## 1 Introduction  
Nowadays, electric vehicles (EVs) are receiving great attention due to their benefits in saving energy, decreasing greenhouse gas emissions, and reducing the dependence on conventional fossil fuels [1, 2]. Driven by policies and regulations, the number of EVs is projected to rise significantly over the coming decades [3, 4]. As the penetration rate of EVs is expected to continue growing exponentially, a lot of energy will be required to support EV charging. The influences of EVs on the power grid may include power losses (feeders overloading and transformer overloading) and power quality (harmonics, unacceptable voltage fluctuations and voltage profile) [5]. Without any control strategies, the charging process of EVs will significantly affect the electric power grid, especially during peak hours [6]. Among the possible solutions to cope with the new peak demand during peak hours is to increase power generation to handle the growth in vehicle charging or to upgrade the distribution system infrastructure. However, these are difficult and expensive solutions [7]. In order to manage and coordinate an EV charging/discharging operation, there is a need for a cost-effective solution, especially in the distribution power network. Different research work and studies have been conducted for EV charging schemes from different perspectives, including distribution transformer aging [8], apartment buildings [9], office buildings [10] and commercial buildings [11]. [PERSON] et al. [8] studied the impact of smart neighborhood load on a distribution transformer aging under a time-varying pricing scheme. The system consisted of a low voltage distribution system across four houses. Each house was equipped with a rooftop photovoltaic (PV) and an energy storage system (ESS). Different scenarios have been investigated considering different EV types. [PERSON] et al. [9] investigated the impact of EV charging strategies in an apartment building equipped with a PV system in terms of an EV utility factor, self-power consumption, and grid impact. [PERSON] et al. [10] discussed the impact of different EV charging strategies in a real office building equipped for EVs, a large photovoltaic system and a combined heat and power (CHP) unit. Charging strategies have been evaluated in terms of self-electricity consumption and grid impact. Energy storage solutions such as battery storage systems were not considered in References [9; 10]. [PERSON] et al. [11] investigated a two-stage energy management strategy for a demand response program considering the operation of EVs and a wind power-based commercial building microgrid with the objective of minimizing the building's operational costs. Charging schemes are classified into four basic types: uncontrolled, indirectly controlled, smart and bidirectional charging [12; 13]. In uncontrolled charging, there is no attempt to schedule EVs as they begin to charge immediately from the charging stations upon their arrival. Indirectly controlled schemes aim to control some parameters such as charging cost and energy price that will affect the charging operation indirectly. Both smart charging schemes and bidirectional charging schemes aim to control the charging parameters directly, such as output power from a charging station, charging time, and charging duration. However, bidirectional charging schemes support vehicle-to-grid (V2G) concepts that enable EV batteries to discharge. Coordinating EV charging has a major impact on end users [14; 15; 16; 17; 18], retailers [19; 20], and power grid operators [21; 22; 23]. With respect to end user preferences, authors in [14; 15] studied the scheduling problem of EV charging in order to minimize customers' electricity payments and reduce the peak to average power ratio (PAPR). [PERSON] et al. and [PERSON] et al. [16; 17] proposed real-time EV load management to minimize the total energy cost for plug-in electric vehicle (PEV) charging and the corresponding grid energy losses.