# Simplified Python Models for Photovoltaic-Based Charging Stations for Electric Vehicles Considering Technical, Economic, and Environmental Aspects

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## Abstract

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## 1. Introduction

_{2}emissions. When it comes to decreasing this global and local pollution, electric vehicles (Evs) are expected to be a vital technology. Thus, electric vehicles must be widely adopted by the transportation sector in order to provide significant environmental benefits. Following that, multiple types of electric vehicles have been marketed by automakers, including hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), and battery electric vehicles (BEVs) [2].

## 2. Modeling of EV Charging/Discharging System with Renewable Energy Resources

#### 2.1. EV-Load Profile Generation

#### 2.2. PV System Design

_{rated}) of the battery, it is possible to compute the maximum and minimum energy stored in the battery (${Eb}_{max}$, ${Eb}_{min}$) as well as the rated energy (E

_{rated}) of the battery.

#### 2.3. EV Charging Station Energy Models

#### 2.4. Environmental Impact Modeling

_{2}emissions since it emits less CO

_{2}compared to a diesel vehicle when traveling the same distance. According to our assumptions, the electric vehicle requires 0.16 kWh/km on average. The total distance traveled may be determined from the generated load profile by dividing the total electricity consumption per year (207,211 kWh/year) by the kWh consumption per km. The total distance traveled per year is calculated to be 1,295,068 km. Given that diesel vehicles can travel on average 12 km/liter, it is estimated that if diesel vehicles are utilized, they will burn 107,922 L/year.

_{2}for every liter of fuel burned. This means that diesel vehicles may release 280.59 tCO

_{2}annually if they travel the assumed distance.

_{2}emitted by the EV depends on the generating source of electricity. In this research, it is assumed to be 0.7 kgCO

_{2}/kWh; therefore, the overall emission, if electric vehicles are used to travel the assumed distance, is equal to 145 tCO

_{2}/year. This suggests that the grid-powered EV vehicles may save 135.6 tCO

_{2}/year, which is comparable to a 48.32% reduction in CO

_{2}/year compared to a diesel vehicle. Now, if a portion of the power consumed by the grid is replaced with PV, the savings will be much greater. The following equation shows the total CO

_{2}savings associated with integrating a PV system to power the electric vehicle charging station.

#### 2.5. Financial Parameters

## 3. Results and Discussion

## 4. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## Appendix A. (Python Codes)

#### Appendix A.1. PV/Grid Charging Station Code

#### Appendix A.2. PV/Grid/Battery Charging Station Code

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Power Level | Charger Location | Typical Use | Typical Power | Charging Time |
---|---|---|---|---|

Level 1 | On-board | Home | 1.3–2.4 kW | 40–50 h |

Level 2 | On-board | Home, Workplace, and Public | 6.6–22 kW | 4–8 h |

Level 3 | Off-board | Public DC Fast Station | 50–350 kW | <1 h |

Vehicle Model | Number of Vehicles | Maximum Charging Power (kW) | Average Charging Duration (min) | kWh per Kilometer |
---|---|---|---|---|

Tesla Model 3 | 50 | 11 | 240 | 0.15 |

Nissan Leaf | 50 | 6.6 | 240 | 0.18 |

Hyundai IONIQ Electric | 50 | 7.2 | 240 | 0.16 |

Input Voltage | 100 V/250 V/380 V (Three Phase) |
---|---|

Input frequency | 47~63 Hz |

Max. output power | 7.6 kW/22 kW (Three Phase) |

Max. output current | 32 A |

Charging interface type | IEC 62196-2, SAE J1772 |

Environment temperature | −40 °C~+80 °C |

Protection degree | IP66 |

Standby power consumption | <8 W |

Number of Chargers | Assumed Session per Day | Actual Sessions per Day | Missed Sessions per Day |
---|---|---|---|

5 | 20 | 12.4 | 7.6 |

10 | 20 | 18.5 | 1.5 |

15 | 20 | 19.9 | 0.1 |

20 | 20 | 19.9 | 0.1 |

Month | EV Avg Monthly Load (kWh) | EV Avg Daily Load (kWh) |
---|---|---|

January | 17,161 | 554 |

February | 15,970 | 570 |

March | 19,000 | 613 |

April | 16,803 | 560 |

May | 16,926 | 546 |

June | 17,208 | 574 |

July | 16,724 | 539 |

August | 19,263 | 621 |

Sepeptember | 16,931 | 564 |

October | 17,200 | 555 |

November | 17,103 | 570 |

December | 16,922 | 546 |

Total | 207,211 | 567 |

Month | Daily Average Irradiance (kWh/m^{2}/day) | POA Irradiance (kWh/m^{2}) | Daily Average Temperature (°C) |
---|---|---|---|

January | 4.20522 | 130.362 | 8.69 |

February | 4.10834 | 115.034 | 10.55 |

March | 6.03906 | 187.211 | 10.31 |

April | 6.32847 | 189.854 | 15.3 |

May | 7.64967 | 237.14 | 18.72 |

June | 7.7606 | 232.818 | 20.37 |

July | 7.89418 | 244.72 | 23.57 |

August | 7.67325 | 237.871 | 23.18 |

September | 6.96279 | 208.884 | 22.25 |

October | 5.78391 | 179.301 | 20.35 |

November | 4.19166 | 125.75 | 15.17 |

December | 3.70693 | 114.915 | 10.03 |

PV Net-Metering | PV Zero-Export | |||||
---|---|---|---|---|---|---|

System Capacity (kW) | 90 | 120 | 140 | 90 | 120 | 140 |

Total PV Generation (kWh) | 149,633 | 199,506 | 232,752 | 97,190 | 103,389 | 105,788 |

PV Energy Consumed locally (kWh) | 97,190 | 103,389 | 105,788 | 97,190 | 103,389 | 105,788 |

Egrid exported (kWh) | 52,442 | 96,117 | 126,964 | 0 | 0 | 0 |

Egrid Imported (kWh) | 110,020 | 103,821 | 101,422 | 110,020 | 103,821 | 101,422 |

Self-Consumption Ratio (%) | 64.95 | 51.82 | 45.45 | 100% | 100% | 100% |

Self-Sufficiency Ratio (%) | 46.90 | 49.89 | 51.05 | 46.90 | 49.89 | 51.05 |

Payback Period (year) | 4.47 | 4.07 | 3.9 | 7.18 | 9.27 | 10.8 |

Levelized Cost of Energy ($/kWh) | 0.057 | 0.056 | 0.055 | 0.088 | 0.108 | 0.122 |

Total CO_{2} Saving (tCO_{2}) | 240.34 | 275.25 | 298.53 | 203.63 | 207.97 | 209.65 |

PV/Battery Net-Metering | PV/Battery Zero-Export | |||||
---|---|---|---|---|---|---|

System Capacity (kW) | 90 | 120 | 140 | 90 | 120 | 140 |

Total PV Generation (kWh) | 149,633 | 199,506 | 232,752 | 137,099 | 152,385 | 156,623 |

PV Energy Consumed locally (kWh) | 137,099 | 152,385 | 156,623 | 137,099 | 152,385 | 156,623 |

Battery Energy (kWh) | 39,905 | 48,993 | 50,832 | 39,905 | 48,993 | 50,832 |

Egrid exported (kWh) | 12,536 | 47,123 | 76,132 | 0 | 0 | 0 |

Egrid Imported (kWh) | 70,111 | 54,825 | 50,587 | 70,111 | 54,825 | 50,587 |

Self-Consumption Ratio (%) | 91.62% | 76.38 | 67.29 | 100% | 100% | 100% |

Self-Sufficiency Ratio (%) | 66.16 | 73.54 | 75.58 | 66.16 | 73.54 | 75.58 |

Payback Period (Year) | 6.75 | 6.14 | 5.90 | 7.37 | 8.07 | 8.85 |

Levelized Cost of Energy ($/kWh) | 0.096 | 0.085 | 0.080 | 0.104 | 0.111 | 0.119 |

CO_{2} Saving (tCO_{2}) | 240.34 | 275.25 | 298.53 | 231.57 | 242.27 | 245.24 |

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**MDPI and ACS Style**

Direya, R.; Khatib, T.
Simplified Python Models for Photovoltaic-Based Charging Stations for Electric Vehicles Considering Technical, Economic, and Environmental Aspects. *World Electr. Veh. J.* **2023**, *14*, 103.
https://doi.org/10.3390/wevj14040103

**AMA Style**

Direya R, Khatib T.
Simplified Python Models for Photovoltaic-Based Charging Stations for Electric Vehicles Considering Technical, Economic, and Environmental Aspects. *World Electric Vehicle Journal*. 2023; 14(4):103.
https://doi.org/10.3390/wevj14040103

**Chicago/Turabian Style**

Direya, Rezeq, and Tamer Khatib.
2023. "Simplified Python Models for Photovoltaic-Based Charging Stations for Electric Vehicles Considering Technical, Economic, and Environmental Aspects" *World Electric Vehicle Journal* 14, no. 4: 103.
https://doi.org/10.3390/wevj14040103