# Orderly Charging and Discharging Group Scheduling Strategy for Electric Vehicles
## 1 Introduction
Additionally, reference [10] concentrates on scheduling strategies for clusters of EVs, crafting charging plans attuned to real-time electricity markets via deep reinforcement learning methods and thereby enhancing scheduling intelligence while accounting for real-time dynamics and offering a novel perspective on EV charging strategy optimization. Electric vehicles are different from traditional electric loads; they are connected to the grid at a time with a strong randomness [11; 12] and use a single timescale to formulate the scheduling plan; thus, they cannot take into account the global nature of the scheduling process and accuracy. (2) The investigation into the control methodologies of electric vehicle (EV) dispatch encompasses both individual and group dispatch strategies, with a focus on scale and the quantity of charging EVs. Reference [13] presents a decentralized two-layer charging strategy grounded in fuzzy data fusion, which enhances the charging cost optimization for an individual electric vehicle by utilizing the decision control variables obtained through fuzzy fusion. Reference [14] delves into charging demand and system load variations across different timescales, proposing an ordered charging/discharging model for two-tier EVs over extended time periods and thereby rationalizing the charging/discharging schedules for each EV. However, advancements in big data and artificial intelligence technology have prompted scholars to explore the effective management of charging and discharging behaviors from the perspective of EV clusters. This approach mitigates communication challenges, reduces algorithmic dimensions, and optimizes computational processes [15; 16]. Reference [17] introduces a travel chain-based EV cluster charging and discharging capacity calculation model, leveraging the spatial and temporal change probability characteristics of trip start and end points. This model guides the EV cluster to function as an energy storage system, promoting charging during off-peak hours. In addressing the unpredictability of current EV users' involvement in vehicle-to-grid (V2G) scheduling, reference [18] establishes an EV indicator evaluation model for the optimal scheduling of EV clusters. Reference [19] suggests a real-time scheduling method utilizing the charging end moment as a cluster characteristic; the method employs a two-layer optimization model to address optimal charging and discharging power concerns for both the entire cluster and individual EVs. However, all of the above clustering strategies subjectively define the optimal scheduling time of EV clusters, and if the load characteristics can be analyzed from the perspective of EV travel patterns, the difficulty of the solution can be greatly reduced, and the speed of the solution can be improved. At the same time user satisfaction must be considered when describing EV clusters [20; 21] to ensure that owner needs are met while EV dwell time is effectively utilized. This holistic perspective underscores the evolving landscape of EV dispatch control methods, incorporating advancements in technology and user-centric considerations. This article describes the EV clustering strategy for day-ahead-intraday multi-timescales in the context of a large number of EVs connecting to the grid in an unorganized manner, which brings great difficulty in grid scheduling. The clustering strategy builds a charging model in the day-ahead phase based on the historical data, and it takes into account the charging willingness and travel plans of EVs in real time in the intraday phase, so that EVs' charging demands can be more accurately predicted and dispatched. The charging demands of EVs are considered.