[2603.28203] Differentiable Power-Flow Optimization

Computer Science > Artificial Intelligence arXiv:2603.28203 (cs) [Submitted on 30 Mar 2026] Title:Differentiable Power-Flow Optimization Authors:Muhammed Öz, Jasmin Hörter, Kaleb Phipps, Charlotte Debus, Achim Streit, Markus Götz View a PDF of the paper titled Differentiable Power-Flow Optimization, by Muhammed \"Oz and 5 other authors View PDF HTML (experimental) Abstract:With the rise of renewable energy sources and their high variability in generation, the management of power grids becomes increasingly complex and computationally demanding. Conventional AC-power-flow simulations, which use the Newton-Raphson (NR) method, suffer from poor scalability, making them impractical for emerging use cases such as joint transmission-distribution modeling and global grid analysis. At the same time, purely data-driven surrogate models lack physical guarantees and may violate fundamental constraints. In this work, we propose Differentiable Power-Flow (DPF), a reformulation of the AC power-flow problem as a differentiable simulation. DPF enables end-to-end gradient propagation from the physical power mismatches to the underlying simulation parameters, thereby allowing these parameters to be identified efficiently using gradient-based optimization. We demonstrate that DPF provides a scalable alternative to NR by leveraging GPU acceleration, sparse tensor representations, and batching capabilities available in modern machine-learning frameworks such as PyTorch. DPF is especially suited...