Constrained Diffusion Models for Synthesizing Representative Power Flow Datasets

TL;DR

This paper introduces a physics-informed diffusion model that generates synthetic power flow datasets, maintaining statistical accuracy and physical feasibility, addressing data access restrictions in power systems.

Contribution

It develops a novel diffusion model with gradient guidance and a variable decoupling strategy to produce realistic, feasible power flow datasets efficiently.

Findings

Outperforms standard diffusion in feasibility and statistical similarity

Ensures AC power flow feasibility in synthetic datasets

Effective across IEEE benchmark systems

Abstract

High-quality power flow datasets are essential for training machine learning models in power systems. However, security and privacy concerns restrict access to real-world data, making statistically accurate and physically consistent synthetic datasets a viable alternative. We develop a diffusion model for generating synthetic power flow datasets from real-world power grids that both replicate the statistical properties of the real-world data and ensure AC power flow feasibility. To enforce the constraints, we incorporate gradient guidance based on the power flow constraints to steer diffusion sampling toward feasible samples. For computational efficiency, we further leverage insights from the fast decoupled power flow method and propose a variable decoupling strategy for the training and sampling of the diffusion model. These solutions lead to a physics-informed diffusion model, generating power flow datasets that outperform those from the standard diffusion in terms of feasibility and statistical similarity, as shown in experiments across IEEE benchmark systems.