Integrating Sociodemographics into Trip Chain Models for Residential Electric Vehicle Charging Schedule Simulation with Large Language Models
Abstract
Accurately forecasting electric vehicle (EV) charging demand is critical for managing peak loads and ensuring grid stability in regions with increasing EV adoption. Residential household peak energy usage and EV charging patterns vary significantly across areas, influenced by geographic accessibility, sociodemographic factors, charging preferences, and EV attributes. Averaging data across regions can overlook these differences, leading to an underestimation of charging demand disparities and risking grid overload during peak periods. This study introduces a spatiotemporal trip chain-based EV charging schedule simulation method to address these challenges. The methodology integrates sociodemographic and geographic data with the large language model to generate trip chains, which serve as the basis for simulating EV charging schedules and aggregating regional energy loads to forecast peak demand. A case study of Pescadero, CA employs synthetic profiles, derived from Census statistics, to model local households as EV owners and validate the practical applicability of this approach. The results emphasize the representativeness of the trip chain generation model and the effectiveness of the EV charging schedule simulation model in accurately forecasting energy consumption patterns and assessing peak load impacts. By combining sociodemographic and geographic insights, this study provides a robust tool for evaluating the peak load impacts of EV charging.
