A Visualization Framework for Exploring Multi-Agent-Based Simulations Case Study of an Electric Vehicle Home Charging Ecosystem

TL;DR

This paper introduces a Python-based dashboard framework that enables detailed exploration and analysis of complex multi-agent simulation data for electric vehicle charging ecosystems, aiding in anomaly detection and system understanding.

Contribution

The paper presents a modular, multi-view visualization system that enhances exploration and root-cause analysis of large, complex MABS datasets in energy systems.

Findings

Supports rapid anomaly detection in simulation data

Facilitates understanding of emergent behaviors in energy systems

Adaptable to various distributed energy resources

Abstract

Multi-agent-based simulations (MABS) of electric vehicle (EV) home charging ecosystems generate large, complex, and stochastic time-series datasets that capture interactions between households, grid infrastructure, and energy markets. These interactions can lead to unexpected system-level events, such as transformer overloads or consumer dissatisfaction, that are difficult to detect and explain through static post-processing. This paper presents a modular, Python-based dashboard framework, built using Dash by Plotly, that enables efficient, multi-level exploration and root-cause analysis of emergent behavior in MABS outputs. The system features three coordinated views (System Overview, System Analysis, and Consumer Analysis), each offering high-resolution visualizations such as time-series plots, spatial heatmaps, and agent-specific drill-down tools. A case study simulating full EV adoption with smart charging in a Danish residential network demonstrates how the dashboard supports rapid identification and contextual explanation of anomalies, including clustered transformer overloads and time-dependent charging failures. The framework facilitates actionable insight generation for researchers and distribution system operators, and its architecture is adaptable to other distributed energy resources and complex energy systems.