POLITYKA ENERGETYCZNA – ENERGY POLICY JOURNAL

2022  Volume 25  Issue 1  175–208

DOI: 10.33223/epj/147329

Marwa M. Ibrahim1

Investigation of a grid-connected solar pv system

for the electric-vehicle charging station of an office
building using pvsol software

Abstract: Electric vehicles are predicted to blossom in Egypt in future years as an emerging technology
in both the transportation and power sectors, contributing significantly to the decrease of fossil-fu-
el usage and CO2 emissions. As a result, to mitigate overloads of the vehicle energy demand on
the nation’s electric grid, a solar PV system can be used to provide the electricity needs of an EV
charging station. This objective of this paper is to present the design, simulation and economic
analysis of a grid-connected solar-power system for an electric-charging station at a workplace
in 6th October city, Egypt using PVSOL simulation tool to supply energy to the charging station
and office-building appliances. The ideal orientation of the PV panels for maximum energy was
determined using data from the photovoltaic geographical information system and predicted load-
-profile patterns. The amount of electricity generated the efficiency of the PV power system, finan-
cial analysis in terms of investment costs and the return on assets, and the ability to reduce CO2
emissions are all estimated in this study. This system also evaluates annual energy predictions and
is used for electric-vehicle charging, grid feeding, and appliance consumption. Due to the relatively
high solar insolation in Egypt; PV production energy was 10,463 kWh per year and the annual yield
is 1,786.69 kWh/kWp. Of the power from PV generation, 66% is utilized for charging the electric

 Corresponding Author: Marwa M. Ibrahim; e-mail: yara_mh2003@yahoo.com

1 Mechanical Engineering Department, National Research Centre (NRC), Dokki, Cairo, Egypt; ORCID iD: 0000-

-0001-8005-4882; e-mail: yara_mh2003@yahoo.com

© 2022. The Author(s). This is an open-access article distributed under the terms of the Creative Commons
Attribution-ShareAlike International License (CC BY-SA 4.0, http://creativecommons.org/licenses/by-sa/4.0/),
which permits use, distribution, and reproduction in any medium, provided that the Article is properly cited.

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 vehicle and 34% for electrical appliances. After applying the financial analysis for 20 years; the
electricity production cost is 0.0032 $/kWh and the payback period for this proposed system is abo-
ut five years. The annual energy costs after the installation of PV systems proposed system created
a financial saving of 21%. The performance ratio of this system inverter is 84% and the monthly
average of the electric vehicle SOC over a year doesn’t decrease out of 27% plus 5 tons of CO2
emissions per year were avoided. This research can be used as a recommendation for stakeholders
who want to use this energy source for vehicle charging.
Keywords: clean solar energy, electrical vehicles, charging station, CO2 emissions, economic appraisal

Introduction

The global expansion in the electrical power demand, the impact of environmental pollu-
tion, and the depletion of fossil fuel supplies have all prompted the development of alternative
power sources. Concerns about energy supply, climate change, and economic competitiveness
are causing a paradigm shift in global energy systems (Alsharif et al. 2021). Climate change has
been identified as the most serious concern facing the world in the coming decades by the ma-
jority of governments. The shift to renewable energy sources (RESs) on distribution systems is
becoming increasingly pronounced, and this trend is expected to continue. Hybrid systems have
been extensively reported to include renewable energy technologies. Solar energy is the most
abundant source of renewable energy. The biggest problem in establishing a solar PV source
in real-time is the source’s cost and availability. PV generating is promising and is widely used
around the world, but the main problem is ensuring a steady supply of energy. Solar energy is
weather-dependent and it has an impact on technical issues like power fluctuation and instability.
Solar photovoltaic resources are expected to generate 5% of the global energy demand in 2030,
escalating to 11% in 2050 (Nhede 2020). Furthermore, the EU has set sustainability goals for the
transportation sector, requiring reductions in GHG emissions by 2050.
In the hope of contributing to reducing green house gas (GHG) emissions, renewable energy
sources, energy efficiency, and new transportation technologies will all require broad exploita-
tion, incentives, and improved integration as a result of decarburization programs. As a conse-
quence, huge avenues and initiatives for the integration of eco-friendly electric cars (EVs) or the
reduction of investments or development incentives for traditional internal combustion engine
vehicles (ICEVs) are already provided or are in progress (Das et al. 2019). Compared to ICEVs,
EVs not only have the potential for low GHG emissions over their lifetime, but they also have the
potential to minimize air and noise pollution. Electric vehicle technology is quickly advancing;
with the potential to one day replace traditional automobiles. Electric vehicles use a rechargeable
lithium-ion battery to power an electric motor instead of an internal combustion engine. Electric
vehicles require charging stations similar to those used by gasoline engines for mobility. The cost
of solar power is falling as photovoltaic technology advances, boosting the practicality of solar

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projects (Sylvia 2020). Electric vehicles (EVs) are vehicles that are powered in part by stored
energy and may travel on roads, highways, trains, planes, and ships. There are two major types
of EVs (Hasan et al. 2021): fully battery electric vehicle (FBEV), hybrid electric vehicle (HEV).
HEVs are usually used in rural and urban areas, and when they operate in the city center, they
can provide significant battery assistance and turn on the motor. PEVs (plug-in electric hybrids)
and FCEVs (fuel cell electric cars) are the two accessible varieties of HEVs. The growing de-
mand for EVs (Fig. 1) (Electric Car Batteries 2022) is driven by cost, long-term battery value
and availability, tax revenue, e-commerce accessibility, power system involvement, and the in-
terface between conventional and electrical automation mobility options. Electric vehicles are
fast becoming more popular around the world, with a global market share of 2.6 percent in 2019
(Ahmad et al. 2021). Over five million electric vehicles have been listed from all over the world.
Electric vehicle revenues increased by 2% in the United States, 3% in Portugal, 5% in China, 7%
in Ireland, 8% in the Netherlands, and 50% of new EVs were sold in Norway (Hasan et al. 2021).
Consumer and government spending on Evs is increasing, and their market share is growing,
indicating a trend towards the electrification of the mobility industry (Kandasamy et al. 2021).
Electricity generation for mobility is particularly beneficial from a climate aspect in countries
like Europe, where the electricity mix has low carbon intensity. Electric vehicles are becoming
increasingly popular in Europe and China (Ghotge et al 2021). Global warming and greenhouse
gas emissions, on the other hand, constitute a hazard to the environment, which can be mitigated

Fig. 1. Electric car deployment in selected countries, 2016–2030 (Electric Car Batteries 2022)

Rys. 1. Liczba samochodów elektrycznych w wybranych krajach w latach 2016–2030

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by increasing the use of electric cars to replace ICEVs. Many countries and businesses have be-
gun to implement policies and encourage their citizens to adopt electric vehicles.
Electric vehicles will help Egypt cut its consumption of petroleum products – a process that
will coincide with the government’s goal of reducing petroleum imports by 10% by 2019 (Farrag
2018). In February 2018 (Is Egypt ready for electric vehicles? 2020), on the Cairo-Suez highway,
the country’s first electric vehicle charging station opened at a state-owned Wataniya gas station.
Bavarian Auto, a BMW distributor, introduced the BMWi, Egypt’s first electric automobile, two
months later. The Wataniya charging station is owned by Revolta Egypt, an electric vehicle tech-
nology business. The company now has seventeen charging stations (Electric vehicles in Egypt
2019) in the country, according to Badawi. Revolta Egypt has ambitious plans to cover nearly
the entire country over the next two years.
Many domestic and international studies have been conducted to investigate the power sup-
ply for EV charging stations. An in-depth look at a current photovoltaic system, electric vehicle,
and battery ideas, as well as how they might be applied to the concept of solar parking lots are de-
scribed in the literature (Osório et al. 2021b). Nguyễn (Nguyễn 2017) built a solar-energy system
for an EV charging station and utilized PVSOL premium software to test the ability to charge an
electric vehicle’s battery. Chandra Mouli et al. (Chandra Mouli et al. 2016) constructed a 10 kW
solar-power system for an electric-car charging station and explored the possibility of using solar
energy to charge battery electric automobiles at work in the Netherlands. Domínguez-Navarro
et al. (Domínguez-Navarro et al. 2018) described a quick charging station for electric vehicles
that incorporates renewable energy and storage devices. Several previous studies (BirnieIII 2009;
Nunesa et al. 2015; Tulpule et al. 2013) looked at the design of an EV charging station based on
solar PV, presenting the mutual benefit of charging EVs with solar energy, the negative effects of
excess solar generation from PV on a national scale, and the economic incentive and CO2 offsets
for PV charging being greater than charging the EV from the grid. Solar- and biogas-energy
charging stations were investigated and economically analyzed using HOMER software by Kar-
maker et al. (Karmaker et al. 2018); cost of energy (COE) of $0.1302/kWh, total net present cost
(NPC) of $56,202, and operating cost of $2,540 are estimated plus the CO2 emissions reduced by
34.68% compared to a conventional grid charging station. Ekren et al. (Ekren et al. 2021) built
a wind-solar hybrid charging-station system using HOMER software and discovered that the
best solution for the hybrid system contains 44.4 percent wind resources. To evaluate the perfor-
mance of an off-grid solar photovoltaic system for the charging of electric vehicles, a feasibility
study (Alsharif et al. 2021) for establishing an EV charging station for the residential area based
on a renewable hybrid system connected with a utility grid was explored. CO2 emissions are
expected to rise in Vietnam in the coming years/near future (Thanh 2021), with electric vehicles
(EVs) playing a significant role in reducing fossil fuel consumption, So Thanh examined the
design, simulation, and economic evaluation of a grid-connected solar-power system for an elec-
tric charging station at Thu Dau Mot University. Three different module areas were considered
for PV panels that are connected to the grid, electric appliances, electric vehicles, and battery
systems, which were then analyzed using PV sol simulation by Pushpavalli et al. (Pushpavalli
et al. 2021) to examine the overall set-up of the system, i.e. module areas of the PV panel with

178
different tracking methods and climate data for the specific location. Srujana et al. (Srujana et al.
2021) reviewed the components of the battery-electric vehicle framework, simulated the model
using MATLAB-Simulink, and established the linked electrical system components and their ac-
companying verification equations. A study by Fotouhi et al. (2019) comprises the development
of an energy-consumption-estimation model for use in an EV fleet management system (FMS);
a commercially available passenger car is modeled using MATLAB/Simulink, and the impact
of energy consumption estimation accuracy on a larger scale for a fleet of EVs is investigated.
Mehrjerdi (Mehrjerdi 2019) built stochastic optimization programming to design an off-grid
charging station for electric and hydrogen vehicles powered by solar panels, and the results show
that the cost of switching stations is covered by the investment cost of the solar system (95%),
the operational cost of the diesel generator (4.5%), and the investment cost of the diesel gener-
ator (0.5%). Another study (Fathabadi 2017); designed and built a novel grid-connected solar/
wind-powered electric-vehicle charging station with the vehicle to grid technology, demonstrat-
ing that the charging station not only provides electric energy to charge electric vehicles but also
helps to balance the load demand in the grid-connected to it. The efficiency of an off-grid solar
photovoltaic system for charging electric vehicles (EVs) in a long-term parking lot is investigat-
ed (Ghotge et al. 2021) with three strategies for prioritizing vehicles with a low state of charge
to best utilize the system’s available energy. An off-grid charging station (OGCS) is required to
meet the energy demand and Improve the charging station’s sustainability, whereas a system has
been proposed (Kumar et al. 2019) that consists of an energy-storage system (ESS) along with
a PV source and an EV charger to increase the use of electric vehicles (EV) in remote locations
while reducing the burden on the grid in urban areas. Colak et al. (Colak et al. 2016) present the
development of a model for a PV-based electrical car that anticipates the total power production
in specific Ankara city conditions; PV cell parameters are determined, and then a PV array with
cells built is formed to compute the cumulative effect. Using improved chicken-swarm optimiza-
tion to optimally place solar-powered charging stations in an IEEE 33 bus system, a comprehen-
sive framework for optimally placing solar-powered charging stations in a distribution network
with an improved voltage profile, minimum power loss, and reduced cost is proposed (Ahmad
et al. 2021). A grid-connected load-following hybrid solar PV and small-hydro micro-grid with
a grid isolated electric-vehicle charging system have been presented (Olatunde et al. 2020). Os-
ório et al. (Osório et al. 2021a) propose an EV fast charger with an integrated PV system as
a news aggregator operator in the energy system, as well as the effectiveness of a solar-pow-
ered EV parking lot. The use of available photovoltaic (PV) electricity to charge EV batteries
while keeping the low-voltage network within its operational constraints is a linear programming
(LP)-based optimization approach for charging electric cars (EVs) in a decentralized manner
as described in the literature (Cortés Borray et al. 2021). Even though two charging strategies
are analyzed and compared using the TRNSYS simulation tool, the investigated smart grid is
designed (Calise et al. 2021) to meet the energy demands of a district, including the energy de-
mand for space heating and cooling, as well as the electric energy of a large number of buildings
occupied by people who only use electric vehicles. The Stochastic Firefly Algorithm (SFA) mod-
el for Maximum Power Point Tracking (MPPT) control to obtain maximum power from the solar

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power plant is used (Goswami and Sadhu 2021) to predict the arrival time, the State of Charge
(SOC), and the charging demand, and the findings show that using SFA enables fast charging
of the batteries while also increasing the charging station’s profit. A Comprehensive optimiza-
tion approach for constructing an off-grid solar-powered charging station to offer electricity to
electric cars (EVs) and hydrogen to hydrogen vehicles was provided by Wang et al. (Wang et al.
2020); the results show that the overall annual cost increases by 13.75 percent. Mclaren et al.
(Mclaren et al. 2016) present a summary of expected emission levels from battery-electric and
plug-in hybrid electric vehicles for four charging scenarios and five power grid profiles, and the
results show that charging during the off hours leads to higher carbon output for all vehicle types
when compared to other charging situations.
An on-grid solar photovoltaic (PV) system for charging electric cars (EVs) parked in parking
lots is investigated in this study. In 6th October, Giza, Egypt, these parking spots, where autos
are parked for eight hours, are frequently positioned adjacent to the designated site’s office buil-
ding. The proposed system’s advantages include the elimination of grid capacity, which would
result in a significant reduction in the capital costs of the installation of EV-charging infrastruc-
ture. This study uses a collection of monthly solar radiation and ambient temperature data for
a specific site produced with high precision and resolution from the Photovoltaic Geographical
Information System (PVGIS). In addition, the actual load demand profile is estimated. The stu-
dy’s key goals are as follows:
)) Estimating percentage of required electricity production which is covered by PV power
or the national grid,
)) Designing a rooftop building solar-power system for an EV charging station,
)) The state of electric car charging is also calculated for a year,
)) Assessing the study’s economic feasibility in terms of total investment costs, electricity ge-
neration costs, and cash flow in and out.
)) Using the PVSOL software package, evaluating the solar-power system’s system performan-
ce ratio and the amount of CO2-emission reduction.
This paper is organized as follows: in Section 2, mathematical modeling for each component
is described, along with the simulation methodology, which is implemented using the PVSOL
software package. In Section 3, we show the structure of the charging of electric vehicles (EVs)
using solar PV, as well as site geography, resource data, load-profile data, as well as specifica-
tions of the system components. Section 4 discusses technical, economical, and environmental
issues. Finally, Section 5, presents the findings of our study as well as some suggestions for
future research.

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 1. Methodology

The proposed technique in this work is separated into two parts: mathematical modeling
and simulation. This investigation is carried out sequentially. The site’s location is picked
first. The solar power system is then installed and equipment is chosen to meet the actual load
requirement. The study of mathematical modeling of system components is also considered.
The PVSOL software tool is then used to model and analyze the solar-power system’s energy
efficiency. Finally, the project’s economic feasibility and the extent of the avoided emissions
are assessed.

1.1. Mathematical modeling

A model is a method of establishing the goals, variables, and constraints of a scenario. PV,
solar panels, and other components can be modeled using a mathematical equation to calculate
the output power under various climatic circumstances and energy policies in Egypt. To improve
the charging station’s reliability, the renewable energy source is run at full capacity.

1.1.1. PV generator capacity

In the grid-connected mode, the solar cells’ output should be able to meet network load
requirements with the addition of a small amount of electricity to compensate for system losses.
The number of PV modules per parallel thread in a series connection, as well as the number of
parallel strings linked in parallel. The modeled equation that is used to calculate the PV system’s
output power is presented in Equation 1 (Alsharif et al. 2021).

G (t )
Ppvout
= (t ) Ppv rated ⋅ ⋅ [1 + αt (Tamb + (0.03125 ⋅ Gt )) − TCstc (1)
1000

where:
Ppvout(t) – the output power generated from PV [watts],
G(t) – refers to solar irradiance [W/m2], and 1000 W/m2 refers to the reference irra-
diance,
PPVrated – denotes as rated power for PV [watts] at standard test condition (STC),
αt – the temperature coefficient (3.7 ⋅ 10–3) 1/C,
TCSTC – the cell temperature [°C],
Tamb – the ambient temperature [°C], respectively. The TCSTC can be obtained by Equ-
ation 2 (Bhandari et al. 2014).

181
  NOCT − 20  (2)
TCSTC = Tamb + G (t ) ⋅  
 800 

where:
NOCT – the manufacturer’s model-able nominal operating cell temperature in degrees Cel-
sius.

1.1.2. Electric vehicle charge station

To improve security and economic sustainability, the battery in electric vehicles is employed
to address several supply restrictions. The rated capacity of an electric-car charging station is
one of the most important factors to consider when modeling it. The rated capacity is calculated
using Equation 3 (Arancibia and Strunz 2012; Cortés Borray et al. 2021).

k ⋅N ⋅P 
Srated =  load slot EV  (3)
 Cos ∅ 
where:
Srated – the rated capacity of the vehicle station,
Cos ∅ – the power factor,
Nslot – the amount of charging slots for each EV,
kload – the overload factor for covering overloading in transients,
PEV – the maximum power rate of each EV.

1.1.3. Inverter

An inverter, with 95% efficiency, is essential for transporting power between renewable so-
urces and loads (Singh et al. 2016). The rating of the inverter was identified to determine the
system’s overall annual cost. The inverter rating Pinv(t) can be calculated using Equation 4 (Du-
fo-López and Bernal-Agustín 2008).

Pinv
= (t ) PLm (t ) ⋅ ηinv (4)

where:
PmL(t) – denotes the peak load demand which is the key in choosing the right inverter,
ηinv – refers to the inverter efficiency.

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 1.1.4. National Grid

If renewable energy sources are insufficient to meet load requirements, the grid can assist in
filling the gap. As indicated in Equation 5 (Barakata et al. 2020), Rgrid is utilized to calculate the
money earned from energy sales to the utility grid.

∑
8760
=Rgrid ratefeed − in xEgrid ( selling ) (5)
t =1

where:
ratefeed - in – refers to the feed-in tariff rate,
Egrid(selling) – the selling price of energy. Furthermore, the cost power from grid
Cgrid is calculated using Equation 6 (Barakata et al. 2020).

∑
8760
C grid
= Cp ⋅ Egrid ( purchased ) (6)
t =1

where:
Cp – the cost of buying electricity from the grid, refers to the total cost of buying power
from the grid for a year in per-hour terms.

1.2. Simulation using PVSOL

Valentin tools create cutting-edge software for design, dynamic simulation, and yield es-
timation (Software 2021). This company offers design tools for photovoltaic (PVSOL), solar
thermal (TSOL), and heat pump (Geo TSOL) systems. PVSOL 2021 R8 was used for this
study as it is a simple, quick, and reliable software tool for the simulation of the solar PV
system (Mehadi et al. 2021). PVSOL is a dynamic simulation tool for designing and optimizing
solar systems in conjunction with appliances, battery systems, and electric vehicles. PVSOL can
design and simulate all types of modern PV systems, from modest rooftop systems with a few
modules to large solar parks with up to 100,000 modules (Software 2021). PVSOL searches
for the best connection between your PV modules and the inverter based on all of the important
parameters, such as location, component specifications, site radiation statistics, and load profile.
The software calculates the solar yield based on the required annual PV energy, solar fraction,
and solar yield data.

183
 2. Design of the proposed on-grid photovoltaic system

This study presents a smart technique for a grid-connected photovoltaic system for office
-building appliances with electric-vehicle charging stations, but it does so by describing the
flow power with the PVSOL software package. The consumption of office-building appliances
and electric-vehicle charging stations is regarded as a daily load, and forecasting the charging
station’s periodic power requirement is highly recommended. Additionally, the electrical grid’s
contribution is merged in this design as a backup option for common advantage when photovol-
taic power is unable to protect the station’s needs, on the one hand, and then when the charge
station’s battery that is required to be charged is fully charged and the photovoltaic system can
incorporate excess energy into the grid.

2.1. Architecture of the charging station

This work is based on the case study of a project with thorough specifications, such as mete-
orological data for PV-panel design and the daily energy demand for office building appliances
and electric vehicle batteries to modify the rated capacity of the solar station. While the grid’s
contribution to the structure remains vital, the controller unit’s power flow algorithms and how
they affect the grid, both positively and negatively present some complex challenges. In this
study, a 5.9 kWp solar-power system with a charging station is installed on the roof of an offi-
ce building. This solar-power system powers both the charging station and the office building.
Electricity generated by photovoltaic power plants can be directly charged to an electric vehicle
during the day. If the amount of electricity generated exceeds the vehicle’s need, a two-way me-
ter can be used to sell the excess energy to the grid. During the night, the office space and char-
ging station receive electricity from the national grid via the two-way meter. Two bidirectional
grids and EV ports, as well as a single unidirectional PV port, are included in the EV–PV charger.
A central DC-link connects the PV converter, grid inverter, and separated EV charging. Because
of the lower conversion steps and higher efficiency, the direct DC link of EV and PV would be
chosen over the AC interface. EV charging is evaluated by standards In the United States (Arar
2020). A layout of the proposed system is shown in Figure 2. This technique provides power to
electric vehicles and appliances and returns excess power to the grid.

184
 Fig. 2. Schematic of on-grid PV solar system proposed for appliances with-electric vehicle charging

Rys. 2. Schemat przyłączenia systemu fotowoltaicznego proponowanego dla urządzeń

z ładowaniem pojazdów elektrycznych

2.2. Collection Data

2.2.1. Site geography

The location is an office building next to a Family commercial plaza in 6th October City,
Giza, Egypt. Next to this office building is an electric-vehicle charging station. Family Mull has
a unique site in the city, as it is located on the 6th of October city’s central axis and amid the city’s
greatest educational edifices. The project is located in one of the city’s most exclusive areas, just
steps from the Al Hosary Mosque, the city’s most famous mosque, and the 6th of October Club,
the city’s largest gathering of people. The Family Mall is located at 29.9 N latitude and 30.9 E
longitude, with an elevation of 226 meters above sea level.

185
 2.2.2. Site resources data

For the heating runtime environment, meteorological data on solar radiation is critical. Solar
radiation and temperature data for the chosen site are used to calculate the design of the system.
Typical meteorological year (TMY) files are used to retrieve satellite-derived irradiance and
meteorological data for the site chosen using the photovoltaic geographical information system
(PVGIS) web-server (PVGIS data 2021). Figure 3 depicts the monthly average of solar radiation
and temperature at the site (PVGIS Data 2021). These meteorological statistics were compiled
using observations of the hourly averages of solar radiation and temperature in the chosen loca-
tion between 2014 and 2020. In this particular location, the monthly averages of global radiation
and temperature are 1544 kWh/m2 and 22°C, respectively. The maximum solar radiation was
2,200 kWh/m2 in June, whereas the maximum temperature was 29.5°C in July.

2500 35
30
2000
25
1500
kWh/m2

20

C
1000 15
10
500
5
0 0

Month
Global Radia�on kWh/m2 Temperature C

Fig. 3. Site resources data (irradiation and temperature), 2014: 2020 (PVGIS Data 2021)

Rys. 3. Dane o zasobach terenu (napromieniowanie i temperatura), 2014: 2020

2.2.3. Load profile

The chosen site office building is estimated to accommodate 1,000 employees in a size that
meets this building requirement, with usage occurring five days per week (Sunday to Thursday)
between the hours of 8 am and 5 pm for a total of nine working hours. Light bulbs, fans, and
PCs are among the wide range of electrical appliances used here. Figure 4a depicts the office

186
building’s daily electrical use, whereas Figure 4b depicts the monthly electric energy demand.
At 10 am, the peak load is 6.3 kW, and the annual energy usage is approximately 60,000 kWh.
The consumption of electric vehicles is also discussed in the following sections.

5400
V

7
6 5200

5 5000
4800

kWh
4
kW

3 4600
2 4400
1 4200
0 4000
00:00
02:00
04:00
06:00
08:00
10:00
12:00
14:00
16:00
18:00
20:00
22:00

Jun
Mar

May

Jul

Nov
Dec
Apr

Aug
Sep
Jan

Oct
Feb
Hour Month

a - Daily load profile b - Monthly electric consumption

Fig. 4. Electric consumption profile of the selected office building in this study

Rys. 4. Profil zużycia energii elektrycznej w wybranym dla przeprawdzenia badań budynku biurowym

2.3. System description and data used

2.3.1. Selection of PV panels

The total capacity of the system to be installed is projected to be 5.88 kWp. A PV panel’s
rated power is 325 W, and the number of panels to be installed is estimated based on:

NPV = PPVrequired/PArray = 5880/325 = 18 panels (7)

where:
NPV – the number of solar panels needed to be installed,
PPVrequired – the power of the PV system needed to be installed,
PArray – the rated power of solar panel.

Since the PV generating surface is 34.9 m2, the selected number of modules is 18, and the
specifications of the solar panels are detailed in Table 1 (Solar panels specifications 2021), and
a graphic coverage design of the concrete roof building is displayed in Figure 5. The selected
roof is concrete and the installation of solar panels on the roof parameters is: PV direction which
facing south and angle of inclination equals 30°.

187
 Table 1. Specifications of PV panels
Tabela 1. Specyfikacje paneli fotowoltaicznych

Characteristic Value
Model STP325S-24/Vem
Dimensions 1.9×0.9×0.04 m
Cell type Si Monocrystalline
Cell count 72 cell
Nominal output 325 W
Total solar capacity 5.88 kWp
Maximum power voltage 36.72 V
Maximum power current 1.73 A
Open circuit voltage 43.9 V
Short circuit current 1.84 A
Efficiency 16.77 %

Source: Solar-panel specifications 2021.

Fig. 5. Building concrete roof graphic coverage scheme

Rys. 5. Schemat graficzny pokrycia dachu budynku betonem

188
 2.3.2. Selection of inverter

The power of the inverter is selected according to the rule by (Mondol et al. 2006; Nguyen
and Hoang) is shown in Equation 2 (Advanced... 2022).

Pinv = PPVrequired/1.17 = 5880/1.17 = 5000 (W) (8)

where:
Pinv – the rated power of the inverter and 1.17 is a compatibility coefficient selected
according to experience. The power of the chosen inverter is 5 kW, and the inver-
ter’s full characteristics are listed in Table 2 (Operation Manual 2013).

Table 2. Specifications of the inverter

Tabela 2. Dane techniczne falownika

Characteristic Value
Model Conext RL 5000 E
Company Schneider Electric
Maximum DC power 5 kW
Maximum DC input voltage 550 V
Maximum DC input current 36 A
AC power rating 5 kW
No of phases 1
Maximum AC power rating 5 kVA
No. of MPP Trackers 2
Efficiency 97%

Source: Operation Manual 2013.

The number of panels to be put in this study is 18, and the inverter has two MPPTs. As a re-
sult, when using the power optimizer mode, the system is separated into two parallel strings, each
with nine solar panels connected in series.

2.3.3. Specifications of electric-vehicle charging station

The electrical charging station can simultaneously charge ten vehicles; Table 3 includes the
specifications of electric automobiles and charging stations (Kia Niro EV Specifications 2021).

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 Table 3. Electric-vehicle parameters and charging-station specifications
Tabela 3. Parametry pojazdów elektrycznych i specyfikacje stacji ładowania

Electric vehicle
Model e-niro 136 (AC charging 10.5 kW)
Manufacturer Kia
Range in accordance with WLTP 289 km
Consumption 15.3 kWh/100km
Battery capacity 39.2 kWh
No of seats 5
Empty weight 1667 kg
Top speed 155 km/h
Engine power 100 kW/136PS
Discharge power 10.5 kW
Charging station
Charging-station technology AC type 2
Charging power 10×10.5 kW
Charging mode PV optimized
Desired range per week 350 km
Time at charging station 8h (from 9 am to 5 pm)
No of trips per week and per vehicle 12 (29.2 km per journey)
Mileage per year 10×18,250 km (27,923 kWh/a)

Source: Kia Niro EV Specifications 2021.

2.3.4. Ac Grid Mains

The grid voltage is 230 V with three phases and the displacement power factor (cos ϕ) is 1.

2.3.5. Cables

The total loss in cables is taken as 2%.

2.4. Simulation

The PVSOL 2021 R8 software package is used to simulate a grid-connected PV system with
electrical appliances and electric cars. Figure 4 shows the block diagram of this suggested sys-

190
tem, together with the required circuits and cables, and Table 6 shows the simulated technical
and financial parameters used.

Fig. 6. Block diagram of proposed PV system connected to grid and electric vehicle

Rys. 6. Schemat blokowy proponowanego systemu PV podłączonego do sieci i pojazdu elektrycznego

Table 4. Simulation parameters for this study

Tabela 4. Parametry symulacji dla tej analizy

Technical Parameters
Simulation period Whole year
Time-step One hour
Financial Analysis Parameters
Assessment period 20 yr
Annual average return on capital employed 1.5%
Energy balance/feed in concept Net-metering
Inflation rate for energy price 3%
Electricity purchasing price 0.103 $/kWh (Egypt electricity prices 2021)
Value-added sales Tax All entries are gross

3. Results

3.1. Overview Results

The planned study, which will take place in 6th October City, Egypt, would use simulation to
estimate the energy, environmental impact, and economic aspects of a PV system for EV char-
ging. Rooftop PV systems are frequently being erected on office buildings adjacent to commer-

191
cial enterprises. Table 5 shows the bill of quantity (BOQ) sheet for this proposed system. Table 6
shows the overall outcomes of the simulation.

Table 5. Bill of quantity of this proposed system

Tabela 5. Przedmiar robót proponowanego systemu

Item Type Manufacturer Name Quantity Unit

1 PV Module Suntech Power STP325S-24/Vem 18 piece
2 Inverter Schneider Electric Conext RL 5000 E 1 piece
3 Power optimizer Solar Edge P3000EU-APAC 18 piece
e-niro 136
4 Electric vehicle Kia 10 piece
(AC charging 10.5 kW)
5 Component Energy-flow Sensor 1 piece
6 Component Bidirectional meter 1 piece
7 Component Office connection 1 piece

Table 6. Overall results of grid-connected PV system with electric vehicle

Tabela 6. Wyniki dla systemu fotowoltaicznego podłączonego do sieci z pojazdem elektrycznym

PV generator energy (AC grid) 10,463 kWh/year

Energy from grid 80,798 kWh/year
Annual yield 1,786.69 kWh/kWp
Performance ratio 84.8 %
Solar fraction 11.5 %
Accrued cash flow (cash balance) 2,087 $
Total investment costs 566 $
Annual revenue or saving 110 $/year
Electricity production cost 0.0032 $/kWh
Payback period 5.1 year
CO2 emissions avoided 4,912 kg/year

3.2. Technical Results

Figure 7 shows the energy flow graph of the system over a year. The total consumption of
this system study is 91,261 kWh/year; 60,000 kWh/year consumption of eclectic appliances for
the office buildings and 31,261 kWh/year consumption of electric vehicle stations. This total

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 Fig. 7. Energy flow graph of proposed grid-connected PV system with electric vehicle

Rys. 7. Wykres przepływu energii proponowanego podłączenia do sieci

consumption is covered by PV power of 10,463 kWh/year and 80,798 kWh/year is covered by

the national grid since there is no electricity sold to the national grid which means the level of
self-sufficiency is 11% as shown in Figure 8. The PV generator output is 5.9 kWp, the amount
of electricity produced from the solar-power system is 10,463 kWh/year, the direct use for the
office building is 5,199 kWh/year and the amount of electricity supplied to the charging vehic-
le station is 5,264 kWh/year. Also, losses due to charging/discharging of electric vehicle are
estimated with 2,621 kWh/year, losses in the battery are 1,098 kWh/year and the consumption
due to kilometers driven is 27,923 kWh. The energy balance for the irradiance of this system is
illustrated in Table 7.
PV module irradiance versus temperature distributions for the whole year is described in
Figure 9 while Figure 10 shows electric-vehicle lower limit state of charge percentage for the
whole year and monthly averages performance ratio of the inverter is illustrated in Figure 11.
As seen in Figure 9; the average monthly irradiance of PV modules over the course of a year
is 880 W/m2 since the highest value in both January and February is around 1,000 W/m2. Simul-
taneously, the highest value module temperature is about 55°C in both September and October.
From Figure 10, it is found that the monthly average of electric vehicle SOC is about 23.8%
since the highest value is 28% in January while in Figure 11, the average performance ratio of an
inverter is 84.4% for the whole year since it also the biggest performance is in both of January
and December.

193
 11%
34%

66%
89%

Appliances Consumption
Consumption Covered by PV Power
Consumption Covered by Grid Electric Vehicle Consumption

Fig. 8. Consumption percentages covered by national grid and PV power (Appliances and Vehicles)

Rys. 8. Procenty zużycia pokrywane przez sieć krajową i energię fotowoltaiczną (urządzenia i pojazdy)

Table 7. PV System energy balance for irradiance

Tabela 7. Bilans energii systemu fotowoltaicznego dla natężenia napromieniowania

Global radiation – horizontal 2,003.57 kWh/m²

Deviation from standard spectrum –20.04 kWh/m² –1.00%
Ground reflection (Albedo) 26.57 kWh/m² 1.34%
Orientation and inclination of the module surface 108.71 kWh/m² 5.41%
Shading 0.00 kWh/m² 0.00%
Reflection on the module interface –33.89 kWh/m² –1.60%
Global radiation at the module 2,084.93 kWh/m²
2,084.93 x 34.926 kWh/m² x m²
=   72,818.85 kWh
Global PV radiation 72,818.85 kWh
Soiling 0.00 kWh 0.00%
STC conversion (rated efficiency of module 16.77%) –60,608.25 kWh –83.23%
Rated PV Energy 12,210.59 kWh
Low-light performance 3.61 kWh 0.03%
Deviation from the nominal module temperature –1,018.17 kWh –8.34%
Diodes –55.98 kWh –0.50%
Mismatch (manufacturer information) 0.00 kWh 0.00%
Mismatch (configuration/shading) 0.00 kWh 0.00%
Power optimizer (DC conversion/down-regulation) –134.27 kWh –1.21%

194
 PV energy (DC) without inverter down-regulation 11,005.79 kWh
Failing to reach the DC start output –1.16 kWh –0.01%
Down-regulation on account of the MPP voltage range 0.00 kWh 0.00%
Down-regulation on account of the max. DC current 0.00 kWh 0.00%
Down-regulation on account of the max. DC power 0.00 kWh 0.00%
Down-regulation on account of the max. AC power/cos ϕ 0.00 kWh 0.00%
MPP matching 0.00 kWh 0.00%
PV energy (DC) 11,004.63 kWh
Energy at the inverter input 11,004.63 kWh
Input voltage deviates from rated voltage 0.00 kWh 0.00%
DC/AC conversion –328.03 kWh –2.98%
Standby consumption (Inverter) –10.95 kWh –0.10%
Total cable losses –213.53 kWh –2.00%
PV energy (AC) minus standby use 10,452.12 kWh
PV generator energy (AC grid) 10,463.07 kWh

3.3. Energy Forecasting Results

Table 8 shows the energy supply account of the planned system for the entire year. Figure 12
depicts the monthly average production energy forecast with consumption, whereas Figure 13 de-
picts the production forecast per inverter.
Table 8 and Figure 9 show that the maximum value consumption of 7976 kWh occurred in
July, and the largest energy production from PV modules also occurred in July. As a result of the
projected PV on-grid system, the overall costs are estimated to be $853 against $963 without
solar PV, saving $110 per year. According to the largest output of PV modules, as listed in Table
8, the average monthly production prediction of an inverter is 871 kWh, with the greatest value
in July is as shown in Figure 13.

3.4. Financial Results

This proposed system is subjected to a twenty-year economic examination. Each year’s ac-
crued cash flow is computed using the investment amount, the export tariff for grid feed-in,
the electricity savings, and the annual cash flow. Figure 14 shows the cash amount at the end
of twenty years, which is predicted to be $2,875. Figure 15 shows the monthly electricity cost
savings before and after PV installation, and Figure 16 shows the evolution of energy costs over
the project’s lifetime.

195
 1200 70

1000 60
50
Irradiance W/m2

Temperature oC
800
40
600
30
400
20
200 10
0 0

Time
Irradiance onto tilted surface [W/m²] Module Temperature [°C]

Fig. 9. PV module irradiance vs. temperature distribution for the whole year

Rys. 9. Naświetlenie modułu fotowoltaicznego a rozkład temperatury przez cały rok

29
28
27
26
SOC %

25
24
23
22
21
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month

Fig. 10. Lower limit SOC of electric-vehicle station over a year

Rys. 10. Dolna granica SOC stacji pojazdów elektrycznych w ciągu roku

196
 90
88
86
84
PR %

82
80
78
76
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month

Fig. 11. Performance ratio of inverters over a year

Rys. 11. Współczynnik wydajności falowników w ciągu roku

From Figure 16, it can be seen that the cost trend before the installation of a solar PV system
is higher than the cost trend after PV system installation. After the project’s twenty-year life cy-
cle, annual energy expenses after installation of PV systems are anticipated to be about $1,400,
while costs before installation are estimated to be around $1,700, indicating that the solar PV
proposed system resulted in a financial saving of 21%.

3.5. Environmental Results

The world today is on the search for pollution-free technology that is environmentally favora-
ble. The rooftop solar installation not only saves money on energy bills, but also helps the environ-
ment by lowering CO2 emissions. Egypt’s grid electricity emission factor is 0.6 kgCO2/kWh
(Carbon Pricing Dashboard 2020) and the price for a ton of CO2 is 50–100 USD (Carbon Pricing
2020). As a result, the quantity of CO2 emissions reduced by solar PV in a year predicted using
PVSOL software is 4,912 kg per year, resulting in savings of roughly $500 per year. When it
comes to solar energy, there are almost no pollutants. As a result, the proposed PV system is
beneficial for the environment.
In the end, when the proposed system is compared to other studies (Kumar et al. 2016; Velaga
and Kumar 2012; Deshmukh and Singh 2019), it is demonstrated that it is efficient due to the
lowest capital cost, payback period, and environmental consequences. The novelty of this paper
includes the elimination of grid capacity, which would result in a significant reduction in the

197
 Table 8. Energy supply account of the proposed system for the whole year

198
Tabela 8. Rachunek dostaw energii dla proponowanego systemu za cały rok

Reference Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Total

Consumption
7,211.95 6,883.97 7,740.69 7,593.92 7,968.40 7,708.80 7,976.84 7,968.87 7,590.77 7,737.78 7,403.19 7,464.61 91,249.780
[kWh)]

Energy production
724.68 723.55 922.63 964.95 966.42 963.01 984.82 979.59 931.07 871.86 733.15 686.38 10,452.120
[kWh]

Energy balance
6,487.27 6,160.42 6,818.06 6,628.97 7,001.97 6,745.79 6,992.02 6,989.28 6,659.70 6,865.91 6,670.04 6,778.23 80,797.660
[kWh]

Costs without solar

76.16774 72.70387 81.75161 80.20129 84.15677 81.41484 84.24581 84.16129 80.16839 81.72065 78.1871 78.83613 963.7155
energy system [$]

Costs with solar

68.51419 65.06194 72.00774 70.01032 73.94968 71.24452 73.84452 73.81613 70.33484 72.5129 70.44452 71.5871 853.3277
energy system [$]

Cost savings value

7.653 7.641 9.743 10.190 10.206 10.170 10.401 10.345 9.833 9.207 7.743 7.249 110.387
[$]

Over the whole observation period, degradation and inflation rates are applied monthly. In the first year, this is completed.
 20000
10000
0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
-10000
Energy kWh

-20000 Month
-30000
-40000
-50000
PV Generator energy AC grid (kWh)
-60000
Appliances (kWh)
-70000
Cummaltive total energy generation (kWh)
-80000
-90000

Fig. 12. Production forecast with consumption for the whole year

Rys. 12. Prognoza produkcji ze zużyciem na cały rok na cały rok

1200

1000

800
Energy kWh

600

400

200

0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Fig. 13. Production forecast per inverter for the whole year

Rys. 13. Prognoza produkcji falownika na cały rok

199
 Table 9. Economic analysis parameters of this proposed system for 20 years

200
Tabela 9. Parametry analizy ekonomicznej proponowanego systemu przez 20 lat

Year Year Year Year Year Year Year Year Year Year Year Year Year Year Year Year Year Year Year Year
1 2 3 4 5 6 7 8 9 10 12 13 14 15 16 17 18 19 20 21
Investments [$] –566 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Electricity Savings [$] 106 110 111 113 115 52 54 56 57 59 61 0.65 2 4 6 8 10 12 14 16
Annual Cash Flow [$] –459 110 111 113 115 52 54 56 57 59 61 0.65 2 4 6 8 10 12 14 16
Accrued Cash Flow [$] –459 –348 –236 –123 –7 44 34 25 19 14 11 10 13 17 24 32 43 53 5 22

Throughout the observation period, degradation and inflation rates are applied monthly. During the first year, this is accomplished.

35000

30000

25000

20000

15000

Cash flow $
10000

5000

0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
-5000

-10000
Year

Fig. 14: Cash flow balance of the proposed system for 20 years

Rys. 14: Bilans przepływów pieniężnych proponowanego systemu za 20 lat

 90
80
Electricity cost saving ($)

70
60
50
40
30
20
10
0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month

Costs without Solar Energy ($) Costs with Solar Energy ($)

Fig. 15. Electricity cost saving of this proposed system for a whole year

Rys. 15. Oszczędność kosztów energii elektrycznej proponowanego systemu przez cały rok

90
80
Electricity cost saving ($)

70
60
50
40
30
20
10
0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month

Costs without Solar Energy ($) Costs with Solar Energy ($)

Fig. 16. Annual developments of energy costs every year

Rys. 16. Roczne zmiany kosztów energii w każdym roku

201
capital costs of EV charging infrastructure installation which can be used as a recommendation
for stakeholders that want to use this energy source for vehicle charging. Higher profits will
motivate utilities to invest in charging stations, resulting in the greater market penetration of
electric vehicles since the Egyptian government is now offering a reasonable price for purcha-
sing electricity generated by rooftop solar-power projects.

Conclusions

Electric-vehicle technology is now advancing at a rapid pace, with the potential to replace
regular automobiles in the future. Based on simulation using PVSOL software, this study is
being carried out to assess the energy, environmental impact, and economic aspects of an on-
grid PV system for electrical appliances of a workplace office building with EV charging. The
suggested system’s sizing is perfect and matches the load demand for the on-grid system. Thro-
ugh simulations, The generated energy is 10,463 kWh/year, the yearly specific yield in kWp is
1,786.69 kWh, and the saved CO2 emissions are 4.9 kg/year. PV power is used to charge electric
vehicles to the tune of 66% and to power electrical appliances to the tune of 34%. The produc-
tion cost of energy is 0.0032 $/kWh after applying the financial analysis for twenty years, and
the payback period for this suggested system is roughly five years. The annual energy costs after
installing the PV systems proposed system were reduced by 21%. The Egyptian government
is now offering a reasonable price for purchasing electricity generated by rooftop solar power
projects; However, the price is expected to fall in the following years, lengthening the payback
period and lowering the system’s economic value. To summarize, this system is technically and
financially feasible in terms of energy output. Higher profits will motivate utilities to invest in
charging stations, resulting in the greater market penetration of electric vehicles.

202
 Nomenclature

Abbreviations
CO2 Carbon dioxide
COE Cost of energy
ESS Energy storage system
EVs Electric vehicles
FBEV Fully battery electric vehicle
GHGs Green house gases
HEV Hybrid electric vehicle
ICEVs Internal combustion engine vehicles
MPPT Maximum power point tracking
NPC Net present cost
OGCS Off-grid charging station
PV Photovoltaic
PVGIS Photovoltaic geographical information system
RESs Renewable energy sources
SFA Stochastic firefly algorithm
SOC State of charge
TMY Typical meteorological year
Letters
8760

∑ Egrid ( purchased )
t =1
Per hour summation of annually buying electricity from the grid for one year

Cgrid Cost power from the grid

Cp Cost of buying electricity from the grid
Egrid(selling) Selling energy price
G(t) Solar irradiance
kload Overload factor for cover overloading in transients
NOCT Nominal operating cell temperature
NPV Number of solar panels needs to be installed
Nslot Amount of charging slots for each EV
PArray Rated power of solar panel
PEV Maximum power rate of each EV
Pinv Rated power of the inverter
Pinv(t) Inverter rating
PmL(t) Peak load demand
Ppv rated Rated power for PV at standard test condition
Ppvout (t) Output power generated from PV
PPVrequired Power of PV system needs to be installed

203
 ratefeed-in Feed-in tariff rate
Rgrid Money earned from energy sales to the utility grid
Srated Vehicle station rated capacity
Tamb Ambient temperature
TCSTC Cell temperature
Greek symbols
Cos ∅ Power factor
αt Temperature coefficient
ηinv Inverter efficiency.

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 Marwa M. Ibrahim

Badanie systemu fotowoltaicznego podłączonego do sieci dla

stacji ładowania pojazdów elektrycznych w budynku biurowym
przy użyciu oprogramowania PVSOL

Streszczenie

Przewiduje się, że w najbliższych latach pojazdy elektryczne rozwiną się w Egipcie jako technologia
wschodząca zarówno w sektorze transportu, jak i energetyki, przyczyniając się znacząco do zmniejszenia
zużycia paliw kopalnych i emisji CO2. Dlatego proponuje się, że aby złagodzić przeciążenia krajowej sieci
elektrycznej wynikające z zapotrzebowania pojazdów na energię, można wykorzystać system fotowoltaicz-
ny do zaspokojenia zapotrzebowania na energię elektryczną w stacjach ładowania pojazdów elektrycznych.
Celem niniejszego artykułu jest przedstawienie projektu, symulacji i analizy ekonomicznej, z wykorzysta-
niem narzędzia symulacyjnego PVSOL, dla podłączonego do sieci systemu zasilania energią słoneczną biu-
ra w mieście Madinat as-Sadis min Uktubar w Egipcie celem dostarczania energii do stacji ładującej i urzą-
dzeń biurowych. Idealną orientację paneli fotowoltaicznych dla uzyskania maksymalnej energii określono
na podstawie danych z fotowoltaicznego systemu informacji geograficznej i przewidywanych wzorców pro-
filu obciążenia. W niniejszym opracowaniu szacowana jest ilość wytworzonej energii elektrycznej, spraw-
ność systemu fotowoltaicznego, analiza finansowa pod kątem kosztów inwestycji i zwrotu z aktywów oraz
zdolność do redukcji emisji CO2. System ten ocenia również roczne prognozy zużycia energii i jest używa-
ny do ładowania pojazdów elektrycznych, zasilania sieci i zaspokojenia zużycia urządzeń. Ze względu na
stosunkowo wysokie nasłonecznienie w Egipcie produkcja energii fotowoltaicznej wyniosła 10 463 kWh
rocznie, a roczna wydajność to 1786,69 kWh/kWp. 66% energii z produkcji fotowoltaicznej jest wyko-
rzystywane do ładowania pojazdów elektrycznych, a 34% do urządzeń elektrycznych. Po przeprowa-
dzeniu analizy finansowej w okresie 20 lat: koszt produkcji energii elektrycznej wynosi 0,0032 $/kWh,
a okres zwrotu nakładów dla proponowanego systemu to około pięć lat. Obliczono, że roczne oszczędności
zużycia energii po instalacji takich systemów PV przyniosły w wymiarze finansowym 21%. Współczynnik
wydajności tego falownika systemowego wynosi 84%, a średnia miesięczna SoC pojazdu elektrycznego
w ciągu roku nie zmniejsza się o 27%, a dodatkowo mamy oszczędność 5 ton emisji CO2 rocznie. Badania
te można wykorzystać jako rekomendację dla interesariuszy, którzy chcą wykorzystać to źródło energii do
ładowania pojazdów.

Słowa kluczowe: czysta energia słoneczna, pojazdy elektryczne, stacja ładowania, emisja CO2, ocena
ekonomiczna