## 2 Power System Impacts of EVs
### Load Profile  
The main factors affecting the load profile are examined under three main headings: charging region, charging level, and charging moment. These factors assist in estimating the EV load profile and impacts on the smart grid [61]. Figure 2 presents the main factors affecting the load profile and their individual properties.  
Charging region: It refers to the location where the EV is connected to the network of the smart grid. Generally, facility utilization is an important argument for a variety of private and public policies. When planning a parking lot, setting up a fire station, or setting up an emergency call center in a city, the question of optimal location arises. In addition, constant changes in population size, market trends, and peripheral factors necessitate the relocation, expansion, and adjustment of plants to keep them up to date [62].  
Charging level: It defines the remaining amount of energy as a result of the amount of energy consumed by an EV in traffic. The electrical energy used by the vehicle is described in two ways: (1) energy consumed while driving and (2) energy drawn from the grid while the vehicle is charging. The amount of energy consumed while driving affects the charge level based on the travel distance, speed, and travel time [63]. When a vehicle is parked  
Figure 2: The main factors affecting the load profile in a smart grid integrated with EVs.  
anywhere, its state of charge (SOC) provides information on the need for charging in the form of available energy capacity. While the vehicle is charging, the speed of the energy consumed from the grid varies according to the power rating of the EVSE. The power rating value of the EVSE determines the charging level duration [64].  
Charging moment: It refers to the time that the vehicle's energy is charged. The charging moment can also be defined as connection time, the start of charging, or the time duration of the grid connection of the EV. In a study [65], the load time was predefined either with a specific start time or duration. In contrast, the start time was sampled using a probability distribution by the researchers in the study [66]. However, these approaches are limited to finding the time to recharge after the final drive or first commute. The researchers [67] also explored the possibility of connecting to car parks with charging facilities for recharging after each trip. In [68], charging times were based on stop-time statistics for commuting or non-shuttle driving trips, and charging times can also be shifted using external and/or charging strategies [61].  
The impact between EVs and the power grid raises some uncommon problems. This means that vehicle loading needs to be predicted early to avoid undesired impacts on power grid operations. Without unique data on each vehicle's usage, it is not possible to fully investigate the desirable and detrimental impacts of vehicles on the smart grid [69]. In order to predict the overall ratings of EVs for the electrical energy demand profile, we need information about when each EV will start charging and the quantity of required electrical energy. The power taken by EVs poses a problem as it has a significant negative impact on the installment of the basic parameters of the smart grid, such as electrical generation, transmission, and distribution [69]. The load profile is the total amount of power that the EV requires at any given time within a certain range. Predicting the charge loading profile is compulsory for assessing how smart grids respond to EVs [70]. The load profile contributes to specifying some basic factors of the distribution network for influence assessments:  
* Influences on local transformers;
* Distribution lines;
* Overloading and voltage unbalance;
* Power quality problems;
* Power losses and electrical efficiency;
* Power system stability and reliability.