1.

INTRODUCTION

In recent years, the global energy crisis and environmental conditions have become increasingly severe, and energy saving and emission reduction have received widespread attention from various countries. Electric vehicles (EVs) have incomparable advantages over traditional fuel vehicles in terms of energy saving and emission reduction, and have therefore been promoted by governments and vehicle enterprises. As the world’s largest market for electric vehicles, China has seen a strong growth in electric vehicles in recent years. Statistics from the Ministry of Public Security show that by the end of 2019, the number of electric vehicles in China had reached 3.81 million. At the same time, China’s General Office of the State Council Guidance on Accelerating the Construction of Electric Vehicle Charging Infrastructure also requires that new residential buildings must be equipped with charging piles as standard or reserved for them. The continued construction and popularity of electrical energy replenishment stations for electric vehicles has greatly improved charging convenience, and electric vehicles will be increasingly accepted and utilized.

However, in contrast to traditional fuel vehicles, EVs and the grid are interconnected and the charging behavior of EVs is highly random and flexible in time and space. As the number of EVs increases, the charging behavior of large-scale EVs connected to the grid in the future will have a significant impact on the grid. The most prominent impact of large-scale electric vehicles on the grid is reflected in the electricity consumption load. The disorderly charging of electric vehicles will increase the peak load of electricity consumption across the grid and exacerbate the peak-to-valley load differential. The peak-to-valley differential on the national grid is widening due to the impact of residential and tertiary electricity consumption, with the maximum load on the national grid peaking at nearly 1.2 billion kilowatts in January 2021, a record high and an increase of over 20% compared to the same period last year. The relevant departments predict, based on statistical data, that by 2035 the number of electric vehicles in large cities will exceed 3 million. When the number of electric vehicles in cities is growing at a high rate and fast charging accounts for a higher percentage, the peak load of the city grid will increase by around 10% in 2035, with the maximum load demand increasing by 1000-4000MW. This increase could, in extreme cases, lead to a shortfall in installed power supply capacity and transmission line capacity in some areas. In addition, the spread of fast charging will increase the grid load while increasing the complexity and uncertainty of the electric vehicle load. In extreme cases, the uncontrolled charging of electric vehicles can also affect the backbone grid, potentially causing transmission blockages and a lack of regional power delivery capacity, increasing the power delivered on heavily loaded transmission lines.

In this case, it is possible to combine distributed photovoltaic plants, electric vehicles, energy storage devices and their control devices in order to suppress or eliminate the negative effects of electric vehicle charging. In a photovoltaic charging station, it is the electricity generated by solar cells that is used as the source of energy for the electric vehicle’s power battery, and the location of the charging station will be more flexible so that the problems mentioned above can be solved to a certain extent. The electric vehicle photovoltaic charging station realizes the in-situ integration of renewable energy and electric vehicles, truly realizing zero pollution and zero emissions, which not only realizes the efficient consumption of renewable energy and guarantees friendly access to the electric vehicle load, but also effectively improves the utilization rate of renewable energy, bringing huge economic and social value.

(Tulpule et al., 2013) study economic and environmental impacts of a PV powered workplace-parking garage charging station. A daytime photovoltaic (PV) based, plug-in electric vehicle charging station located in a workplace parking garage is considered [1]. (Ye et al., 2015) propose a model of solar-powered charging stations for electric vehicles to mitigate problems encountered in China’s renewable energy utilization processes and to cope with the increasing power demand by electric vehicles for the near future [2]. (Torreglosa et al., 2016) study decentralized energy management strategy based on predictive controllers for a medium voltage direct current photovoltaic electric vehicle charging station. An electric vehicle charging station supplied by photovoltaic solar panels, batteries and with grid connection is analyzed and evaluated [3]. In term of the necessity of the re-use of retired electric vehicle battery and the capacity allocation of photovoltaic (PV) combined energy storage stations (Han et al., 2018) present a method of economic estimation for a PV charging station based on the utilization of retired electric vehicle batteries [6]. In order to improve the profitability of the fast-charging stations and to decrease the high energy demanded from the grid, the station includes renewable generation (wind and photovoltaic) and a storage system. (Domínguez-Navarro et al., 2019) study design of an electric vehicle fast-charging station with integration of renewable energy and storage systems [7]. Unlike other papers, this one uses a detailed model of the charging process that considers the arrival time and state of charge of electric vehicles. (Seddig et al., 2019) compare three approaches (heuristic, optimization, and stochastic programming) to schedule the charging process of three different electric vehicles fleets (commuters, opportunity, and commercial fleets) at a common charging infrastructure under uncertainty [8]. (Shin et al., 2020) propose a novel multiagent deep reinforcement learning method for the energy management of distributed electric vehicle charging stations with a solar photovoltaic system and energy storage system [10]. Other influential work includes (Sehar et al., 2017), (Singh et al., 2018), (Ghosh, 2020) [4,5,9].

China is rich in solar resources and most areas are eligible for photovoltaic power generation, so this combination of charging piles and distributed renewable energy generation systems, which means that the construction of renewable energy for local consumption has been widely accepted. Current solar energy utilization technologies include thermal, photovoltaic and photochemical. Among them, photovoltaic cells made by using the principle of photovoltaic effect can convert the sun’s light energy directly into electrical energy for utilization, called photoelectric conversion, that is, photovoltaic power.

Multiple parallel photovoltaic (PV) inverters based microgrid is developed to enhance the reliability and accessibility of electricity in remote areas (Singh et al., 2022) [11]. (Nallolla et al., 2022) aim to design the optimal hybrid renewable energy system, wherein the design consists of PV/BS (1476 kW-solar PV, 417 batteries, electrolyser-200 kW, hydrogen tank-20 kg and 59.6 kW-converter) by comparing the minimum net present cost (NPC: $7.01 M), levelized cost of energy (LCOE: 0.244 $/kWh), and the high renewable fraction (RF: 84.1%) [13]. (Iheanetu, 2022) provide a comprehensive and systematic review of recent advances in solar PV power forecasting techniques with a focus on data-driven procedures [16]. In order to improve the efficiency of photovoltaic panels, a photovoltaic-temperature difference (PV-TE) hybrid power generation system can be formed by combining photovoltaic power generation with the thermoelectric generation, which is based on the Seebeck effect. Based on this (Lin et al., 2023) set up the photovoltaic-temperature hybrid power system test bench for testing [17]. (Liu et al., 2023) discuss the modeling and fast frequency controls, namely virtual inertia and droop control [18]. The parallel operation of two inverters is taken as an example, the power distribution mechanism is derived and the relevant mathematical model is established, and the influencing factors of the parallel inverter power distribution are analyzed (Cui et al., 2023) [19]. Ball milling advantages include the potential for high capacity, predicted fineness in a specific amount of time, reliability, safety, and simplicity, but has disadvantages of high weight, energy consumption and costs, which limit accessibility. To overcome these limitations (Mottaghi et al., 2023) apply the free and open source hardware approach coupled to distributed digital manufacturing to fabricate a ball mill with a simple, customizable design that can be used in a wide range of scientific applications and circumstances including those without access to reliable grid electricity [20]. Other influential work includes (Ilyushin et al., 2022), (Lu et al., 2022), (Zhao et al., 2022) [12,14,15].

2.

APPROACH

The irradiation situation in Lanzhou is depicted in Figure 1, which reveals that the city receives 2446 hours of sunshine per year and has an annual radiation output of around 6.7×106kJ/m². Beijing has a total land area of 1,3085.6 km² and receives an amount of solar radiation annually that is comparable to 2,505 million tons of standard coal. According to QX/T 89-2008 Solar Energy Assessment Methodology, Lanzhou is located in a region of China that has a Class III abundance of solar energy resources. Because of the considerable solar resources available, the construction of photovoltaic charging station is now feasible. Table 1 shows the location information of Lanzhou area in 2020, which will be used as a source for the design of the photovoltaic system.

Figure 1.

Global radiation data sets.

Table1.

The 2020 weather data for the Lanzhou area.

The sun irradiation in the Lanzhou area in the year 2020 is depicted as a heat map in Figure 2. According to the color bands, the higher the irradiation, the redder the color, and as can be observed, the irradiation is rather considerable in summer months. Irradiation levels are typically low during the winter months.

Figure 2.

Total irradiance from weather file in 2020.

According to the national standard GB 50797-2012 Design Specifications for Photovoltaic Power Plants, the prediction of the power generation capacity of a photovoltaic power plant should be based on the solar resources at the location of the photovoltaic power plant, and take into account various factors such as the system design of the photovoltaic power plant, the layout of the photovoltaic arrays and environmental conditions, and then be calculated. Calculation of photovoltaic plant power generation according to equation (1),

Where EP1 - power generation;

H - total horizontal irradiance, kWh/m²;

ESC - irradiance at STC, 1 kW/m²;

PAZ - installed capacity, kW;

K1 - combined efficiency factor. K1 includes: correction factors for PV module type, unavailable solar irradiation losses, correction factors for tilt and azimuth of PV arrays, losses due to operating temperature, availability of photovoltaic power generation system, light utilization rate, inverter efficiency, collector line losses, step-up transformer losses, PV module surface contamination correction factors, PV module conversion efficiency correction factors, string arrangement, line losses, shadow shading, etc.

The irradiance HTX on the inclined surface with azimuth angle is the sum of the direct and diffuse irradiance, and the calculation formula is as formula (2)

Where β - module inclination angle;

R_b-ratio of direct solar radiation on the inclined plane to the horizontal plane;

H₀ - solar radiation on the horizontal plane outside the atmosphere;

H_b - direct radiation on the horizontal plane;

H_d - scattered radiation on the horizontal plane According to the amount.

The solar radiation H₀ on the horizontal plane outside the atmosphere is calculated according to the formula (3):

In the formula, I_SC - solar constant, generally 1367±7 W/m²;

N - number of days from New Year’s Day;

ϕ - latitude;

δ - solar declination angle;

ω_S - sunrise and sunset angle on the horizontal plane.

Solar declination angle ϕ is calculated as formula (4), and sunrise and sunset angle on the horizontal plane ω_S is calculated as formula (5)

The ratio of direct solar irradiation from the inclined plane to the horizontal plane,R_b is calculated as in equation (6)

Where, V - orientation angle, -180°≤V≤180°;

ω_sr, ω_ss - inclined plane of angles at sunrise and sunset, as in equation (7)

H_day, H_month and H_year are the total daily/monthly/yearly horizontal solar irradiance, which can be obtained by integrating the horizontal solar irradiance (kW/m²) at the moment of the I0 sampling period, and by adding up the number of days in the month and the number of months in the year, where T₁ and T₂ are the time periods of mid-day light. The total daily horizontal solar irradiance H_day is the sum of the direct and scattered irradiance H_b and H_d, as in equation (8)

Combining equation (3), we have K_T = H_day/H₀ andH_d is calculated as in equation (9)

In combination with equation (8), the H_d horizontal direct irradiation is obtained. This completes the calculation of HTX for inclined surfaces with an azimuthal angle. In practice, I can be obtained by the irradiance can be obtained by means of an irradiator.

By analyzing the effect of PV plant devices, equipment and environment on the efficiency of the PV system, the nine loss factors are quantified and calculated to obtain a comprehensive system design The combined efficiency coefficient is shown in equation (10), with the subscript i representing the nine types of losses. Table 2 is the PV module loss symbol table.

Table2.

PV module loss symbol table.

The weak light loss is the loss of power generation due to the reduction in conversion efficiency when the irradiation intensity is below 1000 W/m2. The weak light effect at different irradiance levels is weighted and summed to obtain the formula for calculating the weak light loss η_WL as in equation (11)

Where δ_i-the statistical frequency share of different irradiance intensities during the time period (day/month);

η_i - the conversion efficiency of PV modules with different irradiation intensities.

The maximum power output of the PV module decreases with increasing temperature. The maximum power temperature coefficient can be found in the table of technical parameters of the PV module. The temperature loss η_T is shown in equation (12)

Where λ_T - the temperature coefficient of maximum power of the PV module;

T_c - module temperature.

According to the SANDIA temperature model, the module temperature T_c and the PV module backsheet temperature T_m are established and calculated as in equation (13)

Where E - ambient irradiation intensity, W/m²;

E₀ - irradiance at the reference condition irradiance, 1000 W/m²;

T_n - ambient temperature;

WS - ambient wind speed, m/s;

ΔT,a, b - all correlation coefficients

According to the revised “PV manufacturing industry specification conditions” in 2018, the decay rate is as follows: the decay rate of monocrystalline silicon PV modules should not be higher than 3% in the first year, not higher than 0.7% in each subsequent year, and not higher than 20% in 25 years.

The actual power of the PV module often differs from the nominal power, where the optical gain is about +3% and the electrical loss is about -1.8%.

When the output power/current of each module in the same string has a certain deviation, the output current is the minimum value of each module and the total output power of the string is less than the sum of the nominal power of each module, as in equation (14)

where, η_P - PV module mismatch loss;

I - minimum value of output current at module STC;

I_m - nominal output current of the module.

Aggregate cable losses represent DC cable losses. The DC cable loss power is obtained from the power formula by calculating the cable resistance. For example, a cable length of approximately 100 m has a loss of approximately 0.2%.

The inverter losses can be weighted and summed according to the input power variation to calculate the actual inverter efficiency η_N, as in equation (15)

where δ_Pi - the inverter load factor (actual inverter input power to rated inverter input power ratio) at 5%, 10%, 20%, 30%, 50%, 75% and 100% time share respectively;

η_Pi - inverter efficiency values at different load ratios.

The inverter export to parallel network loss, these parameters include AC cable losses and high voltage grid transformer losses. The power loss of the cable can be calculated from the loss of the three-phase four-wire cable line, for instance, for a cable length of 100 m, the loss is approximately 0.5%. Transformer losses include active and reactive losses, which are calculated from the technical parameters of the transformer.

The formula for calculating the system utilization rate is shown in equation (16)

Where, η_X - the system utilization rate;

T_m, T_f - the number of hours of normal maintenance shutdown and troubleshooting of the system in a year.

3.

EXPERIMENTAL SETUP

Table 3 shows the module, inverter, array and analysis parameters information of the designed PV charging station. The photovoltaic system array is shown by Figure 3, which includes length of side, row spacing and module orientation.

Table3.

Parameters.

Figure 3.

Photovoltaic arrays.

4.

RESULTS AND DISCUSSION

A heat map of the power produced by a PV system created for an EV charging station in 2020 is shown in Figure 4. Depending on the color band, the bluer the color the lower the power output and the redder the color the higher the power output. The color blue denotes that the system is not producing any power either because there is insufficient solar irradiation during the night or because there are clouds in the sky. The red color is due to the high solar irradiation at midday and the fact that the light is largely perpendicular to the PV module and the system can provide maximum power generation. It can be seen that the greatest amount of electricity is generated during the day at around 12 noon, and the greatest amount of electricity is generated during the months of the year, from March to May. This is due to the fact that the system output is related to the module temperature, with the optimum operating temperature of the module being around 25°C. The power output of the module will decrease by approximately 0.5% for every 1 °C as the temperature of the module increases. This means, the output power at a module temperature of 20°C is approximately 20% more than the output power at a module temperature of 70°C. In light of this, even though the solar irradiation is at its peak during the summer months, the power output is not at its peak because the temperature of the module rises appropriately.

Figure 4.

Heat map of system power generated in 2020.

The monthly power generation of this PV system based on the solar irradiance of Lanzhou region in 2020 is shown in Figure 5. It can be seen that the month with the largest power generation is May with 10522.6 kWh, and the smallest power generation is December with 6626.25 kWh. in 2020 the average monthly power generation is 8637.49 kWh.

Figure 5.

Monthly energy productions in 2020.

The power heat map of the amount of power a PV charging station can provide to an EV power cell after taking into account inverter efficiency, transformer losses, string arrangement and line losses is shown in Figure 6. In May, the month with the best solar irradiation, the electricity generated by the PV system is 10309.54 kWh, which can provide 275 charging services for EVs at 40 kWh per vehicle. In December, the month with the lowest solar irradiation, the photovoltaic system generates 6607.73 kWh of electricity, which can be used to provide 165 charging cycles for electric vehicles if each vehicle is charged at 40 kWh per cycle.

Figure 6.

System power to battery.

In the whole of 2020, EV distributed photovoltaic charging stations provided 103,749.19 kWh of clean electricity for electric vehicles, equivalent to 41.50 tons of standard coal. If the calculation is based on 40 kWh per vehicle, this electricity can provide 2,594 charging services.

5.

CONCLUSIONS

This paper aims to solve the problem of basic charging facilities for electric vehicles by designing a distributed photovoltaic charging station for electric vehicles, which can provide clean energy for electric vehicles and at the same time provide a feasible solution for stabilizing the power grid. The power generation of this PV charging station is calculated based on the weather data of Lanzhou area in 2020, and the data of this PV system is as follows:

In the circumstances of the contemporary era, the new energy electric vehicle has emerged as the primary development path for China’s automobile industry. The present-day electric vehicles have gradually matured, but this has also introduced new demands for the development of infrastructure supporting electric vehicle charging stations. New energy electric vehicle photovoltaic charging station in the provision of clean energy, stabilization of the grid plays a pivotal role in its development process is also inseparable from the support of relevant national policies, vigorously develop photovoltaic power generation technology, help promote the further development of photovoltaic electric vehicles.

FUNDING

Intelligent grid-connected photovoltaic system for new energy vehicle charging stations (2023B-244)