Open AccessModeling of Electric Vehicle Charging Demand and Coincidence of Large-Scale Charging Loads in Different Charging Locations
Author(s) -
Ilkka Jokinen,
Matti Lehtonen
Publication year - 2023
Publication title -
ieee access
Language(s) - English
Resource type - Journals
ISSN - 2169-3536
DOI - 10.1109/access.2023.3322278
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
Battery electric vehicles (BEVs) are becoming more widespread and consequently the charging load from vehicles is rapidly increasing. For energy system and grid planning, the magnitude and coincidence of these charging loads are crucial parameters. Furthermore, to determine the charging power demand in different charging locations, the coincidence of charging in them must be examined. Thus, in this study, the coincidence factors of charging loads in different charging locations were analyzed for a large-scale BEV fleet, considering available charging power and ambient temperature. In addition, the mean charging load, deviation of load, and flexibility potential within charging events, were examined based on the same parameters. The coincidence factors of charging increased with lower available charging power and lower ambient temperature. By location type, the highest factors were at work, at hotel, and at home, but overall, the coincidence of charging remained low for a large-scale BEV fleet. Moreover, the relative standard deviation of a composite load for a large number of BEVs was low, whereas the opposite was found for a small number of BEVs. The modeling of the charging loads in this study were based on travel survey data, from which 12773 respondents with 40321 trips were included.