AI-Based Approach for Optimal Placement of EVCS and DG With Reliability Analysis

It is expected that future transport will rely on electric vehicles (EVs) due to their sustainability and reduced greenhouse gas emissions. However, the rapid increase in electric load penetration causes several other concerns, including a generation-demand mismatch, increased network active power l...

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Autores principales:	Mohd Bilal, M. Rizwan, Ibrahim Alsaidan, Fahad M. Almasoudi
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Artificial intelligence electric vehicle charging stations radial distribution system distributed generators reliability Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/60ad7576e0b544f584c3f05688ff3204
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spelling	oai:doaj.org-article:60ad7576e0b544f584c3f05688ff32042021-11-24T00:01:55ZAI-Based Approach for Optimal Placement of EVCS and DG With Reliability Analysis2169-353610.1109/ACCESS.2021.3125135https://doaj.org/article/60ad7576e0b544f584c3f05688ff32042021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9599691/https://doaj.org/toc/2169-3536It is expected that future transport will rely on electric vehicles (EVs) due to their sustainability and reduced greenhouse gas emissions. However, the rapid increase in electric load penetration causes several other concerns, including a generation-demand mismatch, increased network active power loss, a degradation in voltage profile, and decreased voltage stability margin. To overcome the issues mentioned earlier, proper integration of electric vehicle charging stations (EVCS) at appropriate locations is essential. The connection of an EVCS to the electricity grid will bring new challenges. Distributed generation (DG) sources are incorporated with EVCS to lessen the impact of EV charging load. In this study, charging stations are combined with DG units, which increases the motivation to use EVs. This study proposes an artificial intelligence (AI) approach, the hybrid of grey wolf optimization and particle swarm optimization, i.e., HGWOPSO, to investigate the suitable nodes for EVCS and DGs in a balanced distribution system. The proposed methodology is verified on the IEEE-33 bus and IEEE-69 bus systems. According to the findings, the obtained results are consistent as compared to other existing techniques. These findings are taken into consideration to analyze the reliability of electrical distribution networks. It is stated that using adequate reliability data of appropriately integrated DG and EVs increases the electrical system’s reliability.Mohd BilalM. RizwanIbrahim AlsaidanFahad M. AlmasoudiIEEEarticleArtificial intelligenceelectric vehiclecharging stationsradial distribution systemdistributed generatorsreliabilityElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 154204-154224 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Artificial intelligence electric vehicle charging stations radial distribution system distributed generators reliability Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Artificial intelligence electric vehicle charging stations radial distribution system distributed generators reliability Electrical engineering. Electronics. Nuclear engineering TK1-9971 Mohd Bilal M. Rizwan Ibrahim Alsaidan Fahad M. Almasoudi AI-Based Approach for Optimal Placement of EVCS and DG With Reliability Analysis
description	It is expected that future transport will rely on electric vehicles (EVs) due to their sustainability and reduced greenhouse gas emissions. However, the rapid increase in electric load penetration causes several other concerns, including a generation-demand mismatch, increased network active power loss, a degradation in voltage profile, and decreased voltage stability margin. To overcome the issues mentioned earlier, proper integration of electric vehicle charging stations (EVCS) at appropriate locations is essential. The connection of an EVCS to the electricity grid will bring new challenges. Distributed generation (DG) sources are incorporated with EVCS to lessen the impact of EV charging load. In this study, charging stations are combined with DG units, which increases the motivation to use EVs. This study proposes an artificial intelligence (AI) approach, the hybrid of grey wolf optimization and particle swarm optimization, i.e., HGWOPSO, to investigate the suitable nodes for EVCS and DGs in a balanced distribution system. The proposed methodology is verified on the IEEE-33 bus and IEEE-69 bus systems. According to the findings, the obtained results are consistent as compared to other existing techniques. These findings are taken into consideration to analyze the reliability of electrical distribution networks. It is stated that using adequate reliability data of appropriately integrated DG and EVs increases the electrical system’s reliability.
format	article
author	Mohd Bilal M. Rizwan Ibrahim Alsaidan Fahad M. Almasoudi
author_facet	Mohd Bilal M. Rizwan Ibrahim Alsaidan Fahad M. Almasoudi
author_sort	Mohd Bilal
title	AI-Based Approach for Optimal Placement of EVCS and DG With Reliability Analysis
title_short	AI-Based Approach for Optimal Placement of EVCS and DG With Reliability Analysis
title_full	AI-Based Approach for Optimal Placement of EVCS and DG With Reliability Analysis
title_fullStr	AI-Based Approach for Optimal Placement of EVCS and DG With Reliability Analysis
title_full_unstemmed	AI-Based Approach for Optimal Placement of EVCS and DG With Reliability Analysis
title_sort	ai-based approach for optimal placement of evcs and dg with reliability analysis
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/60ad7576e0b544f584c3f05688ff3204
work_keys_str_mv	AT mohdbilal aibasedapproachforoptimalplacementofevcsanddgwithreliabilityanalysis AT mrizwan aibasedapproachforoptimalplacementofevcsanddgwithreliabilityanalysis AT ibrahimalsaidan aibasedapproachforoptimalplacementofevcsanddgwithreliabilityanalysis AT fahadmalmasoudi aibasedapproachforoptimalplacementofevcsanddgwithreliabilityanalysis
_version_	1718416094923849728

AI-Based Approach for Optimal Placement of EVCS and DG With Reliability Analysis

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