Study of an Electric Vehicle Charging Strategy Considering Split-Phase Voltage Quality

IF 2.6 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Fulu Yan, Mian Hua, Feng Zhao, Xuan Liang
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引用次数: 0

Abstract

Slow-charging electric vehicle (EV) loads are single-phase loads in the power distribution network (PDN). The random access of these EVs to the network brings to the forefront the split-phase voltage quality issues. Therefore, a two-layer EV charging strategy considering split-phase voltage quality is proposed in this paper. Issues with voltage unbalance (VU), split-phase voltage deviation (VD), and split-phase voltage harmonics (VHs) are included in the optimization objective model. An upgraded version of the multi-objective non-dominated sorting genetic algorithm (NSGA-II) is used in the inner layer of the model and to pass the generated EV phase selection scheme to the outer layer. The outer layer consists of a split-phase harmonic current algorithm based on the forward–backward generation method, and feeds the voltage quality calculation results to the inner layer. After several iterations, the optimal EV phase selection scheme can be obtained when the inner layer algorithm satisfies the convergence condition. The results gained for the example indicate that the suggested EV charging approach can effectively handle the PDN’s split-phase voltage quality. Furthermore, it enhances the energy efficiency of PDN operations and promotes further energy consumption.
考虑分相电压质量的电动汽车充电策略研究
缓慢充电的电动汽车(EV)负载是配电网络(PDN)中的单相负载。这些电动汽车随机接入配电网,使分相电压质量问题凸显出来。因此,本文提出了一种考虑分相电压质量的双层电动汽车充电策略。电压不平衡(VU)、分相电压偏差(VD)和分相电压谐波(VHs)等问题被纳入优化目标模型。模型内层使用了升级版的多目标非支配排序遗传算法(NSGA-II),并将生成的电动汽车相位选择方案传递给外层。外层由基于前向后生成法的分相谐波电流算法组成,并将电压质量计算结果反馈给内层。经过多次迭代,当内层算法满足收敛条件时,就能得到最优的电动汽车相位选择方案。示例结果表明,建议的电动汽车充电方法能有效处理 PDN 的分相电压质量问题。此外,它还提高了 PDN 运行的能效,促进了能源的进一步消耗。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
World Electric Vehicle Journal
World Electric Vehicle Journal Engineering-Automotive Engineering
CiteScore
4.50
自引率
8.70%
发文量
196
审稿时长
8 weeks
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