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Probabilistic Modeling of Nodal Charging Demand Based on Spatial-Temporal Dynamics of Moving Electric Vehicles

机译:基于电动汽车时空动力学的节点充电需求概率建模

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摘要

High penetration of electric vehicles (EVs) as moving loads in power system have drawn increasing concerns about their negative impacts. Due to the spatial-temporal random dynamics of EVs, it is a challenge for identification and positioning of the space and time varying impacts. Most previous studies investigated system-wide EV charging demand based on data analysis with deterministic charging location and time. In this circumstance, this paper proposes a probabilistic model for nodal charging demand based on the spatial-temporal dynamics of moving EVs. Following the introduction to the integrated system with graph theory, a spatial-temporal model of moving EV loads is established based on random trip chain and Markov decision process (MDP). The nodal EV charging demands are derived from the charging probabilities of single and multiple EVs. The system studies show that this model is capable to assess the nodal charging demand due to the spatial-temporal distribution of moving EVs.
机译:随着电力系统中的移动负载,电动汽车(EV)的高普及率引起了人们对其电动汽车负面影响的日益关注。由于电动汽车的时空随机动态,这对于识别和定位时空变化带来了挑战。之前的大多数研究都是基于具有确定性充电位置和时间的数据分析来研究全系统电动汽车的充电需求。在这种情况下,本文基于移动电动汽车的时空动态,提出了一种节点充电需求的概率模型。在引入具有图论的集成系统之后,基于随机行程链和马尔可夫决策过程(MDP)建立了移动电动汽车负载的时空模型。节点EV充电需求来自单个和多个EV的充电概率。系统研究表明,由于移动电动汽车的时空分布,该模型能够评估节点的充电需求。

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