# PV-Powered Charging Station with Energy Cost Optimization via V2G Services

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## Abstract

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## Featured Application

**This article presents a mixed-integer linear programming optimization problem to minimize the energy cost of a charging station powered by photovoltaics via V2G service.**

## Abstract

## 1. Introduction

_{2}emissions of charging EV batteries solely from the grid, from the PVCS, and with internal combustion engine vehicles. They have found that the PVCS concept is more efficient in countries with high annual average irradiance and significant CO

_{2}emissions in their grid, but it remains economically unfeasible due to expensive storage systems. In [4], a supervision control system is presented for smart charging of an EV fleet in a PVCS-based research building. The proposed control strategy is based on a real-time operation to satisfy EV users using PV forecasting and EV charging historical records over four years to predict the EV power profiles. A user-friendly smart charging method, which includes interactions with EV users via an interface, has been developed in [5], where the EV user is a key player in the process of choosing the best scenario among uncoordinated charging, smart charging, and bidirectional smart charging control in a PVCS. The proposed methodology is based on real-time rule-based control and a predictive linear optimization control. The results showed that bidirectional charging control had the best cost reduction, while uncoordinated charging control costs the most. In [6], mixed-integer programming was investigated to minimize the cost of energy traded to a PVCS, where the intermittency of PV power can be compensated by EVs which can also discharge energy to the PVCS, where it does not integrate stationary storage. The EVs are classed in three categories according to their charging behavior, and the results showed that an increase of green EVs, the only category of EVs for which the users can allow discharging of energy into the charging station, could reduce the total cost of the PVCS. In [7], mixed-integer linear programming (MILP) has been applied to optimize the sizing of a PVCS components (PV, stationary storage, and transformer) in order to minimize the investment cost and the total cost considering the uncertainties of PV and EV charging power profiles. The simulation results, with a 1-h step time, showed that EV charging stations powered by PV are more cost-effective than EV charging stations powered by the grid.

- Proposing an energy cost optimization problem in a PVCS with V2G service, taking into consideration the uncertainty of the arrival time of EVs in a real-time simulation;
- Actualizing the optimization problem formulated via MILP at every arrival of a new EV; the arrival of EVs is not modeled based on day-ahead prediction; instead it is randomly generated as unpredicted events in MATLAB;
- Assessing the energy consumption of every EV from each power source and the energy participation among the power sources (PV, energy storage, and grid).

## 2. PV-Powered Charging Station with V2G Service

#### 2.1. PV-Powered Charging Station with V2G Service without Energy Cost Optimization

#### 2.2. PV-Powered Charging Station with V2G Service with Energy Cost Optimization

#### 2.2.1. Prediction Layer

^{2}), and ${T}_{air-test}$ is the fixed air temperature (20 °C).

#### 2.2.2. Human–Machine Interface

#### 2.2.3. Energy Cost Optimization

#### 2.2.4. Operation Layer

## 3. Energy Cost Optimization with V2G Service

#### 3.1. PV Sources

#### 3.2. Stationary Storage

#### 3.3. Grid Connection

#### 3.4. Electric Vehicles

#### 3.4.1. V2G Mode

#### 3.4.2. EV Charging Mode

#### 3.5. Power Balancing

#### 3.6. Objective Function

## 4. Simulation Results for PVCS with V2G Service

- Scenario a: during peak periods, EVs discharge at a constant power and then recharge with the same constant charging power as set by the user until departure time;
- Scenario b: during peak periods, EVs discharge at a maximum power of 50 kW and then recharge again with a variable charging power, irrespective of the charging mode selected by the user, to achieve the desired SOC at departure after V2G service.

#### 4.1. Case 1: Sunny Day

#### 4.1.1. Scenario a: Constant Power

#### 4.1.2. Scenario b: Variable Power

#### 4.2. Case 2: Cloudy Day

## 5. Energy Cost Analyses for PV-Powered Charging Station with V2G Service

## 6. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 3.**Supervisory control system for the PVCS [44].

**Figure 4.**Real PV power ${p}_{PV\mathrm{MPPT}}$ and predicted PV power ${p}_{PV\mathrm{MPPT}\mathrm{pred}}$—29 June 2019.

**Figure 10.**Real PV power ${p}_{PV\mathrm{MPPT}}$ and predicted PV power ${p}_{PV\mathrm{MPPT}\mathrm{pred}}$—10 May 2019.

Parameter | Value | Parameter | Value | Parameter | Value |
---|---|---|---|---|---|

${P}_{G\_I\_max}$ | - | $SO{C}_{S\_min}$ | 20% | ${c}_{G\_NH}$ | 0.1 €/kWh |

${P}_{G\_S\_max}$ | 50 kW | $SO{C}_{S\_max}$ | 80% | ${c}_{G\_PH}$ | 0.7 €/kWh |

${P}_{S\_max}$ | 7 kW | $SO{C}_{EV\_min}$ | 20% | ${c}_{S}$ | 0.01 €/kWh |

${P}_{EV\_fast\_max}$ | 50 kW | $SO{C}_{EV\_max}$ | 100% | ${c}_{PVS}$ | 1.2 €/kWh |

${P}_{EV\_aver\_max}$ | 22 kW | $SO{C}_{{S}_{0}}$ | 50% | ${c}_{EV\_penalty}$ | 2.5 €/kWh |

${P}_{EV\_slow\_max}$ | 7 kW | ${v}_{S}$ | 288 V | ${c}_{SW}$ | 0.05 € |

$E$ | 50 kWh | ${C}_{Bat}$ | 130 Ah | ${p}_{PV\_MPPT}$ | 28.9 kWp |

**Table 2.**Data and preferences of EV users [43].

EVs | $\mathit{S}\mathit{O}{\mathit{C}}_{\mathit{E}\mathit{V}\_\mathit{a}\mathit{r}\mathit{r}}$ | $\mathit{S}\mathit{O}{\mathit{C}}_{\mathit{E}\mathit{V}\_\mathit{d}\mathit{e}\mathit{s}}$ | ${\mathit{t}}_{\mathit{a}\mathit{r}\mathit{r}}$ | ${\mathit{t}}_{\mathit{e}\mathit{s}\mathit{t}\_\mathit{c}\mathit{h}}$ | M | V2G |
---|---|---|---|---|---|---|

EV1 | 31% | 85% | 09:20 | 03 h 52 min | Slow | Yes |

EV2 | 35% | 75% | 10:00 | 0 h 24 min | Fast | No |

EV3 | 50% | 80% | 12:05 | 02 h 8 min | Slow | Yes |

EV4 | 25% | 78% | 13:45 | 01 h 13 min | Average | No |

EV5 | 29% | 72% | 14:25 | 03 h 5 min | Slow | No |

Operation Case | Energy Injected into the Public Grid during V2G Period | Energy Injected into the Grid during the Day (kWh) | |||||
---|---|---|---|---|---|---|---|

PV (kWh) | EVs (kWh) | Total Energy during V2G (kWh) | % EV/Total | % PV/Total | |||

Case 1—constant power scenario | Sim w/o opti | 5.88 | 2.91 | 8.79 | 33.10% | 66.90% | 44.03 |

Sim w/ opti | 0 | 0 | 0 | 0 | 0 | 58.85 | |

Case 1—variable power scenario | Sim w/o opti | 5.88 | 20.83 | 26.71 | 77.98% | 22.02% | 50.95 |

Sim w/ opti | 5.88 | 23.33 | 29.21 | 79.87% | 20.13% | 68.34 | |

Case 2—variable power scenario | Sim w/o opti | 6.21 | 20.83 | 27.04 | 77.04% | 22.96% | 30.52 |

Sim w/ opti | 7.45 | 25 | 32.45 | 77.04% | 22.96% | 40.91 |

Operation Case | Public Grid Cost (c€) | Stationary Storage Cost (c€) | EV Penalty (c€) | Total Cost (c€) | |
---|---|---|---|---|---|

Case 1—constant power scenario | Sim w/o opti | −1106 | 32 | 1750 (Dissatisfied client–Risk of losing client) | −1074 |

Sim w/ opti | −1247 | 9 | 0 | −1238 | |

Case 1—variable power scenario | Sim w/o opti | −1006 | 40 | 0 | −966 |

Sim w/ opti | −2942 | 6 | 0 | −2936 | |

Opti for real conditions | −4210 | 10 | 0 | −4200 | |

Case 2—variable power scenario | Sim w/o opti | −571 | 28 | 0 | −543 |

Sim w/ opti | −1745 | 11 | 0 | −1734 | |

Opti for real conditions | −2710 | 11 | 0 | −2699 |

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**MDPI and ACS Style**

Cheikh-Mohamad, S.; Celik, B.; Sechilariu, M.; Locment, F.
PV-Powered Charging Station with Energy Cost Optimization via V2G Services. *Appl. Sci.* **2023**, *13*, 5627.
https://doi.org/10.3390/app13095627

**AMA Style**

Cheikh-Mohamad S, Celik B, Sechilariu M, Locment F.
PV-Powered Charging Station with Energy Cost Optimization via V2G Services. *Applied Sciences*. 2023; 13(9):5627.
https://doi.org/10.3390/app13095627

**Chicago/Turabian Style**

Cheikh-Mohamad, Saleh, Berk Celik, Manuela Sechilariu, and Fabrice Locment.
2023. "PV-Powered Charging Station with Energy Cost Optimization via V2G Services" *Applied Sciences* 13, no. 9: 5627.
https://doi.org/10.3390/app13095627