algeria, 2015 Sciences & Technology A, no. 41, pp. 49-54, Jun. 2015.

 ISSN: 2088-8708
4730
[22] M. Azzouzi, “Comparaison between MPPT P&O and MPPT Fuzzy Controls in Optimizing the Photovoltaic
Generator,” International Journal of Advanced Computer Science and Applications, vol. 3, no. 12, 2012,
doi: 10.14569/IJACSA.2012.031208.
[23] P. Takun, S. Kaitwanidvilai, and C. Jettanasen, “Maximum Power Point Tracking using Fuzzy Logic Control for
Photovoltaic Systems,” Proceedings of the International Multi Conference of Engineers and Computer Scientists,
Hong Kong, vol. 2, 2011.
[24] Slamet, E. Rijanto, A. Nugroho, and R. A. Ghani, “A robust maximum power point tracking control for PV panel
using adaptive PI controller based on fuzzy logic,” TELKOMNIKA Telecommunication, Computing, Electronics
and Control, vol. 18, no. 6, pp. 2999–3010, Dec. 2020, doi: 10.12928/telkomnika.v18i6.17271.
[25] E. M. Laadissi, J. Khalfi, F. Belhora, C. Ennawaoui, and A. E. Ballouti, “Aging study of a lead-acid storage bank in
a multi-source hybrid system,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 20, no. 3,
pp. 1109–1117, Dec. 2020, doi: 10.11591/ijeecs.v20.i3.pp1109-1117.
[26] A. Khamis, M. R. Ab. Ghani, G. C. Kim, M. S. M. Aras, M. A. Bin Zabide, and T. Sutikno, “Control Strategy for
Distributed Integration of Photovoltaic and Battery Energy Storage System in Micro-grids,” TELKOMNIKA
Telecommunication, Computing, Electronics and Control, vol. 16, no. 5, pp. 2415–2427, Oct. 2018,
doi: 10.12928/telkomnika.v16i5.10249.
[27] L.Wang and H. Zhang, “An adaptive fuzzy hierarchical control for maintaining solar greenhouse temperature,”
Computers and Electronics in Agriculture, vol. 155, pp. 251–256, Dec. 2018, doi: 10.1016/j.compag.2018.10.023.
[28] C.Wang, W. Cao, B. Li, Z. Shi, and A. Geng, “A fuzzy mathematical method to evaluate the suitability of an
evaporative pad cooling system for poultry houses in China,” Biosystems Engineering, vol. 101, no. 3, pp. 370–375,
Nov. 2008, doi: 10.1016/j.biosystemseng.2008.08.005.
BIOGRAPHIES OF AUTHORS
Boutlilis Fatima, Ph.D. teacher-researcher at the Department of Electrical Engineering of Abd
el Hamid ibn Badiss University in Mostaganem, Algeria, in renewable energies. His current
research focuses on methods for dimensioning photovoltaic generators. We include the
comparison of different classical and intelligent controls and their simulations. The objective is
to realize an intelligent photovoltaic power plant to supply the farms and reduce pollution from
the sector.
Chouitek Mama, PhD in electrical engineering option: electrical command, teacher-researcher
at the Institute of Maintenance and Industrial Safety of the University Ahmed Benhmed Oran 2
Algeria. Her current research focuses on the comparison of different classical and intelligent
controls and their simulations in order to be able to build a test bench of the best order for this
variable reluctance machine (6/8).
Bekkouche Benaissa works at the Department of Electrical Engineering of Abd el Hamid ibn
Badiss University in Mostaganem, Algeria, as a research professor. Research field: responsible
for the doctoral training in methodology for the design of renewable energy sources and the
dimensioning of electrical systems.

Design methodology of smart photovoltaic plant

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