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Improved approach for electric vehicle rapid charging station placement and sizing using Google maps and binary lightning search algorithm
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 l...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5722383/ https://www.ncbi.nlm.nih.gov/pubmed/29220396 http://dx.doi.org/10.1371/journal.pone.0189170 |
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author | Islam, Md. Mainul Shareef, Hussain Mohamed, Azah |
author_facet | Islam, Md. Mainul Shareef, Hussain Mohamed, Azah |
author_sort | Islam, Md. Mainul |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5722383 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-57223832017-12-15 Improved approach for electric vehicle rapid charging station placement and sizing using Google maps and binary lightning search algorithm Islam, Md. Mainul Shareef, Hussain Mohamed, Azah PLoS One Research Article 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. Public Library of Science 2017-12-08 /pmc/articles/PMC5722383/ /pubmed/29220396 http://dx.doi.org/10.1371/journal.pone.0189170 Text en © 2017 Islam et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Islam, Md. Mainul Shareef, Hussain Mohamed, Azah Improved approach for electric vehicle rapid charging station placement and sizing using Google maps and binary lightning search algorithm |
title | Improved approach for electric vehicle rapid charging station placement and sizing using Google maps and binary lightning search algorithm |
title_full | Improved approach for electric vehicle rapid charging station placement and sizing using Google maps and binary lightning search algorithm |
title_fullStr | Improved approach for electric vehicle rapid charging station placement and sizing using Google maps and binary lightning search algorithm |
title_full_unstemmed | Improved approach for electric vehicle rapid charging station placement and sizing using Google maps and binary lightning search algorithm |
title_short | Improved approach for electric vehicle rapid charging station placement and sizing using Google maps and binary lightning search algorithm |
title_sort | improved approach for electric vehicle rapid charging station placement and sizing using google maps and binary lightning search algorithm |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5722383/ https://www.ncbi.nlm.nih.gov/pubmed/29220396 http://dx.doi.org/10.1371/journal.pone.0189170 |
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