A Novel Method Based on a Non-Stationary Discrete Markov Chain for Tracking Variations in the Quantity of Reserved Energy and the Number of Electric Vehicles

Abstract

Since the initial suggestion that electrically propelled vehicles could be used on the grid-side, numerous significant investigations have been conducted to showcase the capabilities of these technologies, which have proven to be highly advantageous. Nevertheless, there are still many uncertainties surrounding the integration of electric vehicles into the power grid, which is why it has been likened to a black box. These uncertainties include the number of electric vehicles that will be connected to the grid at any given time, the amount of energy that will be stored in their batteries during both the daytime and overnight, and the impact that their charging profiles will have on the overall load placed on the power system. In addition, there are several unanswered questions that need to be addressed. This article presents a novel model that effectively addresses these uncertainties. It is based on a non-stationary Markov chain, and it was introduced in this paper. The findings of the model provide fascinating insights into the number of electric vehicles connected to the grid and the amount of energy saved over the course of a day, as demonstrated by a case study. In addition, this article analyzes and evaluates the ability of the model to accurately represent the load modeling of electric vehicle charging.