# A Simple Multi-Parameter Method for Efficient Charging Scheduling of Electric Vehicles
###### Abstract
A hierarchical multi-agent system with small computation and communication times and able to satisfy state-of-charge (SoC) targets and all distribution network and EV constraints was designed. In [19], a smart charging decision function focusing on EV user satisfaction and the minimization of grid investments was developed. Three main factors are considered for the charging and discharging of EVs: residual energy, charging efficiency and charging habit. In this case, communication is required between the charging stations and EVs in order to decide if the EVs will charge, discharge or wait at the charging station. It is also assumed that the EVs can store information about the residual energy of their battery and charging habits of the driver. In addition, the charging capacity and the distance of each charging station should be broadcasted from the charging stations. A method considering urgent charging demands is proposed in [20]. EVs could charge in a fast or slow charging mode depending on the charging urgency of the EV. V2G was not considered as an option. It satisfies charging requests and smoothens the power exchanged by the PEVs and the microgrid. A comparison between the first-in-first-serve method, PSO and the Shuffled Frog Leaping Algorithm for dynamic charging of EVs is made in [21]. The developed objective function aimed to minimize the EV charging cost considering the electricity price and charger booking. The effect of the autonomous operation of the microgrid on the electricity price or not is studied for operation scenarios of 20 EVs. The EV discharging scheme was not considered in this work. In [22], the authors took into consideration realistic mobility/parking patterns and developed a centralized charging scheduling system in order to maximize the profit of the PL and satisfy EVs' requirements using the Advanced Interactive MultidimensionalModeling System software. Only slow charging was considered in this work. In [23], the operation of a PL of EVs was studied under real-time pricing. An improved PSO method is proposed for charging and discharging 30 EVs which arrive at the PL at 8:00 and have different departure hours. In [24], PSO is used to determine the charging schedule of PEVs in order to fulfill their requirements and minimize the charging cost at the PL level. Additionally, a heuristics and proportion-based assignment method was used to generate the initial population. Distribution grid loading was not considered in [22; 23; 24]. In [25], an autonomous real-time energy management system is proposed for a workplace microgrid with EV integration and time-of-use (TOU) pricing. Random forest methodology was used to forecast the EV travel pattern. The EVs and the workplace energy management system should exchange information in order to reduce the power consumption in the workplace and the charging costs for EV owners, however the overloading of the local transformer power was not considered. In [26], the effects of coalitions between employer and employee, for the sake of efficient charging scheduling of EVs, are studied using cooperative game theory. It was assumed that in the workplace parking, the EVs could only discharge. The results showcased that the proposed approach is economically beneficial to EV owners (employees) and the employers. In [27], peak shaving and valley filling of power consumption in a non-residential system are achieved with suitable EV charging/discharging scheduling, without minimizing the charging cost of the EVs. Only one type of EV was considered with a battery capacity of 24 kWh with a low charging/discharging rate. A real-time charging method with V2G capability using fuzzy logic was developed in [28]. The aims of the method were the satisfaction of EV charging demands and the minimization of the charging cost. Linear programming was used to solve the EV charging schedule problem. The proposed fuzzy inference system computes the charging/discharging priority of each electric vehicle based on state-of-charging (SoC), remaining charging time and electricity price.