A Grid-Aware Agent-Based Model for Analyzing Electric Vehicle Charging Systems
For researchers and planners of EV charging infrastructure, this model provides a flexible simulation tool to study user-centric and grid-aware dynamics, though the results are preliminary and context-specific.
This paper introduces a grid-aware Agent-Based Model for analyzing EV charging systems, integrating heterogeneous user behavior and power constraints. A workplace scenario shows that infrastructure configuration and charging strategies significantly impact energy delivery, utilization, and load characteristics.
This paper presents a configurable, grid-aware Agent-Based Model (ABM) for the systematic analysis of electric vehicle (EV) charging systems under configurable infrastructure and operational conditions. The model integrates heterogeneous EV behavior, charging column constraints, and a shared Energy Sandbox that regulates aggregate power allocation, enabling the joint study of user-centric charging dynamics and facility-level power behavior. Implemented in Python using the SimPy discrete-event framework, the approach supports scalable, event-driven simulations across varying system sizes, charger compositions, and scheduling strategies. A representative workplace charging scenario is investigated to illustrate how infrastructure configuration and coordination mechanisms influence energy delivery performance, infrastructure utilization, and aggregate load characteristics. The results highlight the context-dependence of infrastructure suitability and demonstrate how charging strategies and charger types reshape both service-level outcomes and grid-facing behavior. The proposed ABM provides a flexible and extensible simulation environment for exploring technical, operational, and grid-aware aspects of EV charging ecosystems, and for serving as a methodological basis for subsequent studies on advanced coordination strategies beyond the specific scenario analyzed in this study.