Constrained Diffusion Models for Synthesizing Representative Power Flow Datasets
This addresses the need for secure and private synthetic data in power systems, though it is incremental as it builds on existing diffusion models with domain-specific constraints.
The authors tackled the problem of generating synthetic power flow datasets for power systems by developing a constrained diffusion model that ensures AC power flow feasibility and statistical accuracy, outperforming standard diffusion models in experiments on IEEE benchmark systems.
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.