New Energy Management System for RES-based Microgrid Operations
using SGO
MADHAB CHANDRA DAS1, PRITAM PATEL1, SARAT CHANDRA SWAIN1,
BINAY KUMAR NAYAK2
1KIIT University,
Bhubaneswar,
INDIA
2Indira Gandhi Institute of Technology,
Sarang,
INDIA
Abstract: - Due to advantages such as abundant energy sources, environmentally friendly perspectives, and
straightforward power extraction, there has been increasing research on integrated microgrids incorporating
photovoltaic (PV), wind, and biogas systems. Efficient utilization of renewable energy sources (RES), backup
distributed generators (DGs), and storage devices within the microgrid is essential to meet power demands.
Consequently, Energy Management Systems (EMS) have been introduced to microgrids, focusing on monitoring
various energy resources and regulating energy consumption at specific locations. In this manner, the EMS
effectively coordinates the integrated DGs within the microgrid to ensure optimal power supply to loads with
minimal operational costs. The aid of decision-makers lies in comprehending a location’s strengths and constraints,
enabling them to regulate usage effectively. To enhance productivity, all potential distributed generators (DGs)
must be integrated into the microgrid and optimized. Despite numerous global research efforts in devising energy
management systems, certain challenges persist. Ensuring a microgrid provides reliable, high-quality power is
demanding, primarily due to geographical dispersion, restricted availability of distributed resources, and the
seasonal and intra-day variability inherent in renewable resources. Managing a microgrid becomes intricate given
these factors.
Key-Words: - Microgrid; renewable energy; energy management; energy storage; distributed generation; Social
group optimization; HOMER Pro software.
Received: September 12, 2023. Revised: July 9, 2024. Accepted: August 11, 2024. Published: September 27, 2024.
1 Introduction
A microgrid is a small-scale power-generating system
that supplies the electricity needed locally. The first
microgrid in the United States was a 64 MW plant
built at the Whitling Refinery in Indiana in 1955,
according to [1], however, the idea behind microgrids
dates back much further. Beginning in the late 1700s,
the microgrid idea uses a tiny local power generating
system to fulfill demand measured in kilowatt-hours.
A decentralized power production and storage system,
able to balance supply and demand and provide
dependable service within its boundaries, should be a
feature of every conventional microgrid. Although
there isn't a set minimum or maximum size for a
microgrid, it should be big enough for a small town,
such as a hospital, school, university, or military base.
However, a single-generation system, often known as
a nano-grid, is any power production system that
supplies a single building, whether it is commercial,
industrial, or residential, [2]. The initial goals of the
microgrid idea were to address energy scarcity,
improve the integration of distributed renewable
energy sources, and reduce the environmental impacts
of traditional power production systems, such as
greenhouse gas emissions and carbon footprint.
Electrifying rural areas, effectively integrating
Distributed Generation (DG), and relieving pressure
on the transmission and distribution systems by
producing electricity where it is required are other
incentives, [3], [4]. By offering an efficient platform
WSEAS TRANSACTIONS on CIRCUITS and SYSTEMS
DOI: 10.37394/23201.2024.23.11
Madhab Chandra Das, Pritam Patel,
Sarat Chandra Swain, Binay Kumar Nayak
E-ISSN: 2224-266X
114
Volume 23, 2024
for integrating and monitoring the DG sources,
microgrids may be defined as the conversion of the
conventional power generating system into a more
dependable, less carbon-intensive, and cost-effective
energy system. Being a scaled-down version of the
main grid, the microgrid also offers closer proximity
between the source and consumption of electricity,
increasing efficiency and lowering transmission
losses. Renewable energy sources including solar,
wind, small hydro, geothermal, waste-to-energy, and
combined heat and power systems can all be
integrated into a microgrid. Based on how they
operate, microgrid technologies may be divided into
two categories: (i) stand-alone or decentralized
modes, and (ii) grid-connected or centralized modes
[5], [6]. The goal of this research project is to assess
the local potential of renewable energy sources, such
as solar and wind power, and to suggest an integrated
energy management system that would enable
microgrid operations to be highly reliable and low-
maintenance. To achieve this, the following objectives
are defined:
I. Using the meteorological information that is
currently available, create a model for
mapping and forecasting the local potential
for solar and wind energy.
II. Estimate ideal land available in the regions
with higher renewable energy potential for the
installation of solar fields and wind farms and
develop a generalized methodology for solar
field and wind farm layout to maximize the
power generation.
III. Investigate an integrated renewable energy
system for stand-alone microgrid operations.
The goal of this paper is to design an energy
management system that takes into account locally
accessible renewable energy sources for microgrid
operations at the local scale. The goal of microgrid
development is to reduce greenhouse gas emissions
while meeting the energy needs of the present and the
future with high dependability and minimal cost in
[7], [8]. The paper defines the process for accurately
assess the potential for renewable energy, settling the
best location for installing an energy system, and
maximize the use of available energy resources. A
new area is select for the arrangement of IRES, and
the method used in this study can also be applied to
other locations with locally acquirable renewable
energy resources and energy desire.
2 Microgrid Architecture
2.1 System Configuration
An endeavor has been created to model Photovoltaic
Solar and Wind Power as Distributed Generation
power sources with energy storage system are
interconnected to the grid in Figure 1 and the system
is tested through the HOMER pro software. This
software simplifies the examination of multiple
configurations of the whole system, considering
different capacities for each component. These
configurations were appraised to determine their
capability to execute the load requirements. The
HOMER Pro software was employed to generate a list
of viable configurations capable of meeting the
demand, which were then organized based on
economic indicators such as the Levelized Cost of
Energy (LCOE) and Net Present Cost (NPC).
Fig. 1: Proposed microgrid with control system
2.2 Modelling Of Photovoltaic System
The calculation for the AC output of a
photovoltaic panel be expressed as follows:
tempinvPVPV ftIrrNAtP
)()(
(1)
where A denotes the panel's area, η denotes panel
efficiency, Irr stands for global horizontal irradiance
(GHI) expressed in W/m2, and ηinv stands for inverter
efficiency. is the derating factor caused by the
temperature that modifies the output power of a PV
panel. It's computed as
)](1[ reftemp Tf
(2)
where,
is the module temperature which is
calculated as
WSEAS TRANSACTIONS on CIRCUITS and SYSTEMS
DOI: 10.37394/23201.2024.23.11
Madhab Chandra Das, Pritam Patel,
Sarat Chandra Swain, Binay Kumar Nayak
E-ISSN: 2224-266X
115
Volume 23, 2024
)(035.0 tIrrT
(3)
where T is the atmospheric temperature.
The monthly predicted solar radiation was
compared with the measured values and plotted for
two test locations (T1) and (T2) in Figure 2. The
result indicates better fitting of value, and prediction
for each month is very near to actual value for all test
locations.
Fig. 2: Comparison between measured values and
predicted values of solar radiation for two test
locations (T1) and (T2) in a year
2.3 Modelling Of Wind System
Temperature, pressure, and relative humidity are the
three meteorological characteristics that have been
considered in order to examine the impact of various
components on wind speed prediction.
Pwind=0.5ρACpV3 (4)
Where A is the swept area of the wind turbine
blades, V is the wind speed, Cp is the power
coefficient, which indicates the turbine's efficiency,
and Pwind is the power taken from the wind.
Figure 3(a) and Figure 3(b) represented measured
and predicted value of wind speed for two test
locations (T1) and (T2). The result shows a closer
approximation between measured and predicted
values of wind speed. As it can be seen in the Figure
3(a) the predicted values of wind speed follow the
trend and are reasonably close to the measured values
of wind speed.
(a)
(b)
Fig. 3: Comparison of measured values and predicted
values of wind speed for two test locations (T1) and
(T2) in a year
The analysis focuses on wind turbines with a
capacity of 3 kW from Diamond Engineering
Enterprises, India, characterized by a capital cost of
Rs. 50,000/-. The predicted monthly mean wind speed
is based on the methodology. Power generation profile
of a chosen wind turbine is shown in Figure 4.
Fig. 4: Power output Vs wind speed
2.4 Modelling Of Battery Energy Storage
System (Bess)
For the battery bank in our investigation, we use
gelled electrolyte-sealed batteries, a particular kind of
valve-regulated lead-acid (VRLA) battery. There is a
connection between the PV panel's output power, the
WSEAS TRANSACTIONS on CIRCUITS and SYSTEMS
DOI: 10.37394/23201.2024.23.11
Madhab Chandra Das, Pritam Patel,
Sarat Chandra Swain, Binay Kumar Nayak
E-ISSN: 2224-266X
116
Volume 23, 2024
load demand at time t, and the battery bank's state at
hour t. A valve is built into every VRLA battery cell
to keep airborne oxygen out and let out any gas
produced during overcharging. Lead acid-sealed
batteries are another name for these VRLA batteries.
In [9], [10], [11] configuration, batteries are connected
in both series and parallel to establish the battery
bank. The total number of batteries is determined by
an Equation which accounts for the arrangement of 83
batteries in series and 48 batteries in parallel.
Every hour, the battery's charge state is updated
by the power that is being charged and discharged.
Equation of charge:
VC
tP
tSOCtSOC
bat
bat
ch
)(
)1()1()(
(5)
VC
tP
tSOCtSOC
bat
bat
dch
)(
)1()1()(
(6)
SOC(t) and SOC (t -1) are the state of charge of the
battery at hour (t) and (t -1), respectively, Cbat = the
capacity of the battery at hour t hour,
PSb NNN
(7)
Ns is the number of batteries in series, NP is the
number of batteries in parallel, and Nb is the total
number of batteries.
V is the nominal battery voltage, σ is the
discharging factor (2.8%) per month, ηch is the
charging efficiency (97%), and ηdch is the discharging
efficiency (94%). The battery power exchanged
during the simulation is plotted in Figure 5.
Fig. 5: Power exchange from the battery
2.5 Modelling Of Load
The monthly average electrical energy demand of
khordha village, is presented in Figure 6(a). Also,
Figure 6(b) shows the hourly load profile for June,
which has the maximum energy demand in the year.
To develop and analyze IRES at the study location,
the electrical energy demand of 100 households along
with the energy demand at a medical center and a
school is considered, which have an approximate
yearly energy demand of 317 MWh.
(a)
(b)
Fig. 6: Annual and hourly load profile for Khordha
district (2023)
3 Energy Management System
To reduce the cost of the energy supply in both modes
of operation, the ideal combination of power
generation from various generating sources is
required. Energy management systems handle the
ideal blend of power generation (EMS). To guarantee
the dependability of the energy supply, the EMS
advises the operator on how to use the system power
efficiently. It is a supply-demand balance, which
forms an optimization problem. When this problem
considers real-world constraints, rather than just an
abstract mathematical function, it becomes more
complex, [12]. The size of the microgrid varies with
applications, however, it is designed/operationalized
to match the supply 13 demand requirements. For
efficient implementation of EMS, it would be
beneficial to have a unified communication interface.
In [13], [14], A typical approach adopted in the sizing
of energy management systems is presented in Figure
7.
WSEAS TRANSACTIONS on CIRCUITS and SYSTEMS
DOI: 10.37394/23201.2024.23.11
Madhab Chandra Das, Pritam Patel,
Sarat Chandra Swain, Binay Kumar Nayak
E-ISSN: 2224-266X
117
Volume 23, 2024
Fig. 7: Typical approach adopted in sizing of energy
management systems
4 Problem Formulation
The innovative energy management system assists in
assessing the viability of each scenario through the
implementation of an economic analysis. This
analysis encompasses economic factors, including the
Levelized Cost of Electricity (LCOE) and Net Present
Cost (NPC) in [15], [16]. The LCOE is computed
using Equation (8) in units of Rs/kWh.
(8)
In this context, where Cann, the total represents the
total annual cost (Rs/yr.), Cboiler signifies the marginal
cost of the boiler (Rs/kWh), Hserved stands for the
overall thermal load served (kWh/yr.), and Eserved
denotes the total electrical load (kWh/yr.). Our study
employs the Social Group Optimization technique to
identify optimal solutions, maximizing the objective
function, as outlined in [17], [18]. The objective
function in this case is the analysis of the financial
benefits associated with Integrated Renewable Energy
Systems (IRES).
)max(CBfobj
(9)
where CB is the financial benefit which can be
calculated as follows:
365
1
24
1
),(
d t
tdbenfCB
(10)
),(),(),( tdPtdpricetdbenf grid
(11)
where benf is the hourly financial benefit, Pgrid is the
hourly power transacted between the utility grid and
the microgrid.
5 Simulation Results
Hourly solar irradiation data for the year 2023-24 in
Khordha, Odisha, serves as the input for the
simulation. The MATLAB platform is utilized for
conducting the simulation. To address the
optimization problem, a Single-Objective Social
Group Optimization (SGO) algorithm has been
applied. The parameters of the SGO algorithm are
detailed in Table 1.
Table 1. Parameters of Sgo
Dimension of the problem, D
50
Population Size, N
50
Maximum Iteration, itermax
270
Limit
[0,20]
Self-introspection parameter, c
0.26
5.1 Comparison of Scenarios and IRES
Analysis
Figure 8 depicts the comparison of the optimal
choices in each scenario concerning Net Present Cost
(NPC) and Initial Capital Cost (ICC). Meanwhile,
Figure 9 provides a comparison of the Levelized Cost
of Electricity (LCOE) among all considered scenarios,
each representing the best combination.
Scenario A: wind + diesel + li-ion battery + AC-DC
Scenario B: PV + diesel + li-ion battery + AC-DC
Scenario C: PV + wind + li-ion battery + AC-DC
Scenario D: wind + fuel cell + electrolyzer +
hydrogen tank + DC-AC
Scenario E: PV + fuel cell + electrolyzer + hydrogen
Tank + DC-AC
Scenario F: Biogas generator
Scenario G: PV + wind + li-ion battery + diesel+
biogas + DC-AC
In Scenario A, where wind turbines are utilized,
both LCOE and NPC experience an increase due to
the turbine's dependency on favorable wind
conditions, rendering them ineffective during low
wind speeds. Conversely, Scenario B exhibits lower
LCOE and NPC values, attributed to the greater
reliability of PV panels and the reduced variability in
solar radiation compared to wind speed in
WSEAS TRANSACTIONS on CIRCUITS and SYSTEMS
DOI: 10.37394/23201.2024.23.11
Madhab Chandra Das, Pritam Patel,
Sarat Chandra Swain, Binay Kumar Nayak
E-ISSN: 2224-266X
118
Volume 23, 2024
Bhubaneswar, Odisha. In Scenario C, where a
combination of wind turbines and PV panels is
employed, there is a slight increase in LCOE and
NPC. Scenarios D and E introduce fuel cells alongside
wind and PV systems, resulting in considerably higher
LCOE and NPC values. This is attributed to fuel cell
technology being relatively new and necessitating
further development as an alternative solution. Thus,
these scenarios (D and E) are regarded as futuristic.
Scenario F, featuring a biogas generator, emerges as a
promising energy resource, displaying the lowest ICC
among all scenarios. However, due to limited
resources, the biogas generator falls short of meeting
the total energy demand. Scenario G (Integrated
Renewable Energy System - IRES), which integrates
various renewable energy sources into a microgrid,
boasts the lowest LCOE and NPC. Figure 8 illustrates
the contribution of different renewable energy sources
in meeting energy demands, with 85% met by PV
panels, followed by 11% and 4% from biogas
generators and wind turbines, respectively. This
underscores that the PV system is the most
economical and feasible option for power generation
in the Khordha district.
Fig. 8: Comparison of all scenarios based on NPC
and ICC
Fig. 9: Comparison of LCOE in all scenarios
Scenario 'C in Figure 8 and Figure 9 was devised
to examine the feasibility of meeting electrical
demand solely through the combination of PV and
wind with a battery, excluding the use of a diesel
generator. This configuration utilizes both wind and
PV, resulting in a renewable energy fraction of 100%.
However, the LCOE for this scenario stands at Rs.
20.75, which is comparable. Still, it has an NPC of Rs.
8.41 crores and an ICC of Rs. 6.81 crores, rendering it
economically less competitive.
5.2 RES for Microgrid Operation
An Integrated Renewable Energy System (IRES) was
developed for a microgrid system to fulfill the
electrical demand of Khordha village, focusing on the
energy requirements of 100 existing houses. The
analysis incorporated various energy generation units,
including photovoltaic (PV), wind, and biogas,
coupled with energy storage units such as batteries.
Seven scenarios, encompassing both realistic and
futuristic options, were formulated with diverse
combinations of energy sources and storage systems
in Table 2. These scenarios were assessed using
HOMER Pro software, considering their Levelized
Cost of Energy (LCOE) and Net Present Cost (NPC).
The futuristic scenarios involved Valve-Regulated
Lead-Acid (VRLA) batteries in conjunction with wind
and/or solar generation systems. The optimal futuristic
scenario exhibited a maximum LCOE of 63.64 Rs.
/kWh. In contrast, the realistic scenarios utilized PV,
solar, wind, and biogas as generation systems, with
the lowest LCOE recorded at 14.49 Rs. /kWh, as
depicted in Figure 9.
Table 2. Analysis of Different Scenarios
Scenarios
COE
(Rs/kWh)
NPC
(Cr. Rs)
ICC
(Cr. Rs)
RE (%)
A
41.78
17.10
1.10
27.39
B
16.62
5.56
3.61
87.32
C
20.74
8.41
6.81
100
D
63.64
25.89
11.43
23.64
E
62.1
25.26
14.8
39.8
F
20.04
8.18
1
100
G
14.49
5.91
4.57
100
6 Conclusion
The study underscores the cost-effective power
generation aspect, indicating that the optimal number
of wind turbines decreases with an increase in wind
WSEAS TRANSACTIONS on CIRCUITS and SYSTEMS
DOI: 10.37394/23201.2024.23.11
Madhab Chandra Das, Pritam Patel,
Sarat Chandra Swain, Binay Kumar Nayak
E-ISSN: 2224-266X
119
Volume 23, 2024
speed for a given size of the wind farm. Furthermore,
a 30 m x 40 m solar field in Khordha district can
generate 1.2 MWh/day with an average solar radiation
of 5.26 kWh/m²/day, capable of meeting the energy
demand of 100 households. In the investigation of
seven different Integrated Renewable Energy System
(IRES) scenarios, it is determined that the IRES
combining PV, wind, and biogas achieves a Levelized
Cost of Electricity (LCOE) of 14.46 Rs/kWh without
subsidies or policy interventions. The feasibility
analysis, considering policy interventions and carbon
abatement costs, results in a reduced LCOE of 8.6
Rs/kWh, establishing it as a sustainable option for the
study region. The application of Social Group
Optimization (SGO) is proven to be a fast, efficient,
and reliable method for solving the optimization
problem.
References:
[1] Achilles, S., Alvarez-Cordero, G., Apostolov,
A., Avramidis, I., Bachiller-Soler, A.,
Badrzadeh, B., ... & Bose, A. 2023 Index IEEE
Power and Energy Magazine Vol. 21.
[2] Kassem, Y., Gokcekus, H., Hamad, O. A. M., &
Fayid, F. M. B. (2023). Economic viability of a
6.5 kW off-grid solar PV with various sun-
tracking systems in Northern Cyprus: A case
study. Engineering, Technology & Applied
Science Research, 13(2), 10608-10621.
[3] Gholami, M., Muyeen, S. M., & Lin, S. (2024).
Optimizing microgrid efficiency: Coordinating
commercial and residential demand patterns
with shared battery energy storage. Journal of
Energy Storage, 88, 111485.
[4] Agajie, T. F., Ali, A., Fopah-Lele, A.,
Amoussou, I., Khan, B., Velasco, C. L. R., &
Tanyi, E. (2023). A comprehensive review on
techno-economic analysis and optimal sizing of
hybrid renewable energy sources with energy
storage systems. Energies, 16(2), 642.
[5] El-Houari, H., Allouhi, A., Salameh, T.,
Kousksou, T., Jamil, A., & El Amrani, B.
(2021). Energy, Economic, Environment (3E)
analysis of WT-PV-Battery autonomous hybrid
power plants in climatically varying regions.
Sustainable Energy Technologies and
Assessments, 43, 100961.
[6] Mishra, S., Pattanaik, D., Pau, R., Dash, R., &
Swain, S. C. (2018, October). A Review on
Different Types of Maximum Power Point
Tracking System & its Application with PR
Current Control Technique. In 2018
International Conference on Applied
Electromagnetics, Signal Processing and
Communication (AESPC) (Vol. 1, pp. 1-6).
IEEE.
[7] Merabet, A., Qin, Z., & Ghias, A. M. (2018,
October). Control of simulated solar PV
microgrid operating in grid-tied and islanded
modes. In IECON 2018-44th Annual
Conference of the IEEE Industrial Electronics
Society (pp. 1729-1734). IEEE.
[8] Swain, S. C., Gogoi, A., Sharma, S., Dash, R.,
& Acharya, A. (2018, July). Some aspects of
fuzzy logic controller for designing MPPT
based SPV system. In 2018 International
Conference on Recent Innovations in Electrical,
Electronics & Communication Engineering
(ICRIEECE) (pp. 3357-3361). IEEE.
[9] Ren, H., Zhao, Y., Chen, S., & Wang, T.
(2019). Design and implementation of a battery
management system with active charge balance
based on the SOC and SOH online estimation.
Energy, 166, 908-917.
[10] Chen, Yu-Kai, et al. "Design and
implementation of energy management system
with fuzzy control for DC microgrid systems."
IEEE Transactions on Power Electronics, 28.4
(2012): 1563-1570.
[11] Merabet, A., Al-Durra, A., & El-Saadany, E. F.
(2022). Energy management system for optimal
cost and storage utilization of renewable hybrid
energy microgrid. Energy Conversion and
Management, 252, 115116.
[12] Ibrahim, O., Bakare, M. S., Amosa, T. I.,
Otuoze, A. O., Owonikoko, W. O., Ali, E. M.,
& Ogunbiyi, O. (2023). Development of fuzzy
logic-based demand-side energy management
system for hybrid energy sources. Energy
Conversion and Management: X, 18, 100354.
[13] Ullah, Z., Wang, S., Lai, J., Azam, M.,
Badshah, F., Wu, G., & Elkadeem, M. R.
(2023). Implementation of various control
methods for the efficient energy management in
hybrid microgrid system. Ain Shams
Engineering Journal, 14(5), 101961.
[14] Xiao, Yulong, et al. "Feasibility study:
Economic and technical analysis of optimal
configuration and operation of a hybrid
CSP/PV/wind power cogeneration system with
WSEAS TRANSACTIONS on CIRCUITS and SYSTEMS
DOI: 10.37394/23201.2024.23.11
Madhab Chandra Das, Pritam Patel,
Sarat Chandra Swain, Binay Kumar Nayak
E-ISSN: 2224-266X
120
Volume 23, 2024
energy storage." Renewable Energy, 225
(2024): 120273.
[15] Vendoti, Suresh, M. Muralidhar, and R.
Kiranmayi. "Techno-economic analysis of off-
grid solar/wind/biogas/biomass/fuel cell/battery
system for electrification in a cluster of villages
by HOMER software." Environment,
Development and Sustainability 23.1 (2021):
351-372.
[16] Xiao, Y., Zou, C., Dong, M., Chi, H., Yan, Y.,
& Jiang, S. (2024). Feasibility study: Economic
and technical analysis of optimal configuration
and operation of a hybrid CSP/PV/wind power
cogeneration system with energy storage.
Renewable Energy, 225, 120273.
[17] Satapathy, S., & Naik, A. (2016). Social group
optimization (SGO): a new population
evolutionary optimization technique. Complex
& Intelligent Systems, 2(3), 173-203.
[18] Naik, A., Satapathy, S. C., & Abraham, A.
(2020). Modified Social Group Optimization—
A meta-heuristic algorithm to solve short-term
hydrothermal scheduling. Applied Soft
Computing, 95, 106524.
Contribution of Individual Authors to the Creation
of a Scientific Article (Ghostwriting Policy)
- Madhab Chandra Das carried out the Ideas;
formulation, simulation, and optimization.
- Pritam Patel has implemented the Algorithm SGO
and is responsible for the Statistics.
- Sarat Chandra Swain has Conducted a research and
investigation process, specifically performing the
experiments and data collection.
- Binay Kumar Nayak has Preparation, creation, and
presentation of the published work, specifically
writing the initial draft.
Sources of Funding for Research Presented in a
Scientific Article or Scientific Article Itself
This research received no external funding.
Conflict of Interest
The authors have no conflicts of interest
Creative Commons Attribution License 4.0
(Attribution 4.0 International, CC BY 4.0)
This article is published under the terms of the
Creative Commons Attribution License 4.0
https://creativecommons.org/licenses/by/4.0/deed.en_
US
WSEAS TRANSACTIONS on CIRCUITS and SYSTEMS
DOI: 10.37394/23201.2024.23.11
Madhab Chandra Das, Pritam Patel,
Sarat Chandra Swain, Binay Kumar Nayak
E-ISSN: 2224-266X
121
Volume 23, 2024
