The paper introduces Penalty + Sequential Linearized Feasibility Seeking (SLFS), a self‑supervised learning framework for solving multiphase AC optimal power flow (AC‑OPF) in distribution systems with topology reconfiguration. SLFS trains directly from the AC‑OPF objective and constraints using a differentiable fixed‑point power flow solver, avoiding the need for labeled optimal solutions. It achieves negligible optimality gaps and near‑zero constraint violations on IEEE feeders up to 8,500 nodes, delivering up to three orders of magnitude speedups over IPOPT while maintaining robustness to large distributional shifts.
By Hoang T. Nguyen, Shaohui Liu, Reetam Sen Biswas, Varsha Pendyala, Nurali Virani, Deepjyoti Deka, Priya L. Donti
arXiv:2608. 03878v1 Announce Type: new Abstract: Synthetic power-grid scenarios are essential for planning, resilience assessment, contingency analysis, and data-driven power-system applications.
By Chenhan Xiao, Xinyu He, Haoran Li, Hanghang Tong, Yang Weng
arXiv:2608. 16494v1 Announce Type: cross Abstract: Modern power systems face growing operational complexity driven by the integration of renewable energy sources, decentralization, and the need for real-time decision-making across a wide range of timescales.
By Martin Sadric, Sebastian P\"utz, Christian Nauck, Veit Hagenmeyer, Frank Hellmann, Dirk Witthaut, Benjamin Sch\"afer
arXiv:2609.23915v1 Announce Type: cross
Abstract: This paper presents an end-to-end framework for reconstructing overhead power utility network topology and extracting span-level physical metadata fr...
By Rishabh Jain, Anuja Saini, Vishal Jain
arXiv:2510. 22048v4 Announce Type: replace Abstract: Power flow (PF) calculations are the backbone of real-time grid operations, across workflows such as contingency analysis (where repeated PF evaluations assess grid security under outages) and topology optimization (which involves PF-based searches over combinatorially large action spaces).
By Ana K. Rivera, Anvita Bhagavathula, Alvaro Carbonero, Priya Donti
Single-task fine-tuning of graph neural networks (GNNs) for power grid problems exhibits a systematic failure mode: models that achieve the lowest in-distribution error degrade the most under topology shift. We term this topology overfitting: the tendency of task-specific gradient signals to encode relational structure particular to the training topologies rather than the underlying physics, causing models to fail on unseen grids despite strong in-distribution performance.