| Title |
A Study on Idle Rate Analysis and Operational Strategies of EV Charging Infrastructure Based on Empirical Data in Jeju |
| Authors |
김수완(SuWan KIM) ; 이개명(Gae-Myoung Lee) |
| DOI |
https://doi.org/10.5370/KIEE.2026.75.6.1442 |
| Keywords |
EV charging infrastructure; Idle rate; AMI; K-Means clustering; Load unbalance; Infrastructure optimization |
| Abstract |
Rapid EV charger expansion has caused spatial mismatches. We analyzed idle rates of 23,769 Jeju EV chargers using 285,228 smart meter records (2024) using one 1 year accumulated data. Defining 'idle' as zero consumption for three consecutive months, chargers were classified into four tiers: Open-Fast, Open-Slow, Private-Fast, and Private-Slow. K-Means clustering and t-tests revealed significant disparities. Suburban 'Open-Slow' chargers showed critical idle rates due to maintenance abandonment and blind public allocation. 'Private-Fast' chargers exhibited extreme loads with zero idleness, while 'Private-Slow' units displayed sporadic idleness globally. Considering legal limits on removing private property, we propose a grid management framework: a negative list restricting new installations in high-idle zones, financial incentives for voluntary relocation, and automatic contract power suspension for idle chargers. This data-driven approach prevents budget waste and reclaims grid hosting capacity. |