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.

中文翻译：

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一种基于非平稳离散马尔可夫链的跟踪储备能量数量和电动汽车数量变化的新方法

摘要

自从最初建议在电网侧使用电力驱动车辆以来，已经进行了大量的重要研究来展示这些技术的能力，事实证明这些技术具有很高的优势。尽管如此，电动汽车并入电网仍存在诸多不确定性，因此被比喻为黑匣子。这些不确定性包括在任何给定时间连接到电网的电动汽车数量、白天和夜间电池中存储的能量以及充电配置对整体的影响施加在电力系统上的负载。此外，还有几个悬而未决的问题需要解决。本文提出了一种有效解决这些不确定性的新颖模型。它基于非平稳马尔可夫链，本文对此进行了介绍。正如案例研究所证明的那样，该模型的研究结果为连接到电网的电动汽车数量以及一天内节省的能源量提供了令人着迷的见解。此外，本文还分析和评估了模型准确表征电动汽车充电负荷建模的能力。