Analyzing heterogeneous electric vehicle charging preferences for strategic time-of-use tariff design and infrastructure development: A latent class approach

Renewable energy resources pose the issue of intermittency and variability, leading to the “duck curve” problem. The electrification of the transportation sector, specifically the electric vehicle (EV), exacerbates this issue as charging is concentrated during evening hours when renewable energy generation decreases. This study addresses these challenges by analyzing the heterogeneous charging preferences of EV drivers using a discrete choice experiment and latent class logit model. By segmenting drivers into three distinct groups based on their sensitivity to charging costs, time, and station distance, this study reveals significant variations in charging behaviors. Using Jeju Island, South Korea, as a testbed, the impact of different time-of-use (TOU) tariff designs and infrastructure expansion scenarios on the power grid are simulated. The findings indicate that tailored TOU tariffs and strategic deployment of charging stations can reduce renewable energy curtailment by 11.3% and gas-fired power generation by 3.8% by 2030. These findings emphasize the importance of incorporating driver behavior insights into energy policy design to enhance grid stability and optimize renewable energy integration. This study provides valuable policy recommendations for achieving carbon neutrality through effective EV charging management and infrastructure planning.