电动汽车和充电站智能电网中负载分接开关和开关并联电容器的优化调度

2013 8th International Conference on Electrical and Electronics Engineering (ELECO) Pub Date : 2013-11-01 DOI:10.1109/ELECO.2013.6713824

S. Deilami, A. S. Masoum, Nasim Jabalameli, M. Masoum

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引用次数: 6

摘要

插电式电动汽车(pev)的随机充电，特别是在高峰负荷时段，可能会损害未来智能电网的性能。本文提出了一种遗传算法(GAs)来优化LTC和开关并联电容器(ssc)的调度，以提高住宅馈线中PEV在消费者场所充电和配电网中PEV充电站(PEV- css)的智能电网性能。首先生成与PEV-CSs和包含pev的住宅馈线相关的预测日负荷曲线，然后将其纳入基于遗传算法的最优LTC和SSC调度解决方案。针对22个低压居民网络中264辆电动汽车随机协调充电的情况，给出了具有5辆电动汽车- css的449节点智能电网系统在无最优调度和最优调度下的仿真结果。

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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Optimal scheduling of load tap changer and switched shunt capacitors in smart grid with electric vehicles and charging stations

Random charging of plug-in electric vehicles (PEVs) particularly during the peak load hours could impairment the performance of future smart grids. This paper presents genetic algorithms (GAs) for optimal scheduling of LTC and switched shunt capacitors (SSCs) to improve the performance of smart grid with PEV charging at consumer premises in residential feeders and PEV charging stations (PEV-CSs) in distribution networks. The forecasted daily load curves associated with PEV-CSs and residential feeders populated with PEVs are first generated and then incorporated in the GA-based optimal LTC and SSC scheduling solution. Simulation results without and with optimal scheduling are presented for a 449 node smart grid system with 5 PEV-CSs considering random and coordinated charging of 264 PEVs in 22 low voltage residential networks.

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来源期刊

2013 8th International Conference on Electrical and Electronics Engineering (ELECO)

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