Capturing Multivariate Dependencies of EV Charging Events: From Parametric Copulas to Neural Density Estimation
This work addresses grid reliability and smart-charging design for electric vehicle infrastructure, representing an incremental advancement by applying existing high-capacity dependence models to a new domain.
The paper tackled the problem of modeling complex dependencies between electric vehicle charging variables by applying Vine copulas and the CODINE neural framework to real-world datasets, showing they outperform traditional methods and remain competitive with state-of-the-art benchmarks in preserving tail behaviors and correlation structures.
Accurate event-based modeling of electric vehicle (EV) charging is essential for grid reliability and smart-charging design. While traditional statistical methods capture marginal distributions, they often fail to model the complex, non-linear dependencies between charging variables, specifically arrival times, durations, and energy demand. This paper addresses this gap by introducing the first application of Vine copulas and Copula Density Neural Estimation framework (CODINE) to the EV domain. We evaluate these high-capacity dependence models across three diverse real-world datasets. Our results demonstrate that by explicitly focusing on modeling the joint dependence structure, Vine copulas and CODINE outperform established parametric families and remain highly competitive against state-of-the-art benchmarks like conditional Gaussian Mixture Model Networks. We show that these methods offer superior preservation of tail behaviors and correlation structures, providing a robust framework for synthetic charging event generation in varied infrastructure contexts.