协调电动汽车充电以减少电网影响的算法比较与分析

IF 5.3 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Open Journal of Vehicular Technology Pub Date : 2024-07-30 DOI:10.1109/OJVT.2024.3435489
Cesar Diaz-Londono;Paolo Maffezzoni;Luca Daniel;Giambattista Gruosso
{"title":"协调电动汽车充电以减少电网影响的算法比较与分析","authors":"Cesar Diaz-Londono;Paolo Maffezzoni;Luca Daniel;Giambattista Gruosso","doi":"10.1109/OJVT.2024.3435489","DOIUrl":null,"url":null,"abstract":"Electric vehicle (EV) adoption has been increasing rapidly, posing new challenges for integrating EV charging infrastructure with the existing electrical grid. Uncoordinated charging of EVs can cause transformers to overload, leading to instability and unreliability in the grid. This article introduces two smart charging coordinators for EV charging pools designed to manage EV charging while considering transformer power limits. The first strategy aims to minimize operational costs, while the second maximizes the charger flexibility. Both coordinators account for uncertainties in EV arrival time and state of charge, as well as inflexible demands on transformers. The strategies are evaluated and compared using grid-aware and grid-unaware methods regarding transformer power limits. Real-world datasets are utilized to assess the performance of the proposed strategies through simulation studies across three scenarios: single charging station behavior, average parking lot occupancy, and worst-case occupancy scenarios. Comparative analysis against uncoordinated and coordinated strategies from the literature reveals that the flexibility maximization strategy provides the most uniform response, effectively mitigating transformer overload events by optimizing charging power and scheduling flexibility. The study underscores the importance of accurate, innovative charging strategies for seamless EV integration and emphasizes the necessity of coordinated charging pools for reliable EV charging operations.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":null,"pages":null},"PeriodicalIF":5.3000,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10614891","citationCount":"0","resultStr":"{\"title\":\"Comparison and Analysis of Algorithms for Coordinated EV Charging to Reduce Power Grid Impact\",\"authors\":\"Cesar Diaz-Londono;Paolo Maffezzoni;Luca Daniel;Giambattista Gruosso\",\"doi\":\"10.1109/OJVT.2024.3435489\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Electric vehicle (EV) adoption has been increasing rapidly, posing new challenges for integrating EV charging infrastructure with the existing electrical grid. Uncoordinated charging of EVs can cause transformers to overload, leading to instability and unreliability in the grid. This article introduces two smart charging coordinators for EV charging pools designed to manage EV charging while considering transformer power limits. The first strategy aims to minimize operational costs, while the second maximizes the charger flexibility. Both coordinators account for uncertainties in EV arrival time and state of charge, as well as inflexible demands on transformers. The strategies are evaluated and compared using grid-aware and grid-unaware methods regarding transformer power limits. Real-world datasets are utilized to assess the performance of the proposed strategies through simulation studies across three scenarios: single charging station behavior, average parking lot occupancy, and worst-case occupancy scenarios. Comparative analysis against uncoordinated and coordinated strategies from the literature reveals that the flexibility maximization strategy provides the most uniform response, effectively mitigating transformer overload events by optimizing charging power and scheduling flexibility. The study underscores the importance of accurate, innovative charging strategies for seamless EV integration and emphasizes the necessity of coordinated charging pools for reliable EV charging operations.\",\"PeriodicalId\":34270,\"journal\":{\"name\":\"IEEE Open Journal of Vehicular Technology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.3000,\"publicationDate\":\"2024-07-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10614891\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Open Journal of Vehicular Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10614891/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Open Journal of Vehicular Technology","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10614891/","RegionNum":0,"RegionCategory":null,"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)的采用率一直在快速增长,这给电动汽车充电基础设施与现有电网的整合带来了新的挑战。不协调的电动汽车充电会导致变压器过载,从而导致电网的不稳定和不可靠。本文介绍了两种用于电动汽车充电池的智能充电协调器,旨在管理电动汽车充电,同时考虑变压器功率限制。第一种策略旨在最大限度地降低运营成本,而第二种策略则最大限度地提高充电器的灵活性。两种协调器都考虑了电动汽车到达时间和充电状态的不确定性,以及对变压器的不灵活需求。在变压器功率限制方面,采用电网感知和电网非感知方法对这两种策略进行了评估和比较。利用真实世界的数据集,通过对三种场景的模拟研究来评估所提出策略的性能:单一充电站行为、停车场平均占用率和最坏情况占用率场景。与文献中的非协调策略和协调策略进行比较分析后发现,灵活性最大化策略提供了最统一的响应,通过优化充电功率和调度灵活性,有效缓解了变压器过载事件。这项研究强调了准确、创新的充电策略对电动汽车无缝集成的重要性,并强调了协调充电池对可靠的电动汽车充电运营的必要性。
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
Comparison and Analysis of Algorithms for Coordinated EV Charging to Reduce Power Grid Impact
Electric vehicle (EV) adoption has been increasing rapidly, posing new challenges for integrating EV charging infrastructure with the existing electrical grid. Uncoordinated charging of EVs can cause transformers to overload, leading to instability and unreliability in the grid. This article introduces two smart charging coordinators for EV charging pools designed to manage EV charging while considering transformer power limits. The first strategy aims to minimize operational costs, while the second maximizes the charger flexibility. Both coordinators account for uncertainties in EV arrival time and state of charge, as well as inflexible demands on transformers. The strategies are evaluated and compared using grid-aware and grid-unaware methods regarding transformer power limits. Real-world datasets are utilized to assess the performance of the proposed strategies through simulation studies across three scenarios: single charging station behavior, average parking lot occupancy, and worst-case occupancy scenarios. Comparative analysis against uncoordinated and coordinated strategies from the literature reveals that the flexibility maximization strategy provides the most uniform response, effectively mitigating transformer overload events by optimizing charging power and scheduling flexibility. The study underscores the importance of accurate, innovative charging strategies for seamless EV integration and emphasizes the necessity of coordinated charging pools for reliable EV charging operations.
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
CiteScore
9.60
自引率
0.00%
发文量
25
审稿时长
10 weeks
期刊最新文献
Digital Twin-Empowered Green Mobility Management in Next-Gen Transportation Networks Fairness-Aware Utility Maximization for Multi-UAV-Aided Terrestrial Networks LiFi for Industry 4.0: Main Features, Implementation and Initial Testing of IEEE Std 802.15.13 Partial Learning-Based Iterative Detection of MIMO Systems Decentralized and Asymmetric Multi-Agent Learning in Construction Sites
×
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