Optimization Research on the Impact of Charging Load and Energy Efficiency of Pure Electric Vehicles

被引:0
|
作者
Xin, Huajian [1 ]
Li, Zhejun [2 ]
Jiang, Feng [2 ]
Mo, Qinglie [2 ]
Hu, Jie [2 ]
Zhou, Junming [2 ]
机构
[1] Guangxi Vocat & Tech Inst Ind, Sch Intelligent Mfg Coll, Nanning 530001, Peoples R China
[2] Guangxi Univ Sci & Technol, Sch Mech & Automot Engn, Liuzhou 545616, Peoples R China
关键词
pure electric vehicles; Monte Carlo method; IEEE model; multi-objective genetic algorithm;
D O I
10.3390/pr12112599
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
In this paper, the negative impact of the charging load generated by the disorderly charging scheme of large-scale pure electric vehicles on the operation performance of the power grid system and the problem of reducing its charging energy efficiency are studied and analyzed. First, based on Matlab 2022a simulation software and the Monte Carlo random sampling method, the probability density model of the factors affecting the charging load is constructed, and the total charging load of different quantities is simulated. Second, the IEEE33-node distribution network model is introduced to simulate the influence of charging load on the grid under different permeability schemes. Finally, the multi-objective genetic algorithm is used to optimize the charging cost and battery life. Taking the 20% permeability scheme as an example, the research results show that, compared with the disorderly charging scheme, the multi-objective optimization scheme reduces the peaking valley difference rate by 24.34%, the charging load power generation cost by 29.5%, and the charging cost by 23.9%. The power grid profit increased by 45.8%, and the research conclusion has practical significance for the energy efficiency optimization of pure electric vehicles.
引用
收藏
页数:26
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