Abstract
The probability distributions of start charging time and daily trip mileage are the key to build a precise charging load model for electric private vehicles. An adaptive kernel density estimation with boundary kernel algorithm is proposed to build the probability distribution models. The proposed method does not require any assumptions about the probability distribution, and can solve the problems of boundary bias and lacking of local adaptability, which improves the precision and adaptability of the probability distributions. Besides, Latin hypercube sampling with cubic spline interpolation algorithm is proposed to figure out the inverse cumulative distribution function of probability distribution. The proposed algorithm has the advantages of high precision and sampling effie iency. Finally, based on these two algorithms, electric private vehicle charging load model is set up. The simulation results demonstrate the effectiveness and adaptability of the proposed method.