EV charging load simulation and forecasting considering traffic jam and weather to support the integration of renewables and EVs

Abstract With the rapid development of electric vehicles (EVs), EV charging load simulation is of significance to tackle the challenges for planning and operating a highly-penetrated power system. However, the lack of historical charging data, as well as consideration on the temperature and traffic, pose obstacles to establish an accurate model. This paper presents a spatial-temporal EV charging load profile simulation method considering weather and traffics. First, the impacts of temperature on battery capacity and air-conditioning power are formulated. Second, the energy consumed by air conditioning and car-driving under various traffic conditions is formulated after defining two traffic-related indices. Third, the refined probabilistic models regarding the spatial-temporal vehicle travel pattern are established to improve accuracy. Daily charging load profiles at multiple regions are generated with inputs of refined models and formulations based on Monte Carlo. The real-world data are used to validate the proposed model under various scenarios. The results show that the magnitude, profile shape and peak time of the charging loads have significant differences in different seasons, traffics, day type and regions. Optimal planning of the distributed wind and solar capacities is made to improve the renewable power supply to the EV charging based on the simulated regional profiles.