Open AccessAutonomous plug and play electric vehicle charging scenarios including reactive power provision: a probabilistic load flow analysis
Author(s) -
Melhorn Alexander C.,
McKenna Killian,
Keane Andrew,
Flynn Damian,
Dimitrovski Aleksandar
Publication year - 2017
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2016.0652
Subject(s) - probabilistic logic , plug in , ac power , voltage , computer science , electric vehicle , automotive engineering , power (physics) , load profile , simulation , control theory (sociology) , engineering , electrical engineering , electricity , physics , quantum mechanics , artificial intelligence , programming language , control (management)
Electric vehicle (EV) charging can have various impacts on low‐voltage distribution systems, along with increasing the uncertainty of network load levels. One method for capturing the statistical uncertainty is probabilistic load flow (PLF). A primary concern of such an approach is determining the correlation between the input variables. Since data are limited, a 1 min resolution bottom‐up time‐variant load model, capturing the changing voltage dependencies of the load, is used for modelling household electrical demand to provide pseudo‐meter data for the three‐phase PLF analysis of a residential distribution network. The correlation between the household and EV charging loads are implicitly taken into account by modelling the EV plug‐in and departure times with the corresponding occupancy model. Two autonomous plug‐and‐play charging scenarios are compared with a standard charging arrangement at both unity and 0.95 capacitive power factors. Four different PLF input correlation scenarios, varying from fully correlated to fully independent, are considered. The proposed charging scenarios and reactive power provision reduce the likelihood of system voltage violations introduced by EVs. The PLF results are verified against those from the initial time‐series analysis providing valuable insight into the uncertainty introduced by EVs and the correlation between household and EV loads.