这篇论文用分层扩散模型学电网数据分布，生成场景不用后处理就能过AC潮流验证，做电力系统研究可参考。
论文arXiv:2608.03878提出可行性感知分布学习框架，用分层扩散模型学习电网拓扑、支路参数和负荷曲线的联合分布。生成过程分为三个阶段：拓扑生成、支路参数生成、负荷曲线生成，并在训练中嵌入AC潮流可行约束。在基准系统上的实验显示，该方法显著提升AC可操作性和故障鲁棒性，同时保持高统计保真度，且无需事后优化。
Operationally Feasible Synthetic Power-Grid Scenarios via Learning the AC-Operable Joint Distribution
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.