Double-Layer Optimal Scheduling Strategy for Charging and Discharging of Electric Vehicles Considering Cluster Schedulability

In response to the power quality issues caused by the large-scale integration of electric vehicles (EVs) into the distribution network, this paper proposes a dual layer zoning optimization scheduling strategy for electric vehicle charging and discharging that considers the schedulability of the cluster, while taking into account the interests of both the grid side and the user side. Firstly, according to the uncertainty of the travel habits of electric vehicles, based on the Monte Carlo sampling method, and based on Minkowski sum, the load storage and dispatching capacity model of the electric vehicle cluster is established. Secondly, the power consumption strategy is formulated by the dispatch center, and electric vehicle agents cooperate to obtain the charging load and discharge output of EVs in each region, minimizing the fluctuation curve of the power grid system during the charging and discharging period, serving as the upper level dispatch model. Finally, the lower level model schedules electric vehicles in different regions based on the EV charging load and discharge output obtained from the upper level, taking into account the EV’s schedulability and user participation willingness, to achieve the lowest user side cost. The improved multiple genetic algorithms were used to solve the model, and the results showed that the model can not only achieve the goal of peak shaving and valley filling, but also maximize user benefits.