Assessing electric vehicles behavior in power networks: A non-stationary discrete Markov chain approach

Abstract

Since the inception of the idea to utilize electrically driven vehicles on the power grid, numerous valuable investigations have been carried out to showcase the advantageous capabilities of such technologies. However, there are still uncertainties surrounding the integration of electric vehicles into the power grid. These uncertainties encompass the number of electric vehicles that will be linked to the grid at any given time, the quantity of energy stored in their batteries during both daytime and nighttime, and the impact of their charging patterns on the overall power grid load. Moreover, there are numerous other unanswered queries that demand attention. This study presents a unique model that effectively addresses these uncertainties by utilizing a non-stationary Markov chain. The utilization of a non-stationary discrete Markov model in this study provides a precise and valuable understanding of the constantly evolving and time-dependent nature of electric vehicle behavior in the power network. Through a comprehensive case study, the outputs of the model offer intriguing insights into the number of electric vehicles connected to the grid and the energy they reserve over a 24-hour period. Furthermore, this study assesses the model's accuracy in representing the load modeling of electric vehicle charging.