## 1 Introduction  
The increasing concerns about greenhouse gas emissions and the energy crisis have accelerated the pace of transportation electrification. Due to the fact that domestic electric vehicles have many advantages, such as higher efficiency in energy utilization, less direct pollution, and more environmental friendliness, they have successfully attracted more attention from governments and institutions around the world [1]. However, with the access of electric vehicles to the grid, extra-large and undesirable peak demands may emerge in distributed power systems because of the huge amount of electrical energy consumed in charging electric vehicles and domestic users' inappropriate charging behavior [2]. According to the National Household Travel Survey (NHTS), domestic users' charging behavior is closely related to the rules of the owners' trip. As a typical example, the 2009 NHTS is widely used in many studies to model domestic users' electric vehicle driving and charging patterns through developing users' travelling probability density functions. According to recent studies, different kindsof assumptions have been made by scholars to simplify domestic users' driving patterns. For example, it is assumed that electric vehicles have pre-specified arrival time in [3], and in [4], a rigid electric vehicle charging schedule is presented. However, these assumptions can lead to inaccuracy of evaluation by ignoring the stochastic nature of driving patterns. To improve the accuracy of evaluation, [PERSON] et al. use the probability method to model the arrival time, departure time, and daily mileage in [5; 6; 7]. In addition, a method to estimate the electric vehicle integration patterns in distributed power systems considering their dispersion in different locations is proposed in [8]. Moreover, it is stated in [9] that the Monte Carlo Simulation is an efficient tool to model electrical characteristics of power systems, which makes it possible to use the Monte Carlo Simulation method to model network total electric vehicle charging loads. Even though the accuracy of evaluation can be improved by the aforementioned literature, many factors still have not been fully considered when modelling users' load charging behavior. For instance, electric vehicle charging loads are sensitive to seasons, while seasonal factors have not been taken into consideration in recent studies. To build a more accurate model of electric vehicle charging loads, it is necessary to analyze the key factors that can affect users' charging behavior. With the rapid growth of electric vehicle charging loads, uncoordinated and random charging activities can increase the burden for distributed systems and may cause network voltage fluctuation, efficiency reduction, and so on [10]. As shown in the 2009 NHTS, numerous electric vehicle owners finish their last trips within a narrow time period, and most of them prefer to charge their vehicles shortly after the last trip; these inappropriate charging activities have negative effects on network operation. Therefore, it is urgent to propose an optimal electric vehicle charging strategy for regulating electric vehicle charging loads, because the proposed strategy can play an important role in load shifting, energy cost saving, and improving energy efficiency for distributed power networks [11; 12; 13]. As two of the most important objectives, reducing system losses and voltage fluctuation have been studied frequently in literature, with the aim of developing an electric vehicle charging strategy. In [14; 15], optimal load charging strategies are proposed for minimizing system losses and improving voltage profiles based on the real-time smart load management (RT-SLM) control strategy. Sensitivity-based and optimization-based methods are proposed for mitigating voltage fluctuation in the presence of plug-in hybrid electric vehicles in [16]. However, with the increase of domestic electric vehicles, the complexity of the aforementioned methods can be significantly increased.