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

• URL: http://arxiv.org/abs/2604.27849v1
• Date: Thu, 30 Apr 2026 13:33:23 GMT
• Title: A Grid-Aware Agent-Based Model for Analyzing Electric Vehicle Charging Systems
• Abstract summary: The model integrates heterogeneous EV behavior, charging column constraints, and a shared Energy Sandbox that regulates aggregate power allocation.<n>The approach supports scalable, event-driven simulations across varying system sizes, charger compositions, and scheduling strategies.
• Score: 0.5872014229110213
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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