Prediction of Electric Vehicle Charging Load with Uncertainty using Probabilistic Methodology

J. Das, Venugopalan K, Johnson Daniel
{"title":"Prediction of Electric Vehicle Charging Load with Uncertainty using Probabilistic Methodology","authors":"J. Das, Venugopalan K, Johnson Daniel","doi":"10.1109/IPRECON55716.2022.10059502","DOIUrl":null,"url":null,"abstract":"The development of Electric Vehicle technology in a region will bring forward new challenges in the reliable operation of the distribution network. Accuracy and reliability in EV load forecasting can effectively comprehend EV load on a large scale. Compared to other load forecasting techniques, there are very few published literature with different EV load prediction. This paper presents a probabilistic approach to predict localized EV load subject to operational and social constraints. The power system under consideration is the urban location of Kochi, Kerala. The uncertainty associated with the input values and the generated EV load has been quantified and presented graphically. This method can be used to predict the variability in the output owing to the variable nature of the input variables used for analysis.","PeriodicalId":407222,"journal":{"name":"2022 IEEE International Power and Renewable Energy Conference (IPRECON)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE International Power and Renewable Energy Conference (IPRECON)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IPRECON55716.2022.10059502","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

The development of Electric Vehicle technology in a region will bring forward new challenges in the reliable operation of the distribution network. Accuracy and reliability in EV load forecasting can effectively comprehend EV load on a large scale. Compared to other load forecasting techniques, there are very few published literature with different EV load prediction. This paper presents a probabilistic approach to predict localized EV load subject to operational and social constraints. The power system under consideration is the urban location of Kochi, Kerala. The uncertainty associated with the input values and the generated EV load has been quantified and presented graphically. This method can be used to predict the variability in the output owing to the variable nature of the input variables used for analysis.
基于概率方法的不确定性电动汽车充电负荷预测
随着电动汽车技术在一个地区的发展,对配电网的可靠运行提出了新的挑战。电动汽车负荷预测的准确性和可靠性可以有效地大规模了解电动汽车负荷。与其他负荷预测技术相比,对电动汽车负荷进行不同预测的文献很少。本文提出了一种基于运行约束和社会约束的电动汽车局部负荷概率预测方法。正在考虑的电力系统是喀拉拉邦科钦的城市位置。与输入值和产生的EV负载相关的不确定性已被量化并以图形表示。由于用于分析的输入变量的可变性质,这种方法可用于预测输出中的可变性。
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 求助全文
来源期刊
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信