LLM-Agent-Assisted PE-GNN for Accelerated MISOCP Power Flow in Hybrid AC/DC Distribution Networks

The integration of power electronics into hybrid AC/DC distribution networks challenges traditional power flow calculations with non-convex convergence bottlenecks, while pure data-driven methods lack physical feasibility guarantees. To overcome this, we propose a model-data hybrid-driven framework. First, a mixed-integer second-order cone programming (MISOCP) model guarantees exact physically feasible solutions. Second, a physics-encoder graph neural network (PE-GNN) rapidly infers high-quality initial states, significantly accelerating the MISOCP solver while adhering to hard physical limits. Finally, a ReAct-based LLM agent translates natural language instructions into API calls for autonomous dispatch, visualization, and diagnosis. The proposed framework ensures efficient, strictly feasible, and highly interactive power flow analysis.