# Electric Vehicle Scenario Simulator Tool for Smart Grid Operators

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## Abstract

**:**

## 1. Introduction

## 2. Electric Vehicles in Smart Grids

- A large number of PHEVs and EVs connected to the grid at the same time may pose a huge challenge to the power quality and stability of the overall power system [15].
- Due to some technical and economic issues, vehicle-to-grid is still less likely to become a reality in the short term [16].
- Effective communications technologies will be extremely important to the successful rollout of EVs [17].

**Table 1.**EV battery specifications [18].

Vehicle class | Battery capacity (kWh) | Charging rates (kW) | ||||
---|---|---|---|---|---|---|

Max | Mean | Min | Slow charge rate | Fast charge rate | ||

BEV | M1 | 72 | 29 | 10 | 2–8.8 | 3–240 |

N1 | 40 | 23 | 9.6 | 1.3–3.3 | 10–45 | |

N2 | 120 | 85 | 51 | 10 | 35–60 | |

L7e | 15 | 8.7 | 3 | 1–3 | 3–7.5 | |

PHEV | M1 | 13.6 | 8.2 | 2.2 | 3 | 11 |

N1 | 13.6 | 8.2 | 2.2 | 3 | 11 | |

EREV | M1 | 22.6 | 17 | 12 | 3–5.3 | - |

N1 | 22.6 | 17 | 12 | 3–5.3 | - |

**Figure 2.**Electric vehicles in smart grids context. It shows a representation of the interaction between electric vehicles and the electric grid in a smart grid environment.

#### 2.1. Driving Behaviour

**Figure 3.**European average travelled per day on weekday [18].

**Figure 4.**Distribution of vehicle trips by trip purpose and start [24].

## 3. EVeSSi Framework

#### 3.1. EVeSSi Parameters

Parameter | Description | Example value |
---|---|---|

initialStateOfBats | Initial state of batteries | 30% |

stepRate | Simulation time step (30 min, 1 hour) | 1 hour |

totalStep | Total number of steps (periods) | 24 |

batteryMaxDoD | Battery max. depth of discharge permitted (DoD) | 80% |

chargingEfficiency 1 | Slow charge mode efficiency | 90% |

chargingEfficiency 2 | Fast charge mode efficiency | 90% |

batteryEfficiency | Battery efficiency | 85% |

evNum | Number of electric vehicles | 2000 |

sameInitalEndBusProb | Probability of the EV to end in the same starting network bus in the simulation scenario | 85% |

parkedAllDay | Cars percentage that are always parked and connected to the grid | 1% |

carsInsideNetwork | Cars percentage that remain inside distribution network | 50% |

carsGoingOutsideNetwork | Cars percentage that leave distribution network | 25% |

carsGoingInsideNetwork | Cars percentage that arrive from other distribution network | 25% |

Parameter | Description |
---|---|

Trip distribution by period | Distribution of trips by each period |

Trip distance distribution | Distribution of travelled distance |

Parameter | Description |
---|---|

Vehicle classes | Specification of vehicles classes present in the network |

Vehicle classes distribution | Distribution of vehicle classes |

Vehicle types | Specification of vehicles types present in the network |

Vehicle types distribution | Distribution of vehicle types |

Parameter | Example value |
---|---|

Battery capacity | 29 kWh |

Slow charging rate | 3 kW |

Fast charging rate | 57 kW |

Average economy | 0.16 kWh/km |

Average km day | 38 km |

Average speed | 35 km/h |

Vehicle type | Plug-in hybrid vehicle |

Vehicle class | M1 |

Tank capacity (hybrid models) | 40 l |

Consumption in hybrid mode | 5 l/100km |

Trip time in hybrid mode | 20% |

#### 3.2. EVeSSi Algorithm

- Distance for each EV;
- Generated scenario.

- $classesNum$ is total number of classes available
- $classesSe{t}_{j}$ is the set of model types i that belong to class j
- $classesWeigt{h}_{j}$ is the weight for class type j (e.g., 90% passenger vehicles, 10% commercial vehicles)
- $evNum$ is the total number of electric vehicles including all models
- $smodelNum$ is the total number of models available
- $techTypesNum$ is the total number of technology types available
- $techTypeSe{t}_{j}$ is the set of model types i that belong to tech type j
- $techWeigt{h}_{j}$ is the weight for technology type j (e.g., 40% BEV, 60% PHEV)
- ${x}_{i}$ is an integer variable where each ${x}_{i}$ represents the number of vehicles of model i

- $\Delta cd$ is the duration of charging, typically $\Delta t=1$
- ${n}_{slowCharge}$ is the charging efficiency in slow charge mode
- ${n}_{fastCharge}$ is the charging efficiency in fast charge mode
- $batCap$ is the limit of battery capacity
- ${E}_{Charge\left(t\right)}$ is the energy charged in period t
- ${E}_{Stored\text{}(t)}$ is the battery’s energy stored in period t
- ${E}_{Trip\text{}(t)}$ is the energy consumed by vehicle trip in period t
- $initialBatState$ is the initial battery state of the battery
- ${P}_{FastChargeRate\left(t\right)}$ is the fast charge rate in period t
- ${P}_{SlowChargeRate\left(t\right)}$ is the slow charge rate in period t
- $T$ is the number of periods
- ${X}_{t}$ is the slow charge binary variable in period t
- ${Y}_{t}\text{}$ is the fast charge binary variable in period t
- ${Z}_{t\text{}}$ is a Boolean for trip decision in period t (0/1) and fixed before optimization

## 4. Experimental Cases

Parameter value | |
---|---|

Battery efficiency | 85% |

Cars parked all day (no movements) | 1% |

Charging efficiency (slow and fast mode) | 90% |

Initial state of battery | 30% |

Maximum depth of discharge | 80% |

Number of EVs | 2000 |

Number of periods | 24 |

Time step | 1 hour |

Model ID | Description | Battery capacity (kWh) | Slow charging rate (kW) | Fast charging rate (kW) | Average economy (kWh/km) | Average speed (km/h) | Average km day (km/day) | Vehicle type | Vehicle class |
---|---|---|---|---|---|---|---|---|---|

1 | Passenger car | 8.7 | 3 | 0 | 0.1122 | 20 | 20 | BEV | L7e |

2 | Passenger car | 28.5 | 3 | 57 | 0.1608 | 35 | 38 | BEV | M1 |

3 | Commercial van | 23.0 | 3 | 46 | 0.1854 | 30 | 56 | BEV | N1 |

4 | Light truck | 85.3 | 10 | 60 | 0.5867 | 40 | 136 | BEV | N2 |

5 | Passenger car | 8.2 | 3 | 0 | 0.1560 | 35 | 20 | PHEV | M1 |

6 | Commercial van | 8.2 | 3 | 0 | 0.1560 | 30 | 20 | PHEV | N1 |

7 | Passenger car | 16.9 | 3 | 0 | 0.2530 | 35 | 20 | EREV | M1 |

8 | Commercial van | 16.9 | 3 | 0 | 0.2530 | 30 | 30 | EREV | N1 |

Vehicle class | Share |
---|---|

L7e | 0.005 |

M1 | 0.870 |

M2 | 0.000 |

M3 | 0.000 |

N1 | 0.100 |

N2 | 0.025 |

N3 | 0.000 |

Vehicle type | Share |
---|---|

BEV | 0.333 |

PHEV | 0.333 |

EREV | 0.333 |

Driving stats | ||
---|---|---|

Total number of cars | 2000 | |

Total trip distance (km) | Cars average | 29 |

Maximum | 482 | |

Minimum | 0 | |

Total distance (km) | 58,438 | |

Total energy consumption (kWh) | 13,306 | |

Mean battery capacity (kWh) | 19 | |

Algorithm execution time | 40 seconds |

**Figure 8.**Cars connected to the grid. Blue line presents the total number of cars connected to the grid and the red line the total number of cars in trip purpose as simulated by EVeSSi tool.

Driving stats | ||
---|---|---|

Total number of cars | 15,000 | |

Total trip distance (km) | Cars average | 33 |

Maximum | 535 | |

Minimum | 0 | |

Total distance (km) | 499,670 | |

Total energy consumption (kWh) | 116,596 | |

Mean battery capacity (kWh) | 19 | |

Algorithm execution time | 352 seconds |

**Figure 10.**Energy and fuel consumption by hour during simulation for the 937 bus scenario with 15,000 EVs.

## 5. Conclusions and Future Work

## Acknowledgments

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© 2012 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).

## Share and Cite

**MDPI and ACS Style**

Soares, J.; Canizes, B.; Lobo, C.; Vale, Z.; Morais, H.
Electric Vehicle Scenario Simulator Tool for Smart Grid Operators. *Energies* **2012**, *5*, 1881-1899.
https://doi.org/10.3390/en5061881

**AMA Style**

Soares J, Canizes B, Lobo C, Vale Z, Morais H.
Electric Vehicle Scenario Simulator Tool for Smart Grid Operators. *Energies*. 2012; 5(6):1881-1899.
https://doi.org/10.3390/en5061881

**Chicago/Turabian Style**

Soares, João, Bruno Canizes, Cristina Lobo, Zita Vale, and Hugo Morais.
2012. "Electric Vehicle Scenario Simulator Tool for Smart Grid Operators" *Energies* 5, no. 6: 1881-1899.
https://doi.org/10.3390/en5061881