A Statistical framework for Electric Vehicle User Behavior impact in Smart Grids

Abstract

Electric Vehicles (EVs) are a key technology for advancing environmental sustainability and achieving net-zero emissions. While studies indicate that power grids can accommodate increasing EV adoption with proper management, there is a significant gap in understanding the uncertainties tied to individual EV user behavior and its implications for power system reliability and urban planning. This research addresses these challenges by introducing a Bayesian Hierarchical Model integrated with Dynamic Bayesian Networks, forming a unified statistical framework. This approach captures the interplay between EV user behavior and other system variables, such as distributed generation and energy storage. The methodology is able to evaluate the impact that the penetration and behavior of EV users has on a smart grid - in terms of system’s availability and failure rate. Results also show how the methodology is used to defined the optimal set of renewable sources to be allocated in the power grid, depending on the EV user behavior.