Problem
Research questions and friction points this paper is trying to address.
electric vehicle charging
grid-aware
agent-based model
charging infrastructure
power allocation
A Grid-Aware Agent-Based Model for Analyzing Electric Vehicle Charging Systems
🤖 AI Summary
Existing simulation models struggle to simultaneously capture user charging behavior, charging infrastructure constraints, and grid interactions, limiting systematic evaluation of electric vehicle (EV) charging systems. This work proposes a grid-aware multi-agent discrete-event simulation framework that, for the first time, couples user-level charging dynamics with facility-level grid responses and introduces a shared energy sandbox mechanism to enable aggregated power regulation. The model integrates heterogeneous EV behaviors, charger constraints, and real-time power allocation algorithms, supporting flexible configuration of infrastructure and scheduling strategies. Experiments in a representative workplace scenario demonstrate that the combination of charging strategies and charger types significantly affects service performance, facility utilization, and grid load, underscoring the high degree of scenario dependency in infrastructure design and deployment.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Agent-Based Model
Grid-Aware
Electric Vehicle Charging
Discrete-Event Simulation
Energy Sandbox
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