# Effect of Electric Vehicles Charging Loads on Realistic Residential Distribution System in Aqaba-Jordan

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## Abstract

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## 1. Introduction

_{2}emissions [3]. For this reason, many governments are taking major measures to reduce reliance on fossil fuel combustion to fulfil their energy needs and to safeguard the environment. Such measures include the switch to utilizing: (i) clean and environmentally friendly renewable energies—in place of traditional fossil-based—to meet the escalating demand of the energy sector [4,5] and (ii) electric vehicles (EVs) that are more energy-efficient and have lower operation and maintenance costs compared to the existing and widely used internal combustion engine (ICE) vehicles [6,7]. As a result, recent years have been marked by a dramatic increase in the use of EVs worldwide [8,9].

- It provides a comprehensive investigation of the effect of EVs on a realistic distribution system in Jordan, which has not previously been reported. It includes a technical evaluation of the effect of the distribution system load and voltage drop in the presence of EVs under different types of charging strategy.
- It presents a new methodology—that has not been reported before—for managing EV loads under a dynamic response strategy for the Jordanian distribution system. The proposed methodology involves two models: the first determines the critical hours in which EVs cause technical violation (feeder loading and voltage drop), and the second investigates the inherent flexibility in EV loads in response to critical hours, in order to modify the EV charging load accordingly.

## 2. Methodology

#### 2.1. Data Collection and Pre-Processing

- Vehicle mobility data are drawn and analysed from transportation surveys to precisely capture driver behavior that is essential in characterizing the charging process (e.g., drive distance, arrival time and departure time).
- A study is conducted in the form of survey questions to obtain data pertinent to driver preferences that have a major influence on the charging load, such as EV types and place of charging.
- Monte Carlo simulation is deployed to simulate the input variables needed to develop the EV charging loads in view of the underlying uncertainty of the random variables. Hence, a multitude of scenarios for EV charging loads is generated and assessed.

#### 2.2. Models of EV Charging Loads

- Step 1: Daily travel distance (DTD): The simulation begins by generating a random number (between 0 and 1) and looks at the inverse daily distance to find the corresponding distance with a probability that equals the generated random number. Once the daily travelled distance is determined for each vehicle, the period and amount of charging can be subsequently determined.
- Step 2: Energy consumption (EC): The estimated daily distance, battery capacity (BC) and electric range (ER) provide the required information to determine the amount of energy consumption. The state of charge (SoC) of each EV after arriving home can be calculated as follows:

- Step 3: Charging duration time (CDT): The estimated energy consumption, charging efficiency (${\eta}_{ch}$), and charging level $\left(c{h}_{L}\right)$ are then used to determine the number of hours needed to charge the vehicle as expressed in the following equation:

- Step 4: Start and end time for charging: Two random numbers are generated to estimate the arrival and departure times from CDFs of home arrival time (AT) and departure time (DT). Two charging scenarios are possible: (i) the vehicle is charged after it arrives home until it is fully charged when the duration between the arrival time and departure time is greater than the required charging time, as expressed in Equation (4); otherwise, (ii) the EV keeps charging until its departure time, as expressed in Equation (5).

#### 2.3. Proposed EV Charging Model Based on Dynamic Critical Hours

#### 2.3.1. Stage 1: Critical Hours Determination

- Step 1: Develop an EV charging load under an uncontrolled mode on an hourly basis using the approach discussed in Section 2.2.
- Step 2: Evaluate the ability of each feeder to meet the EV charging load. At this step, when the EV charging load violates the feeder loading and voltage drop limits, the hours in the load profiles are divided into two groups: (i) critical hours, when the feeder loading and voltage drop limits are violated and (ii) non-critical hours, when the feeder loading and voltage drop limits are satisfied. For the critical hours, an index for the EV load that caused violation (${V}^{EV}$), as shown in Equation (7), is computed. This index is then utilized for defining the number of EVs required to avoid charging during critical hours (${N}_{v}^{EV}$), as indicated by Equation (8).

- Step 3: The aforementioned indices, in addition to the other EV charging parameters, are then used as input for modifying the uncontrolled EV charging load while considering the inherent flexibility of the EV charging load in response to the critical hours.
- Step 4: The adapted EV charging profile is then used for evaluating the distribution system and determining the effectiveness of this model.

#### 2.3.2. Stage 2: EV Charging Load in Response to Critical Hours

- (a)
- Avoid charging during all critical hours: when the stay home hours are more than or equal to the required charging hours plus the critical hours, charging can be avoided during all critical hours. In this case, the EV is charged according to Equations (14) and (15), in which ${T}^{3}$ contains the non-critical hours that intersect with the stay home hours.

- Step 4: The previous procedures (Steps 1 to 3) are repeated sequentially until charging profiles for the entire fleet are simulated.

#### 2.4. Case Study: Aqaba Distribution System

- The Aqaba 2 (A2) main station that has four 132/33 kV transformers and a total capacity of 63 and 40 MVA.
- Aqaba Industrial Estate (AIE) main station that has two 132/33 kV transformers and a rated power of 80 MVA.
- Aqaba Thermal Power Station (ATPS) that has four 132/33 kV transformers and a total capacity of 63 and 80 MVA.
- Disi Main Station that has two 132/33 kV transformers and a capacity of “2 × 63” MVA.
- Quweira Main Station that has three 132/33 kV transformers and a rated power of 16 and 45 MVA.

- Most of its 400 customers are of the residential category, which makes it the perfect candidate for EV home charging.
- It represents a sample of an urban electric network in a continuously growing city with a high possibility of EV adoption.

^{2}and UGCs with a “4 × 35” mm

^{2}cross section, and both types are made of aluminium [29]. The triangles in Figure 9 and Figure 10 are individual homes with the assumption of one vehicle for each home.

## 3. Experiments and Results

#### 3.1. Case A: Impact of an Uncoordinated Arrival Time Charging Scheme under Different EVC Penetration Levels in Summer

#### 3.2. Case B: Impact of Coordinated Charging Schemes under Different EVC Penetration Levels

#### 3.3. Case C: Other Coordinated Charging Scheme by Applying Dynamic Response to the System Critical Hours

## 4. Conclusions

- Considering all types of charging scenarios under different charging penetration levels, the resulting changes in feeder loading and voltage profiles are different due to the differences in the network topology, nature of traditional loads, and number of EVs connected to each feeder.
- In general, electric vehicle loads under different charging types have a noticeable effect on both feeder loading and voltages.
- Since the maximum allowable load for each feeder is 132 KW (415 V, 320 A), Feeders 2 and 3 of Transformer 53 and Feeder 1 of Transformer 740 passed the maximum load in almost all charging scenarios under 40% and 60% EV penetration levels.
- One of the interesting results is that the effect of EVs on feeder loading is not identical to the effect on the voltage, which is due to the aforementioned reasons in point 1.
- In general, electric vehicle loads under different charging types have noticeable effects on both the load and voltage variables.
- The results showed that arrival time charging has a significant effect on both feeder loading and voltage drop. This can be understood by the fact that the time of arrival to home coincides with the start time of high system demand (refer to Figure 2 and Figure 11). This matter calls for appropriate solutions to manage EV demand and ensure a reduction in the impact of EVs.
- Overnight charging is a possible scenario that is often proposed in many studies. When 22:00 was chosen as the starting hour for overnight charging, the results were worse than those of arrival time charging. The reason behind this is that most people have the flexibility to wait for that time and start charging at the same hour, thus causing another peak in the load profile. Therefore, the choice of the starting hour of charging is very important to avoid such a case.
- For the off-peak charging scenario, the charging start time is chosen to be 4 a.m. when the system load profile is light. Compared with the arrival time and overnight charging scenarios, charging using the off-peak scenario significantly reduces the impact of EVs on feeder loading and voltage drop.
- A more significant reduction in the impact of EVs can be achieved if a proper dynamic demand response programme is implemented. However, this necessitates the availability of appropriate infrastructure that enables information sharing and communication between electricity companies and EV owners.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 6.**Main procedures for simulating the EV charging load under overnight and off-peak scenarios.

**Figure 14.**Voltage profile at the end of Transformer 740 in summer with 60% EVC and arrival time charging.

**Figure 17.**Voltage profiles at the end of Transformer 740 in summer with 60% EVC and overnight charging.

**Figure 18.**Voltage profiles at the end of Transformer 740 in summer with 60% EVC & off-peak charging.

**Figure 19.**Load profiles of Transformer 740/feeder 1 in summer at 60% EVC with/without response to the critical hours.

**Figure 20.**Voltage profiles of Transformer 740/feeder 1 in summer at 60% EVC with/without response to the critical hours.

EV Type | Battery Capacity | Range | Specific Energy |
---|---|---|---|

(kWh) | (km) | (kWh/km) | |

Chevrolet Volt | 16 | 56 | 0.284 |

Nissan LEAF | 24 | 118 | 0.203 |

Toyota Prius | 4.4 | 18 | 0.249 |

Tesla S | 85 | 427 | 0.199 |

Charging Level | Voltage | Current | Power |
---|---|---|---|

(V) | (A) | (kW) | |

1 | 120 | 12 | 1.44 |

2 | 240 | 30 | 7.2 |

Transformer | Feeder | Cross Section (mm^{2}) | CCC (A) | Summer Load (kW) | Summer Voltage Drop (%) | Winter Load (kW) | Winter Voltage Drop (%) |
---|---|---|---|---|---|---|---|

53 | 1 | 95 | 320 | 89.21 | 4.25 | 56.81 | 1.31 |

2 | 95 | 320 | 103.89 | 5.55 | 66.24 | 2.40 | |

3 | 95 | 320 | 122.29 | 6.27 | 77.92 | 2.94 | |

740 | 1 | 120 | 380 | 171.03 | 5.60 | 108.05 | 4.31 |

2 | 120 | 380 | 57.43 | −1.95 | 36.12 | −0.71 | |

3 | 120 | 380 | 78.61 | 1.47 | 49.41 | 1.38 |

Transformer | Feeder # | EVs 40% | EVs 60% |
---|---|---|---|

53 | 1 | 15 | 22 |

2 | 17 | 26 | |

3 | 24 | 30 | |

740 | 1 | 29 | 43 |

2 | 10 | 15 | |

3 | 13 | 20 | |

Total | 108 | 156 |

0% EVC | 40% EVC | 60% EVC | |||||
---|---|---|---|---|---|---|---|

Transformer | Feeder | Max Weekday Load (kW) | Max Weekend Load (kW) | Max Weekday Load (kW) | Max Weekend Load (kW) | Max Weekday Load (kW) | Max Weekend Load (kW) |

53 | 1 | 89 | 92 | 116 | 119 | 130 | 133 |

2 | 104 | 109 | 136 | 140 | 151 | 155 | |

3 | 122 | 125 | 157 | 161 | 171 | 176 | |

740 | 1 | 171 | 178 | 210 | 220 | 241 | 248 |

2 | 57 | 64 | 77 | 80 | 88 | 90 | |

3 | 79 | 85 | 103 | 107 | 115 | 118 |

0% EVC | 40% EVC | 60% EVC | |||||
---|---|---|---|---|---|---|---|

Transformer | Feeder | Max Weekday Voltage Drop (%) | Max Weekend Voltage Drop (%) | Max Weekday Voltage Drop (%) | Max Weekend Voltage Drop (%) | Max Weekday Voltage Drop (%) | Max Weekend Voltage Drop (%) |

53 | 1 | 2.14 | 2.24 | 2.84 | 2.99 | 3.22 | 3.45 |

2 | 4.39 | 4.45 | 5.93 | 6.23 | 6.55 | 6.72 | |

3 | 5.35 | 5.61 | 7.13 | 7.42 | 7.78 | 7.95 | |

740 | 1 | 5.35 | 5.84 | 7.08 | 8.17 | 9.76 | 9.82 |

2 | −1.20 | −1.20 | −1.20 | 0.13 | 0.24 | 0.53 | |

3 | 0.77 | 1.02 | 1.46 | 1.66 | 2.07 | 2.21 |

**Table 7.**Loading of the LV network in summer with 0%, 40%, and 60% EVC and different charging schemes.

Transformer | Feeder | 0% Load (kW) | 40% Load (kW) | 60% Load (kW) | ||
---|---|---|---|---|---|---|

Without Charging | Overnight Charging | Off-Peak Charging | Overnight Charging | Off-Peak Charging | ||

53 | 1 | 89 | 124 | 103 | 137 | 117 |

2 | 104 | 145 | 122 | 160 | 137 | |

3 | 122 | 167 | 139 | 181 | 154 | |

740 | 1 | 171 | 228 | 186 | 256 | 218 |

2 | 57 | 83 | 69 | 93 | 69 | |

3 | 79 | 112 | 92 | 122 | 104 |

**Table 8.**Voltage drop at the end of the LV network in summer with 0%, 40%, and 60% EVC, and different charging schemes.

Transformer | Feeder | 0% EVC Voltage Drop (%) | 40% EVC Voltage Drop (%) | 60% EVC Voltage Drop (%) | ||
---|---|---|---|---|---|---|

No Charging | Overnight Charging | Off-Peak Charging | Overnight Charging | Off-Peak Charging | ||

53 | 1 | 2.14 | 3.11 | 2.14 | 3.47 | 2.34 |

2 | 4.39 | 6.29 | 4.63 | 6.92 | 5.28 | |

3 | 5.35 | 7.54 | 5.37 | 8.19 | 6.31 | |

740 | 1 | 5.35 | 7.30 | 5.57 | 10.22 | 8.05 |

2 | −1.20 | −1.20 | −1.57 | 0.36 | −0.17 | |

3 | 0.77 | 0.51 | −0.19 | 2.24 | 1.42 |

**Table 9.**Results for Transformer 740/feeder 1 in summer at 60% EVC with/without response to the critical hours.

Load (kW) | Voltage Drop (%) | ||||
---|---|---|---|---|---|

Base Case No EVs | Uncontrolled Charging | Dynamic Critical Hours | Base Case No EVs | Uncontrolled Charging | Dynamic Critical Hours |

171 | 241 | 171 | 5.35 | 9.76 | 5.35 |

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**MDPI and ACS Style**

Obeidat, M.A.; Almutairi, A.; Alyami, S.; Dahoud, R.; Mansour, A.M.; Aldaoudeyeh, A.-M.; Hrayshat, E.S.
Effect of Electric Vehicles Charging Loads on Realistic Residential Distribution System in Aqaba-Jordan. *World Electr. Veh. J.* **2021**, *12*, 218.
https://doi.org/10.3390/wevj12040218

**AMA Style**

Obeidat MA, Almutairi A, Alyami S, Dahoud R, Mansour AM, Aldaoudeyeh A-M, Hrayshat ES.
Effect of Electric Vehicles Charging Loads on Realistic Residential Distribution System in Aqaba-Jordan. *World Electric Vehicle Journal*. 2021; 12(4):218.
https://doi.org/10.3390/wevj12040218

**Chicago/Turabian Style**

Obeidat, Mohammad A., Abdulaziz Almutairi, Saeed Alyami, Ruia Dahoud, Ayman M. Mansour, Al-Motasem Aldaoudeyeh, and Eyad S. Hrayshat.
2021. "Effect of Electric Vehicles Charging Loads on Realistic Residential Distribution System in Aqaba-Jordan" *World Electric Vehicle Journal* 12, no. 4: 218.
https://doi.org/10.3390/wevj12040218