Service Time Analysis For Electric Vehicle Charging Infrastructure

Abstract

This paper analyzes electric vehicle charging patterns in Jeju City, taking advantage of open software such as MySQL, Hadoop, and R, as well as open data obtained from the real-time charger monitoring system currently in operation. Main observation points lie in average service time, maximum service time, and the number of transactions, while we measure the effect of both temporal and spatial factors to them. According to the analysis result, the average service time is almost constant for all parameters. The charging time of 88.7 % transactions ranges from 10 to 40 minutes, while abnormally long transactions occupy just 3.4 % for fast chargers. The day-by-day difference in the number of charging transactions is 28.6 % at maximum, while Wednesday shows the largest number of transactions. Additionally, geographic information-based analysis tells that the charging demand is concentrated in those regions having many tourist attractions and administrative offices. With this analysis, it is possible to predict when a charger will be idle and allocate it to another service such as V2G or renewable energy integration.

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

“…The population size is 100, while the genetic loop iterates 1,000 times. In addition, occupancy rate is the probability that a charger is used and denotes the charging demand on a charger [12].…”

Renewable energy allocation based on maximum flow modelling within a microgrid

“…Hence, microgrids are required to try to accommodate as many EVs as possible, making their charging more convenient and energy-efficient. Those efforts may include charging facility siting, energy provisioning, and driver-friendly charger reservation services [10], [11]. In addition, scheduled energy consumption, combined with the real-time price adaptation, can reduce the peak load, and avoid the construction of new power plants.…”