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

arXiv: https://arxiv.org/abs/2608.03878 · HTML: https://mlsanity.com/paper/2608.03878/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 30 references that are in the map.
- First posted on arXiv 4 Aug 2026.

## Builds on (most cited first, 14 of 14)

- [DiGress: Discrete Denoising diffusion for graph generation](https://mlsanity.com/paper/2209.14734/) (2022)
- [Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations](https://mlsanity.com/paper/2202.02514/) (2022)
- [Graph Generation with Diffusion Mixture](https://mlsanity.com/paper/2302.03596/) (2023)
- [Beta Diffusion](https://mlsanity.com/paper/2309.07867/) (2023)
- [Advancing Graph Generation through Beta Diffusion](https://mlsanity.com/paper/2406.09357/) (2024)
- [Real-Time Contingency Analysis with Corrective Transmission Switching - Part I: Methodology](https://mlsanity.com/paper/1604.05570/) (2016)
- [Distribution Grid Line Outage Identification with Unknown Pattern and Performance Guarantee](https://mlsanity.com/paper/2309.07157/) (2023)
- [PowerGrow: Feasible Co-Growth of Structures and Dynamics for Power Grid Synthesis](https://mlsanity.com/paper/2509.12212/) (2025)
- [FeederGAN: Synthetic Feeder Generation via Deep Graph Adversarial Nets](https://mlsanity.com/paper/2004.01407/) (2020)
- [A Framework for Generating Synthetic Distribution Feeders using OpenStreetMap](https://mlsanity.com/paper/1910.07673/) (2019)
- [Synthetic Active Distribution System Generation via Unbalanced Graph Generative Adversarial Network](https://mlsanity.com/paper/2108.00599/) (2021)
- [Synthetic Grid Generator: Synthesizing Large-Scale Power Distribution Grids using Open Street Map](https://mlsanity.com/paper/2408.04923/) (2024)
- [A Two-Stage AI-Powered Motif Mining Method for Efficient Power System Topological Analysis](https://mlsanity.com/paper/2412.05957/) (2024)
- [Exploring Variational Graph Autoencoders for Distribution Grid Data Generation](https://mlsanity.com/paper/2509.02469/) (2025)

## Abstract (arXiv)

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.