Procedural simulation method for aggregating charging load model of private electric vehicle cluster

Abstract

The usage of each private electric vehicle (PrEV) is a repeating behavior process composed by driving, parking, discharging and charging, in which PrEV shows obvious procedural characteristics. To analyze the procedural characteristics, this paper proposes a procedural simulation method. The method aggregates the behavior process regularity of the PrEV cluster to model the cluster's charging load. Firstly, the basic behavior process of each PrEV is constructed by referring the statistical datasets of the traditional private non-electric vehicles. Secondly, all the basic processes are set as a simulation starting point, and they are dynamically reconstructed by several constraints. The simulation continues until the steady state of charge (SOC) distribution and behavior regularity of the PrEV cluster are obtained. Lastly, based on the obtained SOC and behavior regularity information, the PrEV cluster's behavior processes are simulated again to make the aggregating charging load model available. Examples for several scenarios show that the proposed method can improve the reliability of modeling by grasping the PrEV cluster's procedural characteristics.

Multi-agent modeling and analysis of EV users’ travel willingness based on an integrated causal/statistical/behavioral model

“…For example, the estimation of charge times during a trip depends on the range and users' range anxiety. Existing causal analysis methods are based on deterministic models based on users' behaviors in typical scenarios, however, the uncertain behaviors of different user groups in different scenarios cannot be represented [16][17][18]. And on the other side, statistical analysis methods provides no joint probability distribution functions of high-dimensional random variables extracted from questionnaires and statistical data [19].…”

Security-Constrained Unit Commitment Considering Differentiated Regional Air Pollutant Intensity

“…Abdollahi et al [22] introduced a cost-emission-based UC taking into account demand response and emission regulation policies. Other critical techniques include: energy storage [23][24][25], electric vehicle [26], and power-to-gas [27,28]. It is noted that most environmental ED/UC models enforce undifferentiated control on emissions or impose uniform incentives to regulate air pollutants.…”