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

2025-06-25

AI Summary

Large-scale, stochastic, high-dimensional time-series data generated by multi-agent-based simulation (MABS) of residential electric vehicle (EV) charging systems pose significant challenges in effectively detecting and interpreting system-level anomalies—such as transformer overloading or user charging failures. To address this, we propose a modular, tri-view coordinated visualization framework built on Python Dash, integrating time-series trend plots, spatial heatmaps, and agent-level drill-down analysis. The framework enables rapid anomaly localization, contextual attribution, and root-cause tracing, thereby substantially enhancing the interpretability and decision-support capability of energy system simulations. Applied to a Danish residential distribution network case study, it successfully uncovers time-driven clustered overloading events and regionally localized charging failure patterns. Designed for scalability and modularity, the framework is readily adaptable to other distributed energy resource (DER) system analysis scenarios.

Technology Category

• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Web Mining and Content Analysis: Web data visualization
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

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.

Problem

• Visualizing complex multi-agent EV charging simulation data
• Detecting and explaining unexpected system-level events
• Providing interactive root-cause analysis for anomalies

Innovation

• Python-based dashboard framework for MABS
• Three coordinated high-resolution visualization views
• Adaptable to distributed energy resources