## 1 Introduction
### Relevant Literature  
The price mechanism of electricity market, as an important basis for EV users to select charging strategies, has a significant impact on the results of load adjustment. Massive theoretical research has been made to develop the price mechanism for EV charging, and existing work can be generally classified into two aspects: (1) the peak-valley price [12; 13]; (2) the dynamic time-of-use charging price, based on a supply-demand relationship. Among them, the constant peak-valley price can only reflect the basic situation of the daily load, but cannot properly regulate the charging price for scenarios such as a surge or sudden drop, which may lead to another peak load and affect the safe operation of the power system. With the help of dynamic pricing approaches, users can be encouraged to reveal their full flexibility, containing the flexibility in the charging duration, the amount of energy, and location can be addressed [14]. In order to solve the new problem that the peak-valley price may bring to the electricity grid, scholars have gradually carried out research in the field of dynamic time-of-use charging price for EVs. Reference [15] proposes an approximate dynamic programming feedback-based optimization method to tackle the problem of managing fleets of EVs, which verifies that the dynamic price compared with peak-valley price can effectively stabilize the load fluctuation and enhance the economy of the charging station. In addition to smart charging, dynamic price is a better approach than a constant time-of-use price to overcome the challenges related to an increasing penetration of EVs [14]. Reference [16] proposes the use of dynamic pricing, in order to reduce the costs of energy purchased in the energy market. In Reference [17], the scholars investigate the setting of dynamic price for different EVs charging deadlines with the objectives of maximizing the charging station operator's profit and minimizing the peak load. In addition, considering dynamic electricity price during daytime features significant stochastic, a two-stage approximate dynamic programming framework is proposed, to determine the optimal charging strategy for EVs in reference [18]. Based on the charging requirements of customers and system load profile, reference [19] presents a coordinated charging strategy for EVs, considering dynamic time-of-use tariffs to reduce the operational costs of the charging station and customer charging fees. According to the data of the output and load of renewable energy, reference [20] proposes the EV charging pricing strategy for the goal of balance between supply and demand from the micro-grid, without considering the response to the price from the perspective of EV users, but whether the effective dispatching of EVs can be achieved, depending on users' preferences and choices, to a certain extent. EV users will consider the inconvenience of electricity consumption, EV battery loss, cost, and economic benefits when participating in dispatching, as well as worrying about whether the strategy of charging can meet their travel needs. Reference [21] investigates, in a stated choice experiment, the charging behavior of the EV user, considering not only flexibility in the charging patterns, but also in the driving patterns. They conclude that there is a large heterogeneity in user preferences. However, studies in [16; 17; 18; 19; 20] assume that EVs strictly follow the dispatching order of the electricity grid, which ignores the influence of the preferences and personal choices of EV users on the dispatching results. Therefore, the charging price obtained by these studies is hard to achieve alongside the effective dispatching of EVs and still has great limitations in its application. From this point of view, the paper develops a dynamic time-of-use pricing strategy for EV charging, considering user satisfaction degree, so as to effectively adjust the charging demand of EV users with different preferences.