Large-scale EV charging scheduling considering on-site PV generation by combining an aggregated model and sorting-based methods

Abstract

Large-scale electric vehicle (EV) charging scheduling is highly relevant for the growing number of EVs, while it can be complex to solve. A few existing studies have applied a two-stage scheduling approach to reduce computation time. The first stage approximates the optimal overall load, and the second prioritizes the charging. This work also attempts to apply such an approach for large-scale EV charging considering on-site photovoltaic (PV) generation at a workplace. However, validation and analysis are missing to address whether and why the two-stage approach is suitable. Besides, the existing studies lack exploring different methods to prioritize charging. This work investigates the two-stage approach. Simulation results show the non-uniqueness of the optimal solution from the optimal individual model, and guided by the optimal overall load, sorting-based methods can often lead to an optimal solution, while non-optimal solutions only cause decreases in the load-matching performance with a median value of less than 1%. The aggregated model usually cannot achieve the optimal overall load due to model simplifications. However, further applying sorting-based methods will reduce the differences between the final and the optimal overall load. Thus, the two-stage approach is suitable for this study, and further simulations show that it can achieve almost the optimal annual performance with around 1/57 of the computation time. Furthermore, this study explores different methods to prioritize charging. Simulation results show no difference in performance, while the Least Laxity First method leads to around 54.6% more switching.