Fast Diffusion with Physics-Correction for ACOPF

TL;DR

This paper introduces a fast diffusion-based method with physics-guided corrections for generating large-scale ACOPF datasets, significantly reducing sampling time while maintaining accuracy and physical feasibility.

Contribution

It proposes a novel diffusion framework combining DDIM with physics-guided corrections, enabling rapid and physically consistent ACOPF data generation.

Findings

Achieves up to 20x faster sampling than standard diffusion models.

Maintains comparable statistical accuracy and physical consistency.

Effective for large-scale power system dataset generation.

Abstract

Generating large-scale, physically consistent AC Optimal Power Flow (ACOPF) datasets is essential for modern data-driven power system applications. The central challenge lies in balancing solution accuracy with computational efficiency. Recent diffusion-based generative models produce high-quality samples; however, their slow sampling procedures limit practical scalability. In this work, we argue that exact physical feasibility is ultimately enforced by power flow solvers or projection steps, and therefore the generative model only needs to produce good initializations rather than perfectly feasible solutions. Based on this insight, we propose a fast diffusion framework using Denoising Diffusion Implicit Models (DDIM) combined with physics-guided corrections during sampling. The proposed method replaces slow stochastic refinement with a small number of deterministic steps and explicit constraint guidance. Experiments on IEEE 6-, 24-, and 118-bus systems show that our approach achieves up to 20 times faster sampling than standard diffusion models while maintaining comparable statistical accuracy and physical consistency. This makes the method well suited for scalable OPF dataset generation and practical power system learning tasks. We release the implementation code at https://github.com/PSquare-Lab/DDIM_OPF.