Research on the analysis strategy of electric vehicle charging demand based on multi-correlation daily scenario generation

Abstract

Abstract With the continuous increase of the penetration rate of OFELECTRIC vehicles, their high random charging loads exert a more significant effect on the stability of the electrical system. Simultaneously, the past charging patterns of electric vehicles indirectly influence the possible future charging activities of these vehicles. Therefore, in order to characterize the correlation between the predicted daily charging behavior of electric vehicles and its historical daily charging behavior, this article uses scene generation theory to fully mine the original multi-correlation Internal characteristics of electric vehicle charging behavior among the original multi-correlation day charging scenario set (OMCDCSS), generating massively generating multi-correlation day charging scenarios (GMCDCS) similar to the probability distribution of OMCDCSS. Secondly, the related scene set (RSS) is screened in the generation of multi-correlation day billing scenario (GMCDCSS) on the basis of weighted Spearman rank (SR). Get the forecast result of electric vehicle load interval.