# Energy Scheduling and Performance Evaluation of an e-Vehicle Charging Station

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## Abstract

**:**

## 1. Introduction

## 2. Conventional EMS of the Charging Station

## 3. Two-Stage Optimization

#### 3.1. First Stage of Optimization

#### 3.2. Second Stage of Optimization

#### 3.3. Real-Time Operation

Algorithm 1 Real-time control. |

1: Control laws |

2: if $t=00:00$ then |

3: $C{O}_{2}(t=1:24)$, ${P}_{pv}(t=1:24)$, $\delta (t=1:24)$ |

4: $So{C}_{t0}$, ${P}_{e{v}_{to}}$ |

5: end if |

6: $k\leftarrow 24$ |

7: for k = 1:24 do |

8: Run first stage of optimization |

9: $u\left(k\right)\leftarrow {P}_{gri{d}_{max}}\left(k\right),{P}_{ba{t}_{max}}\left(k\right)$, |

10: ${P}_{evmax}\left(k\right)\leftarrow {P}_{gri{d}_{max}}\left(k\right)+{P}_{ba{t}_{max}}\left(k\right)+{P}_{pv}$, |

11: end for |

12: for i = 1:288 do |

Require:${P}_{pv}^{\prime}\left(i\right)$, ${P}_{ev}^{\prime}$(i), $So{C}^{\prime}\left(i\right)$ |

13: if ${P}_{ev}^{\prime}\left(i\right)>={P}_{e{v}_{max}}\left(i\right)$ then |

14: ${P}_{ev}^{\prime}\left(i\right)\leftarrow {P}_{e{v}_{max}\left(i\right)}$ |

15: ${P}_{ev}^{\prime}\left(i\right)\leftarrow {P}_{ev}\left(i\right)$ |

16: end if |

17: Run MPC |

18: ${P}_{grid}^{\prime}\left(i\right)$, ${P}_{bat}^{\prime}\left(i\right)$ |

19: Sent control variables to the charging station |

20: end for |

## 4. EMS Platform

- Control and supervise the charging station.
- Real-time measurement of the charging station status, Sonnen Hybrid inverter, and ambient conditions.
- Daily communication with ENTSO-e to request the type of power generation forecast.
- Data storage of power, voltage, and current of every component of the charging station, battery state, and ambient conditions.
- Visualization of the current state of the charging station: PV production, grid power production, emissions, occupancy, and EV power consumption.

#### 4.1. Control and Supervision:

#### 4.2. Data Storage

#### 4.3. Monitoring and Visualization

#### 4.4. Communication

## 5. Analysis and Results

#### 5.1. Energy Analysis

#### 5.2. Environment

#### 5.3. Economic

#### 5.4. General Discussion

## 6. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## Appendix A

**Table A1.**$C{O}_{2}$ emissions for different fuels used in electricity generation ($\beta $ factor).

Fuel Type | kg/kWh |
---|---|

Biomass | 0.857 |

Coal | 0.9 |

Natural gas | 0.3675 |

Hard coal | 0.9 |

Oil | 0.65 |

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**Figure 4.**Normal operation power profile of the charging station (9th to 13th of May 2022) using the conventional EMS.

**Figure 7.**$C{O}_{{2}_{f}}$ variation per hour for each week in May 2022. (

**a**) Week 1: 2nd–6th of May, (

**b**) Week 2: 8th–13th of May, (

**c**) Week 3: 15th–20th of May, (

**d**) Week 4: 22nd–27th of May.

**Figure 8.**Electricity price variation per hour for each week of May 2022. (

**a**) Week 1: 2nd–6th of May, (

**b**) Week 2: 8th–13th of May, (

**c**) Week 3: 15th–20th of May, (

**d**) Week 4: 22nd–27th of May.

Parameters | Conventional EMS | Two-Stage Optimization | Units | ||
---|---|---|---|---|---|

Min | Max | Min | Max | ||

${P}_{grid}$ | −4.1 | 22 | −3.3 | 21 | kW |

${P}_{EV}$ | 0 | 22 | 0 | 21 | kW |

${P}_{inv}$ | −4.1 | 4.1 | −3.9 | 3.9 | kW |

${P}_{pv}$ | 0 | 3.9 | 0 | 3.9 | kW |

${P}_{bat}$ | −3.3 | 3.3 | −3.3 | 3.3 | kW |

$SoC$ | 0 | 100 | 0 | 100 | % |

Week | Conventional EMS | Two-Stage Optimization | ||||
---|---|---|---|---|---|---|

${E}_{from-grid}$ | ${E}_{inv}$ | ${E}_{ev}$ | ${E}_{from-grid}$ | ${E}_{net-inv}$ | ${E}_{ev}$ | |

Week 1 | 316.8 | 118.32 | 412.8 | 242.4 | 95.52 | 334.8 |

Week 2 | 338.4 | 156.6 | 482.4 | 273.6 | 133.2 | 405.6 |

Week 3 | 213.6 | 115.44 | 307.2 | 195.6 | 81.24 | 277.2 |

Week 4 | 236.4 | 141 | 363.6 | 184.8 | 119.52 | 302.4 |

Total | 1105.2 | 535.2 | 1566 | 896.4 | 548.4 | 1320 |

Parameter | Conventional EMS | Two-Stage Optimization | Units |
---|---|---|---|

PV production | 561.6 | 561.6 | MWh |

EV consumption | 1566 | 1320 | MWh |

Battery charge | 250.68 | 227.4 | MWh |

Battery discharge | 220.44 | 211.68 | MWh |

From grid | 1105.2 | 896.4 | MWh |

Feed in | 56.76 | 116.4 | MWh |

**Table 4.**Minimum and maximum values of the $C{O}_{2}$ emissions factor of the electricity grid in Italy in May 2022.

Week | Min | Max | Units |
---|---|---|---|

Week 1 | 0.17 | 0.435 | kg/kWh |

Week 2 | 0.1698 | 0.435 | kg/kWh |

Week 3 | 0.167 | 0.341 | kg/kWh |

Week 4 | 0.211 | 0.364 | kg/kWh |

Week | Conventional EMS | Two-Stage Optimization |
---|---|---|

Week 1 | 739.96 | 547.18 |

Week 2 | 735.71 | 590.99 |

Week 3 | 421.56 | 382.18 |

Week 4 | 548.47 | 415.29 |

Total | 2445.70 | 1935.64 |

Week | Min | Max | Units |
---|---|---|---|

Week 1 | 0.193 | 0.419 | EUR/kWh |

Week 2 | 0.184 | 0.35 | EUR/kWh |

Week 3 | 0.189 | 0.36 | EUR/kWh |

Week 4 | 0.173 | 0.31 | EUR/kWh |

Week | Conventional EMS | Two-Stage Optimization |
---|---|---|

Week 1 | 770.59 | 588.78 |

Week 2 | 652.53 | 529.39 |

Week 3 | 433.26 | 394.76 |

Week 4 | 454.82 | 352.39 |

Total | 2311.20 | 1865.32 |

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**MDPI and ACS Style**

Cabrera-Tobar, A.; Blasuttigh, N.; Massi Pavan, A.; Lughi, V.; Petrone, G.; Spagnuolo, G. Energy Scheduling and Performance Evaluation of an e-Vehicle Charging Station. *Electronics* **2022**, *11*, 3948.
https://doi.org/10.3390/electronics11233948

**AMA Style**

Cabrera-Tobar A, Blasuttigh N, Massi Pavan A, Lughi V, Petrone G, Spagnuolo G. Energy Scheduling and Performance Evaluation of an e-Vehicle Charging Station. *Electronics*. 2022; 11(23):3948.
https://doi.org/10.3390/electronics11233948

**Chicago/Turabian Style**

Cabrera-Tobar, Ana, Nicola Blasuttigh, Alessandro Massi Pavan, Vanni Lughi, Giovanni Petrone, and Giovanni Spagnuolo. 2022. "Energy Scheduling and Performance Evaluation of an e-Vehicle Charging Station" *Electronics* 11, no. 23: 3948.
https://doi.org/10.3390/electronics11233948