







































Y. Kassem et al. /Future Technology                                                                          August 2025| Volume 04 | Issue 03 | Pages 204-215 

204 

 

 

 

Article 

Harnessing wind and solar power for electric 

vehicle charging: a feasibility study at Ikas 

supermarket, Lefkosa, Northern Cyprus 
Youssef Kassem1,2,3*, Hüseyin Çamur 1, Ahmad Hussein1  

1Department of Mechanical Engineering, Engineering Faculty, Near East University, 99138 Nicosia (via Mersin 10, 

Turkey), Cyprus  
2Energy, Environment, and Water Research Center, Near East University, 99138 Nicosia (via Mersin 10, Turkey), Cyprus 
3Science, Technology, Engineering Education Application, and Research Center, Near East University, 99138 Nicosia (via 

Mersin 10, Turkey), Cyprus 

A R T I C L E   I N F O 
 

Article history: 
Received 25 April 2025  
Received in revised form 
08 June 2025 
Accepted 17 June 2025 
 
Keywords:  
Electric car, Renewable resource,   
Cost-benefit analysis, Solar energy, Wind energy 
 
*Corresponding author 
Email address: 
yousseuf.kassem@neu.edu.tr 
youssef.kassem1986@hotmail.com 
 
 
DOI: 10.55670/fpll.futech.4.3.19 

A B S T R A C T 
 

Electric vehicles (EVs) have replaced conventional bio-fuel cars over the past 
ten years. Electric vehicles, or EVs, have become popular for both financial and 
environmental reasons. One of the most significant challenges facing humanity 
today is environmental degradation. From both an economic and ecological 
perspective, it would be highly beneficial if electric automobiles could be 
charged using renewable energy. The use of EVs in Northern Cyprus remains 
in its early stages. Thus, the viability of charging from renewable sources is 
investigated. In addition to comparing fuel-based and electric vehicles and 
determining the economic viability of charging using renewable sources, the 
study explains ways to charge electric vehicles using hybrid wind and solar 
power systems. The costs of the required components have been obtained 
from manufacturers, and the average cost is then taken into account. The 
results demonstrated that the developed system achieved a maximum 
monthly energy output of 13,500 kWh in March and ensured stable 
production throughout the seasons by utilizing solar and wind resources in 
combination. Additionally, it has the capacity to support 58 EV chargers per 
day, which can charge approximately 1,700 EVs per month, including the 
GÜNSEL B9 model. Economically, the system was extremely viable with a 
payback time of just 3.34 years when electricity was sold at $0.31/kWh. 
Moreover, the proposed system offered a significant 96% reduction in carbon 
emissions compared to conventional grid electricity. These results 
demonstrate the hybrid system's success in facilitating sustainable, high-
capacity EV charging, yielding significant environmental and economic 
benefits. Additionally, compared to fuel vehicles, EVs are almost twice as 
advantageous and environmentally friendly.   

 

1. Introduction 

In response to the urgent need to mitigate the effects of 
climate change, nations have lately altered global 
transportation patterns [1, 2]. One of the prospective 
solutions that is gaining traction is the usage of electric 
vehicles (EVs), which offer a more environmentally friendly 
substitute for conventional internal combustion engine 
vehicles [4, 5]. Electric vehicles (EVs) improve air quality, 
reduce dependency on fossil fuels, and significantly reduce 
greenhouse gas (GHG) emissions [6]. Rules and incentive 
programs have been put in place in several countries to 

 

 
 

promote the use of renewable energy sources and ease the 
transition to electric vehicles [7-9]. The environmental and 
energy issues in Northern Cyprus, as in many developing 
countries, are mostly caused by rapid urbanization, 
expanding car ownership, and an increasing reliance on 
imported fossil fuels [10, 11]. One of the biggest contributors 
to air pollution and carbon emissions in the transportation 
sector is personal vehicles [12]. Transport accounts for 
about 24 percent of global direct CO₂ emissions from fuel 
combustion [13]. The economy of developing countries' 
economy is severely impacted by the increasing use of fossil 

 

 

Future Technology 

Open Access Journal 

https://doi.org/10.55670/fpll.futech.4.3.19 

 

Journal homepage: https://fupubco.com/futech 

 

ISSN 2832-0379 

August 2025| Volume 04 | Issue 03 | Pages 204-215 

mailto:yousseuf.kassem@neu.edu.tr/
mailto:youssef.kassem1986@hotmail.com
https://doi.org/10.55670/fpll.futech.4.3.19
https://fupubco.com/futech


Y. Kassem et al. /Future Technology                                                                          August 2025| Volume 04 | Issue 03 | Pages 204-215 

205 

 

fuel-powered automobiles due to fuel imports, volatile 
exchange rates, and rising energy costs, in addition to 
environmental issues [14, 15].  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Air pollution also contributes to respiratory and 
cardiovascular diseases, increased medical expenses, and a 
shorter life expectancy [16]. These factors highlight the 
importance of adopting a more resilient and sustainable 
transportation system. However, there are challenges 
associated with the transition to EVs that require careful 
planning and cooperation. Although replacing all fossil fuel-
powered vehicles at once is not feasible, it makes sense and 
is beneficial to phase them out gradually. This transition can 
be aided by expanding EV infrastructure, especially by 
setting up a huge network of PV-powered charging stations 
[17-19]. Among the countries that have successfully 
promoted the use of EVs are the United States, China, and 
Germany, highlighting the importance of strong legislative 
frameworks, financial incentives, and public awareness 
initiatives [6, 20, 21].  

In general, EVs powered by renewable energy are 
consistent with both national climate pledges and global 
sustainable development goals.  By integrating emission-free 
EVs with low-carbon power generation technologies, 
emissions from internal combustion engine vehicles can be 
significantly decreased [17, 22]. Numerous studies have 
examined the growing use of EVs in conjunction with 
renewable energy sources, including solar [23, 24], wind  
[25-27], and hybrid systems that combine solar and wind 
[27-29]. Combining EVs with renewable energy sources has 
many important advantages. It facilitates increased 
penetration of renewables into the energy mix and also helps 
reduce energy curtailment. Additional benefits include 
reduced greenhouse gas emissions and lower overall energy 
expenditures [27]. As mentioned previously, the global 
transition towards sustainable transport is increasingly 
dependent on the development of renewable energy sources 
for powering electric vehicles (EVs). However, the 
widespread utilization of EVs remains limited, particularly in 
urban areas such as Lefkoşa in Northern Cyprus.   The high 

cost of the grid (Table 1) electricity, which is regulated by 
the Cyprus Turkish Electricity Authority (KIB-TEK), is one of 
the main obstacles to the region's EV adoption.  Due to the 
higher costs, most users cannot afford to charge EVs on the 
grid, which significantly limits the possibility of 
implementing a cleaner transportation system. Therefore, 
the goal of this study is to develop a hybrid model that will 
power cafes and EV charging stations adjacent to the İkas 
Supermarket on the campus of Near East University using 
photovoltaics (PV) and vertical-axis wind turbines (VAWT). 
The primary objective is to improve sustainable energy 
production by integrating clean and renewable energy into 
the transportation infrastructure. The proposed hybrid 
system's performance and feasibility are evaluated by a 
comprehensive techno-economic and environmental 
analysis. The innovative hybrid system design of this 
research, which incorporates PV and VAWT technologies 
into pre-existing infrastructure to guarantee effective land 
utilization and promote the use of clean energy, is its 
strongest point. For local energy supply and sustainable EV 
charging, it focuses on the Near East University campus. A 
prospective charging station powered by wind and solar is 
depicted in Figure 1. 

Table 1. Cost of energy in Northern Cyprus   

 

 

Figure 1. Prospective charging station powered by wind and solar 

 

Energy consumption  Cost [TL/kWh] Cost [USD/kWh] 

0-250 kW 4.8044 0.12 

251-500kW 9.9115 0.25 

501-750 kW 10.6573 0.27 

751-1000kW 11.5519 0.29 

Greater than 1000kW 13.8069 0.35 

 Abbreviations 

EOT Equation Of Time  

EPV The energy output of the PV system  

EVs Electric Vehicles  

EWT Wind turbine's total power output  

Gb Beam Component  

Gd Diffuse Component  

GDH Direct horizontal solar irradiance 

GHG Greenhouse Gas  

Gr Reflected Component  

GSI Incident global solar irradiance on an inclined 

surface   

KIB-TEK Cyprus Turkish Electricity Authority  

LAT local apparent time  

LCOE Levelized cost of energy 

NASA National Aeronautics and Space Administration 

NEU Near East University  

PV Photovoltaics  

SPP simple payback period  

STC Standard Test Conditions  

VAWT vertical axis wind turbines  



Y. Kassem et al. /Future Technology                                                                          August 2025| Volume 04 | Issue 03 | Pages 204-215 

206 

 

2. Materials and methods  

2.1 Study area  
The Ikas Supermarket, situated on the campus of Near 

East University in Lefkoşa, the capital of Northern Cyprus, 
was chosen as the study site.  Near East University (NEU) is 
one of the most recognized and large universities in the 
region. It offers a wide range of academic programs, cutting-
edge research facilities, and a strong emphasis on 
sustainability and technical innovation. Energy consumption 
is consistent and significant on campus due to the diverse 
student body, faculty, and staff, particularly in commercial 
and service sectors such as supermarkets, cafeterias, and 
transit hubs. Northern Cyprus, situated in the eastern 
Mediterranean, boasts a climate ideal for renewable energy 
applications, characterized by abundant sunshine 
throughout the year and moderate wind resources. These 
characteristics make the location ideal for the installation of 
hybrid renewable energy systems. Ikas Supermarket, a busy 
business center located within the university, was selected 
due to its advantageous location, consistent energy usage, 
and potential to serve as a model for integrating renewable 
energy in institutional settings. This site offers a practical 
and important context for evaluating the technical and 
economic feasibility of integrating PV and VAWT systems to 
promote sustainable energy production and reduce reliance 
on fossil fuels. 

2.2 Dataset 
One of the biggest challenges is designing solar and 

wind energy systems in areas lacking measurement data. It 
is especially challenging in Northern Cyprus due to the small 
number of weather stations and the lack of complete 
datasets. To solve this issue, satellite-derived climate 
datasets need to be used; however, their quality and 
dependability must be confirmed by comparing them to 
locally recorded data first. These datasets cannot be used to 
assess the effectiveness of wind and solar energy systems for 
particular areas until such validation has taken place. In this 
context, NASA's Earth Science Research Program is a great 
resource, offering a wide variety of model-based and 
satellite-derived products via its satellite system network. 
Applications for these resources are numerous and include 
studying climate dynamics, improving energy efficiency, and 
meeting agricultural needs. The initiative known as 
Prediction of Worldwide Energy Resources (POWER) stands 
out among the rest. This project provides weather and solar 
data from 1981 to the present on a global grid with a 0.5° × 
0.5° spatial resolution.  By covering regions that lack surface-
based meteorological observations, the NASA POWER 
dataset has become a critical tool for researchers and 
practitioners worldwide. Furthermore, the data is freely 
accessible, further enhancing its utility [29].   

In the literature, solar and wind energy potential was 
evaluated using the NASA dataset [29-34]. For instance, 
Kassem et al. [30] revealed that NASA provided accurate 
solar energy potential estimation in terms of the value of R2,  
for all selected locations (Girne, Güzelyurt, Lefkoşa, and 
Gazimağusa). Gairaa and Bakelli [31] found a high 
correlation between measured data on global solar radiation 
and the NASA database. Arreyndip et al. [32] evaluated the 
feasibility of wind at different places in Cameroon based on 
NASA data (1983-2013) for future wind farm installation. 
Rafique et al. [33] evaluated the feasibility of a 100 MW grid-
connected wind farm in Saudi Arabia using the NASA 
dataset. Gökçekuş et al. [34] utilized the NASA dataset 

database to investigate the distribution of wind speed at 
eight locations in Lebanon.  

Therefore, hourly data, including solar radiation data, 
wind speed, and temperature for the selected location, were 
collected from the NASA POWER database for the period 
between 01-Jan to 31-Dec 2023 in this study.  

2.3 Solar radiation incident on a tilted surface 
Solar radiation received by a surface is composed of 

three main components: direct (Beam) radiation, diffuse 
radiation, and radiation reflected from the Earth’s surface  
[35, 36]. These components contribute to the total solar 
irradiance on a tilted surface. However, due to the limited 
availability of measurement tools and methodologies, it is 
rare to find recorded data specifically for inclined surfaces 
[37]. This challenge necessitates the use of mathematical 
models to estimate the total solar irradiance on tilted 
surfaces based on known parameters. The incident global 
solar irradiance on an inclined surface (GSI) can generally be 
broken down into three components [36]: 
• Beam Component (Gb): This refers to the portion of solar 

radiation that comes directly from the sun and strikes the 
tilted surface without any scattering or reflection. 

• Diffuse Component (Gd): This portion represents the 
scattered solar radiation that reaches the surface after 
being deflected by particles in the atmosphere. 

• Reflected Component (Gr): This is the radiation that is 
reflected from the ground and subsequently received by 
the tilted surface. 

It can be calculated using the following equation [37].  

𝐺𝑆𝐼𝑖 = 𝐺𝑏 + 𝐺𝑑 + 𝐺𝑟                 (1) 

The incidence angle between the sun's rays and the surface 
normal can be utilized for calculating the 𝐺𝑏 . It can be 
expressed as [37]:  

𝐺𝑏 =
𝐺𝐷𝐻

𝑐𝑜𝑠𝜃𝑧
𝑐𝑜𝑠𝜃𝑖               (2) 

Where 𝐺𝐷𝐻 direct horizontal solar irradiance, 𝜃𝑧 is the solar 
zenith angle (Eq. (3)) and θi is the incidence angle of the 
beam radiation on the tilted surface (Eq. (4)). 

𝑐𝑜𝑠𝜃𝑧 = 𝑠𝑖𝑛𝜙 ∙ 𝑠𝑖𝑛𝛿 + 𝑐𝑜𝑠𝜙 ∙ 𝑐𝑜𝑠𝛿 ∙ 𝑐𝑜𝑠𝜔           (3) 

𝑐𝑜𝑠𝜃𝑖 = 𝑠𝑖𝑛𝛿 ∙ 𝑠𝑖𝑛𝜙 ∙ 𝑐𝑜𝑠𝛽 − 𝑠𝑖𝑛𝛿 ∙ 𝑐𝑜𝑠𝜙 ∙ 𝑠𝑖𝑛𝛽 ∙ 𝑐𝑜𝑠𝛼 +
𝑐𝑜𝑠𝛿 ∙ 𝑐𝑜𝑠𝜙 ∙ 𝑐𝑜𝑠𝛽 ∙ 𝑐𝑜𝑠𝜔 + 𝑐𝑜𝑠𝛿 ∙ 𝑐𝑜𝑠𝜙 ∙ 𝑠𝑖𝑛𝛽 ∙ 𝑐𝑜𝑠𝛼 ∙ 𝑐𝑜𝑠𝜔 +
𝑐𝑜𝑠𝛿 ∙ 𝑠𝑖𝑛𝛽 ∙ 𝑠𝑖𝑛𝛼 ∙ 𝑠𝑖𝑛𝜔             (4) 

Where 𝛿 is the solar declination angle, 𝜙 is the location's 
latitude,𝜔 is the hour angle, 𝛽  is the surface tilt angle 
concerning the horizontal plane and 𝛼 is the surface azimuth 
angle.  

The 𝜔 is computed using the local apparent time (LAT). 
Two modifications can be made to the normal time shown on 
a clock to accomplish this: The location's longitude and the 
meridian that serves as the basis for standard time disagree, 
which leads to the first correction. For every degree of 
longitude variation, the adjustment has a magnitude of 4 
minutes. The Earth's orbit and rotational speed are prone to 
minute fluctuations, which is why the second correction is 
known as the equation of time (EOT). Eq. (6) is used to 
calculate the 𝜔 [37].  

𝜔 = 15(12 − 𝐿𝐴𝑇)            (5) 

𝐿𝐴𝑇 = 𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑 𝑡𝑖𝑚 (𝑐𝑙𝑜𝑐𝑘 𝑡𝑖𝑚𝑒) ±
4(𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑 𝑡𝑖𝑚𝑒 𝑙𝑜𝑛𝑔𝑖𝑡𝑢𝑑𝑒 − 𝑙𝑜𝑛𝑔𝑖𝑡𝑢𝑑𝑒 𝑜𝑓 𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛) +
𝐸𝑂𝑇               (6) 



Y. Kassem et al. /Future Technology                                                                          August 2025| Volume 04 | Issue 03 | Pages 204-215 

207 

 

𝐸𝑂𝑇 = 229.18 (0.000075 + 0.001868 ∙ 𝑐𝑜𝑠 (
360∙(𝑁−1)

365
) −

0.032077 ∙ 𝑠𝑖𝑛 (
360∙(𝑁−1)

365
) − 0.014615 ∙ 𝑐𝑜𝑠2 (

360∙(𝑁−1)

365
) −

0.04089 ∙ 𝑠𝑖𝑛2 (
360∙(𝑁−1)

365
))             (7) 

Where 𝑁 is the day of the year. 
It should be noted that the first correction for LAT applies to 
the eastern hemisphere with a negative sign, while the 
western hemisphere applies to the positive sign. 

2.4 Electricity generated by the PV system  
The PV system's monthly energy output can be 

estimated by considering key factors such as the installed 
system capacity, the peak sun hours at the location, and a 
derate factor that accounts for the combined effects of 
component efficiencies, system losses, and environmental 
conditions. The amount of electricity that a photovoltaic 
system is anticipated to produce can be estimated with this 
method. To calculate the energy output of a PV system (𝐸𝑃𝑉) 
in kilowatt-hours (kWh), utilize the formula below [38]: 

𝐸𝑃𝑉 = ∑ 𝜂𝑃𝑉𝑃𝑆𝑇𝐶 (
𝐺

𝐺𝑆𝑇𝐶
) [1 − 𝛼𝑝(𝑇𝐶 − 𝑇𝑆𝑇𝐶)]𝑁∆𝑡𝑖

𝑛
𝑖=1              (8) 

Where 𝜂𝑃𝑉 is the individual PV module derating factor, 
which accounts for wiring losses, inverter inefficiency, 
component failures, soiling, and aging, among other effects 
(𝜂𝑃𝑉  = 0.85 here); PSTC is the nominal power of an 
individual PV module in terms of power output under 
Standard Test Conditions (STC); G is the effective, or plane-
of-array irradiance, i. e. incident irradiance less self-shading 
losses; 𝐺𝑆𝑇𝐶  is the reference plane of-array irradiance under 
STC = 1 kW/m2; 𝛼𝑝 is the PV panel temperature coefficient of 

power; 𝑇𝐶  is the operating cell temperature; 𝑇𝑆𝑇𝐶  is the STC 
operating cell temperature = 25℃; N is the number of 
installed PV modules; and ∆𝑡𝑖 is the duration of the n time 
steps considered. 

2.5 Electricity generated by a wind turbine  
According to previous studies [39], Eq (9) can be used 

to express the wind turbine's total power output (𝐸𝑊𝑇). 
Furthermore, a parabolic law, as provided by [21] (Eq (10)), 
can be used to approximate the wind turbines' power curve. 

𝐸𝑊𝑇 = ∑ 𝑃𝑤𝑡(𝑖)
𝑛
𝑖=1 𝑡             (9) 

𝑃𝑤𝑡(𝑖) =

{
 
 

 
 𝑃𝑟

𝑣𝑖
2−𝑣𝑐𝑖

2

𝑣𝑟
2−𝑣𝑐𝑖

2    (𝑣𝑐𝑖 ≤ 𝑣𝑖 ≤ 𝑣𝑟)

𝑃𝑟

𝑣𝑟
                     (𝑣𝑟 ≤ 𝑣𝑖 ≤ 𝑣𝑐𝑜)

0         (𝑣𝑖 ≤ 𝑣𝑐𝑖𝑎𝑛𝑑𝑣𝑖 ≥ 𝑣𝑐𝑜)

         (10) 

Where 𝑣𝑖  is the vector of possible wind speeds at a 
given site, 𝑃𝑤𝑡(𝑖) is the vector of the corresponding wind 

turbine output power in W, 𝑃𝑟 is the rated power of the 
turbine in W, 𝑣𝑐𝑖  is the cut-in wind speed (m/s), 𝑣𝑟is the 
rated wind speed (m/s) and 𝑣𝑐𝑜 is the cut-out wind speed 
(m/s) of the wind turbine.  

2.6 Mathematical modeling of wind and solar panels  
The electricity generated by the wind turbine and solar 

panels can be used to compute the hybrid system's total 
power production. Power produced (𝐸𝑇𝑜𝑡𝑎𝑙) by a solar-
hybrid system is represented mathematically as given below. 

𝐸𝑇𝑜𝑡𝑎𝑙 = 𝑁𝑊𝐸𝑊𝑇 + 𝑁𝑃𝑉𝐸𝑃𝑉          (11) 

Where 𝑁𝑃𝑉 and 𝑁𝑊 are several solar panels, and wind 
turbines, respectively.  

2.7 Economic viability 
The economic viability of installing a PV system is 

assessed in this study using a variety of financial factors. 
These offer a comprehensive view of the project's financial 
performance and include the simple payback period (SPP) 
and the levelized cost of energy (LCOE) [40]. In the current 
study, the mathematical equations are used to evaluate the 
economic viability of the proposed hybrid system. The 
simple payback period (Eq (12)) provides a quick estimate 
of how long it will take for the initial investment to be 
recouped by the system's energy savings or revenue, making 
it a useful tool for evaluating investment risk and identifying 
projects with faster returns.  

𝑆𝑃𝑃 =
𝐼𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡 𝑐𝑜𝑠𝑡

 𝐴𝑛𝑛𝑢𝑎𝑙 𝑆𝑎𝑣𝑖𝑛𝑔
          (12)  

Furthermore, the LCOE (Eq (13)) represents the 
average cost per kilowatt-hour of electricity generated 
during the system's lifespan and accounts for all capital, 
operating, and maintenance costs. This metric is crucial for 
assessing the PV system's cost-effectiveness in comparison 
to other energy sources and technologies, enabling informed 
decisions about long-term energy planning and investment. 
Collectively, these measures help stakeholders assess the 
short- and long-term financial sustainability of solar energy 
projects. 

𝐿𝐶𝑂𝐸 =
𝐶𝑜+∑

𝐶𝑖,𝑡+𝐶𝑂&𝑀,𝑡

(1+𝑖)𝑡
𝑛
1

∑
𝐸𝑡

(1+𝑖)𝑡
𝑛
1

           (13)  

Where 𝐶𝑜 is the investment cost, 𝑛 is the project's 
economic life, 𝐶𝑖,𝑡 , 𝐶𝑂&𝑀,𝑡 , and 𝐸𝑡 are the investment cost 

(such as replacement cost), operation and maintenance cost, 
and the electricity generated per year, respectively. 

2.8 Carbon mitigation analysis 
The proposed hybrid power plant's carbon mitigation 

analysis is estimated using the following methodology:  
• The greatest CO2 emissions that the hybrid power plant 

might reduce are given by Eq (14).  

𝐶𝑂2 𝑚𝑖𝑡𝑖𝑔𝑎𝑡𝑖𝑜𝑛 𝑏𝑦 ℎ𝑦𝑏𝑟𝑖𝑑 𝑠𝑦𝑠𝑡𝑒𝑚 =
𝐴𝑛𝑛𝑢𝑎𝑙 𝑒𝑛𝑒𝑟𝑔𝑦 𝑔𝑒𝑛𝑒𝑟𝑎𝑡𝑖𝑜𝑛 × 𝐸𝑚𝑖𝑠𝑠𝑖𝑜𝑛 𝑓𝑎𝑐𝑡𝑜𝑟        (14) 

• The hybrid system cannot be fully regarded as an 
emission-free power-producing system. Therefore, it is 
necessary to estimate the amount of CO2 released per kWh 
of power generated by the hybrid system. This uses Eq 
(15) to determine the emission released from the hybrid 
system. 

𝐶𝑂2 𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛 𝑓𝑟𝑜𝑚 ℎ𝑦𝑏𝑟𝑖𝑑 𝑠𝑦𝑡𝑒𝑚 =
𝐴𝑛𝑛𝑢𝑎𝑙 𝑒𝑛𝑒𝑟𝑔𝑦 𝑒𝑔𝑛𝑒𝑟𝑎𝑡𝑖𝑜𝑛 ×
𝐶𝑂2 𝑚𝑖𝑡𝑖𝑔𝑎𝑡𝑖𝑜𝑛 𝑏𝑦 ℎ𝑦𝑏𝑟𝑖𝑑 𝑠𝑦𝑠𝑡𝑒𝑚        (15) 

• The net reduction in CO2 emissions from the hybrid system 
facility is computed using Eq (16). 

𝑁𝑒𝑡 𝐶𝑂2 𝑟𝑒𝑑𝑢𝑐𝑡𝑖𝑜𝑛 = 𝐶𝑂2 𝑚𝑖𝑡𝑖𝑔𝑎𝑡𝑖𝑜𝑛 𝑏𝑦 ℎ𝑦𝑏𝑟𝑖𝑑 𝑠𝑦𝑠𝑡𝑒𝑚 −
𝐶𝑂2 𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛 𝑓𝑟𝑜𝑚 ℎ𝑦𝑏𝑟𝑖𝑑 𝑠𝑦𝑡𝑒𝑚         (16) 

3. Results 

3.1 Energy production  variation  
The present study utilizes hourly solar and wind energy 

data from January 1 to December 31, 2023, to estimate the 
renewable energy production of a 40 kW wind–26 kW solar 
hybrid system at the Ikas supermarket at Near East 
University.  Due to the rapid advancement of technology, 
there are numerous varieties of monocrystalline solar panels 
available on the market, and new models are always being 



Y. Kassem et al. /Future Technology                                                                          August 2025| Volume 04 | Issue 03 | Pages 204-215 

208 

 

released. For the proposed hybrid power system, the Tiger 
Neo N-type 72HL4-(V) 585-watt monocrystalline solar panel 
was selected due to its robust power production, durable 
construction, and high efficiency. This panel achieves an 
efficiency of 22.65% by utilizing N-type TOPCon cell 
technology, which offers greater energy conversion and 
enhanced performance in high-temperature and low-light 
settings. Reliability and long-term power generation are 
supported by its low degradation rate, which is only 0.4% 
annually after the first year and about 1% during the second. 
Additional technical information regarding the solar panel 
may be found in Table 2. 

Moreover, a 10 kW rated RX-SV2 Spiral-Type Vertical 
Axis Wind Turbine was employed In this investigation It was 
designed by Nantong R&X Energy Technology Co., Ltd.. This 
spiral-type Vertical Axis Wind Turbine is created for optimal 
performance and has a low start-up wind speed of 2 m/s. 
When installed at a height of 12 meters, it can operate 
effectively even in mild breeze. With a projected 20-year 
lifespan, the turbine offers a reliable choice for small-scale 
wind energy generation. The complete technical 
characteristics are shown in Table 3. 

Table 2. Solar panel specification  

Parameters Value/Units 

Manufacturer Jinko solar 

Model JKM585N-72HL4 

Panel weight 28 kg 

Wp/panel 585W 

Voltage at Maximum Power 42.52V 

Current at Maximum Power 13.76A 

Open-circuit voltage, Voc 51.16V 

Short-circuit current, Isc 14.55A 

Panel efficiency 22.65 % 

Annual Degradation Rate 0.4% 

Warranty 30 years 

 

Table 3. Wind turbine specification  

Parameters Value/Units 

Model RX-SV2 

Blades Height 2.0 m 

Wind Wheel Diameter 1.2 m 

Rated Power  10 kW 

Rated Speed (m/s) 11 m/s 

Start-Up Speed (m/s) 2 m/s 

Survival Speed (m/s) 45 m/s 

Lifetime (Years) 20 

This is carried out to determine the number of electric 
vehicles that the recommended system can charge. Figure 2 
shows the daily fluctuations in the production from the wind 
and solar systems for each month. It is found that wind 
energy varies greatly, reaching a maximum of 35,000 kWh 
during the winter months of January through February. On 
the other hand, solar energy, which typically ranges from 
100 to 250 kWh per day, is more reliable but has a smaller 
amount. In March, the pattern is still present, with the sun 
making a steady contribution and the breeze showing only 
slight fluctuations. While solar energy steadily and 
considerably increases, often topping 250 kWh/day, wind 
output fluctuates but loses its supremacy in April and May.  
Moreover, during the summer months (June to August), solar 
energy is the most prevalent source, with daily outputs 
consistently exceeding 250 kWh and exhibiting minimal 
volatility, in contrast to wind energy, which is less 
dependable and usually moderate to low. Solar energy starts 
to slightly decline but remains stable in September and 
October, while wind output becomes more erratic with 
sporadic high peaks, particularly in October. In November 
and December, wind energy output increases in activity and 
variety, signaling a return to wind-dominated contributions, 
while solar energy output reaches its lowest points of the 
year.  

Overall, solar energy is more dependable throughout 
the year, especially in the spring and summer, whereas wind 
energy is more irregular but has significant potential 
throughout the winter and transitional seasons. The hybrid 
architecture of the system ensures more consistent energy 
output throughout the year by utilizing the seasonal 
strengths of both sources. The overall daily energy output of 
a hybrid renewable energy system varies hourly for each 
month of the year, as shown in Figure 3. Since wind speeds 
and solar irradiance fluctuate throughout the year, the 
graphs provide insight into the seasonal and diurnal 
behavior of the system. It is found that energy output 
production shows clear diurnal trends, with the highest 
energy generation typically occurring during the day, 
especially between 8:00 and 16:00, which corresponds to 
the hours of greatest solar energy. In many months, 
especially in spring and early summer, the majority of the 
daily energy yield is contributed during these hours. March 
has the largest total energy output of any month, peaking at 
nearly 13,500 kWh between 13:00 and 14:00, indicating 
favorable wind activity and strong solar irradiation, and 
April, June, and November also have significant energy 
outputs, with each month reaching close to or exceeding 
10,000 kWh at midday. However, October and September 
exhibit the lowest energy output, with maximum values of 
no more than 4,000–5,000 kWh, even during peak hours.  

In October, the energy profile is rather flat and does not 
fluctuate much during the day, as shown in Figure 3. This 
could be a result of both decreased wind speeds along 
shorter daylight hours. The lower and irregular output 
pattern in September also points to less-than-ideal 
conditions for solar and wind sources during this summer-
to-fall transition. Moreover, the pattern of energy generation 
becomes more complex during the summer months of June, 
July, and August. The output has a bimodal distribution in 
July and August, with two distinct peaks that may be 
observed in the early morning and late afternoon, suggesting 
that wind energy contributes more during off-solar hours or 
cloud-induced solar intermittency.  

 
 



Y. Kassem et al. /Future Technology                                                                          August 2025| Volume 04 | Issue 03 | Pages 204-215 

209 

 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

  

  

  

  

  

  
 

Figure 2. Daily fluctuations in the production from the wind and solar systems for each month 

 

0

50

100

150

200

0

5000

10000

15000

0 3 6 9 12 15 18 21 24 27 30

P
V

 o
u

tp
u

t 
[k

W
h

]

W
in

d
 o

u
tp

u
t 

[k
W

h
]

Number of dat [-]

Jan.

-50

0

50

100

150

200

250

0

5000

10000

15000

20000

0 3 6 9 12 15 18 21 24 27

P
V

 o
u

tp
u

t 
[k

W
h

]

W
in

d
 o

u
tp

u
t 

[k
W

h
]

Number of dat [-]

Feb.

0

100

200

300

0

10000

20000

30000

40000

0 3 6 9 12 15 18 21 24 27 30
P

V
 o

u
tp

u
t 

[k
W

h
]

W
in

d
 o

u
tp

u
t 

[k
W

h
]

Number of dat [-]

Mar.

0

100

200

300

0

5000

10000

15000

1 3 5 7 9 11131517192123252729

P
V

 o
u

tp
u

t 
[k

W
h

]

W
in

d
 o

u
tp

u
t 

[k
W

h
]

Number of dat [-]

Apr.

0

100

200

300

0

5000

10000

15000

1 4 7 10 13 16 19 22 25 28 31

P
V

 o
u

tp
u

t 
[k

W
h

]

W
in

d
 o

u
tp

u
t 

[k
W

h
]

Number of dat [-]

May

0

200

400

0

5000

10000

15000

1 3 5 7 9 11131517192123252729

P
V

 o
u

tp
u

t 
[k

W
h

]

W
in

d
 o

u
tp

u
t 

[k
W

h
]

Number of day [-]

Jun.
Wind PV

240

250

260

270

280

290

0

2000

4000

6000

1 4 7 10 13 16 19 22 25 28 31

P
V

 o
u

tp
u

t 
[k

W
h

]

W
in

d
 o

u
tp

u
t 

[k
W

h
]

Number of day [-]

Jul.

0

100

200

300

0

5000

10000

15000

1 4 7 10 13 16 19 22 25 28 31

P
V

 o
u

tp
u

t 
[k

W
h

]

W
in

d
 o

u
tp

u
t 

[k
W

h
]

Number of day [-]

Aug.

0

50

100

150

200

250

300

0

5000

10000

15000

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

P
V

 o
u

tp
u

t 
[k

W
h

]

W
in

d
 o

u
tp

u
t 

[k
W

h
]

Number of day [-]

Sep.

0

50

100

150

200

250

0

5000

10000

15000

1 4 7 10 13 16 19 22 25 28 31

P
V

 o
u

tp
u

t 
[k

W
h

]

W
in

d
 o

u
tp

u
t 

[k
W

h
]

Number of day [-]

Oct.

0

50

100

150

200

0

10000

20000

30000

40000

1 3 5 7 9 11131517192123252729

P
V

 o
u

tp
u

t 
[k

W
h

]

W
in

d
 o

u
tp

u
t 

[k
W

h
]

Number of day [-]

Nov.

0

50

100

150

200

0

5000

10000

15000

1 4 7 10 13 16 19 22 25 28 31 P
V

 o
u

tp
u

t 
[k

W
h

]

W
in

d
 o

u
tp

u
t 

[k
W

h
]

Number of day [-]

Dec.
Wind PV



Y. Kassem et al. /Future Technology                                                                          August 2025| Volume 04 | Issue 03 | Pages 204-215 

210 

 

July is noteworthy for having two mild peaks at 6:00 
and 18:00, as well as a noon decline, suggesting that wind 
energy becomes more prevalent during these periods due to 
either lower solar irradiance or PV module overheating, 
which reduces efficiency. In the winter months of January, 
February, and December, the system generates a 
considerable amount of energy, despite the production curve 
being considerably compressed. Given that the wind 
component compensates for less solar exposure, the hybrid 
system performs well even with fewer daylight hours, as 
evidenced by the fact that January peaks at roughly 11,000 
kWh between 11:00 and 13:00, and December reaches up to 
8,500 kWh. In February, the output curve is flat and peaks at 
roughly 7,500 kWh, indicating consistent but decreased 
generation. The energy generation profiles for April and May 
are balanced; April's energy output is constant between 8:00 
and 17:00, averaging between 9,000 and 10,000 kWh during 
these hours, while May's high is approximately 8,500 kWh. 
These months provide the ideal conditions for solar energy 
production due to the longer daylight hours and milder 
temperatures that enhance PV performance. The most 
productive seasons in terms of total daily energy generation 
are spring (March) and late fall to early winter (November–
January) because of a balance between the availability of 
solar and wind resources. Late summer and early fall 
witness somewhat reduced outputs, most likely due to 
slower wind speeds and possible decreases in PV efficiency 
caused by hot weather. 

3.2 Results of estimating the number of EVs and 
chargers  
In general, Nissan Leaf (battery capacity = 62 kWh) 

Tesla Model 3 (battery capacity = 82 kWh), Kia e-Niro 
(battery capacity = 64 kWh), Renault Zoe (battery capacity = 
41kWh), BMW i3 (battery capacity = 42 kWh), GÜNSEL B9 
(battery capacity = 53 kWh) and GÜNSEL J9 (battery 
capacity = 85 kWh) are the most available EVs in Northern 
Cyprus. In this study, a 22 kW public/commercial charger 
was used. Accordingly, Figure 4 shows the number of EVs, 
assuming one complete recharge per vehicle every session, 
that could potentially be fully charged by a public or 
commercial 22 kW charging station. According to data, the 
BMW i3 and Renault Zoe have the highest monthly charging 
capacity, with 3,500–3,700 vehicles in March. The monthly 
charging volume of the Tesla Model 3 is 800 to 1,800 cars, 
which is comparatively stable despite being lower. March 
and October see the highest and lowest variations for the 
Nissan Leaf and Kia e-Niro, respectively, indicating 
periodicity in supply or demand. The GÜNSEL J9 has a lower 
monthly total generally; however, the GÜNSEL models, 
especially the B9, have consistent average numbers.  

It should be noted that the power used for charging is 
provided by a hybrid renewable energy system that 
combines wind turbines and photovoltaic solar panels. Since 
shorter charging times are required, smaller-capacity 
vehicles such as the Renault Zoe and BMW i3 are charged 
more frequently and reach monthly peaks of over 3,500 
vehicles. EVs with higher batteries, such as the Tesla Model 3 
and Kia e-Niro require fewer monthly recharges. Seasonal 
variations are present in all models, with increased charging 
capacity in months such as March, presumably optimum 
solar irradiation and wind speed conditions that maximize 
the hybrid setup's energy contribution. However, September 
and October show a decline in numbers, which could be the 
result of a lower supply of renewable energy. To develop and 
manage an effective charging infrastructure, it is critical to 

understand how EV characteristics, energy system 
performance, and seasonal energy supply interact. 

Moreover, Figure 5 illustrates the number of EV 
chargers that can be sustained per day each month from the 
energy produced by a hybrid renewable energy system.  The 
number of supported chargers and the energy availability 
vary significantly across days and months. It peaks in March 
and November, when the system may be able to handle up to 
58 chargers a day, perhaps as a result of favorable wind and 
solar energy combinations. While August, September, and 
December have comparatively fewer chargers available, 
June, April, and February also exhibit relatively frequent 
peaks, suggesting lesser hybrid energy generation 
throughout these months. Further, the range is extremely 
high even within the same month, indicating the intermittent 
nature of renewable energy. On some days, for example, 
there are very few chargers (under 5), whereas on other 
days, it exceeds 20 or even 50 easily. This necessitates 
flexible energy management and storage systems to balance 
out charging availability. 

3.3 Results of economic viability   
To assess the economic feasibility, certain assumptions 

have been made based on literature and previous 
investigations regarding Northern Cyprus, considering that 
there are, at present, no hybrid renewable EV charging 
schemes in Northern Cyprus. The initial investment in 
hybrid solar and wind EV charging stations is USD 111,078. 
This includes 45 PV panels costing USD 200 (USD 9,000) and 
4 wind turbines costing USD 6,000 per unit (USD 24,000), 
which is USD 33,000 for renewable energy equipment. The 
Growatt inverter costs USD 15,000 with a USD 45,000 
lifespan cost. Seven public chargers are priced at USD 3,000 
each, totaling USD 21,000. Other costs are 3% contingency, 
8.6% installation and spares, and 0.6% feasibility and 
engineering charges. This is a well-thought-out breakdown 
of the costs that ensures all significant items and charges are 
captured in the project budget. For financial calculations, the 
discount rate of 6% has been applied to capture the time 
value of money, as per local economic estimates and 
previous research on renewable energy investments. An 
inflation rate of 8% was also assumed following local 
economic trends. Operation and maintenance (O&M) costs 
were considered at a standard of 1.5% of the total cost of 
capital per year, which is a general rate applied in global 
renewable energy feasibility studies.  

The Simple Payback Period (SPP) of the proposed 
hybrid (solar + wind) electric vehicle charging station has 
been calculated at different selling prices (SP) of electricity 
to observe how pricing affects the payback of the initial 
investment. The results demonstrate a clear trend: the more 
expensive the selling price per kilowatt-hour, the shorter the 
payback period. As shown in Figure 6, with a reduced selling 
price of USD 0.10/kWh, the payback period turns out to be 
very long, around 10.35 years, which may not attract 
investors. With an increased price of USD 0.31/kWh, 
corresponding to the current local grid electricity price, the 
payback period reduces to around 3.34 years, and the 
project turns out to be much more financially attractive. 
When the price is set at a premium rate of USD 0.428/kWh, 
the payback period shortens even more to around 2.42 
years. The premium rate not only accelerates the return on 
investment more quickly but also recognizes the value added 
of clean, renewable energy and growing consumer demand 
for green EV charging options.  

 



Y. Kassem et al. /Future Technology                                                                          August 2025| Volume 04 | Issue 03 | Pages 204-215 

211 

 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

  

  

  

  

  

  

 

Figure 3. Hourly total energy output production from the hybrid system for each month 

 

0

2000

4000

6000

8000

10000

12000

0 2 4 6 8 10 12 14 16 18 20 22 24

To
ta

l o
u

tp
u

t 
p

ro
d

u
ct

io
n

 
[k

W
h

]

Number of hour [h-

Jan

0

2000

4000

6000

8000

0 2 4 6 8 10 12 14 16 18 20 22 24

To
ta

l o
u

tp
u

t 
p

ro
d

u
ct

io
n

 
[k

W
h

]

Number of hour [h-

Feb

0

5000

10000

15000

0 2 4 6 8 10 12 14 16 18 20 22 24

To
ta

l o
u

tp
u

t 
p

ro
d

u
ct

io
n

 
[k

W
h

]

Number of hour [h-

Mar

0

2000

4000

6000

8000

10000

12000

0 2 4 6 8 10 12 14 16 18 20 22 24

To
ta

l o
u

tp
u

t 
p

ro
d

u
ct

io
n

 
[k

W
h

]

Number of hour [h-

Apr

0

2000

4000

6000

8000

10000

0 2 4 6 8 10 12 14 16 18 20 22 24

To
ta

l o
u

tp
u

t 
p

ro
d

u
ct

io
n

 
[k

W
h

]

Number of hour [h-

May

0

2000

4000

6000

8000

10000

0 2 4 6 8 10 12 14 16 18 20 22 24

To
ta

l o
u

tp
u

t 
p

ro
d

u
ct

io
n

 
[k

W
h

]

Number of hour [h-

Jun

0

2000

4000

6000

0 2 4 6 8 10 12 14 16 18 20 22 24

To
ta

l o
u

tp
u

t 
p

ro
d

u
ct

io
n

 
[k

W
h

]

Number of hour [h-

Jul

0

2000

4000

6000

8000

0 2 4 6 8 10 12 14 16 18 20 22 24

To
ta

l o
u

tp
u

t 
p

ro
d

u
ct

io
n

 
[k

W
h

]

Number of hour [h-

Aug

0

1000

2000

3000

4000

5000

0 2 4 6 8 10 12 14 16 18 20 22 24

To
ta

l o
u

tp
u

t 
p

ro
d

u
ct

io
n

 
[k

W
h

]

Number of hour [h-

Sep

0

1000

2000

3000

4000

0 2 4 6 8 10 12 14 16 18 20 22 24

To
ta

l o
u

tp
u

t 
p

ro
d

u
ct

io
n

 
[k

W
h

]

Number of hour [h-

Oct

0

2000

4000

6000

8000

10000

0 2 4 6 8 10 12 14 16 18 20 22 24

To
ta

l o
u

tp
u

t 
p

ro
d

u
ct

io
n

 
[k

W
h

]

Number of hour [h-

Nov

0

2000

4000

6000

8000

10000

0 2 4 6 8 10 12 14 16 18 20 22 24

To
ta

l o
u

tp
u

t 
p

ro
d

u
ct

io
n

 
[k

W
h

]

Number of hour [h-

Dec



Y. Kassem et al. /Future Technology                                                                          August 2025| Volume 04 | Issue 03 | Pages 204-215 

212 

 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
Figure 5. The estimated number of EV chargers that can be 
sustained per day, each month  

 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
It is such a pricing mechanism that supports quicker 

capital recovery, enhances profitability, and stimulates the 
adoption of environmentally friendly transportation. 
However, the proposed solar and wind power-enabled 
hybrid charging station can produce an average estimated 
107,327.61 kWh of clean electricity annually. Because all the 
energy resources utilized in the system are renewable, the 
system has virtually no operational carbon dioxide (CO₂) 
emissions. In a calculation involving the overall life cycle 
from manufacturing to installation, maintenance, and 
decommissioning, an estimated emission intensity of 
approximately 30 grams of CO₂ per kWh is expected, which, 
annually, amounts to a total of around 3,219.83 kg of CO₂. To 
put this into perspective, if the same amount of electricity is 
produced by the power grid in Northern Cyprus, which is 
highly dependent on fossil fuels such as diesel and heavy fuel 
oil, the emissions would amount to 83,716.53 kg of CO₂ per 
year based on an average emission factor of 780 grams of 

0
5

10
15
20
25
30
35
40
45
50
55
60

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30N
u

m
b

e
r 

o
f 

ch
ar

ge
rs

 [
-]

Number of day [-]

Jan Feb Mar Apr

May Jun July Aug

Sep Oct Nov Dec

  

  

  

 
 

Figure 4. Estimated number of cars using public or commercial 22 kW charging station 

 

0
250
500
750

1000
1250
1500
1750
2000
2250
2500

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

N
u

m
b

e
r 

o
f 

ca
r 

[-
]

Car#1: Nissan Leaf

0

500

1000

1500

2000

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

N
u

m
b

e
r 

o
f 

ca
r 

[-
]

Car#2: Tesla Model 3

0
250
500
750

1000
1250
1500
1750
2000
2250
2500

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

N
u

m
b

e
r 

o
f 

ca
r 

[-
]

Car#3: Kia e-Niro 

0

1000

2000

3000

4000

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

N
u

m
b

e
r 

o
f 

ca
r 

[-
]

Car#4: Renault Zoe

0

1000

2000

3000

4000

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

N
u

m
b

e
r 

o
f 

ca
r 

[-
]

Car#5:  BMW i3

0
250
500
750

1000
1250
1500
1750
2000
2250
2500
2750
3000

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

N
u

m
b

e
r 

o
f 

ca
r 

[-
]

Car#6: GÜNSEL B9 

0

500

1000

1500

2000

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

N
u

m
b

e
r 

o
f 

ca
r 

[-
]

Car#7: GÜNSEL J9 



Y. Kassem et al. /Future Technology                                                                          August 2025| Volume 04 | Issue 03 | Pages 204-215 

213 

 

CO₂ per kWh. This translates to the estimated hybrid system 
achieving an average savings of approximately 80,496.70 kg 
of CO₂ equivalent to a 96% decrease in emissions from usual 
grid electricity. 

 

 
Figure 6. SPP vs. SP  

Moreover, when electric cars are charged using the 
electricity generated by the suggested system, the resulting 
emissions equal 2–6 g of CO₂ per kilometer. On the other 
hand, grid charging contains around 156 grams of CO₂ per 
kilometer, while conventional petrol and diesel engines 
release around 185g and 160g of CO₂ per kilometer, 
respectively. All such comparisons demonstrate that the 
proposed system significantly reduces greenhouse gases and 
is open to sustainability and climate mitigation strategies. 

 
3.4 Limitations and future work 

The viability of a hybrid solar-wind power system for 
EV charging at Ikas Supermarket in Lefkoşa, Northern 
Cyprus, is examined in this study. Although the outcome is 
encouraging and suggests that locally accessible renewable 
energy could be used to sustainably charge EVs, it is 
important to identify some limitations to understand the 
results and influence future research. The analysis was 
performed using wind and solar data from the NASA POWER 
database. Due to a lack of on-site measurements or 
validation of real-time data, the results may not be accurate, 
which are influenced by climate conditions. Besides, the 
system's design is based on general consumption rate 
assumptions and assumed EV charging load rather than 
measured actual load profiles and traffic patterns for the 
supermarket. This adds uncertainty to economic calculations 
and energy sizing. Additionally, the existing model does not 
fully incorporate some technological factors due to scope 
limits, such as the long-term degradation of PV panels, the 
lower efficiency of windmills at low wind speeds, the 
integration of battery storage, and dynamic control of 
systems.  

For accurate system performance analysis and financial 
modeling, these are necessary. In order to enhance 
subsequent research, it is suggested that energy 
consumption, solar irradiance, and wind speed be measured 
on-site, that real-time system monitoring be established, and 
that a sensitivity analysis of important technical and 
financial aspects be conducted. In Northern Cyprus and 
other comparable settings, such renewable-based EV 
charging systems will also become more dependable and 
scalable with the addition of battery storage systems, load 
control techniques, and realistic user behavior. 

 

3.5 Conclusions  
This study provides an in-depth examination of the 

energy potential, charging capacity, and environmental and 
economic sustainability of a hybrid solar and wind-powered 
electric vehicle charging station designed for Northern 
Cyprus. Based on the results, solar energy provides 
consistent daily output, especially in the spring and summer, 
while wind energy makes a substantial contribution in the 
winter and transitional months. These seasonal fluctuations 
are expertly complemented by the hybrid system 
architecture, which provides a steady energy output all year 
round. With a peak energy generation of 13,500 kWh in 
March and the ability to support up to 58 chargers per day, 
the system shows a strong capacity to meet local EV charging 
needs. At a selling price of $0.31/kWh and a payback period 
of 3.34 years, the project becomes economically viable. Once 
again demonstrating the system's sustainability, it offers a 
96% reduction in CO₂ emissions when compared to grid-
based electricity.  The system's viability and usability are 
still supported by its monthly capacity to charge thousands 
of EVs, including regional models such as the GÜNSEL B9. 
For countries that are importing oil, EVs would have saved 
the money spent on importing fossil fuel for cars that run by 
fuel or vehicles. Many foreign currencies have been used in 
importing such forms of fuel, which also helps to reduce the 
trade deficit. Besides financial benefits, since electricity 
generated using solar energy can be used to charge EVs, it 
can go a long way in improving our environment by reducing 
CO2 emissions. The government can provide simple 
incentives such as a lower rate of taxation for the use of 
electric vehicles compared to fuel-based vehicles. In 
addition, a cut in the import duty of the EVs and an increase 
in the import duty of the fuel cars can be in their favor 
regarding adaptability. The authority must invest in electric 
vehicle technologies research and development at the local 
level and build electric vehicle charging stations across the 
nation. The feasibility study shows that wind-solar charging 
can be profitable for charging various available types of EVs, 
and thus, the research findings could have great implications 
for countries with imported fuel at a high price. If it had been 
particularly constructed to use solar power in recharging 
EVs, and not batteries and the plant price had been reduced 
by almost half of the total amount, then the usefulness of this 
research could be more relevant. The research brought out a 
new dimension to the utilization of electric vehicles powered 
by only renewable sources of energy, a new contribution to 
what already exists in the literature. The findings of the 
study are technologically feasible, economically feasible, and 
environmentally friendly and therefore merit being 
employed by policymakers in any country. 

Ethical issue 
The authors are aware of and comply with best practices in 
publication ethics, specifically with regard to authorship 
(avoidance of guest authorship), dual submission, 
manipulation of figures, competing interests, and compliance 
with policies on research ethics. The author adheres to 
publication requirements that the submitted work is original 
and has not been published elsewhere. 

Data availability statement 
The manuscript contains all the data. However, more data 

will be available upon request from the authors. 

Conflict of interest 

The authors declare no potential conflict of interest. 

y = 0.007x2 - 0.1272x + 0.674
R² = 0.9784

0

0.1

0.2

0.3

0.4

0.5

0 1 2 3 4 5 6 7 8 9 10 11

Se
lli

n
g

p
ri

ce
 [

U
SD

/k
W

h
]

Simple Payback Period  [Year]

Grid Price 



Y. Kassem et al. /Future Technology                                                                          August 2025| Volume 04 | Issue 03 | Pages 204-215 

214 

 

References 

[1] Mehranfar, S., Banagar, I., Moradi, J., Andwari, A. M., 
Könnö, J., Gharehghani, A., ... & Kurvinen, E. (2024). 
The perspective of energy storage systems 
advancements and challenges for electric vehicle 
applications; metric, mechanism, mode, and mitigation 
framework. Future Sustainability, 2(4), 22-32. 
https://doi.org/10.55670/fpll.fusus.2.4.4 

[2] Elkelawy, M., Saeed, A. M., Atta, Z. A., Sayed, M. M., 
Hamouda, M. A., Almasri, A. M., & Seleem, H. E. (2025). 
Transforming Conventional Vehicles into Electric: A 
Comprehensive Review of Conversion Technologies, 
Challenges, Performance Enhancements, and Future 
Prospects. Pharos Engineering Science Journal, 2(1), 
197-212.  
https://doi.org/10.21608/PESJ.2025.375197.1028 

[3] Costa, C. M., Barbosa, J. C., Castro, H., Gonçalves, R., & 
Lanceros-Méndez, S. (2021). Electric vehicles: To what 
extent are environmentally friendly and cost-
effective?–Comparative study by European 
countries. Renewable and Sustainable Energy 
Reviews, 151, 111548. 
https://doi.org/10.1016/j.rser.2021.111548 

[4] Al-Ghaili, A. M., Kasim, H., Aris, H., & Al-Hada, N. M. 
(2022). Can electric vehicles be an alternative to 
traditional fossil-fuel cars with the help of renewable 
energy sources towards energy sustainability 
achievement?. Energy Informatics, 5(Suppl 4), 60. 
https://doi.org/10.1186/s42162-022-00234-3 

[5] Qadir, S. A., Ahmad, F., Al-Wahedi, A. M. A., Iqbal, A., & 
Ali, A. (2024). Navigating the complex realities of 
electric vehicle adoption: A comprehensive study of 
government strategies, policies, and incentives. Energy 
Strategy Reviews, 53, 101379. 
https://doi.org/10.1016/j.esr.2024.101379 

[6] Okoh, A. S., & Onuoha, M. C. (2024). Immediate and 
future challenges of using electric vehicles for 
promoting energy efficiency in Africa’s clean energy 
transition. Global Environmental Change, 84, 102789. 
https://doi.org/10.1016/j.gloenvcha.2023.102789 

[7] Martins, H., Henriques, C. O., Figueira, J. R., Silva, C. S., & 
Costa, A. S. (2023). Assessing policy interventions to 
stimulate the transition of electric vehicle technology 
in the European Union. Socio-Economic Planning 
Sciences, 87, 101505. 
https://doi.org/10.1016/j.seps.2022.101505 

[8] Srivastava, A., Kumar, R. R., Chakraborty, A., Mateen, A., 
& Narayanamurthy, G. (2022). Design and selection of 
government policies for electric vehicles adoption: A 
global perspective. Transportation Research Part E: 
Logistics and Transportation Review, 161, 102726. 
https://doi.org/10.1016/j.tre.2022.102726 

[9] Akçaba, S., & Eminer, F. (2022). Sustainable energy 
planning for the aspiration to transition from fossil 
energy to renewable energy in Northern 
Cyprus. Heliyon, 8(6). 
https://doi.org/10.1016/j.heliyon.2022.e09813  

[10] Barton, D. (2025). An overview of the sustainability of 
emerging energy technologies in mitigating climate 
change. Future Sustainability, 3(3), 35-46. 
https://doi.org/10.55670/fpll.fusus.3.3.5 

[11] Ercan, T., Onat, N. C., Keya, N., Tatari, O., Eluru, N., & 
Kucukvar, M. (2022). Autonomous electric vehicles can 
reduce carbon emissions and air pollution in 
cities. Transportation Research Part D: Transport and 

Environment, 112, 103472. 
https://doi.org/10.1016/j.trd.2022.103472 

[12] Yu, X., LeBlanc, S., Sandhu, N., Wang, L., Wang, M., & 
Zheng, M. (2024). Decarbonization potential of future 
sustainable propulsion—A review of road 
transportation. Energy Science & Engineering, 12(2), 
438-455. https://doi.org/10.1002/ese3.1434 

[13] Romero-Ocaño, A. D., Cosío-León, M. A., Valenzuela-
Alcaraz, V. M., & Brizuela, C. A. (2022). The impact of 
gradually replacing fossil fuel-powered vehicles with 
electric ones: A bi-objective optimisation 
approach. Expert Systems with Applications, 194, 
116546. https://doi.org/10.1016/j.eswa.2022.116546 

[14] Holechek, J. L., Geli, H. M., Sawalhah, M. N., & Valdez, R. 
(2022). A global assessment: can renewable energy 
replace fossil fuels by 2050?. Sustainability, 14(8), 
4792. ; https://doi.org/10.3390/su14084792 

[15] Chen, F., & Chen, Z. (2021). Cost of economic growth: 
Air pollution and health expenditure. Science of the 
Total Environment, 755, 142543. 
https://doi.org/10.1016/j.scitotenv.2020.142543 

[16] Malik, M. A. I., Kalam, M. A., Ikram, A., Zeeshan, S., & 
Zahidi, S. Q. R. (2025). Energy transition towards 
electric vehicle technology: Recent 
advancements. Energy Reports, 13, 2958-2996. 
https://doi.org/10.1016/j.egyr.2025.02.029 

[17] Majid, M. A., & Ahmed, A. (2024). Advances in electric 
vehicles for a self-reliant energy ecosystem and 
powering a sustainable future in India. e-Prime-
Advances in Electrical Engineering, Electronics and 
Energy, 10, 100753. 
https://doi.org/10.1016/j.prime.2024.100753 

[18] Alanazi, F. (2023). Electric vehicles: Benefits, 
challenges, and potential solutions for widespread 
adaptation. Applied Sciences, 13(10), 6016. 
https://doi.org/10.3390/app13106016 

[19] Ehsan, F., Habib, S., Gulzar, M. M., Guo, J., Muyeen, S. M., 
& Kamwa, I. (2024). Assessing policy influence on 
electric vehicle adoption in China: An in-Depth 
study. Energy Strategy Reviews, 54, 101471.  
https://doi.org/10.1016/j.esr.2024.101471 

[20] Zhang, X., Xie, J., Rao, R., & Liang, Y. (2014). Policy 
incentives for the adoption of electric vehicles across 
countries. Sustainability, 6(11), 8056-8078. 
https://doi.org/10.3390/su6118056 

[21] Shang, H., Sun, Y., Huang, D., & Meng, F. (2024). Life 
cycle assessment of atmospheric environmental 
impact on the large-scale promotion of electric 
vehicles in China. Resources, Environment and 
Sustainability, 15, 100148. 
https://doi.org/10.1016/j.resenv.2024.100148 

[22] Baringo, L., Boffino, L., & Oggioni, G. (2020). Robust 
expansion planning of a distribution system with 
electric vehicles, storage and renewable units. Applied 
Energy, 265, 114679. 
https://doi.org/10.1016/j.apenergy.2020.114679 

[23] Keller, V., English, J., Fernandez, J., Wade, C., Fowler, M., 
Scholtysik, S., ... & Rowe, A. (2019). Electrification of 
road transportation with utility controlled charging: A 
case study for British Columbia with a 93% renewable 
electricity target. Applied Energy, 253, 113536. 
https://doi.org/10.1016/j.apenergy.2019.113536 

[24] Arif, A., & Milanovic, J. (2021, October). Data-Driven 
Distribution System Expansion Planning Considering 
High EV and PV Penetration. In 2021 IEEE PES 
Innovative Smart Grid Technologies Europe (ISGT 

https://doi.org/10.55670/fpll.fusus.2.4.4
https://doi.org/10.1016/j.rser.2021.111548
https://doi.org/10.1016/j.esr.2024.101379
https://doi.org/10.1016/j.gloenvcha.2023.102789
https://doi.org/10.1016/j.seps.2022.101505
https://doi.org/10.1016/j.tre.2022.102726
https://doi.org/10.1016/j.heliyon.2022.e09813
https://doi.org/10.55670/fpll.fusus.3.3.5
https://doi.org/10.1016/j.trd.2022.103472
https://doi.org/10.1002/ese3.1434
https://doi.org/10.1016/j.eswa.2022.116546
https://doi.org/10.3390/su14084792
https://doi.org/10.1016/j.scitotenv.2020.142543
https://doi.org/10.1016/j.prime.2024.100753
https://doi.org/10.3390/app13106016
https://doi.org/10.1016/j.esr.2024.101471
https://doi.org/10.1016/j.resenv.2024.100148
https://doi.org/10.1016/j.apenergy.2020.114679


Y. Kassem et al. /Future Technology                                                                          August 2025| Volume 04 | Issue 03 | Pages 204-215 

215 

 

Europe) (pp. 01-05). IEEE. 
https://doi.org/10.1109/ISGTEurope52324.2021.963
9991 

[25] de Quevedo, P. M., Muñoz-Delgado, G., & Contreras, J. 
(2017, July). Joint expansion planning of distribution 
networks, EV charging stations and wind power 
generation under uncertainty. In 2017 IEEE Power & 
Energy Society General Meeting (pp. 1-5). IEEE.  
https://doi.org/10.1109/PESGM.2017.8273783  

[26] Santos, R. D. C. O., Abud, T. P., Lopes, Y., & Borba, B. S. 
M. C. (2025). Systematic Literature Review of electric 
vehicles within the expansion planning of electrical 
power systems. Results in Engineering, 104500. 
https://doi.org/10.1016/j.rineng.2025.104500 

[27] Abdi, H., Moradi, M., & Rashidi, R. (2022). Hybrid 
transmission expansion planning and reactive power 
planning considering the real network 
uncertainties. International Journal of Numerical 
Modelling: Electronic Networks, Devices and 
Fields, 35(1), e2937. 
https://doi.org/10.1002/jnm.2937 

[28] Tabandeh, A., Hossain, M. J., & Li, L. (2022). Integrated 
multi-stage and multi-zone distribution network 
expansion planning with renewable energy sources 
and hydrogen refuelling stations for fuel cell 
vehicles. Applied Energy, 319, 119242. 
https://doi.org/10.1016/j.apenergy.2022.119242 

[29] Siddiqui, T., & Mustaqeem, M. (2023). Performance 
evaluation of software defect prediction with NASA 
dataset using machine learning techniques. 
International Journal of Information Technology, 
15(8), 4131-4139. https://doi.org/10.1007/s41870-
023-01528-9 

[30] Kassem, Y., Çamur, H., & Alhuoti, S. M. A. (2020). Solar 
energy technology for Northern Cyprus: Assessment, 
statistical analysis, and feasibility 
study. Energies, 13(4), 940. 
https://doi.org/10.3390/en13040940 

[31] Gairaa, K., & Bakelli, Y. (2013). Solar energy potential 
assessment in the Algerian south area: Case of 
Ghardaïa region. Journal of Renewable Energy, 
2013(1), 496348. 
https://doi.org/10.1155/2013/496348 

[32] Arreyndip, N. A., Joseph, E., & David, A. Wind energy 
potential assessment of Cameroon’s coastal regions for 
the installation of an onshore wind farm. Heliyon. 
2016, 2(11).  

[33] Rafique, M., Rehman, S., Alam, M., & Alhems, L. 
Feasibility of a 100 MW Installed Capacity Wind Farm 
for Different Climatic Conditions. Energies. 2018, 
11(8), 2147. https://doi.org/10.3390/en11082147 

[34] Gökçekuş, H., Kassem, Y., & Al Hassan, M. Evaluation of 
Wind Potential at Eight Selected Locations in Northern 
Lebanon Using Open Source Data. International 
Journal of Applied Engineering Research. 2019, 
14(11), 2789–2794. 

[35] Demain, C., Journée, M., & Bertrand, C. (2013). 
Evaluation of different models to estimate the global 
solar radiation on inclined surfaces. Renewable 
energy, 50, 710-721.  
https://doi.org/10.1016/j.renene.2012.07.031 

[36] Othman, A. B., Belkilani, K., & Besbes, M. (2018). Global 
solar radiation on tilted surfaces in Tunisia: 
Measurement, estimation and gained energy 
assessments. Energy Reports, 4, 101-109. 
https://doi.org/10.1016/j.egyr.2017.10.003 

[37] Shukla, K. N., Rangnekar, S., & Sudhakar, K. (2016). 
Mathematical modelling of solar radiation incident on 
tilted surface for photovoltaic application at Bhopal, 
MP, India. International Journal of Ambient Energy, 
37(6), 579-588. 
https://doi.org/10.1080/01430750.2015.1023834 

[38] Marion, W. F. (2010). Overview of the PV module 
model in PVWatts. National Renewable Energy 
Laboratory. 

[39] Sulaeman, S., Brown, E., Quispe-Abad, R., & Müller, N. 
(2021). Floating PV system as an alternative pathway 
to the amazon dam underproduction. Renewable and 
Sustainable Energy Reviews, 135, 110082. 
https://doi.org/10.1016/j.rser.2020.110082 

[40] Manoj Kumar, N., Sudhakar, K., & Samykano, M. (2019). 
Techno-economic analysis of 1 MWp grid connected 
solar PV plant in Malaysia. International Journal of 
Ambient Energy, 40(4), 434-443. 
https://doi.org/10.1080/01430750.2017.1410226 

 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

  

This article is an open-access article distributed under the 

terms and conditions of the Creative Commons Attribution 

(CC BY) license 

(https://creativecommons.org/licenses/by/4.0/). 

https://doi.org/10.1109/ISGTEurope52324.2021.9639991
https://doi.org/10.1109/ISGTEurope52324.2021.9639991
https://doi.org/10.1109/PESGM.2017.8273783
https://doi.org/10.1016/j.rineng.2025.104500
https://doi.org/10.1002/jnm.2937
https://doi.org/10.1016/j.apenergy.2022.119242
https://doi.org/10.1007/s41870-023-01528-9
https://doi.org/10.1007/s41870-023-01528-9
https://doi.org/10.3390/en13040940
https://doi.org/10.1155/2013/496348
https://doi.org/10.3390/en11082147
https://doi.org/10.1016/j.renene.2012.07.031
https://doi.org/10.1016/j.egyr.2017.10.003
https://doi.org/10.1080/01430750.2015.1023834
https://doi.org/10.1016/j.rser.2020.110082
https://creativecommons.org/licenses/by/4.0/

