AutoB2G: Agentic Simulation and Reinforcement Learning for Spatio-Temporal Grid-Interactive Building Control

Grid-interactive building control can improve demand-side flexibility, but realistic evaluation requires coordinated co-simulation of buildings, reinforcement learning (RL), and distribution grids. Translating user intent into executable co-simulation workflows is labor-intensive, requiring the manual integration and configuration of heterogeneous simulators and interfaces. We introduce AutoB2G, a grid-aware building-grid co-simulation environment integrating CityLearn V2 with Pandapower and OpenDSS. It supports balanced and three-phase unbalanced grid simulation and a broad range of grid-side evaluation metrics. AutoB2G organizes simulation components as a directed acyclic graph (DAG), explicitly encoding module dependencies and execution order for dependency-aware retrieval and composition. Building on this structure, we develop a large language model (LLM)-based agentic framework that automatically retrieves, composes, executes, verifies, and repairs simulation pipelines from natural-language task descriptions. Experiments on realistic distribution networks show improved reliability and executability of complex building-grid workflows, demonstrating the potential of agentic LLMs for scientific simulation orchestration in energy systems.