Abstract
The accurate modeling of the charging behaviors for electric vehicles (EVs) is the basis for the charging load modeling, the charging impact on the power grid, orderly charging strategy, and planning of charging facilities. Therefore, an accurate joint modeling approach of the arrival time, the staying time, and the charging capacity for the EVs charging behaviors in the work area based on ternary symmetric kernel density estimation (KDE) is proposed in accordance with the actual data. First and foremost, a data transformation model is established by considering the boundary bias of the symmetric KDE in order to carry out normal transformation on distribution to be estimated from all kinds of dimensions to the utmost extent. Then, a ternary symmetric KDE model and an optimum bandwidth model are established to estimate the transformed data. Moreover, an estimation evaluation model is also built to transform simulated data that are generated on a certain scale with the Monte Carlo method by means of inverse transformation, so that the fitting level of the ternary symmetric KDE model can be estimated. According to simulation results, a higher fitting level can be achieved by the ternary symmetric KDE method proposed in this paper, in comparison to the joint estimation method based on the edge KDE and the ternary t-Copula function. Moreover, data transformation can effectively eliminate the boundary effect of symmetric KDE.
Highlights
Nowadays, a considerable part of the total emissions is due to contribution of road traffic [1,2].For this reason, the development of the new technologies (as electric vehicles (EVs) and bicycles for sustainable mobility) is necessary to reduce the emissions
The deviation of the joint density estimation generated by the above errors is large, causing a large deviation between the simulation results and the actual results. With this as the cutting point, this paper introduces an accurate joint modeling approach of the arrival time, the staying time, and the charging capacity for the EVs charging behaviors in the work area based on ternary symmetric kernel density estimation (KDE) in accordance with the actual data
In order to verify the effectiveness of the method proposed in this paper, the joint estimation method based on the edge KDE and the Copula function [21,25,28] was compared, defined, and simulated in the following four cases: Case 1: Ternary symmetric KDE was conducted on the original data, and simulation data were generated with the Monte Carlo method; Case 2: After conducting normal transformation on the original data, ternary symmetric KDE
Summary
A considerable part of the total emissions is due to contribution of road traffic [1,2]. Considering that drivers’ driving times and distances are constant, it is common to study the modeling of the EVs charging behaviors with fuel vehicle data. The probability distribution of the variable to be estimated is gained directly under the drive of EVs operating data In this way, deviations caused by wrong selection of PE models can be effectively addressed. The deviation of the joint density estimation generated by the above errors is large, causing a large deviation between the simulation results and the actual results With this as the cutting point, this paper introduces an accurate joint modeling approach of the arrival time, the staying time, and the charging capacity for the EVs charging behaviors in the work area based on ternary symmetric KDE in accordance with the actual data.
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