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

arXiv: https://arxiv.org/abs/2604.27849 · HTML: https://mlsanity.com/paper/2604.27849/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 25 references that are in the map.
- First posted on arXiv 30 Apr 2026.

## Builds on (most cited first, 3 of 3)

- [EV-EcoSim: A grid-aware co-simulation platform for the design and optimization of electric vehicle charging infrastructure](https://mlsanity.com/paper/2401.04705/) (2024)
- [Multi-Agent Based Simulation for Decentralized Electric Vehicle Charging Strategies and their Impacts](https://mlsanity.com/paper/2408.10790/) (2024)
- [Assessing EV Charging Impacts on Power Distribution Systems: A Unified Co-Simulation Framework](https://mlsanity.com/paper/2505.21773/) (2025)

## Abstract (arXiv)

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.