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Electric vehicles (EVs) play a crucial role in achieving sustainability goals, mitigating energy crises, and reducing air pollution. However, their rapid adoption poses significant challenges to the power grid, particularly during peak charging periods, necessitating advanced load management strategies. This study introduces an artificial intelligence (AI)-integrated optimal charging framework designed to facilitate fast charging and mitigate grid stress by smoothing the “duck curve”. Data from Caltech’s Adaptive Charging Network (ACN) at the National Aeronautics and Space Administration (NASA) Jet Propulsion Laboratory (JPL) site was collected and categorized into day and night patterns to predict charging duration based on key features, including start charging time and energy requested. The AI-driven charging strategy developed optimizes energy management, reduces peak loads, and alleviates grid strain. Additionally, the study evaluates the impact of integrating 1.5 million, 3 million, and 5 million EVs under various AI-based charging strategies, demonstrating the framework’s effectiveness in managing large-scale EV adoption. The peak power consumption reaches around 22,000 MW without EVs, 25,000 MW for 1.5 million EVs, 28,000 MW for 3 million EVs, and 35,000 MW for 5 million EVs without any charging strategy. By implementing an AI-driven optimal charging optimization strategy that considers both early charging and duck curve smoothing, the peak demand is reduced by approximately 16% for 1.5 million EVs, 21.43% for 3 million EVs, and 34.29% for 5 million EVs.
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This content will become publicly available on August 1, 2026
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.
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- Award ID(s):
- 2330504
- PAR ID:
- 10655347
- Publisher / Repository:
- The American Society of Michanical Engineers
- Date Published:
- Journal Name:
- ASME Journal of Engineering for Sustainable Buildings and Cities
- Volume:
- 6
- Issue:
- 3
- ISSN:
- 2642-6641
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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