# Orderly Charging and Discharging Group Scheduling Strategy for Electric Vehicles
## 1 Introduction  
As of June 2023, the national ownership of new energy vehicles reached 16.2 million, constituting 4.9% of the overall vehicle count. Within this figure, pure electric vehicles totaled 12.594 million, representing 77.8% of the overall new energy vehicle count. As a new type of controllable load, electric vehicles can serve as mobile energy storage devices when connected to the grid; they can not only obtain electricity from the grid but also provide energy to the grid through the price incentive mechanism to realize the networking of vehicles. However, with the increase in the number of electric vehicles, the disorderly charging of a large number of electric vehicles will have an impact on the power grid, which will lead to the increase in the load and voltage fluctuation of the power grid and affect the normal operation of the power grid [1; 2]. Providing appropriate guidance for the charging of large-scale electric vehicles upon connection to the power grid can not only mitigate the negative impacts resulting from haphazard charging but also address issues such as the intensification of peak-valley differences and the compromised reliability and economicefficiency of the distribution network; it can also provide auxiliary services for power grid optimization, realize peak shaving and valley filling [3], stabilize the power grid load, and reduce network loss [4] and, at the same time, bring certain economic benefits to the power grid and to car owners. In recent years, scholars have made significant advancements both domestically and internationally in the research on the systematic management of charging and discharging for electric vehicles, as well as the vehicle-to-grid mode. (1) n the intricate domain of electric vehicle (EV) scheduling, diverse temporal dimensions, which are notably embedded within the context of vehicle-to-grid (V2G) responsiveness, warrant scholarly investigation. Pioneering endeavors have delved into temporal intricacies via the prism of day-ahead optimal scheduling [5; 6; 7] and real-time optimal scheduling [8; 9; 10]. Leveraging historical data, the optimization scheduling [5] meticulously models and scrutinizes the travel parameters of EVs to prognosticate the temporal distribution of charging and discharging demands for the ensuing day. Reference [6] formulates day-ahead optimal charging schedules for EVs; these schedules are predicated on an integrated game model which is cognizant of the vehicle owners' interests. Reference [7] introduces an analytical approach deploying polyhedral approximation models to succinctly articulate the dispatchable region for large-scale EVs, thereby facilitating the involvement of EV aggregators in the distribution grid for effective day-ahead optimal scheduling. Nevertheless, forecasting EV loads based on historical statistics has the potential for significant deviations between actual dispatch outcomes and day-ahead optimization projections. The extant optimization procedures necessitate adaptation in response to the actual access patterns of EVs, engendering challenges in devising charging and discharging strategies for individual EVs. Addressing the uncertainties surrounding factors such as the arrival and departure times of EVs, reference [8] compounds a real-time optimal scheduling strategy for large-scale electric vehicles, utilizing a dynamic non-cooperative game approach to formulate charging and discharging strategies for each electric vehicle within the cluster through the ADMM algorithm. Reference [9] proposes the concept of transferable charging/discharging margins for EVs, quantifying the flexibility of charging/discharging scheduling by considering dispatchable time/power and user willingness to participate. With the incremental proliferation of EVs connecting to the system in the future, real-time optimal scheduling requires heightened solution speeds and efficiency.