ATEAM4Py: An Efficient and Scalable Python-Based Model for Charging Demand
- Argonne National Laboratory (ANL), Argonne, IL (United States)
- Exelon Generation, Chicago, IL (United States)
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.
- Research Organization:
- Argonne National Laboratory (ANL), Argonne, IL (United States)
- Sponsoring Organization:
- US Department of Energy; Exelon Generation, Chicago, IL (United States)
- DOE Contract Number:
- AC02-06CH11357
- OSTI ID:
- 2587834
- Report Number(s):
- ANL--25/31; CRADA-A16155; 198383
- Country of Publication:
- United States
- Language:
- English
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