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使用谷歌地图和二进制闪电搜索算法的电动汽车快速充电站布局与规模确定的改进方法

Improved approach for electric vehicle rapid charging station placement and sizing using Google maps and binary lightning search algorithm.

作者信息

Islam Md Mainul, Shareef Hussain, Mohamed Azah

机构信息

Department of Electrical and Electronic Engineering, Uttara University, Uttara Model Town, Dhaka, Bangladesh.

Department of Electrical Engineering, United Arab Emirates University, 1 Al-Ain, UAE.

出版信息

PLoS One. 2017 Dec 8;12(12):e0189170. doi: 10.1371/journal.pone.0189170. eCollection 2017.

Abstract

The electric vehicle (EV) is considered a premium solution to global warming and various types of pollution. Nonetheless, a key concern is the recharging of EV batteries. Therefore, this study proposes a novel approach that considers the costs of transportation loss, buildup, and substation energy loss and that incorporates harmonic power loss into optimal rapid charging station (RCS) planning. A novel optimization technique, called binary lightning search algorithm (BLSA), is proposed to solve the optimization problem. BLSA is also applied to a conventional RCS planning method. A comprehensive analysis is conducted to assess the performance of the two RCS planning methods by using the IEEE 34-bus test system as the power grid. The comparative studies show that the proposed BLSA is better than other optimization techniques. The daily total cost in RCS planning of the proposed method, including harmonic power loss, decreases by 10% compared with that of the conventional method.

摘要

电动汽车(EV)被视为应对全球变暖和各类污染的优质解决方案。尽管如此,一个关键问题是电动汽车电池的充电问题。因此,本研究提出了一种新颖的方法,该方法考虑了运输损耗、堆积以及变电站能量损耗的成本,并将谐波功率损耗纳入最优快速充电站(RCS)规划中。提出了一种名为二进制闪电搜索算法(BLSA)的新型优化技术来解决该优化问题。BLSA也被应用于传统的RCS规划方法。通过使用IEEE 34节点测试系统作为电网,进行了全面分析以评估这两种RCS规划方法的性能。对比研究表明,所提出的BLSA优于其他优化技术。所提方法在RCS规划中的每日总成本,包括谐波功率损耗,与传统方法相比降低了10%。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/621e/5722383/832153164467/pone.0189170.g001.jpg

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