A novel spatial–temporal model for charging plug hybrid electrical vehicles based on traffic-flow analysis and Monte Carlo method. (August 2021) - Afshar, Mostafa; Mohammadi, Mohammad Reza; Abedini, Mohamad
Abstract: The increase of using electric vehicles (EVs) may increase power demand and therefore major effects on the power system. Therefore, if we do not have an appropriate program for utilizing and managing battery charging in the EVs, the charging process may coincide with the peak of power consumption and cause severe network problems. This paper deals with modeling the problem and estimating load consumption in EVs connected to the system. The EVs have been considered based on a random load and a probabilistic process. To manage the EVs, a traffic method called the spatial–temporal scheme, which uses the Monte Carlo algorithm, has been proposed. Also, the proposed method can be used to represent a model of electric vehicles changes in their battery charge and their need for charging at different times of a full day in different zones of the city. The proposed method has been simulated in the Matlab software and implemented for Hamedan province. The results show that using this method of estimation can improve the management of EVs. Highlights: The vehicle's behavior across the city is simulated using Hamadan's urban traffic data. Battery charge changes and need for charging at different times of a full day in different zones of the city are then calculated. The estimation of EV's charging value per hour and in each area in network has been shown. The results show that using the estimating method can be reduced the consume fuel of EVs.