M. Cenký, J. Bendík, Ž. Eleschová, A. Beláň, B. Cintula, P. Janiga
{"title":"Probabilistic Model of Electric Vehicle Charging Demand to Distribution Network Impact Analyses","authors":"M. Cenký, J. Bendík, Ž. Eleschová, A. Beláň, B. Cintula, P. Janiga","doi":"10.1109/EPE.2019.8777973","DOIUrl":null,"url":null,"abstract":"Future distribution networks will be quite different to those, we know today. One of the main factors of impact regarding the distribution net, is the progress in the e-mobility sector. Most accurate modelling of such network is by using real data from the consumers, mainly gathering data from the e-mobiles. Availability of such data in our research regions (mostly where e-mobility is expected to be growing) is extremely low. This is due to complexity of gathering such data on large scale. There's a need for accurate predictions of users behaviour in long term. This article is dealing with modelling the customer's behaviour based on the international statistics and its implementation into the probabilistic model itself.","PeriodicalId":117212,"journal":{"name":"2019 20th International Scientific Conference on Electric Power Engineering (EPE)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 20th International Scientific Conference on Electric Power Engineering (EPE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EPE.2019.8777973","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
Future distribution networks will be quite different to those, we know today. One of the main factors of impact regarding the distribution net, is the progress in the e-mobility sector. Most accurate modelling of such network is by using real data from the consumers, mainly gathering data from the e-mobiles. Availability of such data in our research regions (mostly where e-mobility is expected to be growing) is extremely low. This is due to complexity of gathering such data on large scale. There's a need for accurate predictions of users behaviour in long term. This article is dealing with modelling the customer's behaviour based on the international statistics and its implementation into the probabilistic model itself.