Load Modeling and Identification Based on Ant Colony Algorithms for EV Charging Stations

Abstract

Charging load modeling for electric vehicles (EVs) is a challenge due to its complexity. However, it serves as a foundation for related studies such as the impact assessment of EV charging behaviors on power system and power demand side management for EVs. The decisive factors affecting charging load profile include the power curve, the duration, and the start time of each charging process. This paper introduces the charging traffic flow (CTF) as a discrete sequence to describe charging start events, where CTF contains both spatial and temporal properties of a charging load. A set of equations are proposed to build a probabilistic load model, followed by simulation iteration steps using a flow chart. The parameter identification method based on ant colony (AC) algorithms is then studied in depth, and the pheromone update and the state transition probability are used to implement route finding and city selection, respectively. Finally, an actual case of battery swapping station is applied to verify the proposed model in both identification and simulation. The results show that the model has satisfactory accuracy and applicability.