基于加权复杂网络的电动汽车充电指导

IF 3.2 Q2 AUTOMATION & CONTROL SYSTEMS Systems Science & Control Engineering Pub Date : 2022-10-27 DOI:10.1080/21642583.2022.2135632
Jin Cao, Yuan Ge, Dongdong Wang, Qiyou Lin, Renfeng Chen
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引用次数: 0

摘要

充电站故障可能会拒绝在充电站为电动车辆提供充电服务。因此,车辆需要选择其他站点。如何做出最佳选择对业主来说很困难。提出了一种基于加权复杂网络的最优充电引导策略,供车主选择最优站点。所有充电站都被建模为一个复杂的网络,其中充电站和充电站之间的道路分别被定义为节点和边缘。此外,每个边缘由车辆的充电状态(SOC)、充电价格以及这两个站点之间的距离和交通状况进行加权。边缘权重越大表示边缘的一个节点处的所有者选择边缘的另一个节点进行充电的概率越小,反之亦然。基于加权复杂网络模型,提出了故障站点车辆充电的局部负荷再分配方法。因此,在本文提出的制导策略下实现了车辆的最优调度。最后,通过对比实验验证了该方法的有效性和优越性。
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Electric vehicle charging guidance based on weighted complex network
The charging station malfunction may deny the charging service for the electric vehicles at the station. As a result, the vehicles need to select other stations. How to make an optimal selection is difficult for the owners. An optimal charging guidance strategy based on a weighted complex network is proposed for the owners to select the optimal station. All the charging stations are modelled as a complex network in which the stations and the roads among them are defined as nodes and edges, respectively. Furthermore, each edge is weighted by the state of charge (SOC) of the vehicle, the charging price, and the distance and traffic conditions between these two stations. The bigger edge weight indicates the smaller probability that the owner at one node of the edge select the other node of the edge for charging, and vice versa. Based on the weighted complex network model, the local load redistribution method is presented to guide the charging of vehicles at the malfunctioning station. Consequently, the optimal scheduling of the vehicles is realized under the guidance strategy proposed in this paper. Finally, some contrast experiments are carried out to illustrate the effectiveness and the superiority of the proposed method.
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来源期刊
Systems Science & Control Engineering
Systems Science & Control Engineering AUTOMATION & CONTROL SYSTEMS-
CiteScore
9.50
自引率
2.40%
发文量
70
审稿时长
29 weeks
期刊介绍: Systems Science & Control Engineering is a world-leading fully open access journal covering all areas of theoretical and applied systems science and control engineering. The journal encourages the submission of original articles, reviews and short communications in areas including, but not limited to: · artificial intelligence · complex systems · complex networks · control theory · control applications · cybernetics · dynamical systems theory · operations research · systems biology · systems dynamics · systems ecology · systems engineering · systems psychology · systems theory
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