Open AccessDesign of a Monitoring-combined Siting Scheme for Electric Vehicle Chargers
Author(s) -
Junghoon Lee,
Gyung-Leen Park
Publication year - 2018
Publication title -
international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.277
H-Index - 22
ISSN - 2088-8708
DOI - 10.11591/ijece.v8i6.pp5303-5310
Subject(s) - computer science , scheme (mathematics) , electric vehicle , term (time) , cluster (spacecraft) , binary number , standard deviation , real time computing , fitness function , reduction (mathematics) , genetic algorithm , automotive engineering , simulation , engineering , mathematics , statistics , operating system , mathematical analysis , power (physics) , physics , arithmetic , quantum mechanics , machine learning , geometry
This paper designs a siting scheme for public electric vehicle chargers based on a genetic algorithm working on charger monitoring streams. The monitoring-combined allocation scheme runs on a long-term basis, iterating the process of collecting data, analyzing demand, and selecting candidates. The analysis of spatio-temporal archives, acquired from the fast chargers currently in operation, focuses on the per-charger hot hour and proximity effect to justify demand balancing in geographic cluster level. It leads to the definition of a fitness function representing the standard deviation of per-charger load and cluster-by-cluster distribution. In a chromosome, each binary integer is associated with a candidate and its static fields include the index to the cluster to which it is belonging. The performance result obtained from a prototype implementation reveals that the proposed scheme can stably distribute the charging load with an addition of a new charger, achieving the reduction of standard deviation from 8.7 % to 4.7 % in the real-world scenario.