Modeling the Charging Behaviors for Electric Vehicles Based on Ternary Symmetric Kernel Density Estimation

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.

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• http://dx.doi.org/10.3390/en13071551 under the license https://creativecommons.org/licenses/by

• https://www.mdpi.com/1996-1073/13/7/1551/pdf under the license http://creativecommons.org/licenses/by/3.0/

• https://www.mdpi.com/1996-1073/13/7/1551,

https://doaj.org/toc/1996-1073 under the license cc-by

• https://www.mdpi.com/1996-1073/13/7/1551,

https://www.mdpi.com/1996-1073/13/7/1551/pdf,

https://ideas.repec.org/a/gam/jeners/v13y2020i7p1551-d337436.html,

https://academic.microsoft.com/#/detail/3013857337

• https://www.mdpi.com/1996-1073/13/7/1551/pdf,

http://dx.doi.org/10.3390/en13071551

under the license https://creativecommons.org/licenses/by/4.0/