Autonomous Electric Vehicle Charging Station Along a High-Traffic Road as a Model for Efficient Implementation of Emission-Free Economy

Abstract

The growing demand for electric vehicles (EV) has increased the need for reliable and sustainable charging infrastructure. To address this challenge, autonomous charging stations powered by renewable energy sources (RES) are a promising solution. This paper presents a simulation-based study that determines the optimal contribution of wind farms, photovoltaic systems, and energy storage to power an autonomous EV charging station. The simulation takes into account historical weather data, EV charging patterns, and renewable energy storage capacity. The results show that by combining RES and batteries, the charging station can operate autonomously minimizing the dependence on the power grid. Battery energy storage plays a key role in balancing intermittent RES generation and variable demand from the charging station. The simulation highlights the importance of adjusting parameters to optimize the energy utilization of the charging station and minimize the dependence on the grid. Further research is warranted to optimize the design, operation, and integration with advanced energy management systems to increase the efficiency and effectiveness of these charging stations. The development of a widespread autonomous charging infrastructure powered by renewable energy sources can accelerate the transition to clean transportation and support the energy system.

1. Introduction

Electric vehicles currently have significantly longer battery loading times compared to re-fueling time of internal combustion engine vehicles [1]. Therefore, providing adequate charging infrastructure is a necessary condition for the development of this type of transportation. The coming decades are likely to be a period of rapid expansion of energy infrastructure related to the development of renewable energy sources and electromobility [2]. The integration of EV into the power grid affects its stability, causing potential problems such as an increase in peak energy demand [3]. Smart grids are important for the effective integration of RES and EV [4,5]. One solution to reduce investments, which also generate CO2 emissions like any other, is the construction of autonomous electric vehicle charging stations. Autonomous electric vehicle charging stations will reduce the need to expand the power grid by utilizing local energy sources and energy storage.

Policy drivers: the development of electric vehicle charging infrastructure is shaped by specific regulations and directives. The European Union replaced the previous Directive 2014/94/EU on the development of alternative fuels infrastructure (AFID) with the AFIR regulation in July 2023, which entered into force on 13 April 2024 [6]. According to provisions of the AFIR regulation, the total capacity of the charging infrastructure in Poland should be adapted to the number of registered electric cars with the location of electric vehicle charging stations every 60 km and hydrogen refueling stations every 100 km [7]. In addition, the regulations specify the requirement to ensure sufficient power at each newly registered station for electric vehicles.

In Poland, the number of electric vehicles is small. The EV sales share in Poland was 5.7%, where the EV share in Europe was 22.0% in 2024 [8]. Currently, the provisions of the regulation are met in terms of the appropriate power of the charging network. In December 2024, the installed capacity in the network of publicly available charging infrastructure was 363 MW, which means that taking into account the existing fleet of electric vehicles in the amount of 215,275, we exceed the level of installed capacity at charging stations required by the AFIR regulation by 69%. According to the forecasts of the Polish Alternative Fuels Association, in connection with the planned development of electromobility in Poland, in 2027 and in the following years, the power of charging stations may be insufficient in relation to the planned number of vehicles on the roads in these years. The situation is similar in other European countries, although the dynamics of capacity growth is different [9].

With the ongoing decarbonization, the energy system will become less reliant on hydrocarbons and more dependent on electricity. Decarbonizing transportation and other sectors of the economy by reducing the use of natural gas, coal, and petroleum will increase the demand for electrical energy [10]. According to the International Energy Agency (IEA), achieving net-zero carbon dioxide emissions by 2050 will require increasing the share of electricity produced from renewable sources from 20% to 50% [11].

Achieving the goals set in the AFIR will require various solutions to fulfill them. One of them could be the operation of charging stations based on hybrid power sources. Then, a microgrid will be created as a system in which local energy sources, such as photovoltaic panels, wind turbines, or biogas generators, are connected to a local energy storage and distribution network. Thanks to the power microgrid, the autonomous charging station can use local, renewable energy sources, as well as store excess energy for later charging of vehicles.

Technical factors there are also a number of technical issues that play an important role in ensuring the efficient and stable operation of the charging infrastructure. These include long charging times, the impact on the stability of the power grid, and variability of renewable energy sources.

2. Source Overview

Energy balancing is an important issue in renewable energy systems, such as PV farms and wind farms (WF). The purpose of energy balancing is to maintain equilibrium between energy supply and demand, ensure the reliability and stability of the system, and improve its efficiency. To achieve this, energy storage systems like batteries are used to store surplus energy and deliver it to the grid or consumers when needed. Various aspects of utilizing renewable energy sources to support Electric Vehicle Charging Stations (EVCS) have been analyzed in [12]. The use of hybrid energy systems that can manage renewable energy sources, ensuring a consistent power output and uninterrupted service has been explored in [13]. An interesting algorithm for managing EV charging stations in DC microgrids, taking into account power constraints and driver choices, was proposed in [14], with simulation results confirming the effectiveness of the proposed strategy. The energy management technique described in [15] confirms the possibility of operating an isolated microgrid in the form of an EV charging station powered by highly variable sources when supported by an energy storage. Different energy management strategies and battery sizes were analyzed in the context of sustainable energy supply in such microgrids. A procedure for selecting appropriate sizes of energy storage devices and converters was proposed [16].

3. Materials and Methods

The study presents an energy balance needed to power stations from various sources and simulates their optimal participation

$$E_{EV}=E_{WF}+E_{PV}+\left|E_{BAT}\right|,$$

(1)

Energy demand (left side of the equation) results from the number of vehicles being charged at a given power over time, taking into account losses due to energy conversion and transmission. This can be described by the formula:

$$E_{EV}={\sum }_{i=1}^n{P}_i{t}_i\left(1-x_i\right),$$

(2)

where EEV is the demand for energy of vehicles, P_i is the charging power of the i-th vehicle at time t, and x_i is the losses.

Charging time = battery capacity/(charging power × efficiency factor × power factor), where the efficiency factor takes into account energy losses during charging, and the power factor considers changes in charging power depending on the battery’s state of charge.

Supplying the station with energy (right side of Equation (1)) compiles the energy production from various sources with energy storage, which can be described by the following formulas:

Wind energy generation

$$p={\displaystyle \frac{1}{2}}\rho A{v}^3{C}_p,$$

(3)

where p is the power generated by the turbine in W, ρ is air density in kg/m³, A is the cross-sectional area of the blades in m², v is wind speed in m/s, and C_p is the power coefficient, which depends on the shape and pitch angle of the blades. This is the basic formula for determining the power of a wind turbine.

The output power from a wind turbine under normal pressure and temperature conditions can be expressed by Equation (4) [17],

$$P_{WT}\left(t\right)=\left\{\begin{array}{c}0,v\left(t\right)\le v_{cut-in} or v\left(t\right)\ge v_{cut-out} \\ P_r \left({\displaystyle \frac{v^3\left(t\right)-v_{cut-in}^3}{v_r^3-v_{cut-in}^3}}\right), v_{cut-in}<v\left(t\right)<v_r\\ P_r,{ v}_r\le v\left(t\right)<v_{cut-in}\end{array}\right.,$$

(4)

where P_r, v_r, v_cut-in, and v_cut-out denotes the rated power, rated velocity, cut-in velocity, and cut-out velocity of the wind turbine.

3.1. Solar Energy Generation

The basic formula for generating energy from photovoltaic panels is as follows:

$$E_{PV}=A\times r\times H\times PR,$$

(5)

where E_PV is the energy generated by the farm in kWh/year, A is the total surface area of the panels in m², r is the efficiency of the panels in kWh/m², H is the average annual sunlight in kWh/m², and PR is the performance ratio, which takes into account losses due to the tilt angle, orientation, temperature, conversion, and transmission.

The output power of the PV generator during the year is calculated by Equation (6) [18],

$$P_v\left(t\right)=R_{pv}{D}_{pv}\left({\displaystyle \frac{G_T\left(t\right)}{G_{T,STC}}}\right)\left[1+{\alpha }_p\left(T_{cell}\left(t\right)-T_{cell,STC}\right)\right],$$

(6)

where P_v is the rated capacity (kW), D_pv is the P_V derating factor (%), G_T is incident solar radiation (kW/m²), GT,STC is the radiation in STC (standard test conditions), αp is the power temperature coefficient (%/°C), T_cell and T_{cell, STC} is the cell temperature (°C) under operating and STC conditions, respectively.

3.2. Battery Energy Storage

Battery energy storage is used to store excess electrical energy and deliver it back to the system when needed. The formula for energy stored by the battery is as follows:

$$E_{BAT}=Q\times V,$$

(7)

where E is the energy stored by the battery (BAT) in Wh, Q is the battery’s electric charge in Ah, and V is the battery voltage in V.

In the proposed hybrid system in the study, battery storage plays a significant role as it is responsible for storing surplus energy generated from renewable sources and supplying it back to the system. It is crucial to characterize the battery system to adapt to the dynamic energy demand of the designed hybrid system [19]. Detailed mathematical modeling related to renewable energy can be found in the literature [20].

The Global Solar Atlas [21] and Global Wind Atlas [22] were used to determine the necessary resources.

The article presents a simulation study of an autonomous charging station for electric vehicles powered by renewable energy sources. However, its limitations result primarily from the level of detail and scope of the simulation, which, although useful for initial assessment, may affect the precision and generality of the conclusions.

The basic operational parameters of the charging station were determined, including the following:

• Traffic-related parameters such as the number of vehicles charged per day, the load structure of the station by charged vehicles during the day, and seasonality;
• The characteristics of the batteries and charging were also taken into account, such as the average battery capacity, battery charge level, and charger power.
• For above parameters appropriate power supply from renewable energy sources was selected, taking into account the characteristic windiness and insolation. In order to limit the supply of energy to the station from the grid, the energy storage was included in the model.

Figure 1 shows a flowchart describing the simulation steps.The project uses an iterative approach, enabling gradual refinement of the optimal configuration. Excel analysis allows for flexible modeling of various scenarios. A key element is to consider the known EV charging profile as a constant around which the power system configuration is optimized, simplifying the design process.

Based on the characteristic wind and insolation of the location, the appropriate power supply from renewable energy sources was selected. This includes wind farms and photovoltaic systems to generate electricity for charging stations. In addition, it was considered that energy storage in the form of batteries would provide a stable power supply.

To simulate the optimal proportion of these power sources, a mathematical model was developed. The model takes into account the generation of energy from wind farms and photovoltaic systems based on historical weather data for a given location. It also takes into account the energy consumption of the charging station, which is determined by the number of vehicles charged per day and their charging patterns.

The model aims to find the optimal allocation of energy from wind farms, photovoltaic systems, and batteries to meet the energy demand of the charging station while maximizing the use of renewable energy sources. It takes into account factors such as the availability of wind and solar resources, battery capacity and charging efficiency.

The simulation is carried out for different scenarios, taking into account differences in vehicle charging patterns, seasonality, and renewable energy efficiency. The goal is to determine the optimal combination of power sources and their share to ensure reliable and sustainable operation of the charging station.

The main problem is the modeling of hybrid systems, which often leads to significant over-sizing of individual system components, such as energy generators and storage units, due to the specific nature of the load (e.g., fast EV chargers requiring high power in a short time). This over-sizing also results from the instability of renewable energy sources and the difficulty in forecasting them.

4. Results of the Electric Vehicle Charging Station Operating Model Based on Energy from RES and Energy Storage

This chapter presents the results obtained from the operational model of an electric vehicle charging station powered by renewable energy sources (RES) and supported by an energy storage. After a detailed discussion of the initial assumptions, the description of the reference station, and the adopted model design parameters in the previous sections, this part focuses on the presentation and analysis of the key results resulting from the simulation of the system operation under defined conditions.

4.1. Preliminary Assumptions

European electricity Transmission System Operators (TSOs) play a key role in developing electricity system into green technology [23]. The problem of ensuring the operational efficiency of the National Transmission System when connecting to the RES system and energy consumers in the form of charging stations has been considered. One of the solution is to create a smart grid, the elements of which are so-called Smart Grids will operate autonomously, connecting producers of energy from conventional and renewable sources, line owners, and consumers, automatically adapting to their needs [24].

Due to the low flexibility of the power system in Poland in the context of further increasing the share of renewable energy sources in the EE System [25], various Smart Grid configurations, including off-grid systems, will play an increasingly important role.

One of such elements may be a hybrid fuel-electric or fully electric vehicle charging station located in places of high energy demand, such as highways. When constructing the model of the charging station, it was assumed that the station would be powered in a hybrid way from its own RESs such as WF and PV supported by BAT.

The work omitted the issue of introducing electricity from RESs to the power grid and Smart Grid. Interesting observations on this subject include study [26].

In order to quickly charge the vehicle, the use of direct current is preferred, and commercial stations are equipped with such chargers. Charging with direct current makes it possible to significantly shorten this process, because power from 41 to 150 kW is used [27].

In the time needed to charge a single electric vehicle, about 30 combustion vehicles can be refueled because refueling a combustion vehicle without paying formalities takes about 1 min (the dispenser provides 40 L/min). Therefore, in order to charge a similar number of electric vehicles as internal combustion vehicles, the power of the chargers and/or the number of charging points should be increased. Both of these methods lead to increased power requirements. At a station located on a highway, the availability of the charger and its appropriate power are important, allowing you to shorten the charging time as much as possible. The solution is direct current (DC) fast chargers with a minimum power of 150 kW.

4.2. Description of the Existing Charging Station and Its Operating Capacity

To construct the model, traffic load data from an existing petrol station located on the A4 motorway in southern Poland was used. It is a large petrol station with 12 dispensers for passenger cars, 6 dispensers for trucks, and 4 chargers for electric vehicles. Figure 2 shows an overview map with the charging station in question marked.

The station supports transit traffic from Poland to Germany and communication between the city of Wrocław and the mountainous region of the Sudety Mountains, which is of great tourist importance and is a reservoir of employees and students for the agglomeration.

The station currently allows you to charge electric cars from four fast Tesla chargers with a capacity of up to 150 kW. Currently, they are heavily loaded, due to the fact that it is the first charging station in Poland equipped with fast chargers for vehicles coming from abroad.

The station is located in a favorable location due to the wind. A wind farm consisting of nine turbines with a capacity of 3 MW each was built near the station.

4.3. Design Assumption of Their Charging Station

4.3.1. Station Charging Data

• refueling time/number of vehicles

Table 1. The number of cars charged per hour depending on the time of day and month.

Hour	Jan	Feb	Mar	Apr	May	June	July	Aug	Sept	Oct	Nov	Dec
0–1	12	12	10	10	12	12	12	12	12	10	10	12
1–2	8	8	7	7	8	8	8	8	8	7	7	8
2–3	6	6	5	5	6	6	6	6	6	5	5	6
3–4	6	6	5	5	6	6	6	6	6	5	5	6
4–5	8	8	7	7	8	8	8	8	8	7	7	8
5–6	30	30	27	27	30	30	30	30	30	27	27	30
6–7	50	50	45	45	50	50	50	50	50	45	45	50
7–8	50	50	45	45	50	50	50	50	50	45	45	50
8–9	50	50	45	45	50	50	50	50	50	45	45	50
9–10	40	40	36	36	40	40	40	40	40	36	36	40
10–11	30	30	27	27	30	30	30	30	30	27	27	30
11–12	30	30	27	27	30	30	30	30	30	27	27	30
12–13	40	40	36	36	40	40	40	40	40	36	36	40
13–14	40	40	36	36	40	40	40	40	40	36	36	40
14–15	50	50	45	45	50	50	50	50	50	45	45	50
15–16	60	60	54	54	60	60	60	60	60	54	54	60
16–17	70	70	63	63	70	70	70	70	70	63	63	70
17–18	60	60	54	54	60	60	60	60	60	54	54	60
18–19	50	50	45	45	50	50	50	50	50	45	45	50
19–20	50	50	45	45	50	50	50	50	50	45	45	50
20–21	40	40	36	36	40	40	40	40	40	36	36	40
21–22	30	30	27	27	30	30	30	30	30	27	27	30
22–23	30	30	27	27	30	30	30	30	30	27	27	30
23–24	20	20	18	18	20	20	20	20	20	18	18	20
Sum	860	860	772	772	860	860	860	860	860	772	772	860

shows the number of cars charged per hour depending on the time of day and month.

• characteristics of EV charging station

It was assumed that the station will have 20 DC chargers with a maximum power of 150 kW.

Table 2. Parameters of the charging station.

EV Charging Station	Value	Unit
Installed power of 1 charging point	150	kW
Charging time	18	Min
Energy stored in the EV	48	kWh
Energy spent per 1 EV	53.34	kWh
Number of charging points at the station	20
Max power requirement	3	MW
Station utilization factor	0.70
Average number of vehicles loaded per day	830.7
Daily consumption of EE	44.3	MWh
Annual demand for EE	16.17	GWh

shows main parameters of the charging station that were adopted in the analysis.

• EV charging station load

Table 3. Demand for EE by EV [kWh] on a daily basis in a given month.

Hour	Jan	Feb	Mar	Apr	May	June	July	Aug	Sept	Oct	Nov	Dec
0–1	640	640	533	533	640	640	640	640	640	533	533	640
1–2	427	427	373	373	427	427	427	427	427	373	373	427
2–3	320	320	267	267	320	320	320	320	320	267	267	320
3–4	320	320	267	267	320	320	320	320	320	267	267	320
4–5	427	427	373	373	427	427	427	427	427	373	373	427
5–6	1600	1600	1440	1440	1600	1600	1600	1600	1600	1440	1440	1600
6–7	2667	2667	2400	2400	2667	2667	2667	2667	2667	2400	2400	2667
7–8	2667	2667	2400	2400	2667	2667	2667	2667	2667	2400	2400	2667
8–9	2667	2667	2400	2400	2667	2667	2667	2667	2667	2400	2400	2667
9–10	2133	2133	1920	1920	2133	2133	2133	2133	2133	1920	1920	2133
10–11	1600	1600	1440	1440	1600	1600	1600	1600	1600	1440	1440	1600
11–12	1600	1600	1440	1440	1600	1600	1600	1600	1600	1440	1440	1600
12–13	2133	2133	1920	1920	2133	2133	2133	2133	2133	1920	1920	2133
13–14	2133	2133	1920	1920	2133	2133	2133	2133	2133	1920	1920	2133
14–15	2667	2667	2400	2400	2667	2667	2667	2667	2667	2400	2400	2667
15–16	3200	3200	2880	2880	3200	3200	3200	3200	3200	2880	2880	3200
16–17	3733	3733	3360	3360	3733	3733	3733	3733	3733	3360	3360	3733
17–18	3200	3200	2880	2880	3200	3200	3200	3200	3200	2880	2880	3200
18–19	2667	2667	2400	2400	2667	2667	2667	2667	2667	2400	2400	2667
19–20	2667	2667	2400	2400	2667	2667	2667	2667	2667	2400	2400	2667
20–21	2133	2133	1920	1920	2133	2133	2133	2133	2133	1920	1920	2133
21–22	1600	1600	1440	1440	1600	1600	1600	1600	1600	1440	1440	1600
22–23	1600	1600	1440	1440	1600	1600	1600	1600	1600	1440	1440	1600
23–24	1067	1067	960	960	1067	1067	1067	1067	1067	960	960	1067
Suma	45,868	45,868	41,175	41,175	45,868	45,868	45,868	45,868	45,868	41,175	41,175	45,868

shows the demand for electric energy by EV on a daily basis in a given month.

4.3.2. Characteristics of the Weather Conditions of the Selected Location

Based on the data collected by the Global Solar Atlas and the Global Wind Atlas, the weather conditions for the selected location were assumed. These tools allow you to choose the optimal location due to the weather conditions ensuring the operation of RES or, as in our case, to determine the parameters of wind and insolation for the previously selected location and select the appropriate turbines and photovoltaic panels, determining their efficiency. Figure 3 shows the characteristics of weather conditions for the solar farm in the selected location. It has been generated from The Global Solar Atlas software.

The weather at this location in Wichrów, Poland, is characteristic of a temperate climate with significant cloudiness. While not an extremely high-insolation area, it possesses a good potential for solar power generation, particularly when photovoltaic systems are optimized by tilting them at a 36° angle to the south. The moderate average temperature is also a positive factor for solar panel efficiency. The data points to a climate with distinct seasons, where a significant amount of the annual solar energy is received during the summer months.

Table 4. PV system configuration.

PV System	Ground-Mounted Large Scale
Azimuth of PV panels	Defaults (180°)
Tilt of PV panels	39°
Installed capacity	3358 kWp
Energy spent per 1 EV	53.34
Annual averages
Total photovoltaic power output and Global tillted irradiation
3676 GWh per year	1347.90 kWh/m2 per year

shows the entire configuration of the PV system, including the amount of energy generated individually and during the year in the selected location.

Figure 4 shows the characteristics of weather conditions for the wind farm in the selected location. It has been generated from The Global Wind Atlas software (https://globalwindatlas.info/en/, accessed on 18 May 2025).

This area possesses a strong and commercially attractive wind resource. The key characteristics are good wind speeds and power density at a typical turbine operational height and a clear and dominant wind direction from the southwest, which simplifies wind farm design and optimization.

To ensure energy from wind turbines within the assumed parameters, energy from three turbines with a capacity of 3 MW is needed, supplemented with energy from a photovoltaic farm and an energy storage facility with the parameters listed below.

The initial selection of wind turbines (3 × 3 MW) and a photovoltaic (PV) installation (3.36 MWp) resulted from taking into account the existing infrastructure: a large petrol station on the A4 motorway in southern Poland, near which a wind farm consisting of nine turbines with a capacity of 3 MW each was already built. In the next iteration of the project, considering the characteristic windiness and insolation for this location, the smallest possible wind farm power (9 MW) and its complementary photovoltaic (PV) installation power (3.36 MWp PV) were determined. The remaining energy deficits are covered by the battery (BAT) energy storage, which acts as a buffer, storing surpluses, and releasing them during periods of insufficient production, effectively eliminating dependence on the external power grid.

Figure 5 characterizes the performance profile of a specific wind turbine technology. By combining this power curve with the location-specific wind data (like the mean wind speed of 7.3 m/s and the wind frequency distribution from the previous analyses), one can calculate the Annual Energy Production (AEP) and the capacity factor, which are crucial metrics for assessing the economic viability of a wind power project in Wichrów.

4.3.3. Calculation of Energy Demand and Its Generation by Selected RES

The selected location is characterized by average wind and sunshine. The selected rotor is characterized by the possibility of operation even at low wind speed of 3 m/s; however, the optimum wind level for the rotor is the wind speed in the range of 8–12 m/s. In the selected location, the maximum wind speed is 9.5 m/s in the best months, i.e., in winter and autumn. In spring and summer, a PV farm is an effective supplement to wind generation. The PV and WF parameters specified above allow us to generate the amount of energy specified in the

Table 5. Summary of WF and PV parameters.

Parameters of the PV and WF Installation	PV	WF	Unit
Installation PV area	48,000		m2
Number of panels	6336		pcs
Panel power	530		Wp
Installation power	3358	9000	kW
Power factor	0.228	0.37
Average daily energy production	18.4	79.9	MWh
Energy production per year	7	29	GWh

.

To illustrate the variability of RES operation, tables showing the amount of energy obtained at a given time of operation, compared with the demand of the vehicle charging station on a daily basis in a given month, were presented.

Table 6. EE generation by wind turbines [kWh].

Hour	Jan	Feb	Mar	Apr	May	June	July	Aug	Sept	Oct	Nov	Dec
0–1	5280	5280	5280	3516	3516	2175	2175	1212	2175	3516	5280	7446
1–2	5280	5280	5280	3516	3516	2175	2175	1212	2175	2175	5280	7446
2–3	5280	5280	5280	3516	3516	2175	2175	1212	2175	2175	5280	7446
3–4	5280	7446	5280	3516	2175	3516	2175	1212	2175	2175	5280	7446
4–5	5280	7446	5280	3516	2175	2175	2175	1212	2175	2175	5280	7446
5–6	5280	7446	5280	3516	2175	1212	1212	1212	2175	2175	5280	7446
6–7	5280	7446	5280	2175	1212	552	552	552	2175	2175	5280	7446
7–8	5280	5280	3516	1212	552	552	552	552	1212	2175	5280	7446
8–9	5280	5280	3516	1212	552	1212	552	552	1212	2175	5280	7446
9–10	5280	3516	3516	1212	1212	1212	552	552	1212	1212	5280	7446
10–11	3516	3516	3516	1212	1212	1212	552	552	1212	1212	3516	5280
11–12	3516	3516	3516	1212	1212	1212	1212	552	1212	1212	3516	5280
12–13	3516	3516	3516	1212	1212	1212	1212	552	1212	1212	3516	5280
13–14	3516	3516	3516	1212	1212	1212	1212	552	1212	1212	3516	5280
14–15	5280	3516	3516	2175	1212	2175	1212	552	2175	1212	3516	5280
15–16	5280	3516	3516	1212	1212	2175	1212	552	2175	1212	3516	5280
16–17	5280	3516	3516	1212	2175	2175	1212	552	2175	2175	5280	7446
17–18	5280	5280	5280	2175	2175	2175	1212	1212	2175	2175	5280	7446
18–19	7446	5280	5280	2175	2175	2175	1212	1212	2175	2175	5280	7446
19–20	7446	3516	5280	2175	2175	2175	2175	2175	2175	2175	5280	7446
20–21	7446	5280	5280	2175	3516	3516	2175	2175	2175	3516	5280	7446
21–22	7446	5280	5280	3516	3516	3516	2175	3516	2175	3516	5280	7446
22–23	7446	5280	5280	3516	3516	3516	2175	3516	2175	3516	5280	7446
23–24	7446	5280	5280	5280	3516	3516	2175	3516	2175	3516	5280	7446

shows electric energy generation by wind turbines.

Table 7. Surplus/Demand for EE of the charging station after the use of wind turbines [kWh].

Hour	Jan	Feb	Mar	Apr	May	June	July	Aug	Sept	Oct	Nov	Dec
0–1	4640	4640	4747	2983	2876	1535	1535	572	1535	2983	4747	6806
1–2	4853	4853	4907	3143	3089	1748	1748	785	1748	1802	4907	7019
2–3	4960	4960	5013	3249	3196	1855	1855	892	1855	1908	5013	7126
3–4	4960	7126	5013	3249	1855	3196	1855	892	1855	1908	5013	7126
4–5	4853	7019	4907	3143	1748	1748	1748	785	1748	1802	4907	7019
5–6	3680	5846	3840	2076	575	−388	−388	−388	575	735	3840	5846
6–7	2613	4779	2880	−225	−1455	−2115	−2115	−2115	−492	−225	2880	4779
7–8	2613	2613	1116	−1188	−2115	−2115	−2115	−2115	−1455	−225	2880	4779
8–9	2613	2613	1116	−1188	−2115	−1455	−2115	−2115	−1455	−225	2880	4779
9–10	3147	1383	1596	−708	−921	−921	−1581	−1581	−921	−708	3360	5313
10–11	1916	1916	2076	−228	−388	−388	−1048	−1048	−388	−228	2076	3680
11–12	1916	1916	2076	−228	−388	−388	−388	−1048	−388	−228	2076	3680
12–13	1383	1383	1596	−708	−921	−921	−921	−1581	−921	−708	1596	3147
13–14	1383	1383	1596	−708	−921	−921	−921	−1581	−921	−708	1596	3147
14–15	2613	849	1116	−225	−1455	−492	−1455	−2115	−492	−1188	1116	2613
15–16	2080	316	636	−1668	−1988	−1025	−1988	−2648	−1025	−1668	636	2080
16–17	1547	−217	156	−2148	−1558	−1558	−2521	−3181	−1558	−1185	1920	3713
17–18	2080	2080	2400	−705	−1025	−1025	−1988	−1988	−1025	−705	2400	4246
18–19	4779	2613	2880	−225	−492	−492	−1455	−1455	−492	−225	2880	4779
19–20	4779	849	2880	−225	−492	−492	−492	−492	−492	−225	2880	4779
20–21	5313	3147	3360	255	1383	1383	42	42	42	1596	3360	5313
21–22	5846	3680	3840	2076	1916	1916	575	1916	575	2076	3840	5846
22–23	5846	3680	3840	2076	1916	1916	575	1916	575	2076	3840	5846
23–24	6379	4213	4320	4320	2449	2449	1108	2449	1108	2556	4320	6379

shows the surplus or demand for elecrticity of the charging station after the use of wind turbines.

Table 8. EE generation by PV [kWh].

Hour	Jan	Feb	Mar	Apr	May	June	July	Aug	Sept	Oct	Nov	Dec
0–1	0	0	0	0	0	0	0	0	0	0	0	0
1–2	0	0	0	0	0	0	0	0	0	0	0	0
2–3	0	0	0	0	0	0	0	0	0	0	0	0
3–4	0	0	0	0	0	0	0	0	0	0	0	0
4–5	0	0	0	0	14.72	42	15	0	0	0	0	0
5–6	0	0	0	24	126	149	123	48	0	0	0	0
6–7	0	0	13	266	391	398	350	283	161	8	0	0
7–8	0	13	371	695	822	804	761	706	576	331	6	0
8–9	93	400	817	1169	1264	1219	1188	1156	1013	763	265	67
9–10	381	865	1186	1547	1606	1546	1509	1539	1352	1034	577	354
10–11	547	1066	1407	1752	1806	1736	1690	1758	1522	1187	797	459
11–12	736	1210	1527	1847	1874	1796	1702	1783	1582	1306	968	568
12–13	763	1237	1541	1835	1850	1797	1651	1744	1564	1332	913	548
13–14	584	1175	1444	1671	1666	1621	1507	1600	1382	1147	661	437
14–15	367	892	1139	1382	1367	1343	1270	1331	1087	819	345	270
15–16	143	517	804	1008	1056	1062	1026	1003	748	476	88	27
16–17	0	92	444	615	673	716	701	653	398	75	0	0
17–18	0	0	72	216	292	349	355	270	69	0	0	0
18–19	0	0	0	23	96	138	132	49	0	0	0	0
19–20	0	0	0	0	4	34	24	440	0	0	0	0
20–21	0	0	0	0	0	0	0	0	0	0	0	0
21–22	0	0	0	0	0	0	0	0	0	0	0	0
22–23	0	0	0	0	0	0	0	0	0	0	0	0
23–24	0	0	0	0	0	0	0	0	0	0	0	0

shows the elecrticity generation by PV farm.

Table 9. Surplus/Demand for EE of the charging station after the use of PV [kWh].

Hour	Jan	Feb	Mar	Apr	May	June	July	Aug	Sept	Oct	Nov	Dec
0–1	4640	4640	4747	2983	2876	1535	1535	572	1535	2983	4747	6806
1–2	4853	4853	4907	3143	3089	1748	1748	785	1748	1802	4907	7019
2–3	4960	4960	5013	3249	3196	1855	1855	892	1855	1908	5013	7126
3–4	4960	7126	5013	3249	1855	3196	1855	892	1855	1908	5013	7126
4–5	4853	7019	4907	3143	1763	1791	1763	785	1748	1802	4907	7019
5–6	3680	5846	3840	2100	701	−239	−265	−341	575	735	3840	5846
6–7	2613	4779	2892	41	−1064	−1717	−1765	−1832	−330	−217	2880	4779
7–8	2613	2627	1487	−493	−1293	−1311	−1354	−1409	−878	106	2886	4779
8–9	2706	3013	1933	−19	−850	−235	−927	−959	−442	538	3145	4846
9–10	3527	2247	2782	839	684	624	−72	−42	431	326	3937	5666
10–11	2463	2982	3483	1524	1418	1348	642	710	1133	959	2873	4138
11–12	2652	3126	3603	1619	1486	1408	1314	735	1194	1078	3044	4248
12–13	2145	2620	3137	1126	928	876	730	163	643	624	2509	3695
13–14	1966	2557	3040	962	744	699	586	18	461	439	2257	3583
14–15	2980	1741	2254	1157	−88	851	−185	−784	595	−369	1461	2884
15–16	2223	833	1440	−660	−932	36	−962	−1645	−277	−1192	724	2107
16–17	1547	−125	600	−1533	−885	−842	−1820	−2528	−1160	−1110	1920	3713
17–18	2080	2080	2472	−489	−733	−676	−1633	−1718	−956	−705	2400	4246
18–19	4779	2613	2880	−202	−395	−354	−1323	−1406	−492	−225	2880	4779
19–20	4779	849	2880	−225	−487	−458	−468	−52	−492	−225	2880	4779
20–21	5313	3147	3360	255	1383	1383	42	42	42	1596	3360	5313
21–22	5846	3680	3840	2076	1916	1916	575	1916	575	2076	3840	5846
22–23	5846	3680	3840	2076	1916	1916	575	1916	575	2076	3840	5846
23–24	6379	4213	4320	4320	2449	2449	1108	2449	1108	2556	4320	6379

shows the surplus or demand for elecrticity of the charging station after the use of wind turbines and PV.

The above tables show the synergy of WF operation with PV; however, due to the instability of wind energy and the lack of sufficient energy generation in the morning and evening hours from the photovoltaic farm, it is necessary to operate an energy storage that, supplied with energy from WF, balances the demand in critical periods. The energy storage must have a large capacity and power to charge electric vehicles in the absence of sufficient generation of electricity from RES.

Table 10. Parameters of the selected BAT energy storage.

Energy Storage	Value	Unit
Capacity	4	MWh
Power	0.6	MW
Possible working time	4	h
Number of charged EV	13.3	pcs

shows the main parameters of the selected BAT energy storage.

5. Results and Discussion

The simulation results provide detailed information on the operation of the proposed model of an electric vehicle charging station powered by renewable energy sources with an energy storage. The station is designed to support significant EV traffic (20 DC chargers, each with a capacity of 150 kW) and is located in a location with favorable renewable energy potential. The design parameters of the charging station indicate a maximum power demand of 3 MW and an annual energy demand of 16.17 GWh. The annual energy balance reveals a significant surplus of locally generated renewable energy compared to the total demand of the station. The simulation performed indicates an annual electricity consumption of the charging station of about 16.17 GWh. In turn, the total energy production from the planned wind farm and photovoltaic system is 35.9 GWh per year, including 6.7 GWh from PV and 29.2 GWh from WF. This significant oversizing of generating capacity (generated approximately 2.2 times greater than demand) is a deliberate design choice to provide a high level of energy self-sufficiency and operational resilience, particularly during periods of low or intermittent renewable generation. A characterization of weather conditions for the selected site, based on Global Solar Atlas and Global Wind Atlas data, shows the variability of available wind and solar resources throughout the year and day. Hourly wind and PV generation data illustrate seasonal and diurnal variations in wind and solar output, which are then overlaid on a table showing the hourly energy demand of the EV charging station for each month.

The projected energy demand for the charging station shows significant diurnal and seasonal variations, reflecting traffic load data from the existing gas station. The simulation clearly demonstrates the critical role of the battery storage system in the operation of the station. The battery energy storage acts as a key buffer, storing the surplus energy generated by the wind turbines and solar panels during periods when generation exceeds the immediate charging demand. This stored energy is then discharged to meet the station load during periods of insufficient RES generation. The use of the energy storage effectively mitigates the variability of renewable energy generation on the one hand and the variability of energy demand from the customers of the EV charging station on the other. This dynamic management of the energy flow between generation, storage, and demand is the basis for achieving high operational autonomy.

A key indicator resulting from the simulation is the excess electricity factor (ExEi), which measures the amount of RES energy that cannot be used in the charging station or stored in the battery. The calculated ExEi for the simulated year of operation of the EV station is 54.9%. This high percentage indicates that more than half of the generated renewable energy is potentially not used by the station in its current configuration. This may happen because the battery is full and the generation still exceeds the demand, or simply because the generation significantly exceeds the demand even when the battery is operating.

The simulation results based on the assumptions made regarding the generation and consumption profiles and the size of the energy storage allow us to conclude that the designed system allows the station to operate autonomously without the need to import energy from the external power grid. Table 9 shows the energy deficit that occurs when the instantaneous RES generation falls below the station demand; however, according to the model parameters, this deficit is fully covered by the energy discharged from the battery storage system, as shown in

Table 11. Stored EE of the charging station after using WF, PV, and BAT energy storage [kWh].

Hour	Jan	Feb	Mar	Apr	May	June	July	Aug	Sept	Oct	Nov	Dec
0–1	0	0	0	0	0	0	0	0	0	0	0	0
1–2	0	0	0	0	0	0	0	0	0	0	0	0
2–3	0	0	0	0	0	0	0	0	0	0	0	0
3–4	0	0	0	0	0	0	0	0	0	0	0	0
4–5	0	0	0	0	0	0	0	0	0	0	0	0
5–6	0	0	0	0	0	3761	3735	3659	0	0	0	0
6–7	0	0	0	0	2936	2283	2235	2168	3670	3783	0	0
7–8	0	0	0	3507	1643	2689	2646	2591	3122	0	0	0
8–9	0	0	0	3488	793	3765	3073	3041	3558	0	0	0
9–10	0	0	0	0	0	0	3928	3958	0	0	0	0
10–11	0	0	0	0	0	0	0	0	0	0	0	0
11–12	0	0	0	0	0	0	0	0	0	0	0	0
12–13	0	0	0	0	0	0	0	0	0	0	0	0
13–14	0	0	0	0	0	0	0	0	0	0	0	0
14–15	0	0	0	0	3912	0	3815	3216	0	3631	0	0
15–16	0	0	0	3340	3068	0	3038	2355	3723	2808	0	0
16–17	0	3875	0	2467	3115	3158	2180	1472	2840	2890	0	0
17–18	0	0	0	3511	3267	3324	2367	2282	3044	3295	0	0
18–19	0	0	0	3798	3605	3646	2677	2594	3508	3775	0	0
19–20	0	0	0	3775	3513	3542	3532	3948	3508	3775	0	0
20–21	0	0	0	0	0	0	0	0	0	0	0	0
21–22	0	0	0	0	0	0	0	0	0	0	0	0
22–23	0	0	0	0	0	0	0	0	0	0	0	0
23–24	0	0	0	0	0	0	0	0	0	0	0	0

. Thus, for the simulated period and conditions, the dependence on the grid is effectively eliminated, confirming the high level of independence that can be achieved with the proposed hybrid system. The results are inherently sensitive to the input data, in particular the assumed traffic profiles from the existing station in Table 1 and the weather conditions presented in the wind and solar resource data Figure 3, Figure 4 and Figure 5, Table 4. Changes to these factors can alter the balance between demand and generation, affecting both the required dispatch of the energy storage and the resulting ExEi value. Further analysis investigating the sensitivity to different traffic scenarios or taking into account weather variability throughout the year could provide a more robust understanding of the system’s performance range. Table 11 shows stored elecricity of the charging station after using WF, PV, and BAT energy storage.

An increase in demand by 10% causes the station’s own energy shortage only in August between 17:00 and 23:00, and by 20% causes the station’s operation to need to be supported from the grid in July and August. Shortages occur mainly between 16 and 24 h. A 10% drop in the wind power utilization factor causes small energy shortages in August between 17 and 20 h, amounting to a maximum of 1.6 MWh. This would reduce the possibility of charging vehicles at that time by 22% or force the station to be supplied from the grid. Further work will develop methods to enhance the stability of the proposed solution.

In summary, the simulation confirms the technical feasibility of operating a high-power electric vehicle charging station, operating mainly on the basis of local RES and energy storage. The obtained results indicate the possibility of operating autonomous hybrid systems, which can have a significant contribution to the development of renewable energy sources and electromobility. The challenge of managing significant energy surpluses is also significant, which is an area of potential future optimization of the management of produced and consumed energy.

6. Conclusions

The development of autonomous charging stations for electric vehicles powered by renewable energy sources is a promising solution for the future of electromobility. The AFIR Regulation in the European Union emphasizes the need to build charging infrastructure and provide sufficient power for it. The simulation carried out showed the possibility of operating a large vehicle charging station based on renewable energy sources of production and storage. The proposed energy sources compensated for their generation in both the daily and monthly cycles. The small negative values of energy demand obtained in certain periods indicate the need to supplement energy shortages occurring mainly in the summer, when the wind is weaker and the solar panels overheat and do not work at full power. The BAT storage, charged during periods of excess generated energy, prevents further oversizing of energy generators and provides a buffer, securing the operation of the charging station.

The presented model of the autonomous vehicle charging station operation was based on simple solutions enabling calculations in a spreadsheet using parameters obtained from the Global Solar Atlas and Global Wind Atlas. The constructed model is characterized by a high value of the ExEi coefficient, indicating how much generated energy was not used by the vehicle charging station. In the analyzed case, the ExEi coefficient was 54.9%. This is a value characteristic of projects operating based on RES, where the main determinant is ensuring continuity of operation [28]. The simulation results show that thanks to the use of a combination of wind farms, photovoltaic systems, and an energy storage, charging stations can operate autonomously regardless of the power grid failure and reduce its load. The discussed solution allows for reducing expenditure on network infrastructure and directly affects the reduction in greenhouse gas emissions and other air pollutants. The next step should be further optimization of energy generators, energy storage facilities and considering the possibility of greater load diversity of charging stations, taking into account a possible barrier or price promotion forcing demand for the service at a specific RES energy generation.

Further optimization of energy sources and energy storage devices should be carried out, and the possibility of greater load diversification for charging stations should be considered, including potential price barriers or promotions influencing demand for the service at a specific renewable energy generation. Sensitivity analysis demonstrated that worsening wind conditions or increased demand does not cause a collapse in charging supply. Only in critical months (summer) might there be a temporary limitation of supply. It is worth considering potential price barriers if the station’s power supply is limited due to economic or technical reasons.