Operationally Feasible Synthetic Power-Grid Scenarios via Learning the AC-Operable Joint Distribution

2026-08-04

AI Summary

This work addresses the limitations of existing synthetic power grid scenarios, which often lack AC power flow feasibility and operational robustness, thereby hindering their utility in real-world system analysis. The authors propose a feasibility-aware hierarchical diffusion generative framework that embeds AC power flow equations and operational constraints directly into the generation process, enabling joint modeling of network topology, branch parameters, and time-varying load within a physically feasible distribution. By integrating domain-specific engineering knowledge through a three-stage architecture, the method significantly enhances power flow convergence, contingency robustness, and statistical fidelity of the generated scenarios—without requiring post-hoc optimization—thus achieving efficient, high-dimensional, and physically consistent grid scenario synthesis.

Abstract

Synthetic power-grid scenarios are essential for planning, resilience assessment, contingency analysis, and data-driven power-system applications. Recent synthetic grid generation methods have improved structural realism and operational feasibility by incorporating engineering knowledge through post-generation validation, optimization, or physics-aware generation. However, generated scenarios may still exhibit low AC feasibility and robustness, limiting their practical value for downstream power-system studies. This paper proposes a feasibility-aware distribution-learning framework that learns the AC-operable joint distribution of network topology, branch electrical parameters, and time-varying load profiles. Instead of enforcing feasibility after generation, the proposed framework incorporates AC power-flow convergence and operational constraints into hierarchical diffusion-based distribution learning. This enables the generator itself to produce operationally feasible grid scenarios through efficient diffusion sampling. The hierarchical architecture decomposes the high-dimensional generation task into three engineering-motivated stages: topology and bus-attribute generation, branch-parameter generation conditioned on the generated structure, and load-profile generation conditioned on both network structure and electrical characteristics. Experiments on benchmark systems demonstrate that the proposed framework significantly improves operational feasibility and contingency robustness while maintaining strong statistical fidelity and eliminating optimization-based post-processing.

Problem

• synthetic power-grid scenarios
• AC feasibility
• operational constraints
• contingency robustness
• distribution learning

Innovation

• AC-operable distribution learning
• hierarchical diffusion model
• synthetic power-grid generation
• operational feasibility
• contingency robustness