Synthetic Power Flow Data Generation Using Physics-Informed Denoising Diffusion Probabilistic Models
This work addresses data scarcity for smart grid applications, offering a domain-specific solution that is incremental in integrating physics constraints into generative models.
The paper tackles the problem of limited access to real-world power flow data in smart grids by developing a physics-informed generative framework using Denoising Diffusion Probabilistic Models to synthesize feasible data, demonstrating improved feasibility, diversity, and accuracy over baseline models on IEEE benchmark systems.
Many data-driven modules in smart grid rely on access to high-quality power flow data; however, real-world data are often limited due to privacy and operational constraints. This paper presents a physics-informed generative framework based on Denoising Diffusion Probabilistic Models (DDPMs) for synthesizing feasible power flow data. By incorporating auxiliary training and physics-informed loss functions, the proposed method ensures that the generated data exhibit both statistical fidelity and adherence to power system feasibility. We evaluate the approach on the IEEE 14-bus and 30-bus benchmark systems, demonstrating its ability to capture key distributional properties and generalize to out-of-distribution scenarios. Comparative results show that the proposed model outperforms three baseline models in terms of feasibility, diversity, and accuracy of statistical features. This work highlights the potential of integrating generative modelling into data-driven power system applications.