考虑可靠性的径向配电网络中电动汽车充电站和电容器的优化同步分配

IF 5.7 1区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Journal of Modern Power Systems and Clean Energy Pub Date : 2024-04-18 DOI:10.35833/MPCE.2023.000674
B. Vinod Kumar;Aneesa Farhan M A
{"title":"考虑可靠性的径向配电网络中电动汽车充电站和电容器的优化同步分配","authors":"B. Vinod Kumar;Aneesa Farhan M A","doi":"10.35833/MPCE.2023.000674","DOIUrl":null,"url":null,"abstract":"The popularity of electric vehicles (EVs) has sparked a greater awareness of carbon emissions and climate impact. Urban mobility expansion and EV adoption have led to an increased infrastructure for electric vehicle charging stations (EVCSs), impacting radial distribution networks (RDNs). To reduce the impact of voltage drop, the increased power loss (PL), lower system interruption costs, and proper allocation and positioning of the EVCSs and capacitors are necessary. This paper focuses on the allocation of EVCS and capacitor installations in RDN by maximizing net present value (NPV), considering the reduction in energy losses and interruption costs. As a part of the analysis considering reliability, several compensation coefficients are used to evaluate failure rates and pinpoint those that will improve NPV. To locate the best nodes for EVCSs and capacitors, the hybrid of grey wolf optimization (GWO) and particle swarm optimization (PSO) (HGWO_PSO) and the hybrid of PSO and Cuckoo search (CS) (HPSO_CS) algorithms are proposed, forming a combination of GWO, PSO, and CS optimizations. The impact of EVCSs on NPV is also investigated in this paper. The effectiveness of the proposed optimization algorithms is validated on an IEEE 33-bus RDN.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"12 5","pages":"1584-1595"},"PeriodicalIF":5.7000,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10505134","citationCount":"0","resultStr":"{\"title\":\"Optimal Simultaneous Allocation of Electric Vehicle Charging Stations and Capacitors in Radial Distribution Network Considering Reliability\",\"authors\":\"B. Vinod Kumar;Aneesa Farhan M A\",\"doi\":\"10.35833/MPCE.2023.000674\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The popularity of electric vehicles (EVs) has sparked a greater awareness of carbon emissions and climate impact. Urban mobility expansion and EV adoption have led to an increased infrastructure for electric vehicle charging stations (EVCSs), impacting radial distribution networks (RDNs). To reduce the impact of voltage drop, the increased power loss (PL), lower system interruption costs, and proper allocation and positioning of the EVCSs and capacitors are necessary. This paper focuses on the allocation of EVCS and capacitor installations in RDN by maximizing net present value (NPV), considering the reduction in energy losses and interruption costs. As a part of the analysis considering reliability, several compensation coefficients are used to evaluate failure rates and pinpoint those that will improve NPV. To locate the best nodes for EVCSs and capacitors, the hybrid of grey wolf optimization (GWO) and particle swarm optimization (PSO) (HGWO_PSO) and the hybrid of PSO and Cuckoo search (CS) (HPSO_CS) algorithms are proposed, forming a combination of GWO, PSO, and CS optimizations. The impact of EVCSs on NPV is also investigated in this paper. The effectiveness of the proposed optimization algorithms is validated on an IEEE 33-bus RDN.\",\"PeriodicalId\":51326,\"journal\":{\"name\":\"Journal of Modern Power Systems and Clean Energy\",\"volume\":\"12 5\",\"pages\":\"1584-1595\"},\"PeriodicalIF\":5.7000,\"publicationDate\":\"2024-04-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10505134\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Modern Power Systems and Clean Energy\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10505134/\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Modern Power Systems and Clean Energy","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10505134/","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0

摘要

电动汽车(EV)的普及提高了人们对碳排放和气候影响的认识。城市交通的扩张和电动汽车的采用导致电动汽车充电站(EVCS)基础设施的增加,对径向配电网络(RDN)产生了影响。为了降低电压降的影响、减少增加的功率损耗 (PL)、降低系统中断成本以及合理分配和定位 EVCS 和电容器,这些都是必要的。本文的重点是通过净现值(NPV)最大化,考虑减少能量损失和中断成本,在 RDN 中分配 EVCS 和电容器的安装。作为可靠性分析的一部分,本文使用了多个补偿系数来评估故障率,并找出可提高净现值的补偿系数。为确定 EVCS 和电容器的最佳节点,提出了灰狼优化(GWO)和粒子群优化(PSO)混合算法(HGWO_PSO)以及 PSO 和布谷鸟搜索(CS)混合算法(HPSO_CS),形成了 GWO、PSO 和 CS 优化的组合。本文还研究了 EVCS 对净现值的影响。本文在 IEEE 33 总线 RDN 上验证了所提优化算法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Optimal Simultaneous Allocation of Electric Vehicle Charging Stations and Capacitors in Radial Distribution Network Considering Reliability
The popularity of electric vehicles (EVs) has sparked a greater awareness of carbon emissions and climate impact. Urban mobility expansion and EV adoption have led to an increased infrastructure for electric vehicle charging stations (EVCSs), impacting radial distribution networks (RDNs). To reduce the impact of voltage drop, the increased power loss (PL), lower system interruption costs, and proper allocation and positioning of the EVCSs and capacitors are necessary. This paper focuses on the allocation of EVCS and capacitor installations in RDN by maximizing net present value (NPV), considering the reduction in energy losses and interruption costs. As a part of the analysis considering reliability, several compensation coefficients are used to evaluate failure rates and pinpoint those that will improve NPV. To locate the best nodes for EVCSs and capacitors, the hybrid of grey wolf optimization (GWO) and particle swarm optimization (PSO) (HGWO_PSO) and the hybrid of PSO and Cuckoo search (CS) (HPSO_CS) algorithms are proposed, forming a combination of GWO, PSO, and CS optimizations. The impact of EVCSs on NPV is also investigated in this paper. The effectiveness of the proposed optimization algorithms is validated on an IEEE 33-bus RDN.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Journal of Modern Power Systems and Clean Energy
Journal of Modern Power Systems and Clean Energy ENGINEERING, ELECTRICAL & ELECTRONIC-
CiteScore
12.30
自引率
14.30%
发文量
97
审稿时长
13 weeks
期刊介绍: Journal of Modern Power Systems and Clean Energy (MPCE), commencing from June, 2013, is a newly established, peer-reviewed and quarterly published journal in English. It is the first international power engineering journal originated in mainland China. MPCE publishes original papers, short letters and review articles in the field of modern power systems with focus on smart grid technology and renewable energy integration, etc.
期刊最新文献
Contents Contents Regional Power System Black Start with Run-of-River Hydropower Plant and Battery Energy Storage Power Flow Calculation for VSC-Based AC/DC Hybrid Systems Based on Fast and Flexible Holomorphic Embedding Machine Learning Based Uncertainty-Alleviating Operation Model for Distribution Systems with Energy Storage
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1