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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EDP Sciences
2021-01-01
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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 |
work_keys_str_mv |
AT xulin researchonelectricvehicleloadforecastingbasedontraveldata AT wangbing researchonelectricvehicleloadforecastingbasedontraveldata AT chengmingxi researchonelectricvehicleloadforecastingbasedontraveldata AT fangshangshang researchonelectricvehicleloadforecastingbasedontraveldata |
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