Abstract
In the paper, the effect of the charging behaviours of electric vehicles (EVs) on the grid load is discussed. The residential traveling historical data of EVs are analyzed and fitted to predict their probability distribution, so that the models of the traveling patterns can be established. A nonlinear stochastic programming model with the maximized comprehensive index is developed to analyze the charging schemes, and a heuristic searching algorithm is used for the optimal parameters configuration. With the comparison of the evaluation criteria, the multiobjective strategy is more appropriate than the single-objective strategy for the charging, i.e., electricity price. Furthermore, considering the characteristics of the normal batteries and charging piles, user behaviour and EV scale, a Monte Carlo simulation process is designed to simulate the large-scale EVs traveling behaviours in long-term periods. The obtained simulation results can provide prediction for the analysis of the energy demand growth tendency of the future EVs regulation. As a precedent of open-source simulation system, this paper provides a stand-alone strategy and architecture to regulate the EV charging behaviours without the unified monitoring or management of the grid.
Highlights
With the gradually deteriorated air quality, environments and energy crisis caused by the fuel-powered vehicles around the world, renewable vehicles, i.e., electric vehicles (EVs) are greatly promoted by all the governments, and many policies have been issued related to their development, where EVs will become the main transportation tools in the future along with the increasing improved technologies and infrastructure construction
This paper investigates large-scale EV charging behaviours as a multiobjective optimization problem, and the main contributions are summarized as follows: (1) From the benefits of the EV users and power suppliers perspectives, the comprehensive evaluation index system has been developed with three key factors: load peak value, charging bills, and traveling rate, which are used as the objective to describe the EV scale Traveling data Variables
The variance coefficient of the comprehensive Y value (calculated by equation (13)) can be converged lower than 0.5%, where the reliability is satisfied with the Monte Carlo (MC) simulation requirement
Summary
With the gradually deteriorated air quality, environments and energy crisis caused by the fuel-powered vehicles around the world, renewable vehicles, i.e., electric vehicles (EVs) are greatly promoted by all the governments, and many policies have been issued related to their development, where EVs will become the main transportation tools in the future along with the increasing improved technologies and infrastructure construction. From the analysis of the Danish national transportation survey data, the EV traveling model can be established with the driving distance and driving periods as the statistical data, so that the power demand and expected charging time from EVs can be determined [6]. 40 km driving distance can be used to determine whether the EV battery capacity can meet the daily traveling requirement. EVs can be considered as the mobile load connected between the power system and the transportation system, where the random mobility and charging of the EVs are dependent on the stochastic traveling patterns
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