DOE OSTI · 2587834
ATEAM4Py: An Efficient and Scalable Python-Based Model for Charging Demand
Abstract
This report details the development and implementation of ATEAM4Py, a Python-based simulation model that projects demand for battery electric vehicle (BEV) charging based on adoption trends and consumer behavior. With Exelon’s support, Argonne National Laboratory converted the original Java-based Agent-based Transportation Energy Analysis Model (ATEAM) into Python, resulting in a faster and more efficient tool for forecasting the timing, location, and scale of charging demand growth. ATEAM4Py tackles key challenges in simulation efficiency and runtime, supporting the strategic development of cost-effective grid capacity expansion strategies and ensuring reliable service for stakeholders.
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Siddique, Nazib [Argonne National Laboratory (ANL), Argonne, IL (United States)], Barua, Limon [Argonne National Laboratory (ANL), Argonne, IL (United States)], Zhou, Yan [Argonne National Laboratory (ANL), Argonne, IL (United States)], Tatara, Eric [Argonne National Laboratory (ANL), Argonne, IL (United States)] (ORCID:0000000179274255), Perk, Sinem [Argonne National Laboratory (ANL), Argonne, IL (United States)], Macal, Charles [Argonne National Laboratory (ANL), Argonne, IL (United States)], Derr, Brian [Exelon Generation, Chicago, IL (United States)], Karathanou, Argyro [Exelon Generation, Chicago, IL (United States)]. 2025-01-01. ATEAM4Py: An Efficient and Scalable Python-Based Model for Charging Demand. https://doi.org/10.2172/2587834
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