Orderly Charging and Discharging Group Scheduling Strategy for Electric Vehicles

To address the challenge of optimizing the real-time scheduling for electric vehicles on a large scale, a day-ahead-intraday multi-timescale electric vehicle cluster division strategy is proposed based on the different expected charging completion times of the accessed electric vehicles. In the pre-day phase, historical travel statistics are used to model and determine the moments when the electric vehicles are on-grid and off-grid. In the intraday phase, the EV clusters are carefully divided by real-time data collection, taking into full consideration the response willingness and ability of vehicle owners. For each scheduling period, a real-time optimal scheduling model for EV clusters based on the V2G mode is established by taking into account the constraints of the power grid, vehicle owners, batteries, and other parties. The model is divided into two layers to find the charging and discharging plans: the upper model aims to determine the aggregate charging and discharging power of the cluster during the current time period by targeting the distribution grid's minimum variance load curve within the scheduling interval; the lower model takes the lowest cost to the EV owner as the goal to find the charging and discharging plan of a single EV and, at the same time, introduces the scheduling penalty factor to adjust the difference with the cluster charging and discharging plan. The simulation outcomes indicate that the suggested approach successfully mitigates load fluctuations and has a good optimization effect and fast solution speed for dealing with large-scale EV access problems. The simulation results show that the proposed strategy can effectively smooth load fluctuations and can significantly reduce the difficulty of optimal real-time scheduling for electric vehicles on a large scale, and it has a better optimization effect and faster solution speed for dealing with large-scale electric vehicle access problems.