L. DJORDJEVI] et al.: SOLAR TECHNOLOGY: EMPOWERING SERBIA’S RENEWABLE ENERGY FUTURE
33–39
SOLAR TECHNOLOGY:
EMPOWERING SERBIA’S RENEWABLE ENERGY FUTURE
TEHNOLOGIJA IZKORI[^ANJA ENERGIJE SONCA:
USPOSABLJANJE SRBIJE ZA OBNOVLJIVO ENERGIJSKO
PRIHODNOST
Luka Djordjevi}, Slavica Prvulovi}
*
, Mi}a Djurdjev, Borivoj Novakovi},
Mihalj Bakator
University of Novi Sad, Technical Faculty "Mihajlo Pupin", 23101 Zrenjanin, Serbia
Prejem rokopisa – received: 2023-07-14; sprejem za objavo – accepted for publication: 2023-12-07
doi:10.17222/mit.2023.944
This study investigated the solar power potential and performance in Serbia, a country with favorable solar conditions but lim-
ited resource utilization. The principal objectives of the investigation were to analyze the performance ratio (PR) and assess the
electricity production of simulated solar power plants in different distribution areas. The Photovoltaic System software (PVsyst
7.3) was employed to simulate five grid-connected solar power plants, each with a capacity of 10 MW, in five distribution areas
of the Serbian Public Electric Utility Company. The study found that the PR values varied from 81.7 % to 84 %, indicating fa-
vorable conditions for harnessing the solar potential. The simulations projected an annual electricity delivery to the grid of
64.41 GWh. According to the study’s estimates, installed solar power plants would be capable of meeting 0.22 % of the electric-
ity needs in Serbia.
Keywords: solar energy, Serbia, performance ratio
V ~lanku avtorji opisujejo {tudijo o raziskovanju potencialov in mo`nem izkori{~anju son~ne energije v Srbiji. Srbija je de`ela
z velikim potencialom glede izkori{~anja energije sonca vendar z omejenimi finan~nimi in drugimi sredstvi za njeno
izkori{~anje. Glavna objekta njihove raziskave sta bila analiza oziroma dolo~itev performan~nega razmerja (PR, angl.: Perfor-
mance Ratio) oziroma razmerja u~inkovitosti in ocena proizvodnje elektrike na simulirani son~ni (fotovoltai~ni) elektrarni na
petih razli~nih geografskih delih ozemlja oziroma podro~jih (okro`jih) distribucije elektri~ne energije. Avtorji ~lanka so
uporabili ra~unalni{ko programsko orodje Photovoltaic System software (PVsyst 7.3) za simulacijo petih v mre`o povezanih
son~nih elektrarn s kapaciteto 10 MW na petih distribucijskih okro`jih srbskega elektrogospodarstva (Elektroprivreda Srbije). S
pomo~jo {tudije so ugotovili, da PR faktor varira med 81,7 % in 84 %, kar ka`e na to, da so izbrana geografska (distribucijska)
okro`ja v Srbiji potencialno zelo ugodna za izkori{~anje son~ne energije. Simulacije so pokazale, da je mo`na letna dobava
elektri~ne energije v dr`avno elektro omre`je enaka 64,41 GWh. V skladu s temi ocenami bi bile in{talirane son~ne elektrarne
sposobne pokriti 0,22 % vseh potreb po elektri~ni energiji Srbije.
Klju~ne besede: energija sonca, Srbija, razmerje u~inkovitosti
1 INTRODUCTION
Faced with increasing challenges regarding energy
sustainability and environmental protection, it is becom-
ing increasingly clear that using renewable energy
sources is vital for preserving our planet.
1–3
Conventional
energy sources, such as fossil fuels, have significant neg-
ative environmental consequences, including the emis-
sion of harmful gases, degradation of water resources
and destruction of biodiversity.
4–6
In this context, solar
energy has become a key player in transitioning to a sus-
tainable energy system. Harnessing solar potential offers
numerous advantages, including reducing greenhouse
gas (GHG) emissions, air and water pollution and inde-
pendence from limited reserves of fossil fuels.
7–9
According to the International Renewable Energy
Agency (IRENA), the installed capacity of solar power
plants worldwide exceeded 1.05 terawatts (TW) in 2022,
marking a significant leap compared to previous years.
10
According to the data from the European Solar Industry
Association (SolarPower Europe), the solar power capac-
ity installed in the EU surpassed 200 GW in 2022. Fur-
thermore, the organization forecasts that by 2030, the cu-
mulative solar PV capacity in the EU will reach 920
GW.
11
In the Republic of Serbia, the situation regarding re-
newable energy sources is still challenging.
12,13
Most of
the country’s energy mix still relies on conventional
sources such as coal and oil.
14,15
However, the awareness
of the need for a transition to renewable energy sources,
including solar energy, is steadily growing in Serbia.
16,17
The country has more sunlight hours compared to many
European nations, ranging from 1500 to 2200 hours per
year.
18
On an annual basis, the average radiation energy
ranges from 1200 kWh/m
2
/year in the northwest to
1550 kWh/m
2
/year in the southeast. These favorable con-
ditions make solar energy a promising avenue for further
Materiali in tehnologije / Materials and technology 58 (2024) 1, 33–39 33
UDK 502.21:523.9:005.336.1 ISSN 1580-2949
Original scientific article/Izvirni znanstveni ~lanek MTAEC9, 58(1)33(2024)
*Corresponding author's e-mail:
prvulovicslavica@yahoo.com (Slavica Prvulovi})

development in Serbia.
19,20
Figure 1 show Serbia’s PV
power potential from 1994–2018.
21
According to reference
22
, the Public Enterprise Elec-
tric Power Industry of Serbia had a total electricity gen-
eration capacity of 7391 MW in 2022. Out of the overall
generation capacity, thermal power plants and combined
heat and power plants accounted for 4376 MW or
59.20 %, while hydro power plants accounted for
3015 MW or 40.80 %. In terms of energy production,
thermal power plants and combined heat and power
plants contributed 22,166 GWh or 71.21 %, while hydro
power plants contributed 8964 GWh or 27.89 %. The
power delivered to customers through GreenEPS con-
tracts in 2022 amounted to 1644.52 GWh, i.e., only
4.58 % of the total electricity consumption in Serbia. So-
lar power plants had a production capacity of 13 MW,
generating 14,630 MWh of electricity. These numbers
indicate that although the Republic of Serbia possesses
significant solar potential, it has not effectively utilized
this resource, leading to underutilization.
2 MATERIALS AND METHODS
The present study used the Photovoltaic System soft-
ware (PVsyst 7.3) to simulate solar power plants. PVsyst
7.3 is an extensive software tool designed for the design,
simulation and performance analysis of photovoltaic
(PV) systems.
23–26
PVsyst provides detailed insights into
a system performance, including energy generation and
overall efficiency. The meteo data for the simulations
were obtained from Metronome version 8.1. These data
play a vital role in accurately assessing the potential of
solar power plants and simulating their performance.
Simulations of five grid-connected solar power
plants, each with a capacity of 10 MW, were conducted
L. DJORDJEVI] et al.: SOLAR TECHNOLOGY: EMPOWERING SERBIA’S RENEWABLE ENERGY FUTURE
34 Materiali in tehnologije / Materials and technology 58 (2024) 1, 33–39
Figure 1: Serbia’s PV power potential [kWh/kWp], © 2020 The
World Bank, source: Global Solar Atlas 2.0, solar resource data:
Solargis, (accessed on 01 July 2023)
Figure 2: Locations of simulated power plants and distribution areas
Table 1: Locations and characteristics of the simulated solar power plants
DA Novi Sad DA Belgrade DA Kraljevo DA Kragujevac DA Ni{
Zrenjanin Beograd Tutin Kragujevac Vranje
Latitude 45.36 N 44.86 N 42.98 N 43.98 N 42.50 N
Longitude 20.44 E 20.23 E 20.33 E 20.86 E 21.92 E
Altitude [m] 79 84 907 234 446
Tilt – optimized 36 36 36 36 38
Azimuth – optimized –2 –1 –1 –1 –1
No. of modules 25,002
Pnom total 10 MWp
Module area [m
2
] 51,819

in five distribution areas (DA) of the Serbian Public
Electric Utility Company (EPS). The EPS is divided into
five distribution areas established according to the terri-
torial principle: DA Novi Sad (DA 1), DA Belgrade (DA
2), DA Kraljevo (DA 3), DA Kragujevac (DA 4) and DA
Ni{ (DA 5). The data on the locations and characteristics
of the simulated solar power plants are shown in Ta-
ble 1.
The locations for these simulations were carefully se-
lected based on the solar radiation map, focusing on ar-
eas with the highest irradiation levels. This approach en-
sures that the analysis is grounded in real-world
conditions and the solar energy potential at these loca-
tions. The locations of the simulated power plants and
distribution areas are shown in Figure 2.
The simulations used a monocrystalline/N-type
(Cello technology) PV module. To achieve the desired
power output of a single solar power plant, a total of
25,002 units of this PV module were used. The power
plant design incorporated a total of 22 inverters. Detailed
technical descriptions of the PV modules and inverters
can be found in Table 2.
Table 2: Technical descriptions of the PV modules and inverters
PV module
Model LG 400 N2W-A5
Cell type Monocrystalline / N-type
Dimensions (L × W × H) 2024 × 1024 × 40 mm
Maximum power (Pmax)* 400
MPP voltage (Vmpp)* 40.6
MPP current (Impp)* 9.86
Open circuit voltage (Voc)* 49.3
Short circuit current (Isc)* 10.47
Module efficiency* 19.3
*STC (standard test condition): irradiance of 1000 W/m
2
, ambient tempera-
ture of 25 °C, AM 1.5
Inverter
Model
SUN2000-100KTL-M1-
400Vac
Nom. power 100 kWac
Operating voltage 200–1000 V
Pnom ratio 1.3
The PR is a significant parameter within the PVsyst
software, providing crucial insights into the modeled
system. It is a metric used to assess the efficiency and
overall performance of a PV system or solar power plant.
The PR allows an overall assessment of a PV array’s per-
formance. The PR is obtained with Equation (1).
27,28
PR
Y
Y
=⋅
A
R
100 (%) (1)
The array yield (Y
A
) allows evaluating and compar-
ing the performance of PV modules by measuring their
energy output relative to their rated power. It represents
the time, measured in hours per day, during which a PV
array needs to operate at its nominal power to generate
the total energy produced. Y
A
is calculated with Equation
(2).
29
Y
E
P
A
PV
nom
= (h) (2)
The reference yield (Y
R
) is defined as the ratio of the
insolation (H) to the irradiance under standard test condi-
tions, specifically for an irradiance level of 1000 W/m
2
.
It provides a standardized measure of the energy produc-
tion potential under ideal conditions. Y
R
calculation is
given in Equation (3).
30
Y
H
G
R
STC
= (h) (3)
L. DJORDJEVI] et al.: SOLAR TECHNOLOGY: EMPOWERING SERBIA’S RENEWABLE ENERGY FUTURE
Materiali in tehnologije / Materials and technology 58 (2024) 1, 33–39 35
Table 3: Values of the PR, GHI, GI and ambient temperature average
Zrenjanin Belgrade Tutin Kragujevac Vranje
GHI [kWh/m
2
] 1276.2 1285.5 1386.0 1318.6 1448.7
GI [kWh/m
2
] 1463.6 1466.3 1577.5 1509.8 1680.7
Ambient temperature average [°C] 12.51 12.85 7.89 11.78 11.71
Month Performance ratio
January 0.873 0.888 0.903 0.898 0.911
February 0.869 0.884 0.896 0.884 0.884
March 0.845 0.854 0.863 0.854 0.851
April 0.822 0.835 0.847 0.837 0.843
May 0.805 0.816 0.835 0.815 0.826
June 0.792 0.804 0.819 0.808 0.816
July 0.784 0.797 0.811 0.797 0.802
August 0.790 0.796 0.814 0.803 0.804
September 0.808 0.820 0.831 0.824 0.816
October 0.835 0.841 0.856 0.844 0.857
November 0.848 0.862 0.875 0.878 0.876
December 0.877 0.886 0.902 0.894 0.897
Average 0.817 0.828 0.845 0.833 0.840

3 RESULTS
The simulations provided valuable insights into the
plants’ performance, represented by the calculated PR
values. These PR values are graphically depicted in Fig-
ure 3. Additionally, Table 3 presents the numerical val-
ues of the PR, global horizontal irradiation (GHI), global
irradiation in the collector plane (GI), and ambient tem-
perature average.
Table 4 presents comparative data on the electricity
consumption per month in distribution areas, along with
L. DJORDJEVI] et al.: SOLAR TECHNOLOGY: EMPOWERING SERBIA’S RENEWABLE ENERGY FUTURE
36 Materiali in tehnologije / Materials and technology 58 (2024) 1, 33–39
Figure 3: Obtained PR values
Table 4: Data on DA electricity consumption
31
and the value of electricity supplied to the grid from the solar power plants [GWh]
Month
DA 1
consumtion
Zrenjanin
DA 2
consumtion
Belgrade
DA 3
consumtion
Tutin
DA 4
consumtion
Kragujevac DA 5
consumtion
Vranje
I 800.5 0.535 905.8 0.51 722.2 0.75 212.2 0.62 496.8 0.79
II 690.3 0.67 741.4 0.73 619.2 0.9 180.5 0.83 422.9 0.99
III 761.5 1.07 805.5 1.1 675.8 1.23 197.7 1.13 464.1 1.27
IV 643.1 1.2 629.1 1.2 556.1 1.21 165 1.18 371.6 1.25
V 576.4 1.33 527.5 1.33 496.7 1.37 143.3 1.3 325 1.41
VI 593.8 1.36 539.9 1.37 489.3 1.44 139.6 1.38 316.9 1.46
VII 625.3 1.46 558.5 1.47 529.8 1.47 149.9 1.42 336.7 1.48
VIII 606.9 1.38 541.9 1.4 515.5 1.48 148.7 1.4 332.9 1.48
IX 567.8 1.07 509.3 1.11 499.7 1.23 139.1 1.15 322.9 1.27
X 620.1 0.95 567.8 0.91 553.2 1.12 155.6 0.98 361.8 1.14
XI 685.9 0.57 682.1 0.59 599.8 0.78 170.3 0.67 385.9 0.83
XII 759.1 0.33 815.1 0.37 670.9 0.59 196 0.48 439.2 0.71
 7930.9 11.96 7821.1 12.14 6928.3 13.62 1998.2 12.58 4577 14.11
Figure 4: Graphical representation of the electricity demand satisfaction in DAs

the data, obtained through simulations of the solar power
plants, on the amount of electricity delivered to the grid.
Table 5 presents data on the percentage of electricity
demand fulfillment in the distribution areas using the
electricity generated from the solar power plants, dis-
played on a monthly basis. Additionally, Figure 4 shows
a graphical representation of the obtained values depict-
ing the percentage of electricity demand satisfaction.
Table 5: Percentage of electricity demand satisfaction in DAs
Month DA 1 DA 2 DA 3 DA 4 DA 5
I 0.07 % 0.06 % 0.10 % 0.29 % 0.16 %
II 0.10 % 0.10 % 0.15 % 0.46 % 0.23 %
III 0.14 % 0.14 % 0.18 % 0.57 % 0.27 %
IV 0.19 % 0.19 % 0.22 % 0.72 % 0.34 %
V 0.23 % 0.25 % 0.28 % 0.91 % 0.43 %
VI 0.23 % 0.25 % 0.29 % 0.99 % 0.46 %
VII 0.23 % 0.26 % 0.28 % 0.95 % 0.44 %
VIII 0.23 % 0.26 % 0.29 % 0.94 % 0.44 %
IX 0.19 % 0.22 % 0.25 % 0.83 % 0.39 %
X 0.15 % 0.16 % 0.20 % 0.63 % 0.32 %
XI 0.08 % 0.09 % 0.13 % 0.39 % 0.22 %
XII 0.04 % 0.05 % 0.09 % 0.24 % 0.16 %
Average 0.15 % 0.16 % 0.20 % 0.63 % 0.31 %
4 DISCUSSION
As presented in Figure 3 and Table 3, the PR values
obtained from the simulations demonstrate favorable
conditions for installing solar power plants. The lowest
PR value was observed for DA 1 in July, amounting to
78.4 %, while the highest value was recorded for DA 5 in
January, reaching 91.1 %. The PR values across all simu-
lations were higher during the winter than the summer
period, indicating the dependence of solar panel effi-
ciency on the temperature, where an increased tempera-
ture leads to a decreased efficiency.
32,33
The highest aver-
age PR value was observed for DA 3, amounting to
84.5 %, while the lowest value of 81.7 % was recorded
for DA 1.
According to the solar radiation map, the GHI values
align with the expected values. The lowest GHI value of
1276.2 kWh/m
2
was observed in the northernmost DA 1,
while the highest value of 1448.7 kWh/m
2
was recorded
in the southernmost DA 5. The GHI values are inversely
proportional to the PR, meaning that during the summer
months, the GHI is higher, while during the winter pe-
riod, it is lower (up to 7 times lower). The average ambi-
ent temperature also significantly influences the effi-
ciency of solar power plants, as lower temperatures
result in higher PR values.
The selected locations and the obtained results high-
light the significance of the temperature impact on the
efficiency of solar panels. Furthermore, this study under-
scores the interconnectedness between the location and
altitude in determining the performance of solar power
plants. Higher altitudes, as exemplified by the geo-
graphic context of Serbia within a continental climate,
are characterized by low average temperatures that are
conducive to enhanced solar panel efficiency. The results
of this research substantiate this correlation as the loca-
tion with the highest altitude (DA 3) exhibits the highest
performance ratio (PR). In contrast, the site with the lowest
altitude (DA 1) records the lowest PR. This observation
reinforces the idea that elevated locations with reduced
average temperatures offer a favorable environment for
solar energy production. These findings have practical
implications for an optimal placement and operation of
solar power facilities in regions with varying altitudes
and climate conditions.
From Table 4, it can be observed that the lowest
quantity of electricity delivered to the grid was recorded
in December in DA 1, amounting to 0.33 GWh. Addi-
tionally, DA 1 achieved the lowest electricity production
among all the simulated solar power plants, with an an-
nual quantity of electricity delivered to the grid of
11.96 GWh. The highest value was observed in DA 3 in
August, with a quantity of 1.48 GWh. Similar values
were achieved in DA 5 in July and August, albeit slightly
lower. The highest quantity of electricity delivered to the
grid was achieved in DA 5, with a quantity of
14.11 GWh.
The percentage of electricity demand fulfillment
shown in Table 5 and Figure 4 greatly depends on a
DA’s population density. Similar fulfillments of electric-
ity demand are visible in DA 1, DA 2 and DA 3. This is
mainly due to similar numbers of consumers and similar
electricity consumptions in these areas. A slightly higher
fulfillment of electricity demand is observed in DA 5,
while DA 4 exhibits values that are, on average, three
times higher. A common characteristic of all the distribu-
tion areas is the fact that during the summer period, the
fulfillment of electricity demand is significantly higher.
This is primarily due to a lower electricity consumption
during the summer months and an increased electricity
production during that period.
5 CONCLUSIONS
Several key conclusions can be drawn based on the
analysis of the provided data. The PVsyst software simu-
lations revealed favorable conditions for installing solar
power plants in Serbia. The performance ratio values in-
dicated an efficient operation. The global horizontal irra-
diation values were consistent with the expectations.
Lower ambient temperatures positively impacted the PR
values, indicating an improved performance in cooler
conditions.
The electricity production analysis indicated varia-
tions among different distribution areas. DA 1 exhibited
the lowest electricity amount delivered to the grid, while
DA 3 had the highest value. The percentage of electricity
demand fulfillment varied based on the population den-
sity of each distribution area. DA 1, DA 2 and DA 3 ex-
L. DJORDJEVI] et al.: SOLAR TECHNOLOGY: EMPOWERING SERBIA’S RENEWABLE ENERGY FUTURE
Materiali in tehnologije / Materials and technology 58 (2024) 1, 33–39 37

hibited similar levels of fulfillment due to comparable
consumer numbers and electricity consumptions, while
DA 4 displayed the highest average values. Installed so-
lar power plants would meet 0.22 % of the electricity
needs in the Republic of Serbia.
This study provides researchers with crucial insights
and is a valuable resource for future solar power plant in-
vestments in Serbia. The projected installation of a solar
power capacity of 300 MW by the end of 2025
34
empha-
sizes the importance of such research. Investors can uti-
lize the findings to make informed decisions regarding
the site selection, efficiency optimization, and meeting
future electricity demands. The study’s comprehensive
analysis contributes to the knowledge base of renewable
energy sources and supports the transition towards a
more sustainable energy landscape in Serbia.
Acknowledgment
This research was conducted within project "Creating
laboratory conditions for research, development, and ed-
ucation in the field of the use of solar resources in the
Internet of Things" at the Technical Faculty, Mihajlo
Pupin, Zrenjanin, financed by the Provincial Secretariat
for Higher Education and Scientific Research, Republic
of Serbia, Autonomous Province of Vojvodina, project
number 142-451-3118/2022-01.
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