A Grid-Aware Agent-Based Model for Analyzing Electric Vehicle Charging Systems

TL;DR

This paper introduces a flexible, grid-aware Agent-Based Model for analyzing electric vehicle charging systems, capturing user behavior, infrastructure constraints, and grid interactions to support scalable simulations.

Contribution

The paper presents a novel, configurable ABM framework that integrates heterogeneous EV behaviors, infrastructure constraints, and grid regulation for comprehensive EV charging analysis.

Findings

Infrastructure configuration significantly affects energy delivery and utilization.

Charging strategies influence both service quality and grid load characteristics.

The model demonstrates the importance of coordination mechanisms in EV charging systems.

Abstract

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.