通过学习交流（AC）可运行联合分布实现可操作的合成电网场景
Operationally Feasible Synthetic Power-Grid Scenarios via Learning the AC-Operable Joint Distribution
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中文总结 AI 辅助
本文提出感知可行性的分层扩散分布学习框架，学习电网拓扑、参数与负荷的交流可操作联合分布，生成的合成电网场景可行性与鲁棒性显著提升，且消除了优化后处理。
中文摘要 AI 辅助
合成电网场景对于规划、弹性评估、故障分析及数据驱动的电力系统应用至关重要。近期的合成电网生成方法通过在生成后验证、优化或采用物理感知生成的方式融入工程知识，提升了结构真实性与运行可行性，但生成的场景仍可能存在交流可行性与鲁棒性较低的问题，限制了其在电力系统下游研究中的实用价值。本文提出一种感知可行性的分布学习框架，用于学习电网拓扑、支路电气参数及时变负荷曲线的交流可操作联合分布。该框架未在生成后强制可行性，而是将交流潮流收敛性与运行约束融入基于分层扩散的分布学习中，使生成器可通过高效扩散采样直接生成可操作的电网场景。分层架构将高维生成任务分解为三个符合工程逻辑的阶段：拓扑与母线属性生成、基于已生成结构的支路参数生成、基于电网结构与电气特性的负荷曲线生成。在基准系统上开展的实验表明，该框架在保持强统计保真度、消除基于优化的后处理的同时，显著提升了运行可行性与故障鲁棒性。
英文摘要
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.
