A Study on Idle Rate Analysis and Operational Strategies of EV Charging Infrastructure Based on Empirical Data in Jeju

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.