Hierarchical control for collaborative electric vehicle charging to alleviate network congestion and enhance EV hosting in constrained distribution networks

IF 9 1区 工程技术 Q1 ENERGY & FUELS Renewable Energy Pub Date : 2024-06-15 DOI:10.1016/j.renene.2024.120823
Amaia González-Garrido , Mikel González-Pérez , Francisco Javier Asensio , Andrés Felipe Cortes-Borray , Maider Santos-Mugica , Ibon Vicente-Figueirido
{"title":"Hierarchical control for collaborative electric vehicle charging to alleviate network congestion and enhance EV hosting in constrained distribution networks","authors":"Amaia González-Garrido ,&nbsp;Mikel González-Pérez ,&nbsp;Francisco Javier Asensio ,&nbsp;Andrés Felipe Cortes-Borray ,&nbsp;Maider Santos-Mugica ,&nbsp;Ibon Vicente-Figueirido","doi":"10.1016/j.renene.2024.120823","DOIUrl":null,"url":null,"abstract":"<div><p>This paper introduces a novel hierarchical architecture aimed at enhancing coordination between distribution system operators and electric vehicle aggregators in order to minimize Electric Vehicle (EV) charging costs for users while optimizing EV hosting capacity to alleviate network congestion. Real-world distribution networks are employed to evaluate EV charging strategies and their impact on medium and low-voltage networks. Two distinct EV charging optimization strategies are proposed to ensure fair power allocation among EV Aggregators (EVAs), alleviating congestion while managing EV charging power efficiently. Results demonstrate that the proposed collaborative EV charging effectively flattens the load curve, reducing peak power and avoiding grid congestion. The main findings underscore the importance of incentivizing EV flexibility to support Distribution System Operator (DSO) objectives beyond static tariffs. Furthermore, a battery degradation model is introduced into the optimization problem, reducing high currents and capacity decay. Despite capturing a higher mean electricity price, the total cost of EV charging is reduced.</p></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":null,"pages":null},"PeriodicalIF":9.0000,"publicationDate":"2024-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0960148124008917/pdfft?md5=548157205041245bde8557b19561516e&pid=1-s2.0-S0960148124008917-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Renewable Energy","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0960148124008917","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0

Abstract

This paper introduces a novel hierarchical architecture aimed at enhancing coordination between distribution system operators and electric vehicle aggregators in order to minimize Electric Vehicle (EV) charging costs for users while optimizing EV hosting capacity to alleviate network congestion. Real-world distribution networks are employed to evaluate EV charging strategies and their impact on medium and low-voltage networks. Two distinct EV charging optimization strategies are proposed to ensure fair power allocation among EV Aggregators (EVAs), alleviating congestion while managing EV charging power efficiently. Results demonstrate that the proposed collaborative EV charging effectively flattens the load curve, reducing peak power and avoiding grid congestion. The main findings underscore the importance of incentivizing EV flexibility to support Distribution System Operator (DSO) objectives beyond static tariffs. Furthermore, a battery degradation model is introduced into the optimization problem, reducing high currents and capacity decay. Despite capturing a higher mean electricity price, the total cost of EV charging is reduced.

Abstract Image

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
分级控制电动汽车协同充电,缓解网络拥堵并提高受限配电网络中的电动汽车托管能力
本文介绍了一种新颖的分层架构,旨在加强配电系统运营商与电动汽车聚合商之间的协调,以最大限度地降低用户的电动汽车(EV)充电成本,同时优化电动汽车托管容量,缓解网络拥塞。我们采用真实世界的配电网络来评估电动汽车充电策略及其对中低压网络的影响。提出了两种不同的电动汽车充电优化策略,以确保在电动汽车聚合器(EVA)之间公平分配电力,在有效管理电动汽车充电功率的同时缓解拥堵。结果表明,所提出的电动汽车协同充电能有效平滑负载曲线,降低峰值功率,避免电网拥塞。主要发现强调了激励电动汽车灵活性的重要性,以支持配电系统运营商(DSO)超越静态电价的目标。此外,在优化问题中引入了电池退化模型,减少了高电流和容量衰减。尽管平均电价较高,但电动汽车充电的总成本却降低了。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Renewable Energy
Renewable Energy 工程技术-能源与燃料
CiteScore
18.40
自引率
9.20%
发文量
1955
审稿时长
6.6 months
期刊介绍: Renewable Energy journal is dedicated to advancing knowledge and disseminating insights on various topics and technologies within renewable energy systems and components. Our mission is to support researchers, engineers, economists, manufacturers, NGOs, associations, and societies in staying updated on new developments in their respective fields and applying alternative energy solutions to current practices. As an international, multidisciplinary journal in renewable energy engineering and research, we strive to be a premier peer-reviewed platform and a trusted source of original research and reviews in the field of renewable energy. Join us in our endeavor to drive innovation and progress in sustainable energy solutions.
期刊最新文献
Influence of temperature dependent short-term storage on thermal runaway characteristics in lithium-ion batteries Energetic and exergoeconomic analysis of different configurations of power and hydrogen generation systems using solar based organic Rankine cycle and PEM electrolyzer A multi-parameter estimation of layered rock-soil thermal properties of borehole heat exchanger in a stratified subsurface Effect of the intermittency of non-conventional renewable energy sources on the volatility of the Colombian spot price H2S mitigation for biogas upgrading in a full-scale anaerobic digestion process by using artificial neural network modeling
×
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