LLM-to-Map: Transparent Conversational Tool Orchestration for Real-Time Multi-Domain Simulation

摘要

Coupled power-traffic simulations are valuable for studying EV charging stress and outage propagation, but existing tools typically require scripts and opaque configurations. We present LLM-to-Map, a demo system that exposes a real-time multi-domain simulator through structured LLM tool orchestration. Natural-language requests are mapped to typed tool calls that coordinate SUMO traffic simulation, PyPSA power-flow analysis, V2G actions, and map operations. The agent operates over a fixed tool schema and emits auditable execution logs in the UI so users can inspect every action and parameter. Tool execution is deterministic: each request resolves to explicit API calls, and unsafe or destructive actions can require confirmation. The tool layer provides JSON schemas and parameter validation, preventing arbitrary code execution and enabling reproducible runs. The agent receives live system state (loads, EV statistics, V2G status, time, temperature) to support context-aware multi-step commands; when an LLM is unavailable, a deterministic parser preserves the same tool interface. The backend synchronizes cross-domain events (EV charging demand and substation failures) and streams state updates to a 3D map interface. We describe the architecture, coupling and synchronization, and a demo workflow that showcases multi-step scenario control and reporting for non-experts without writing scripts. Demo video: https://youtu.be/CpEgCZPl_2g; code: https://github.com/MarouaneBenbrahim/Map-LLM.