This paper is connected to two main research categories, namely the managed charging of EVs and ML techniques, which are reviewed in the upcoming subsections.
2.1. Managed Charging of Electric Vehicles
Many studies have been conducted showing the negative impact of the uncoordinated charging of EVs on the distribution grid. Clement-Nyns et al. [
8] and Karmaker et al. [
9] studied the impact of charging EVs on the grid. It is seen that the instant charging of EVs, without coordination, at large scales causes large voltage deviations and power losses. In addition, Clement-Nyns et al. [
8] demonstrate that such effects can be more adverse when EV penetration levels increase. Karmaker et al. [
9] also reveal that despite the possible use of renewable energy to mitigate such disadvantages, problems still occur when no proper regulation of power quality takes place.
Yilmaz and Krein [
10] and Habib et al. [
11] reviewed the use of V2G technologies to mitigate the risk of charging EVs on the distribution grid. It is signified that the V2G technologies can help in improving the efficiency, stability and reliability of the grid. Additionally, according to Yilmaz and Krein [
10], the advantages of V2G technologies include the regulation of power, load balancing and current harmonics filtering. However, V2G technologies can cause the deep discharging of EVs. This comes at a cost in the form of battery degradation in EVs, which decreases the battery’s lifetime and, in turn, decreases customer satisfaction. As a result, it is not considered as the most ideal solution as only the power grid is taken into consideration, without examining the user’s point of view.
Moreover, Lunz et al. [
12] reviewed the impact of different charging strategies on the costs of charging and battery degradation. Different charging strategies were tested, including uncoordinated charging, unidirectional charging and bidirectional charging. It was seen that the use of intelligent charging strategies can greatly increase battery lifetime and decrease charging and battery degradation costs simultaneously, especially with the use of time of use electricity pricing strategies.
Clairand et al. [
13] researched the coordinated charging of EVs taking into consideration the user. The optimization model aims to minimize the cost with constraints for the power system’s limitations. The system’s benefits become more prominent as EV penetration levels increase and the charging cost differences can reach up to 50%, compared to the uncoordinated charging of EVs. However, the system needs the installation of smart meters to be able to collect real-time data regarding the charging of EVs and fast charging is not considered.
In addition, Clairand et al. [
14] investigated the charging of EVs with the high penetration of renewable energy, which can pose great challenges due to the high energy requirements for charging of EVs and possible instability of renewable energy generation. The results of the charging scheme showed significantly decreased costs, with up to 8% decreases compared to the uncoordinated charging case. Furthermore, carbon dioxide emissions are greatly decreased, providing a more environmentally friendly solution to EV charging.
Similar to Clairand et al. [
14], Fanti et al. [
15] researched the use of renewable energy, energy storage systems and electric vehicles to synthesize an optimal energy management system. The authors utilized a linear programming problem to maximize the use of the day ahead purchased energy and to minimize the real-time additional costs. The case study performed on three buildings using the proposed system successfully decreased the energy costs and increased the reliability of the grid. Another home energy management system was proposed by Amer et al. [
16], where a multiobjective optimization problem was formulated for the optimization of the scheduling of a number of loads and supplies based on different pricing schemes. The results showed that the system can minimize energy costs and meet customer demand, as well as minimize the loss of life of the transformer for the utility operator.
Much research has been conducted on the utilization of a number of optimization techniques such as quadratic programming and dynamic programming. Sortomme et al. [
17] worked on minimizing distribution system losses using heuristic or sequential methods. The developed method showed that the minimization of the load variance is more practical than the minimization of the losses as it provides the same overall result in a smaller time period. Additionally, it is independent of the system topology used and can be utilized for all distribution grids.
Similar to Sortomme et al. [
17], Deilami et al. [
18] studied the minimization of power losses and the improvement of the voltage profile in a real-time coordination system, which was built upon the minimization of the total cost of energy generation and power losses. The algorithm used the maximum sensitivities selection optimization technique and assumes the random plugging in of EVs. The real-time system is able to improve the efficiency of the distribution grid and reduces the system overload.
Similarly, Jian et al. [
19] researched the optimization of the load variance in a single household microgrid. Quadratic programming is used to minimize the load variance in a single household microgrid. The system is seen to have a positive impact on the overall distribution grid as the load variance of every household is minimized, which, in turn, minimizes the load variance of the distribution grid. However, some parameters were considered to be known, such as the load curve, which might not be realistic in real-life applications and slightly reduces the effectiveness of the system.
Furthermore, Masoum et al. [
20] also proposed an EV coordinated charging method based on peak shaving to minimize power losses and enhance the voltage profile. The system also considered the preferred charging time for users through a priority selection scheme. The system was able to achieve a reduced peak demand and increased the efficiency of the power grid.
Ma et al. [
21] considered the coordinated charging of a large number of EVs in a decentralized topology system. The proposed system is based on non-cooperative games. The system works on minimizing the cost of generating electricity by filling the valley in a load curve and decreasing the peak-to-valley difference. The main benefit of such a system is that the constant communication between EVs and centralized control cannot be achieved. Nevertheless, the formulated optimization problem minimizes electricity costs and fully charges EVs, and factors such as power losses and voltage fluctuations are not examined. Additionally, the system considers other loads, not EVs, to be predictable, which might not be the case in real-life situations.
Another solution to the coordination of EVs is the use of scheduling algorithms. Iacobucci et al. [
22] researched the scheduled charging of EVs using two parallel control optimization algorithms. The first algorithm wa utilized over long time scales to minimize wait times and charging costs. The second algorithm optimizes the routing of EVs over short time scales to minimize wait times. A case study was run to test the model, where it decreased charging costs significantly, whilst having little effect on wait times. Additionally, the model can provide higher cost savings in cases of high price variance. Nevertheless, EVs are assumed to be able to charge as soon as they arrive and it is assumed that a charger will always be available, which might not always be the case in terms of public charging stations.
Additionally, Liu et al. [
23] considered the utilization of an aggregative game model for scheduling the charging of EVs. The game model was used to model the impact of EV charging demands on the electricity price, and quadratic programming was employed to find the equilibrium of the game model. A Nash equilibrium was proved to exist and the day ahead EV charging schedule was successfully created. Additionally, the model is shown to be able to cope with the random nature of EV charging. Nevertheless, the model assumes a constant charging rate and the use of V2G technologies is not examined.
Rezaei et al. [
24] studied the energy management of hybrid EV batteries through the minimization of the energy consumption using the catch energy saving opportunity method. In addition, the system considers both charging and discharging for EV batteries. However, such a method provides a solution for the user requirements but fails to take into consideration the requirements of the distribution grid and its constraints.
Wei et al. [
25] examined the use of a park and charge system with the main objective of minimizing the battery degradation cost. Moreover, the system is modelled with constraints pertaining to the user and the charging company as well. Firstly, the battery degradation cost is minimized, and then the charging cost is minimized, which were both seen to be achieved in the results obtained from the system. The main drawback of the system is the assumption of a maximum capacity of eight EVs, which can be considered a small number of EVs for a commercial charging station.
To add to this, Chaudhari et al. [
26] proposed the prediction of electricity usage using an agent-based model, based on parameters such as initial and final states of charge, charging duration, charging station location, etc., to coordinate the charging of EVs. Several whole day simulations were used to test the model and provided reliable results, which can be attributed to the consideration of the influence of human behavior on the charging demand of EVs. The system uses charging stations that have both fast and slow charging options, but the use of V2G is not examined.
Moreover, Azizipanah-Abarghooee et al. [
27] studied the enhancement of power flow in a distribution grid using EVs. The system uses a fuzzy logic controller for the charging and discharging of EVs. In order to optimize the operation cost, a new algorithm is created. The algorithm proves its usefulness in minimizing charging costs with high efficiency. However, single charging rates are studied; therefore, the utilization of fast and conventional charging concurrently is not examined.
Zhang et al. [
28] showed the coordination of EVs for single output multiple charging spots. Stochastic programming is used to minimize the annual costs and probabilistically simulate the coordinated charging of the EVs to check the influence of the charging on the charging station. The system improves the operating costs of EVs and profitability of charging stations. Furthermore, the system considers the need for a few emergency fast charging sockets in the case of users needing to leave before the calculated departure time. Nonetheless, the method does not review factors such as power losses and voltage fluctuations.
In addition, Wang et al. [
29] investigated the massive EV charging, with the utilization of renewable energy. A two-stage method is formulated which predicts future energy requests, and the charging rate of EVs is coordinated and has an algorithm to reduce the complexity of finding the solution. The system was able to reduce energy costs and the peak-to-valley difference. Additionally, the fluctuating power produced by renewable energy sources does not significantly impact the system’s performance. However, like the work conducted by Zhang et al. [
29], the minimization of power losses and voltage fluctuations is not examined.
The major disadvantage of the aforementioned research is the utilization of optimization techniques such as dynamic programming, quadratic programming, etc., for optimizing the operation of the power grid and the user requirements. The use of such techniques requires careful and time-consuming problem formulation and solving procedures. In this paper, ML is used as an optimization technique, which is less complex in its usage. ML only requires having an appropriate dataset and then ML algorithms function automatically, with the exception of tuning hyperparameters, which is based on heuristics and trial and error. Therefore, the formulation of complex equations and optimization constraints is not needed. In addition, transfer learning can be utilized for transferring ML models for different applications to be used for the desired application with a few modifications.
The proposed system in this paper focuses on managing the charging of EVs by optimizing the voltage profile, the power losses, the transformer loading and the charging cost using ML, which will, in turn, reduce power overloads in the grid and enhance the voltage profile of the grid. Thus, the system takes into consideration both the distribution system constraints and the satisfaction of the user.
Essentially, the distinction between the method in this paper and previous research is that it uses ML as an optimization technique to manage the charging of a fleet of EVs, while considering both the distribution grid and user demands and utilizing conventional charging, fast charging and V2G technologies.