Agent-Based Model for Electric Vehicle Charging Systems Analysis
A new configurable, grid-aware Agent-Based Model (ABM) has been created by researchers to examine electric vehicle (EV) charging systems across diverse operational and infrastructure conditions. This model incorporates varied EV behaviors, constraints of charging columns, and a communal Energy Sandbox that governs overall power distribution, facilitating a comprehensive analysis of user-oriented charging dynamics alongside facility-level power interactions. Developed in Python with the SimPy discrete-event framework, it allows for scalable, event-driven simulations tailored to different system sizes, charger types, and scheduling methods. A typical workplace charging scenario was analyzed to demonstrate how infrastructure setup and coordination affect energy delivery efficiency, infrastructure use, and overall load characteristics. Findings underscore the context-specific nature of infrastructure effectiveness. The paper can be accessed on arXiv with identifier 2604.27849.
Key facts
- The paper presents a configurable, grid-aware Agent-Based Model (ABM) for EV charging systems.
- The model integrates heterogeneous EV behavior, charging column constraints, and a shared Energy Sandbox.
- The Energy Sandbox regulates aggregate power allocation.
- The model enables joint study of user-centric charging dynamics and facility-level power behavior.
- Implemented in Python using the SimPy discrete-event framework.
- Supports scalable, event-driven simulations across varying system sizes, charger compositions, and scheduling strategies.
- A representative workplace charging scenario was investigated.
- Results highlight the context-dependence of infrastructure suitability.
Entities
Institutions
- arXiv