Influencing Factors of Solar-Powered Electric Vehicle Charging Stations in Hail City, Saudi Arabia

Abstract

As part of the global endeavor to encourage sustainable urban growth and lower carbon emissions, Hail City is leading the way in implementing cutting-edge technologies with which to improve its urban infrastructure. Initiatives for energy resilience and the environment heavily rely on shifting to electric vehicles (EVs). This work describes the strategic planning required to implement a network of solar charging stations and analyzes the parameters that affect this, supporting cleaner transport options. In addition to meeting the growing demand from an increased number of EVs, constructing a network of solar charging stations positions the city as a leader in integrating renewable energy sources into urban areas. A solar electric vehicle charging station (EVCS) will also be designed. This study highlights a competitive attitude in establishing international standards for sustainable practices and critically examines the technical factors affecting the required charging stations. Regarding the latter, the following results were obtained. The ideal number of station slots is 200. Less efficient vehicles with higher consumption rates require a more comprehensive charging infrastructure, and increasing the charging power leads to an apparent decrease in the number of stations. The influence of battery capacity on the required NSs is limited, especially at charger power values above 30 kWh. By taking proactive measures to address these factors, Hail City hopes to improve its infrastructure effectively and sustainably, keeping it competitive in a world where cities are increasingly judged on their ability to adopt new technology and green projects. A solar station was designed to supply the EVCS with a capacity of 700.56 kWp.

1. Introduction

The transition to sustainable energy is gaining momentum worldwide, with solar energy emerging as a key player in reducing carbon emissions and promoting environmental sustainability. One crucial application of solar energy is powering electric vehicle (EV) infrastructure, particularly charging stations. As EVs become more popular, the availability and distribution of charging stations have become critical to supporting their widespread adoption. Saudi Arabia, which was previously dependent on oil, has started to shift to renewable energy sources (RESs). This change aligns with a national goal to reduce environmental impact and diversify energy sources. The region’s abundance of solar energy presents an ideal way to meet energy needs, particularly for supplying electricity for a recently established network of EV charging stations.

Hail City, located in the north-central region of Saudi Arabia, is a model case study due to its geographic and climatic conditions, which are highly conducive to solar energy utilization. However, despite its potential, the development of solar-powered charging infrastructure in Hail City is still in its nascent stages. Understanding the factors that impact the expansion of such an infrastructure is essential for city planners, policymakers, and investors aiming to promote renewable energy (RE) and support the growth of EV usage. Hail City’s urban area has grown significantly with the growth in its population, positioning the city for continued expansion. Urban infrastructure must be re-evaluated in light of this expansion to handle the growing population while minimizing adverse environmental effects, particularly those resulting from transportation. There is a global movement toward electric automobiles to lessen dependency on non-RESs and carbon emissions; with growing interest and investment in EV infrastructure, Hail City reflects this trend.

This study explored various technological factors influencing the number of solar electric vehicle charging stations (EVCSs) and policy-related aspects. By identifying these factors, this research aimed to provide insights into how Hail City can enhance its infrastructure to meet the increasing demand for sustainable energy solutions in the transportation sector. A solar station was also designed to supply an EVCS with a capacity of 700.56 kWp.

2. Literature Review

Recently, there has been a focus on research related to all aspects of EVs, including their impact on the local grid, the use of RE to charge them, the issuance of necessary legislation, the obstacles that prevent their deployment, and more. The EU’s Directive 2018/844 [1] describes the regulations and technical requirements for EV charging stations. This document states that member states should encourage the installation of recharging infrastructure by enacting legislation that private property owners encounter when setting up recharging stations in their parking lots. Szumska [2] examined whether European Union nations had EV rapid-charging facilities built into their current infrastructure. According to the most recent data, the author reported that the Netherlands has 1.18 semi-public charging stations per square kilometer. The Netherlands is one of five nations in the world, along with Norway, Iceland, Sweden, and China, where the total proportion of electric passenger cars is greater than 1%. Regarding infrastructure, the Netherlands has more charging stations per capita than any other country [3]. According to published Polish data, the country’s infrastructure for EV charging has been widely expanding [4]. Almasri et al. [5] reviewed the existing research regarding solar-powered EVCSs. The results showed that alongside parking lot shading projects, for an on-grid system, the electricity cost ranged from USD 0.0032 to 0.5645/kWh, while the electricity consumption varied from 0.139 to 0.295 kWh/km. The authors reported that the payback period varied between one and fifteen years.

Integrating RE with the transportation infrastructure is attractive and increasingly studied in academic environments. According to Barman et al. [6], optimizing the environmental advantages of EVs requires combining RESs with effective EV charging. Developments in this area have made it possible to employ sustainable and environmentally friendly EV charging technology, decreasing the cost of charging. To schedule the charging of many EV fleets in a parking lot with a photovoltaic (PV) system and a transformer limit for charging, Seddig et al. [7] considered a stochastic optimization model. The findings demonstrate that charging various EV fleets in a parking lot is doable, even considering technical limitations and signal uncertainty. EV charging controllers that improve the architecture and implementation of charging station structures were studied by Taghizad-Tavana et al. [8]. Academics were given access to the most recent advancements in EV and RES grid integration by Sinha et al. [9], who also highlighted open research questions. A thorough design of a fuel cell and electrolyzer system or a fuel cell-free PV energy-based EVCS was proposed by Enescu et al. [10] by using RETScreen in a residential property in Pitești city, Romania. The car battery capacity (BC) of the power-following strategy-based EVCS design was roughly 20 times less than that of the reference design. Furthermore, the EVCS design using a power-following methodology was nearly 50% cheaper than the reference design. The power-following method was used to analyze three different EVCS operation scenarios. A thorough overview of the essential elements of PV–EV charging, such as power converter architectures, charging protocols, and control mechanisms for various PV system types, was provided by Bhatti et al. [11]. An effective energy management strategy for PV–electric systems that charge EV batteries was suggested by Tran et al. [12]. The system’s operating strategy is to enable the PV cells or distribution grid to charge an EV effectively. The suggested energy management strategy can lessen unforeseen peak power use and assist in the deployment of vehicle-to-grid technology. The outcomes of the experiment and simulation demonstrated that the proposed approach can alleviate the effects of high EV penetration.

In many papers, solar-powered EVCSs have been studied using MATLAB 2024a. For example, Nair et al. [13] projected a combined control policy for the EVCS. The energy storage unit at the charging station allows for the economical use of solar systems while guaranteeing uninterrupted EV charging. Droop, master–slave, and snubber circuits combined with EVCS management can result in faster battery charging as well as discharging and improved direct-current (DC) bus voltage regulation. Using MATLAB, Atawi et al. [14] also created and assessed a stand-alone PV-powered charging station (PVCS). The robust agreement between the simulation and experimental results confirmed the effectiveness of the suggested system. According to the findings, the EV battery charges steadily and constantly under various degrees of solar exposure. Shariff et al. [15] showed how to build and operate a contemporary solar-assisted EVCS in India’s Aligarh Muslim University parking lot. The PV was tested using standard parameters. Compared to empirical approaches that rely on assumed data or the direct and applied approach with defined data, the MATLAB model can efficiently size and operate an entire PV–EV charging system faster. Fachrizal and Munkhammar [16] used MATLAB to assess innovative PV EVCS options for residential buildings in Sweden. Aa maximum charging power rate of 3.7 kW and a charging efficiency of 90% were established. The authors believed a significant drop in residential peak loads may result from intelligent charging networks. The study aimed to determine how many of these stations are best distributed over large route networks. Umair et al. [17] used MATLAB to investigate the performance dynamics of a solar-integrated charging system. The authors stated that the output power of the solar PV system increased significantly by 47% when the solar irradiation was increased from 400 W/m² to 1000 W/m².

A multi-agent deep reinforcement learning approach was presented by Shin et al. [18] for energy management in distributed solar EVCSs and energy storage systems (ESSs). A thorough performance evaluation showed that the suggested method lowered operational costs for EVCSs. It was asserted that the proposed method was designed explicitly for PV/ESS-enabled EVCSs, successfully managing fluctuations in charging-related data in real time and producing the desired performance results. Using an ESS, Chaudhari et al. [19] presented a hybrid algorithm to reduce the cost of solar EVCSs in Singapore using three test cases of EV charging stations. Due to the high price of EVCSs, the study looked into the subsidies required to build nearby PV systems profitably. Therefore, using the suggested technique, ESSs on the distribution side can make deploying PV systems close to EVCSs easier at a reasonable cost.

An optimization model was presented by Ji et al. [20] to help determine the locations and sizes of solar-assisted EVCSs within a city. The tests used historical data from 297 customers of an EV rental business in Beijing, China. The outcomes demonstrated that the suggested method can generate superior selections in a computationally feasible time. Future research should prioritize developing efficient charging strategies in light of the growing number of EVs. PVCS roofs are attractive because of their integration of RE and EV charging potential. Consequently, Zhang et al. [21] suggested a method of energy management for PVCSs that accommodates several EVs. The process takes into account the objectives of EV owners as well as those of the stations. They asserted that the suggested strategy uses a learning algorithm with a two-level mechanism, producing effective energy distribution. Compared to stations without the proposed management system, the results demonstrated that the suggested method functions effectively for many charging stations.

Feizi et al. [22] proposed a system for calculating the PV generation dispatch limitations in distribution networks with EVCS connectivity to account for related PV generation and EV uncertainties. The findings showed that adding charging capabilities to EVs can influence PV generation dispatch restrictions while raising budget uncertainty. The EV market, including its technology requirements, charging structure, and energy management strategies, was covered in detail by Alrubaie et al. [23]. The authors also looked at studies on various EVs and solar system setups: they carried out a comprehensive examination of global grid connection and EV charging standards and provided an overview of the current state of EVs. Lastly, suggestions and concerns regarding future infrastructure development for EV charging and grid connectivity were assessed. It has been demonstrated that PV-grid charging is commercially feasible. The low capacity of PV panels and batteries may make the power system unfeasible.

Potential problems with the infrastructure for EV charging have been discussed. Studies show that a system that encourages investment in RE-powered charging stations is necessary to expand the adoption of EVs. Encouraging EV use is also advantageous. According to Funke et al. [24], home charging is the most common in many countries. This trend is anticipated to persist in other countries as well. Different nations have different pricing structures: according to the authors’ research, national regulations on charging infrastructure may not be very broadly applicable. According to Stańczyk and Hyb [25], electromobility has three main obstacles: a lack of a rapid charging station infrastructure, prolonged charging times, and a limited EV range due to battery size. Wide-ranging national networks are necessary to improve traveler comfort. Eastern Polish respondents to a study by Stoma and Dudziak [26] discussed the difficulties impeding EVs’ general market implementation. The primary concerns were exorbitant prices, poor infrastructures, and a finite amount of car BC per charge. According to Halbey et al. [27], integrating fast-charging stations into the infrastructure would be wise. Increased car battery capacity, thoughtful consideration of charging station locations, and grid density are critical components that may improve acceptance and reduce range anxiety. Almutairi [28], in Saudi Arabia, and Bailey et al. [29], in Canada, discovered that more than 60% of participants chose their homes as the ideal places at which to charge EVs. More than 70% of participants in Sheldon and Dua’s [30] survey said that they traveled between 10 and 60 km every day. To offer insights into the potential adoption and impacts of EVs in Hail City, Saudi Arabia, as well as the move away from conventional cars, Al-fouzan and Almasri [31] conducted a survey. The results show a clear preference for EVs, with 37.9% of participants stating they would switch to an EV. Xu et al.’s research [32] examined EVs’ effects in Regina, Canada. According to a survey, most Regina residents support wind farms, and 25% of those surveyed stated they would put solar panels on their roofs for free. According to research by Osório et al. [33], parking lots are home to 26% of all EV charging outlets worldwide. The United States (US) uses various strategies and cutting-edge program designs to align EV loads with RESs.

Tripathi et al. [34] reviewed the integration of PV solar systems with EV charging infrastructure. According to the authors, solar-powered EVs can significantly improve energy sustainability and urban mobility, and case studies from around the world have demonstrated the potential benefits of large-scale solar EV charging infrastructure. The authors stated that compatibility issues, grid integration problems, and technological complexity need to be resolved. The review emphasized the necessity of an ongoing stakeholder research partnership. Etukudoh et al. [35] examined EVCSs in Africa, the United States, and Canada. According to the authors, infrastructure spending is driven by foreign finance, public–private partnerships, and government grants, which promote economic growth and job creation. Their investigation highlighted the need for customized communication techniques by identifying various behavioral and cultural elements that impact EV adoption. Ultrafast charging, wireless technologies, and intelligent ecosystems are all part of the future, which calls for cooperative solutions to grid capacity and standardization issues. Singh et al. [36] discussed the infrastructure for charging electric vehicles and how it connects to the grid. Additionally, there was a complete examination of the advantages and disadvantages of each component of current grid integration and charging infrastructure. This paper included insights into methods for overcoming current barriers and suggestions for further research. The authors stated that developing electric vehicle charging infrastructure in the future necessitates a thorough and cooperative approach. By resolving standardization issues, bolstering security measures, adopting innovative safety practices, promoting the sustainability of electronic waste, and making user-centric research a top priority, the electric vehicle industry may clear the way for a safe, open, and environmentally friendly future. To ensure a stable electricity supply to EVCSs in the Çukurova region of Adana, Turkey, Güven et al. [37] investigated various hybrid system scenarios using HOMER Pro 3.14.2. According to the authors, the scenario with a levelized cost of energy of USD 0.0215 and a total net present cost of USD 611,283.50 was the most practicable system. In total, 1507 MWh of energy was produced, and 1420 MWh was consumed annually. The system’s payback period is shortened because the energy produced is more than what is obtained from the grid. These results demonstrate how renewable hybrid systems might improve EVCS performance in solar-rich areas economically and sustainably. Chen et al. [38] investigated how economic, technological, commercial, governmental, social, and environmental aspects affected China’s public electric charging infrastructure from the viewpoint of its consumers. The study’s findings included the following conclusions: first, the demand for public charging infrastructure will not be significantly increased by lowering operating costs; second, technical considerations cannot directly encourage the development of public charging infrastructure; and third, that the primary driver of the electric car industry’s growth is the expanding market demand, whilst the social environment and incentive policies can also indirectly encourage the construction of public charging infrastructure.

The ability of distribution networks to manage the higher electricity load from the increasing number of EVs is a significant concern, and this can be significantly worse in some places, as noted by Nogueira et al. [39]. The authors demonstrated the urgent need to fortify distribution networks because of the impending advent of EV development. The infrastructure needs for fast charging in Sweden and Norway were examined by Gnann et al. [40]. They concluded that connecting a charging station to the nearby electrical grid was a significant obstacle. The authors stated that one 150 kW fast-charging station may be found for every 1000 electric vehicles. The findings suggest that they may be financially successful if these stations are close to commercial and service buildings. Al-fouzan and Almasri [41] also demonstrated the need to build strategic infrastructure to deal with the expected rise in EV adoption for Hail City, Saudi Arabia. The authors reported that a 1047.35 kWp PV system has an expected profitability of 11.69 years, and they also calculated the CO₂ savings.

From the above, it is clear that there are numerous studies on EVs, the use of solar energy, their impact on the grid, and their economic as well as environmental benefits. However, further research is needed into the factors affecting charging stations and the integration of solar energy systems in hot regions such as the Arabian Gulf. The importance of this study lies in filling this gap. This paper is divided into five sections: 1. Introduction, 2. Literature Review, 3. Methodology, 4. Results and Discussion, and 5. Conclusions and Recommendations.

3. Methodology

In this paper, the work of Al-fouzan and Almasri [31,41] is followed up using survey data on transportation and the likelihood of switching to EVs, and a network of solar-powered charging stations in Hail City, Saudi Arabia, is proposed. Hail City’s current transport system is mainly intended for conventional cars. A vast network of solar charging stations is critical to this infrastructure as the city welcomes EVs. As EV adoption rates rise, these stations are scalable to meet future and existing demands. Military services comprise most of Hail City’s urban footprint, around 27% of the area. The second-largest area comprises residential regions: these cover less than 27% of the area and are mainly located around Aja Mountain along the north–south axis. According to the authorized land-use plan, these residential zones will grow considerably, making up more than 56% of the total area. This indicates a trend towards urban expansion and monofunctional planning. The new plan’s anticipated rapid decline to less than 5% of the metropolitan area, which currently contains government institutions and public facilities and makes up about 26% of the area, has raised concerns about the availability of public services in the future. It is suggested that the existing sparse industrial land use be concentrated in 16% of the area, mostly north of the city [42].

3.1. Station Demand

A geographic information system (GIS) with OpenStreetMap data and Open Route Service (ORS) was employed, with questionnaire data from [31]. The design and study of the factors affecting the number and distribution of stations were based on real data obtained from a questionnaire and the technical specifications of electric vehicles available in the global market, which may make these results closer to reality than modeling methods.

Table 1. Calculations of station numbers.

Step	Calculation Method	Equation
Measure area (A) via Google Earth	Utilizing Google Earth to understand the geographic extent.	N/A
2. Calculate total population (P)	Multiplying the area (A) by the average population density (D) as per [42].	$P=A\times D$
3. Determine average family size (M)	Survey data were used to calculate a weighted average of family sizes as per [42].	$M={\displaystyle \frac{\sum _{i=1}^N(x_i\times w_i)}{\sum _{i=1}^N\left(w_i\right)}}$
4. Compute the total number of families (F)	Dividing the entire population (P) by the typical family size (M).	$F={\displaystyle \frac{P}{M}}$
5. Calculate average car ownership (O)	Utilizing survey data on car ownership to calculate a weighted average per the questionnaire.	$O={\displaystyle \frac{\sum _{i=1}^m(y_i\times v_i)}{\sum _{i=1}^m\left(v_i\right)}}$
6. Estimate total number of cars (C)	Multiplying the number of families overall (F) by the typical car ownership (O).	$C=O\times F$
7. Forecasted number of EVs (E)	Multiplying the total number of cars (C) by the conversion percentage to EVs (T).	$E=C\times T\times (1-\%PC)$
8. Determine average trip length per day (L)	Survey data will be used to calculate a weighted average of trip lengths as per the questionnaire.	$L={\displaystyle \frac{\sum _{i=1}^m(L_i\times z_i)}{\sum _{i=1}^m\left(z_i\right)}}$
9. Calculate the car battery consumption (CBC)	Multiplying the EV consumption rate (R) by (L).	$CBC=R\times L$
10. Determine average recharging days (RD)	Dividing the average car battery capacity (BC) by the daily CBC.	$RD=BC/CBC$
11. Calculate hours needed for full charge (HF)	Dividing BC by the charger power (CP).	$HF=BC/CP$
12. Compute the fully charged number of charging cycles (Nf)	Dividing the operational hours by the hours needed for a full charge (HF).	$Nf=(24-5)/HF$
13. Estimate station capacity per day (SD)	Multiplying the number of car slots (CSs) by the number of complete charging cycles (Nf).	$SD=CS\times Nf$
14. Calculate station capacity (SC)	Multiplying the station capacity per day (SD) by the average recharging days (RDs).	$SC=RD\times SD$
15. Determine the number of stations (NS)	Dividing the expected number of EVs (E) by the station capacity (SC).	$NS=E/SC$
16. Adjust the number of stations for maintenance (NS’)	Increasing the number of stations (NS) by 10% to account for maintenance.	$NS\prime =NS\times 1.10$

describes the detailed multistep method used in Hail City to determine the required number of EVCSs. Using Google Earth, the procedure started with geographic measurements of the city zones. After this, calculations were performed that incorporated demographic data, vehicle ownership data, and EV conversion rates. Formulas that determine the total population, average family size, number of cars overall, and anticipated number of EVs in the city were essential to this process. Furthermore, the study used average trip length per day, L, and consumption rate, R, which account for the daily battery use of EVs, to determine the total required number of stations, NSs, and the necessary capacity of charging stations. Adjustments were made for station upkeep and idle times, ensuring a robust system supporting the anticipated quantity of EVs.

This study examined how essential factors change and affect the number of EVCSs that are needed. Al-Wosyataa’s central location (zone 7) made it a perfect sample area for this study. It probably represents a higher concentration of EV usage and charging demands, offering insightful information for planning urban EV infrastructure. The estimated future number of EVs in Hail City was also calculated. This study determined the impact of technical factors affecting the NSs using MATLAB, including route length, EV consumption rate, car BC, car slots per station (CSs), and CP. PV*Sol 2022 R6 was also used to design the solar system required for the station as a model. The four primary parameters of the study fluctuated [41]:

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EV consumption rate, R: ranged from 0.05 to 0.40 kWh/km in steps of 0.05 kWh/km.

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Car BC: varied from 25 to 100 kWh in increments of 5 kWh, reflecting the energy storage capacity of EVs.

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CS per station: Ranged from 50 to 500 slots in increments of 50. This indicates the capacity of each charging station in terms of the number of cars it can accommodate.

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CP: varied from 20 to 75 kWh in increments of 5 kWh, indicating each charger’s power output.

In total, 15,360 examples for all possible combinations of these factors were investigated in this study. This wide range of situations allowed for a thorough understanding of how various factors influence the infrastructure needs of EVCSs. A standard set of assumptions was used to analyze the need for EVCSs throughout Hail City’s 17 zones. This methodology offers a fundamental synopsis of the charging station requirements in every zone, enabling thorough comprehension of the infrastructure requirements. The following assumptions were made when determining the NS’ required for the 17 zones: R = 0.20 kWh/km; BC = 75 kWh; CSs = 200; and CP = 45 kWh.

A crucial practical aspect was added to these conventional assumptions to represent actual charging behaviors: Based on assumptions, Bailey et al. [29] and Almutairi [28] suggested that 60% of EVs are charged at home or by using private chargers. This illustrates the expanding popularity of at-home charging options and how they affect the need for public charging infrastructure. Calculating the remaining BC for different trip lengths while considering the various battery capacities and energy consumption rates typical of various vehicles, such as compact cars, sedans, and luxury SUVs, is part of the methodology for analyzing battery performance across different vehicle types. This method makes it easier to comprehend how various cars lose battery power in other ways over predetermined distances. The primary equation used to calculate the remaining battery life is as follows:

$$Battery Remaining=BC-(R\times L)$$

The results can inform urban planning, infrastructure development, and policy decisions related to sustainable transportation and EV charging infrastructure, enabling decision-makers to make informed plans and policies with which to support the transition to electric mobility and address future transportation needs.

3.2. PV System

The same design steps for solar charging stations and assumptions as in [41] were followed, but with less capacity than the previous one for the Scientific Center in Hail City. The Scientific Center in Hail City, illustrated in Figure 1, was selected as the perfect location at which to establish an EVCS due to its easy access through the central educational district of Hail City. The choice of this venue was influenced by its strategic positioning and convenient access, which makes it an excellent site for deploying advanced RE technologies. The Scientific Institute serves as a typical charging station and provides a unique opportunity to explore the integration of solar energy solutions in an educational environment. The purpose of constructing a solar carport PV system at this location was to serve as a model for comparable installations in urban and semi-urban settings. The performance, warranty, and availability of the PV module type in the local market were considered in the process of choosing it for this study [43]. Because of their high module efficiency and compatibility with the local environment, Jinko Solar modules were selected for use. The specifications of these new modules are detailed in

Table 2. The PV module specifications used [44].

PV Panel Type	N-type Mono-Crystalline
Maximum power at STC (W)	630 Wp
Module efficiency (%)	22.54%
PTC power rating (W)	474
Optimum operating current (Imp)	13.69 A
Optimum operating voltage (Vmp)	46.02 V
Short-circuit current (Isc)	14.39 A
Open-circuit voltage (Voc)	55.85 V
Nominal operating cell temperature (NOCT) (°C)	45–85

.

4. Results and Discussion

This section examines and assesses the research’s conclusions and offers recommendations for how to proceed with creating a solar charging station network, focusing on Hail City. The information was examined to find noteworthy patterns that might influence the creation of effective, long-lasting, and user-friendly infrastructure for EV charging.

4.1. Site Spatial Parametric Study

A GIS with OpenStreetMap data and ORS was employed, and questionnaire data from [31] were also used. All zones of the city were included in the analysis. It was discovered that these zones have an average driving distance of up to 37 km. As such, this distance was set as the study’s upper bound, guaranteeing that the research appropriately captured usual driving behaviors. The investigation focused on the battery profiles of EVs throughout various trip lengths, namely 9 km to 37 km, which correspond to the main commuter routes.

Table 3. An overview of trip durations and times for typical driving situations in Hail City.

North to north Shortest distance (black) = 16.00 km Shortest period (red) = 0.335 h
North to middle Shortest distance (black) = 24.73 km Shortest period (red) = 0.444 h
Middle to middle Shortest distance (black) = 9.581 km Shortest period (red) = 0.164 h
Southern to northern Shortest distance (black) = 37.77 km Shortest period (red) = 0.564 h
Southern to middle Shortest distance (black) = 16 km Shortest period (red) = 0.324 h
Southern to southern Shortest distance (black) = 19.35 km Shortest period (red) = 0.291 h

lists the hours of travel and distances for trips. ORS v7.2.1 was used in the study to determine the shortest and fastest routes for a variety of travel circumstances. It was found that a maximum one-way distance of 37 km, accounting for a round-trip of around 74 km, corroborated the survey findings in association with L, which was found to be approximately 77 km/day by a thorough survey [31].

4.2. Charging Stations

4.2.1. Required Station Numbers

A comprehensive analysis of Hail City’s projected automotive landscape in 2030 was conducted, drawing on market data and estimates from several trustworthy sources. By 2030, Hail City’s population is expected to reach 582,152, according to [42]. According to [45], Hail City is anticipated to account for 2.3% of Saudi Arabia’s car market. Furthermore, data in [46] indicate that 53.8% of the market comprises the private automotive category. A more accurate estimate, which accounts for average family sizes and projected population increases, suggests that there may be close to 304,060 automobiles overall.

The key regions were required to have the maximum number of charging stations in the scenario where private charging was not adopted, represented as 0% PC. The NSs and influencing factors were calculated. Specifically, zone 4 required the most significant NSs—seven—while zones 6 and 7 required only six stations. This pattern aligns with the expected rise in demand for public charging stations if private charging options are unavailable. However, when the model incorporated a 60% PC rate, there was a discernible decrease in the requirement for public charging stations, indicating a substantial uptake of private charging options. This scenario demonstrated that the NSs in zone 4 was reduced to four station, while the NSs in zones 6 and 7 were reduced to 3 and 4, respectively.

Table 4. Expected number of stations at different private charging rates in Hail City [41].

Share of Private Charging (%)	0	20	40	60
Zone 4	7	6	5	4
Zone 6	6	5	4	3
Zone 7	6	5	4	3
Zone 8	5	4	3	3
Zone 9	3	2	2	2

illustrates the required NS’ in zones 4, 6, 7, 8, and 9 at different private charging rates, while other zones needed only two stations in all cases. These results highlight the critical influence that private charging infrastructure has on the need for public charging stations. Notably, at least two stations are maintained in every zone, irrespective of the PC rate, to guarantee operational reliability and accommodate maintenance requirements. This baseline guarantees that EV consumers consistently have access to public charging alternatives, even in areas with lower demand.

4.2.2. Factors Affecting the Number of Stations

Impact of Average Car Consumption Rate (R) and Charger Power (CP) on Station Numbers

The data show that the average R significantly influences the number of EVCSs needed. Figure 2 demonstrates the precise relationship between vehicle efficiency and the need for charging infrastructure through a step-by-step increase in the NS’ as R values climb. For example, the station requirement increases from 6 to 41 when comparing the extremes of R values (0.05 vs. 0.40) for a mid-range CP of 45 kWh. This significant disparity highlights that vehicle efficiency is essential when developing urban EV infrastructure. In some circumstances, the link is described in depth as follows.

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Lowest R = 0.05 kWh/km: At this optimal efficiency level, the NS’ required started at 13 for the lowest CP of 20 kWh. As CP increases, a notable decrease in station requirements is observed. For instance, at CP values of 40 and 45 kWh, the NS’ drops to seven and six, respectively. The highest CP of 75 kWh requires the fewest stations, with only four needed.

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Medium R = 0.20 kWh/km: With moderate efficiency, the station requirement escalates noticeably. At a CP of 20 kWh, the NS’ almost quadruples to 46 compared to the lowest R. Even at higher CP levels, such as 60 and 75 kWh, the numbers are significantly higher than in the lowest R scenario, requiring 16 and 13 stations, respectively.

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Highest R = 0.40 kWh/km: This scenario demands the most substantial infrastructure and represents the least efficient EVs: the station requirement at a CP of 20 kWh skyrockets to 91. Even at the highest CP of 75 kWh, the requirement remains high at 25 stations, highlighting a more than sixfold increase from the most efficient scenario.

Effects of Car Slots (CSs) per Station and Average Car Consumption Rate (R)

Analyzing the relationship between CSs and the required NS’ reveals significant insights, particularly concerning vehicle efficiency. For vehicles with an R-value of 0.05 kWh/km, the NS’ needed decreases sharply from nearly 13 stations at 50 CSs to 5 stations at 100 CSs, as shown in Figure 3. This trend is consistent across different R values, although less efficient vehicles (e.g., R = 0.40 kWh/km) show a more significant reduction under the same conditions. The diminishing returns on increasing the number of CSs become apparent beyond 200 slots, where the NS’ plateaus, suggesting an optimal range for station capacity depending on the average vehicle efficiency in the fleet.

Car Battery Capacity’s Role in Charging Infrastructure Needs

Figure 4 presents the required NS’ as a function of BC for EVs, stratified by different R values. It illustrates the response of charging infrastructure needs across a BC range of 25 kWh to 100 kWh, with R values varying between 0.05 and 0.40 kWh/km. Vehicles with an R = 0.40 kWh/km show a generally increasing trend in the required NS’ as BC increases until 65 kWh. Notably, there is a sharp decrease in station requirements between 80 and 95 kWh, indicating a potential threshold beyond which significantly less infrastructure is needed to support larger BCs in highly efficient vehicles. It is also evident from the figure that the same effect applies to the required NS’ with an increase in R, where a greater NS’ required. Additionally, the impact of increasing BC on the NS’ at low R values is almost constant, at 0.05 and 0.10 kWh/km for 7 and 13 stations, respectively. Higher R values generally correspond to more stations required across most BCs, illustrating that less efficient vehicles (higher R) demand more extensive charging infrastructure.

Effect of Car Slots (CSs) per Station and Charger Power (CP) on Station Number

This analysis looks at the relationship, especially for a medium average R = 0.20 kWh/km, between the number of CSs and CP at each station and the number of charging stations needed. The information given includes a variety of CP values in addition to CS values from 50 to 500. Under certain conditions, the following is a detailed description of the link.

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Lower CSs (50 Cars): The station numbers start relatively high at the lowest CSs: 50. For instance, as many as 46 stations are required. As CP increases, station numbers gradually decline. Notably, at the highest CP of 75 kWh, this number drops to 13.

▪

Mid-range CSs (200 to 250 Cars): A marked reduction in the NS’ is observed when the CSs are increased to 200 and 250. For CP values of 20 and 75 kWh, the station requirements decrease to 13 and 4 for CSs = 200 and further down to 10 and 4 for CSs = 250, respectively.

▪

Higher CSs (500 Cars): The number of required stations decreases significantly at the upper limit of CSs (500 cars). Only six stations are needed for a CP of 20 kWh, which remains consistently low across higher CP values, reaching a minimum of three stations for a CP of 75 kWh.

This trend highlights a clear inverse relationship between CSs and CP and the number of charging stations required. As the capacity of each station increases (higher CSs), the total NS’ needed across the city decreases. For instance, comparing the scenarios of CSs = 50 and CSs = 500 at a CP of 45 kWh, the required NS’ drops from 24 to just 4.

These findings have significant ramifications for the design of urban EV infrastructure. Higher CSs, or individual charging station capacity, can result in a substantial decrease in the total NS’ needed. This approach might provide a more effective use of available space and resources, particularly in crowded urban locations with less room for additional infrastructure. Additionally, this research indicates that the best course of action for places such as Hail City may involve a balanced strategy combining various high-capacity and well-placed charging stations. The charging network can be made as efficient and accessible as possible by providing sufficient coverage with smaller stations and catering to high-demand locations with larger stations.

An in-depth look at how the number of CSs at each charging station and the CP affect the required number of EVCSs in a city, specifically when the average rate of charge is 0.20 kWh/km, is shown in Figure 5. The graph delineates a series of scenarios, ranging from lower CSs (50 cars) to higher CSs (500 vehicles), across various CP values from 20 to 75 kWh. Each line represents a different CS value, illustrating the corresponding change in the required charging stations as CP increases. This visual representation provides an insightful understanding of the interplay among SC, CP, and the infrastructural demands of an evolving urban EV landscape. It highlights the potential efficiencies that can be gained by optimizing CSs and CP in planning and developing EV charging networks, an essential consideration for urban planners and policymakers. The figure is a critical component in illustrating the findings and implications of this study, underscoring the importance of strategic infrastructure planning in the transition toward sustainable urban mobility.

Efficiency Gains from Increasing Car Slots and Battery Capacity

Figure 6 shows the effect of CSs per station and BC on the required NS’. It is evident from the figure that increasing the number of CSs up to 200 leads to an apparent decrease in the required NS’, after which it remains almost constant. On the other hand, the effect of BC on the NS’ required is limited. This confirms that the CSs per station required should be around 200 slots.

Influence of Charger Power (CP) and Car Battery Capacity (BC)

Figure 7 shows the effect of CP and BC on the required NS’. It is evident from the figure that increasing the CP leads to an apparent decrease in the number of stations, while the effect of the BC on the NS’ required is limited, especially for values of CP greater than 30 kWh.

4.3. Car Battery Profile over 24 H

The focus here was on analyzing the battery state of different types of vehicles (sedan, truck, and bus) over a typical 24 h period. This analysis included how battery levels fluctuate with usage patterns throughout the day, offering a realistic view of battery demands and charging needs. A MATLAB code was designed to simulate the battery state of different types of EVs, considering their charging patterns and usage. The hypothesis behind this analysis was that the vehicle type, defined by battery size and R, combined with variable charging probabilities and user interface factors, significantly influences the battery state throughout the day. Considering that the charging amount is the minimum charging requirement or remaining capacity, the discharge amount was calculated as the product of a random L and R.

Table 5. Car battery profile parameters.

Variable Parameter	Value	Description
Vehicle type	Sedan, truck, and bus	Different vehicle categories.
Car battery capacity, BC (kWh)	Compact car (25, 50), sedan (75), and luxury SUV (100)	Varying battery capacities for each vehicle type [47].
Charging requirement (kW)	7, 15, and 25	Maximum charging rates for vehicles.
Consumption rate, R (kWh/km)	0.05 to 0.40	Energy consumption per kilometer.
Charging probability	Varies hourly (0.05 to 0.9)	Probability of vehicle charging at each hour.

describes the parameters used and their variations.

Figure 8 shows the relationship between L and the battery remaining at different R (depending on the type of car) values. The BC of a compact automobile with a 50 kWh battery and an efficient R of 0.1 kWh/km remains relatively high, even after lengthy travel. After a 37 km journey, the average L per societal driving habit remains around 46.3 kWh, as shown in Figure 8. In contrast, the BC remains the same in a less efficient car that consumes 0.25 kWh/km, resulting in a noticeable reduction. Following a 37 km trip, the remaining capacity decreases to 40.75 kWh. Premium SUVs equipped with a 100 kWh battery exhibit strong and consistent energy storage capability regardless of the rate of energy usage. Following a 37 km journey with an R of 0.25 kWh/km, the battery retains a capacity of 90.75 kWh, emphasizing the advantage of larger batteries in sustaining range even with increased consumption. It is generally observed that during a trip (L = 37 km), only 5 to 10 kWh was consumed, depending on the type of car.

4.4. Design of PV System

The PV*SOL Premium Simulator was used in this investigation to create an EVCS scenario and evaluate how solar integration affects EV charging capabilities. When solar PV was installed at the Scientific Center, several significant aspects of the module areas were found through feasibility modeling. The system considered was a carport-mounted system planned to charge EVs. It was also assumed that EV stations will stay alive all day. The maximum number of EVs that can be charged, estimated at 2400 vehicles, can be charged each day at different times through solar and grid-connected systems. For simulation purposes, it was considered that all EVs are initially charged to 0%. The consumption of solar energy on-site is ~100%, considering that auxiliary consumption is partially covered by solar energy and balanced by the local grid. Five chargers are divided into 11, 22, 50, 100, and 150 kW, whereas each EV group contains 120 vehicles that can be observed easily through a single-line diagram. An overview of the solar array installations throughout the facility can be found in

Table 6. PV array and the quantity of installed modules in the Scientific Center’s EVCS.

Area Overview	Total Surface Area (m2)	Quantity of Modules
Carport 01	523.6	44 × 2 × 2 = 176
Carport 02	255.9	43 × 2 = 86
Carport 03	238.0	80 × 1 = 80
Carport 04	238.0	80 × 1 = 80
Carport 05	426.0	80 × 2 = 160
Carport 06	1204.9	135 × 3 = 405
Carport 07	321.9	125 × 1 = 125
Total combined PV module area	3108.2	1112

. The information supplied includes details of the total area the arrays occupy and the number of modules placed in each section. Details regarding the design of a PV system are given in

Table 7. PV system specifications.

Number of PV Modules	1112
PV generator output	1112 × 0.630 = 700.56	kWp
PV generator surface	3308.2	m2
Number of inverters [48]	6 GW100K-HT × 100	kW
No. of vehicles	Up to 2400 *	EV/day e-Charge

* The number of charging vehicles depends on CP and the time needed to charge each vehicle fully.

. In sum, 1112 PV modules make up the system. The layout of the solar PV systems mounted in the carport is shown in Figure 9.

To evaluate the overall effectiveness and efficiency of the developed PV arrays, the energy balance of the PV system for the Scientific Centre solar project was examined. The initial radiation measurement was recorded at 2221 kWh/m². However, after accounting for factors such as spectral deviations and module orientation, the final net global radiation at the module surface was determined to be 2210 kWh/m². The irradiance was subsequently spread across the entire module area, which measures 3108.4 m², resulting in potential energy generation of 6869.564 MWh. The module efficiency, rated under standard test conditions (STCs), is 22.54%. This results in a helpful energy output of precisely 1548.340 MWh. Additional losses, including partial shading, temperature variations, diode losses, and mismatched module information and configuration, were all considered in the calculation. The DC energy is lowered to 1410.455 MWh through a series of deductions, at which point the inverter regulates the remaining energy. When all relevant elements were considered, the system’s alternate current (AC) production of 1373.552 MWh was finally computed. The production was adjusted to 1373.659 MWh when minor regulatory and standby consumptions were considered.

Table 8. PV system energy balance.

Global radiation—horizontal, kWh/m2	2221
Deviation from the standard spectrum, kWh/m2	−22.21
Ground reflection (albedo), kWh/m2	3.34
Orientation and inclination of the module surface, kWh/m2	8.08
Global radiation at the module, kWh/m2	2210
Available solar energy, 2210 kWh/m2 × 3108.4 m2 = 6,869,564 kWh
STC conversion (rated efficiency of module, 22.54%), MWh	−5322.052
Rated PV electricity, MWh	1548.340
Low-light performance, kWh	7702
Deviation from the nominal module temperature, kWh	−103,840
Diodes, kWh	−7262
Mismatch (manufacturer information), kWh	−28,902
Mismatch (configuration/shading), kWh	−5746
PV energy (DC) without inverter down-regulation, MWh	1410.455
Down-regulation on account of max. AC power/cos phi, kWh	−480
MPP matching, kWh	−2138
PV electricity (DC) (energy at the inverter input), MWh	1407.837
Input voltage deviates from the rated voltage, kWh	−1989
DC/AC conversion, kWh	−32,189
Standby consumption (inverter), kWh	−107
Total cable losses, kWh	0.00
PV energy (AC) minus standby use, MWh	1373.659
PV Generator energy (AC grid), MWh	1373.552

displays the corresponding energy balance, a methodical organization of the specific examination of these values, and their respective impacts.

Table 9. PV*SOL simulation output.

Charge at beginning, kWh	54,000
Charge of the EV (total), MWh/year	3069.628
Charge of the EV (PV system), MWh/year	1359.120
Charge of the EV (grid), MWh/year	1710.508
Losses due to charging/discharging, kWh/year	276,925
Losses in battery, kWh/year	218,703
Consumption due to kilometers driven, MWh	2628.000
Mileage per year, km	10,950,000
Mileage per year from PV, km	4,848,262

contains details about the results of the system design via PV*SOL.

Table 10. Technical, economic, and environmental system data.

Start of operation of the system	10/4/2025
PV generator output, kWp	700.56
Assessment period, years	30
Interest on capital (%)	1
PV generator energy (AC grid), MWh/year	1373.659
Direct own use, kWh/year	11,838
Charge of the EV, MWh/year	1359.120
Grid feed-in, kWh/year	2700
Auxiliary electricity consumption (annual), kWh/year	25,500
Own power consumption (%)	99.80
Solar fraction (%)	44.30
Yield factor, kWh/kWp	1961
PR (%)	88.70
Yield reduction due to shading, %/Year	0.3
CO₂ emissions avoided, kg/year	645,569

summarizes the annual energy output and efficiency metrics of a 700.56 kWp PV system integrated with EV charging capabilities. It boasts a high power consumption rate of 99.8% and a solar fraction of 44.3%. Table 10 shows detailed information on the PV system, scheduled to commence operations on October 4, 2025. A modest 2700 kWh is expected to be fed into the grid by the system in its first year of operation, accounting for the effects of module deterioration. This relates to the overall annual energy output that was previously described, of which a sizeable percentage is used for EV charging and direct use. The system’s performance parameters, which span a 30-year assessment period and include a yearly yield factor of 1961 kWh/kWp and a high-performance ratio (PR) of 88.7%, show that it is dependable and efficient at transforming solar energy into usable power.

Over 30 years, this degradation process has had a cumulative impact, leading to a substantial decrease in the system’s productivity. The panels are initially assumed to have 100% efficiency. Utilizing the pace of degradation, we determined the following.

• After 1 year: The efficiency decreases slightly to 99.6% of its original capacity.
• After 10 years: The panels maintain 96.04% of their initial output. This shows a modest decline and suggests that the panels continue to perform robustly a decade into operation.
• After 20 years: The efficiency drops further to 92.19%. This indicates more pronounced wear, but the retaining of a significant portion of the productive capacity.
• After 30 years: The efficiency reaches about 88.07%. This marks a noticeable reduction from the original output, aligning with the standard solar panel longevity and effectiveness expectations.

The findings suggest that the solar panels would exhibit a progressive decrease in efficiency, amounting to around 11.93% over 30 years, as shown in Figure 10. This progressive deterioration is crucial to consider in financial and operational strategizing, as it affects the profitability and long-term viability of the solar installation. Including maintenance schedules and a budget for possible panel replacements or upgrades in the later stages of the system’s lifespan is crucial.

5. Conclusions and Recommendations

As part of the global effort to promote sustainable urban development and reduce carbon emissions, Hail City is setting the standard for using state-of-the-art technology to enhance its urban infrastructure. This study examined the factors, such as car consumption rate, charger power, car slots per station, and battery capacity, influencing solar charging station network deployment and outlined the necessary strategic plans. It was decided to create a solar-powered electric car charger. This study thoroughly analyzed the technical aspects influencing the number of required charging stations. Hail City wants to enhance its infrastructure effectively and sustainably by adopting proactive measures to address these concerns. Regarding the factors affecting the NS’, the following results were obtained:

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The diminishing returns on increasing car slots become apparent beyond 200 slots, where the NS’ plateaus, suggesting an optimal range for station capacity depending on the average vehicle efficiency in the fleet.

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Higher consumption rates generally correspond to a higher number of required stations across most battery capacities, illustrating that less efficient vehicles with higher consumption rates demand a more extensive charging infrastructure.

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Increasing the car slots up to 200 leads to an apparent decrease in the required NS’, after which it remains almost constant. On the other hand, the effect of battery capacity on the NS’ required is limited. This confirms that the car slots per station required should be around 200.

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Increasing the charger power leads to an apparent decrease in the number of stations, while the effect of the battery capacity on the NS’ required is limited, especially for values of charger power more significant than 30 kWh.

Regarding the PV system designed at one of the sites with a capacity of 700.56 kWp, the following data were obtained:

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The total annual energy output was 1373.659 MWh, with 1359.12 MWh used for charging EVs. This equates to a higher power consumption rate of 99.8% and a solar fraction of 44.3%.

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The system’s efficiency and dependability are demonstrated by its performance measures, which show its output during 30 years of 1960.7 kWh/kWp and a high-performance ratio of 88.7%.

If the results are applied, this study will help make Hail City more sustainable. The results can also be used to study other areas similar to Hail City with regard to climatic, social, and technical conditions. The authors propose building a pilot solar electric vehicle charging station in Hail City, exploring the practical case, and continuing to study the impact of electronic control system loads on the local grid, considering grid integration constraints, transformer capacities, and load balancing mechanisms.