Design and Techno-Economic Feasibility Study of a Solar-Powered EV Charging Station in Egypt

Abstract

This research focused on determining the technical and economic feasibility of the design of a solar-powered electric vehicle charging station (EVCS) in Cairo, Egypt. Using HOMER Grid, hybrid system configurations are assessed technically and economically to reduce costs and ensure reliability. These systems incorporate photovoltaic (PV) systems, lithium-ion battery energy storage systems (ESS), and diesel generators. A comprehensive analysis was conducted in Cairo, Egypt, focusing on small vehicle charging needs in both grid-connected and generator-supported scenarios. In this study, a 468 kW PV array integrated with 29 units of 1 kWh lithium-ion batteries and supported by time-of-use (TOU) tariffs, were used to optimize energy utilization. This study demonstrated the feasibility of the system in a case of eight chargers of 150 kW each and forty chargers of 48 kW. Conclusions suggest that the PV + ESS has the lowest pure power costs and reduced carbon emissions compared to traditional network-dependent solutions. The optimal configuration of USD 10.23 million over 25 years, with lifelong savings, results in annual savings of tool billing of around USD 409,326. This study concludes that a solar-powered EVC in Egypt is both technically and economically attractive, especially in the light of increasing energy costs.

1. Introduction

The rapid depletion and environmental concerns associated with fossil fuels have accelerated the global shift toward renewable energy sources (RESs) [1,2]. To address these challenges, EVs have been introduced, reducing fossil fuel dependency and emissions. They are charged through utility or RESs [3], employing various charger designs customized to system parameters [4]. Charging strategies like vehicle-to-grid ($\mathrm{V}2\mathrm{G}$), vehicle-to-building ($\mathrm{V}2\mathrm{B}$), and vehicle-to-vehicle ($\mathrm{V}2\mathrm{V}$) enhance system flexibility and resilience [5,6,7].

EV charging uses $\mathrm{A}\mathrm{C}$ or $\mathrm{D}\mathrm{C}$ types, governed by standards such as IEC, SAE, and CHAdeMO [8]. Charging methods and configurations impact battery life, energy efficiency, and charge duration. Despite the increase in EV adoption, many charging systems continue to rely on electricity generated from fossil fuels. This has led to research on integrating RESs into EV charging. Several approaches have emerged: urban power management for small EVs [9], off-grid PV strategies [10], grid-interfaced converters [11], and PV-based charging in parking lots [12,13]. Recent studies proposed advanced energy management systems to optimize solar-powered EVCS and reduce grid dependence [14]. Further research explores hybrid systems combining RESs for EV charging. The economic benefits of integrating RESs with EVCS infrastructure are further supported in [15,16], highlighting energy storage optimization, carbon reduction, and enhanced grid stability. The authors of [17] developed optimal scheduling model for solar-powered EVCSs using competitive swarm optimization. PV-EV sizing strategies with smart charging were examined in [18]. These works, i.e., [17,18], emphasize load management and cost minimization through advanced optimization techniques.

However, while the existing literature provides valuable insights into hybrid energy integration for EV charging, many of these studies lack realistic load profiles, localized modeling, and policy scenarios like carbon pricing or TOU tariffs, particularly in North African contexts. Additionally, much research ignores the larger technical and policy frameworks. These include the impact of dynamic tariffs and the way in which EVCS planning is impacted by smart grid integration. It also ignores regionally specific deployment challenges. These challenges include ambiguous regulations, inadequate grid infrastructure for high-power charging, and a lack of public acceptance of new charging technologies in countries such as Egypt.

Some common technical and financial adaptation studies have been carried out for schemes outside the web, and the break-even distance for web extensions has been determined. No study has included the payback time, the profitability index (PI), and the internal rate of return (IRR). In [19], researchers used HOMER software to design and adapt a hybrid outside the PV web and the wind turbine system for an EVCS in Turzmir, Turkey. Their findings indicated that the ideal configuration consisted of 250 kW from solar cell systems and 200 kW from wind turbines, with a level cost of USD 0.064 per kWh (COE) and a net power cost (NPC) of USD 697,704.

Other countries have been the focus of study in the area of RES optimization of EVCSs in general, revealing opportunities to minimize carbon emissions and to achieve energy security through the integration of RES with utilities. Güven and Yücel (2023) applied HOMER to examine hybrid charging systems in six cities in Turkey. Another study investigated a hybrid system of RES and ESS for EVCSs in Libya. It pointed to reducing dependence on fossil fuel and strengthening energy security [20].

Similar applications were made in India, where scientists developed EVCSs that used biomass and RES. Additionally, HOMER was utilized in this study to optimize these systems, proving their viability from an economic and ecological perspective. One of the more recent developments is the incorporation of green hydrogen into renewable energy systems [21]. The potential for RES systems combined with green hydrogen production for use in the residential and EV sectors was also noted by Das et al. (2024a) [22]. Other studies within the academic literature also serve to illustrate the technical and financial benefits of hydrogen when combined with RESs toward system efficiency and dependability. The global literature also reveals the increasing popularity of ESS across the world to tackle renewable energy fluctuations. The role of battery energy storage systems in energy arbitrage for hybrid solar–wind EVCSs has been analyzed in studies by Alanazi, F. et al. (2024) and Alharbi et al. (2025). These systems provide an economic viability of charging stations by supplying the stations with steady power through the lowering of energy costs [23,24]. Markedly, in their role as international benchmarks, these studies have largely disregarded a specific set of techno-economic circumstances in Egypt, consisting of solar potential, energy pricing schema, reforms on subsidies, and EVCS load characterization in urban territories. Likewise, such conditions set by the Egyptian energy market, such as the progressive cancellation of electricity subsidies and the application of peak-hour tariffs, have shown to have a significant bearing on the feasibility of large-scale PV integration within EVCSs, as explained in the work of Ibrahim (2022) [25]. Thus, these are very important contextual factors for appropriate modeling but are left out of many international studies.

This study attempts to fill this research gap by answering the question of whether it is technically and economically possible to have solar-powered charging stations for electric vehicles in Cairo while considering real urban operating conditions and changing energy tariffs. It greatly contributes to the body of knowledge and so performs a detailed techno-economic analysis of solar-powered EVCSs under specific local Egyptian energy and economic conditions. While studies have been undertaken for similar systems in other regions, this is among the first to incorporate a detailed analysis of small EV charging requirements with an emphasis on the Egyptian context, both grid-connected and off-grid. By utilizing HOMER software, this research explores several hybrid configurations and compares their technical and economic feasibility in Cairo, Egypt, under real-world conditions.

Moreover, this study offers the possibility of energy saving and environmental protection. The results show that in solar EVCSs, particularly those with energy storage, the least costly means are used while carbon emissions are avoided to a great extent. This study’s new findings pave the way for better insight into renewable energy-powered EVCSs and provide valuable insights for policymakers, investors, and urban planners to take the next steps in scaling sustainable energy infrastructures in Egypt and other similar regions above sub-Saharan Africa.

This research is novel because it integrates HOMER Grid simulations with Egypt-specific time-of-use tariffs, carbon emissions policies, and sensitivity analyses of PV and battery costs. Furthermore, it introduces a structured comparison of four system configurations and their economic indicators—NPC, LCOE, IRR, and payback period—in different technical and policy scenarios.

Moreover, this research expands the scope of RES designs by incorporating a comprehensive financial analysis and carbon reduction strategies. It also pioneers the integration of advanced technologies such as TOU tariffs. It supports national sustainability goals and climate goals. Lastly, this study provides a replicable modeling approach and a comparative framework. This can be modified for use in other MENA cities. Section 2 presents a methodology and modeling framework using HOMER Grid. Section 3 outlines the simulation results. Section 4 presents the integrated results and the discussion. Section 4 provides the conclusion and outlines recommendations for future research.

2. Materials and Methods

2.1. Equations for Modeling in HOMER Grid

The optimal final power of the PV system in HOMER Grid is evaluated using the following equation [26]:

$$P_{PV}={\gamma }_{PV}{f}_{PV}\left\{{\displaystyle \frac{{\overline{G}}_T}{{\overline{G}}_{T,STC}}}\right\}\left[1+a_p\left(T_C-T_{C,STC}\right)\right]$$

(1)

where

$P_{PV}$: Output power of the PV system in kW.

${\gamma }_{PV}$: Output power of the PV system at STC in kW.

$f_{PV}$: Power derating factor for the PV system.

${\overline{G}}_T$: Solar radiation incident on the PV system in W/m².

${\overline{G}}_{T,STC}$: Solar radiation at STC in W/m².

$a_p$: Temperature coefficient of power in %/°C

$T_C$: Ambient temperature in °C.

$T_{C,STC}$: STC temperature in °C.

The software models the maximum power of the battery set using the following equation [27]:

$$p_{bac,cpmax,mcc}={\displaystyle \frac{N_{bat}{I}_{max}{V}_{nom}}{1000}}$$

(2)

where

$p_{bac,cpmax,mcc}$: Maximum battery charge power in kW.

$N_{bat}$: Total number of batteries.

$I_{max}$: Maximum charge current in A.

$V_{nom}$: Nominal voltage of the battery in V.

The renewable fraction is configured as follows [25]:

$$f_{RF}=1-{\displaystyle \frac{E_{nonren}}{E_{served}}}$$

(3)

where

$f_{RF}$: Renewable fraction.

$E_{nonren}$: Energy consumed from the grid in kWh.

$E_{served}$: Total energy consumed by the system in kWh.

The NPC of a system is the present value of all the costs incurred over the system’s useful life minus the present value of all the revenues received over the system’s life. The costs include capital expenses, replacement costs, operation and maintenance costs, fuel costs, environmental penalties, and the cost of energy acquisition from the grid. The revenue flows are the residual value and income from selling energy to the grid. The total NPC is obtained by combining the discounted cash flows over the project life.

The levelized cost of energy is calculated using the following relation [26,27]:

$$LCOE={\displaystyle \frac{C_{ann,tot}}{E_{served}}}$$

(4)

where

$LCOE$: Levelized cost of energy in S/kWh.

$C_{ann,tot}$: Total annualized cost of the system in S.

The internal rate of return (IRR) is the discount rate at which the reference case and the optimized system have the same net present cost. The IRR is calculated by dividing the current value of the difference between the two cash flow sequences by the discount rate. A customized hourly charging load profile was used to model the EV’s energy absorption. The charging efficiency is set at 92% for DC fast chargers and 88% for AC chargers, in accordance with IEC 61851-23 and IEC 61851-1. The model assumes constant-power charging phases followed by a tapering current stage close to the full state of charge. Auxiliary loads (cooling, control electronics) are taken into account in the absorption profile at a rate of about 1.5% of the energy delivered.

The PV array cost was set at 850 USD/kW (468 kW total = USD 397,800), and the Li-ion battery storage system cost at 350 USD/kWh (29 kWh total = USD 10,150). Balance-of-system costs, inverter costs, and installation were estimated at 15% of PV and storage combined, resulting in a total initial investment of approximately USD 469,000. Three representative charging scenarios were defined for simulation: (1) public DC fast charging at 150 kW, (2) public AC charging at 22 kW, and (3) fleet charging with 50 kW DC chargers. Each scenario specified connector types (CCS2 for DC; Type 2 for AC), grid connection requirements (400 V three-phase for AC; 800 V for DC), and minimum service bay capacity.

The novelty of the methodology lies in the integration of real TOU electricity pricing and carbon penalties, which are rarely considered in similar studies for EVCS feasibility in developing countries. In order to ensure model reliability, the modeling results were verified by contrasting system outputs with established benchmarks from international studies carried out in Turkey, India, Libya, and Malaysia. According to its architecture, the base system’s components included the Huarmey district’s electric grid, the PV array, the inverter, the batteries, and the charging station, which represented energy consumption. The arrangement of these elements is visually presented in Figure 1, where the overall configuration of the system along with its respective components is illustrated.

2.2. Study Limitations

The load profile is simplified to a single representative year and does not include possible seasonal behavioral shifts or special-event demand spikes. Furthermore, the absence of night charging in the modeled profile reflects the selected site type and may not represent all Egyptian EVCS scenarios, particularly highway or fleet depots.

3. Results and Discussion

This study employs a techno-economic feasibility analysis utilizing HOMER Grid software to optimize the design of a solar-powered EVCS in Egypt. This study utilizes real-world energy consumption and solar resource data for Cairo, Egypt. The site location is as follows: latitude: 30° 2.67′ N; longitude: 31° 14.14′ E; time zone: Africa/Cairo. The average daily energy consumption is $2794 \mathrm{k}\mathrm{W}\mathrm{h}$, with a peak demand of $511.03 \mathrm{k}\mathrm{W}$ recorded in October (Figure 2). This modeled station represents a public DC fast-charging site located in a commercial district of Cairo, with operational hours largely concentrated between 08:00 A.M.–10:00 P.M. Nighttime charging demand is minimal in such locations due to absence of overnight parking and domestic night charging. The energy consumption patterns are shown in Figure 3. The tariffs are considered USD 0.048/kWh during on-peak times, USD 0.031/kWh during mid-peak times, and USD 0.021/kWh during off-peak times.

The study evaluates four EVCS configurations:

• PV + Storage (Li-ion Battery) + TOU;
• PV + Generator + TOU;
• Generator + ESS (Li-ion Battery) + TOU;
• Generator + TOU.

A comparison of the configurations based on cost, CO₂ emissions, and savings is presented in

Table 1. System Configurations and economic metrics.

System	$\mathbf{NPC} \left(\mathbf{U}\mathbf{S}\mathbf{D}\right)$	LCOE $(\mathbf{U}\mathbf{S}\mathbf{D}/\mathbf{k}\mathbf{W}\mathbf{h})$	${\mathbf{CO}}_2 \mathbf{Emissions} (\mathbf{t}/\mathbf{y}\mathbf{r})$	$\mathbf{Annual} \mathbf{Savings} \left(\mathbf{U}\mathbf{S}\mathbf{D}\right)$	$\mathbf{Payback} \mathbf{Period} \left(\mathbf{Y}\mathbf{e}\mathbf{a}\mathbf{r}\mathbf{s}\right)$
PV + ESS	$\mathrm{3,248,125}$	$0.346$	$145.5$	$\mathrm{409,326}$	$5.8$
PV + Generator	$\mathrm{330,550,300}$	$37.556$	$445.5$	$-\mathrm{116,333}$	-
Generator + ESS	$\mathrm{331,927,000}$	$37.158$	$633.7$	$-\mathrm{212,428}$	-
Generator Only	$\mathrm{332,558,600}$	$37.875$	$603.7$	$-\mathrm{355,425}$	-

. Various EVCS configurations were simulated to minimize NPC, carbon emissions, and maximize annual utility bill savings. The PV + ESS configuration was identified as the most optimal. It provides $\mathrm{U}\mathrm{S}\mathrm{D} 10.23$ million in lifetime savings (Figure 4). It also achieved the lowest CO₂ emissions ($145.5 \mathrm{t}/\mathrm{y}\mathrm{r}$).

To assess economic feasibility, the following metrics were calculated:

• LCOE: Assesses cost per $\mathrm{k}\mathrm{W}\mathrm{h}$
• IRR: Measures investment profitability.

The economic feasibility metrices are shown in

Table 2. Economic feasibility metrics.

Parameter	PV + ESS	PV + Generator	Generator + ESS	Generator Only
NPC $\left(\mathrm{U}\mathrm{S}\mathrm{D}\right)$	$\mathrm{3,248,125}$	$\mathrm{330,550,300}$	$\mathrm{331,927,000}$	$\mathrm{332,558,600}$
LCOE ($\mathrm{U}\mathrm{S}\mathrm{D}/\mathrm{k}\mathrm{W}\mathrm{h}$)	$0.346$	$37.556$	$37.158$	$37.875$
IRR $(\%)$	$8.2\%$	-	-	-
AnnualSavings $\left(\mathrm{U}\mathrm{S}\mathrm{D}\right)$	$\mathrm{409,326}$	$-\mathrm{116,333}$	$-\mathrm{212,428}$	$-\mathrm{355,425}$

, and the utility bill for different configurations is given in Figure 5. These results demonstrate that $\mathrm{P}\mathrm{V}+\mathrm{E}\mathrm{S}\mathrm{S}$ configurations not only reduce lifetime energy costs but also offer reliable returns for investors.

A sensitivity analysis was conducted to evaluate the impact of varying economic and technical parameters on the system’s financial and environmental performance. The following key factors were considered:

• PV investment costs;
• Battery storage costs;
• Electricity price variations;
• Carbon penalty policies;

Each parameter was varied individually to examine the effects on NPC, LCOE, IRR, and carbon emissions. Figure 6 shows peak daily profiles of PV power output, grid purchases, and battery state of charge. Emissions are based on an assumption of the grid’s generation sources. The decrease in CO₂ emissions from solar-powered configurations is massive. Diesel and grid-dependent alternatives, in this case, represent the baseline. The decrease in emissions relative to systems that use diesel-based generation only is more considerable, namely higher than 75%. This theory highlights the environmental gains when the wireless power transfer pathway for rural water pumping systems works with renewable energy. This finding corresponds with the results of earlier works carried out in Turkey and Libya, wherein the integration of PV brought about 60–80% reductions in emissions for similar charging applications. Therefore, these results prove PV-based EVCSs as a considerable emission abatement tool in urban transport.

• Impact of PV Investment Cost

To evaluate the effect of PV investment cost variations, different cost reduction scenarios were analyzed. As

Table 3. Impact of PV cost reduction on economic performance.

PV Cost Reduction (%)	NPC (USD)	LCOE (USD/kWh)	IRR (%)
0% (Baseline)	3,248,125	0.346	8.2
10%	3,125,000	0.310	9.5
20%	2,980,000	0.280	10.8
30%	2,800,000	0.250	12.3

illustrates, a decrease in PV costs results in lower LCOE and NPC. This makes the PV-ESS more financially viable. Table 3 indicates that a 30% reduction in PV cost decreases LCOE from USD 0.346/kWh to USD 0.250/kWh. NPC also falls by about 14%, and IRR rises from 8.2% to 12.3%. These results show that lower PV costs make the PV-ESS configuration more attractive to investors and confirm that the system remains stable as technology prices continue to decline.

b

Impact of Battery Storage Cost

Battery costs were varied to examine their effect on system viability. As

Table 4. Effect of battery cost on economic viability.

Battery Cost Change (%)	NPC (USD)	LCOE (USD/kWh)	Payback Period (yrs)
−30% (Lower Cost)	3,000,000	0.325	5.5
Baseline (0%)	3,248,125	0.346	5.8
+30% (Higher Cost)	3,500,000	0.370	6.4

demonstrates, higher battery costs increase LCOE, but the effect is less pronounced than PV costs due to the smaller contribution of batteries to total investment.

• • A 30% decrease in battery cost reduces NPC by 7.6%, lowering total investment.
  • Payback time improves, making ESS more financially attractive.
  • LCOE impact is moderate, indicating that battery cost is not as dominant as PV cost in overall system feasibility.

c

Effect of Electricity Price Variations

Electricity price fluctuations directly impact projected savings. The analysis considered three electricity price increase scenarios: 5%, 10%, and 20% annually.

Table 5. Sensitivity of financial savings to electricity prices.

Electricity Price Increase (%)	Annual Savings (USD)	Payback Period (yrs)
0% (Baseline)	409,326	5.8
5%	450,000	5.2
10%	500,000	4.9
20%	620,000	4.5

shows annual savings vs. electricity price growth.

• • Higher electricity prices improve annual savings, reducing the payback period.
  • A $20\%$ price increase reduces payback to 4.5 years, accelerating investment returns.

d

Impact of Carbon Penalty Policies

Carbon penalties were introduced to evaluate their effect on investment decisions.

Table 6. Effect of carbon tax on system profitability.

Carbon Price (USD/ton)	CO2 Reduction (t/yr)	Annual Savings (USD)	LCOE (USD/kWh)
0 (Baseline)	465.3	409,326	0.346
20	465.3	500,000	0.310
50	465.3	610,000	0.280
100	465.3	750,000	0.250

illustrates that with higher CO₂ costs, PV+ ESS become more financially competitive compared to fossil fuel-based alternatives. Table 6 outlines CO₂ emission costs and project viability.

• • Higher carbon prices make renewable systems more competitive.
  • A $100 \mathrm{U}\mathrm{S}\mathrm{D}/\mathrm{t}\mathrm{o}\mathrm{n}$ CO₂ price reduces LCOE to $0.250 \mathrm{U}\mathrm{S}\mathrm{D}/\mathrm{k}\mathrm{W}\mathrm{h}$, enhancing financial viability.

The findings of the selectivity studies can be summarized in the following points:

• PV costs have the strongest impact on LCOE and overall savings. A $30\%$ PV cost reduction lowers LCOE by $28\%$
• Carbon penalties make PV-ESS more attractive, improving financial performance.

These findings suggest that policy incentives, carbon pricing, and PV cost reductions can greatly accelerate the adoption of solar-powered EVCS. The results of this study provide a strong foundation for the wider adoption of renewable energy in EV infrastructure. By prioritizing sustainable energy integration, Egypt can accelerate its transition toward a low-carbon transportation sector, aligning with global climate action goals and national energy security strategies.

Table 7. Comparative analysis of the current study with previous studies.

Study Region	Best Configuration	Emissions Reduction	LCOE (USD/kWh)	Payback Period (yrs)
Egypt (Current study)	PV + ESS (Li-ion Battery) + TOU	>75%	0.15–0.25	5.8 years
Egypt [25]	PV + ESS (Li-ion)	>70%	0.15–0.25	6–8 years
Turkey [17]	PV + Wind + ESS	60–80%	0.064	5–9 years
Sweden [18]	PV workplace charging	68%	0.13–0.20	8 years
Indonesia [28]	Grid-connected PV for urban EVCS	Up to 75%	0.18–0.23	7 years
West Virginia (USA) [29]	PV + ESS (with policy incentives)	40–60%	0.21–0.28	+10 years

shows a comparative analysis of this work with previous studies.

• • The current study, carried out in Egypt, demonstrates the highest emissions reduction (>75%) among all regions compared, with an LCOE in line with prior Egyptian and international benchmarks and the shortest payback period (5.8 years).
  • TOU played a significant role in improving economic returns in the current research.
  • International studies validate that hybrid systems with strong solar resource access and dynamic policy support consistently lead to superior financial and environmental outcomes.
  • In the case of the USA, payback periods stretch when incentives or carbon penalties are removed; this further underscores the policy context’s importance.

4. Conclusions and Future Work

4.1. Conclusions

This study confirms the technical and economic viability of a solar-powered EVCS in Cairo, Egypt. Using HOMER Grid simulations, four system configurations were assessed. The load profile used was simplified to a single year located in a commercial district of Cairo with minimal nighttime charging demand. The novelty of this study lies in combining Egypt-specific tariffs, potential carbon pricing, and real-world pilot site data within HOMER Grid simulations, supported by a sensitivity analysis of PV and ESS costs. This integrated approach fills a gap in the existing EVCS literature by explicitly linking local regulatory frameworks to investment viability. The PV + ESS + TOU option proved to be the most effective. It achieved the lowest NPC of $\mathrm{U}\mathrm{S}\mathrm{D} 3.25 \mathrm{m}\mathrm{i}\mathrm{l}\mathrm{l}\mathrm{i}\mathrm{o}\mathrm{n}$, resulting in significant lifetime savings of $\mathrm{U}\mathrm{S}\mathrm{D} 10.23 \mathrm{m}\mathrm{i}\mathrm{l}\mathrm{l}\mathrm{i}\mathrm{o}\mathrm{n}$ over $25$ years. The LCOE for the optimal system is $\mathrm{U}\mathrm{S}\mathrm{D} 0.346/\mathrm{k}\mathrm{W}\mathrm{h}$, which is much lower than fossil fuel-based options. The solar-based system reduces the annual CO₂ of emissions by $145.5 \mathrm{t}/\mathrm{y}\mathrm{e}\mathrm{a}\mathrm{r},$ compared to $633.7 \mathrm{t}$ from generator-based systems, demonstrating its environmental superiority. Economic viability is demonstrated with a $\mathrm{I}\mathrm{R}\mathrm{R}$ of $8.2\%$ and a payback period of $5.8$ years. These results highlight its investment appeal. The PV-ESS configuration was consistently preferred over generator-based alternatives based on both financial and environmental metrics. Sensitivity analyses revealed that system performance is further enhanced by rising electricity prices, carbon penalties, and decreases in PV and battery costs. This study offers a strong basis for expanding Egypt’s solar-powered EV infrastructure. It supports national goals for cleaner transportation, lower emissions, and enhanced energy security. This model is adaptable to other regions, especially those with strong solar resources.

4.2. Future Work

Future research could involve the following areas.

• Scalability and expansion: Evaluation of how this model can be used in Egypt and other areas outside, especially in areas with high solar capacity.
• Advanced ESS solutions: Further increasing the system’s efficiency in order to verify the capabilities of the next generation of battery technologies, such as solid-state batteries and hydrogen storage.
• Politics and incentive analysis: Investigation of the effect of state grants, carbon credit, and input collection for the economic attraction of solar-driven EVCs.
• Integration with smart grids: Exploring the potential for $\mathrm{V}2\mathrm{G}$ and $\mathrm{V}2\mathrm{B}$ applications, enabling EVs to serve as distributed energy storage units.