A comprehensive data processing approach to the future smart grid

Yamazaki, Jiro; Yoshino, Daishi; Fukuhara, Hideyuki; Hayashi, Takafumi

doi:10.1109/icrera.2015.7418567

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…Most modern facilities, not restricted to charging stations, are connected to a management coordinator via ubiquitous and cheap communication channels [4]. In our city, namely, Jeju, Republic of Korea, which is making an extensive effort to prompt the deployment of EVs, many chargers are under construction and report their real-time working status to a central server [5].…”

Section: Introductionmentioning

confidence: 99%

Design of a Monitoring-combined Siting Scheme for Electric Vehicle Chargers

Lee

Park

2018

IJECE

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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.

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Section: Introductionmentioning

confidence: 99%