Research on electric vehicle load forecasting based on travel data

Due to the rapid promotion of electric vehicles, large-scale charging behavior of electric vehicles brings a large number of time and space highly random charging load, which will have a great impact on the safe operation of distribution network. This paper proposes a planning method of electric veh...

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Main Authors: Xu Lin, Wang Bing, Cheng Mingxi, Fang Shangshang
Format: Article
Language:English
Published: EDP Sciences 2021-01-01
Series:E3S Web of Conferences
Online Access:https://www.e3s-conferences.org/articles/e3sconf/pdf/2021/33/e3sconf_aesee2021_01017.pdf
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spelling doaj-6179f53e80f54df79cd2fb48255798682021-05-28T12:42:12ZengEDP SciencesE3S Web of Conferences2267-12422021-01-012570101710.1051/e3sconf/202125701017e3sconf_aesee2021_01017Research on electric vehicle load forecasting based on travel dataXu Lin0Wang Bing1Cheng Mingxi2Fang Shangshang3College of Energy and Electrical Engineering, Hohai UniversityCollege of Energy and Electrical Engineering, Hohai UniversityCollege of Energy and Electrical Engineering, Hohai UniversityCollege of Energy and Electrical Engineering, Hohai UniversityDue to the rapid promotion of electric vehicles, large-scale charging behavior of electric vehicles brings a large number of time and space highly random charging load, which will have a great impact on the safe operation of distribution network. This paper proposes a planning method of electric vehicle charging station based on travel data. Firstly, the didi trip data is processed and mined to get the trip matrix and other information. Then, the electric vehicle charging load forecasting model is established based on the established unit mileage power consumption model and charging model, and the charging demand distribution information is predicted by Monte Carlo method. Finally, the simulation analysis is carried out based on the trip data of some areas of a city, which shows the effectiveness of the established model feasibility.https://www.e3s-conferences.org/articles/e3sconf/pdf/2021/33/e3sconf_aesee2021_01017.pdf
collection DOAJ
language English
format Article
sources DOAJ
author Xu Lin
Wang Bing
Cheng Mingxi
Fang Shangshang
spellingShingle Xu Lin
Wang Bing
Cheng Mingxi
Fang Shangshang
Research on electric vehicle load forecasting based on travel data
E3S Web of Conferences
author_facet Xu Lin
Wang Bing
Cheng Mingxi
Fang Shangshang
author_sort Xu Lin
title Research on electric vehicle load forecasting based on travel data
title_short Research on electric vehicle load forecasting based on travel data
title_full Research on electric vehicle load forecasting based on travel data
title_fullStr Research on electric vehicle load forecasting based on travel data
title_full_unstemmed Research on electric vehicle load forecasting based on travel data
title_sort research on electric vehicle load forecasting based on travel data
publisher EDP Sciences
series E3S Web of Conferences
issn 2267-1242
publishDate 2021-01-01
description Due to the rapid promotion of electric vehicles, large-scale charging behavior of electric vehicles brings a large number of time and space highly random charging load, which will have a great impact on the safe operation of distribution network. This paper proposes a planning method of electric vehicle charging station based on travel data. Firstly, the didi trip data is processed and mined to get the trip matrix and other information. Then, the electric vehicle charging load forecasting model is established based on the established unit mileage power consumption model and charging model, and the charging demand distribution information is predicted by Monte Carlo method. Finally, the simulation analysis is carried out based on the trip data of some areas of a city, which shows the effectiveness of the established model feasibility.
url https://www.e3s-conferences.org/articles/e3sconf/pdf/2021/33/e3sconf_aesee2021_01017.pdf
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