Optimal Power Flow Distribution

Latest papers

• PowerZooJax: A JAX-based Power System Benchmark for Reinforcement Learning

  Zhanhua Pan, Xiao Liu, Zhilong Cao, Jianhong Wang, Dawei Qiu · 30 September 2026

  Power system operation is a safety-critical sequential decision-making problem, making it a natural testbed for reinforcement learning (RL). However, existing RL environments for power systems are often narrow in scope and computationally limited by CPU-based simulation workflows, making large-scale…

• AC Power Flow Contingency Analysis Using a Single Deep Neural Network

  Md Obaidur Rahman, Junjie Qin, Vassilis Kekatos · 28 September 2026

  Contingency analysis using the AC power flow (AC-PF) model is a critical tool for accurate grid security assessment, but its computational burden increases with the number of operating scenarios and outage configurations to evaluate. Recent ML-based approaches typically require outage-specific train…

• GridSFM: A Foundation Model for Solving AC Optimal Power Flow

  Luke Bhan, Weiwei Yang, Margaret Capetz, Baosen Zhang · 25 September 2026

  We introduce GridSFM, a framework that combines a pretrained foundation model across grid topologies with physics-informed fine-tuning for solving AC Optimal Power Flow (AC-OPF) at scale. It is a $15$ million parameter physics-inspired graph neural network pretrained across $54$ topologies of $500$ …

• From Graphs to Feeders: Constraint-Guided Diffusion for Rule-Compliant Feeder Generation

  Yu Qin, Andrew Glaws, Aadil Latif, Ryan King · 25 September 2026

  Generative modeling approaches often focus on recovering broad statistical characteristics from the training data. In the context of graph generation, this may refer to degree distributions, clustering coefficients, or spectral properties. However, generating usable distribution feeders when detaile…

• Reachability-Based Formal Verification of Graph Neural Networks with Node and Edge Features

  Anne M. Tumlin, Ben Wooding, Zhenxuan Shao, Diego Manzanas Lopez, Tyler Derr, Taylor T. Johnson · 25 September 2026

  Graph neural networks (GNNs) have become a prominent approach for developing fast, topology-aware surrogates in electric power systems, supporting tasks such as power flow (PF) analysis, optimal power flow (OPF) estimation, and cascading failure analysis (CFA). Despite this growing use, formally ver…

• Towards Hierarchical GNNs for multi-grid power flow: generalization across operating scenarios

  Carmine Delle Femine, Leire Garin Atxaga, Asier Diaz-Iglesias, Juan Pablo Maroto Herrera, Ane Miren Florez-Tapia, Marco Quartulli. Izaro Goienetxea Urziku · 23 September 2026

  Hierarchical latent communication improves the generalization of a multi-grid power-flow model to new operating scenarios. The module exchanges information through two reduced graphs within a GENCO-based corrective network. We compare Kron-derived transports, a same-anchor Quotient construction and …

• Unified Heterogeneous Graph Neural Network solver for Power Flow, Optimal Power Flow and State Estimation

  Ferran Bohigas-Daranas, Hamid Latif-Mart\'inez, Eduardo Prieto-Araujo, Oriol Gomis-Bellmunt, Pere Barlet-Ros · 16 September 2026

  Power Flow (PF), Optimal Power Flow (OPF), and State Estimation (SE) are fundamental problems in power system analysis, but solving them is computationally expensive. Graph Neural Networks (GNNs) have been proposed as fast surrogates, yet existing solvers are trained for a single problem at a time, …

• Scaling Laws for Physics-Aware ACOPF Surrogate Learning

  Yijiang Li, Emon Dey, Stefano Fenu, Massimiliano Lupo Pasini, Teja Kuruganti, Kibaek Kim · 16 September 2026

  Learning-based surrogates for AC optimal power flow (ACOPF) promise large speedups over classical solvers, but their operational value depends on physical feasibility as much as predictive accuracy. Physics-aware objectives such as the augmented Lagrangian (AL) improve constraint satisfaction at add…

• Risk and Anomaly Identification for Distribution Network Optimal Operation Based on Reinforcement Learning and Uncertainty Quantification

  Ziqi Zhang · 4 September 2026

  Reliable operation of modern distribution networks requires timely identification of operational risks and anomalous events under pervasive uncertainty. In practice, operators must identify risks that are inherent in stochastic yet in-distribution conditions, and anomalies that correspond to out-of-…

• RestoreBench: Can AI Agents Restore Power Flow Convergence?

  Riccardo Mansutti, Andrea Pomarico, Robert Jakob, Qian Zhang, Alberto Berizzi, Kevin O'Sullivan · 2 September 2026

  Large Language Model (LLM) agents increasingly automate multi-step engineering workflows through tool use, interpretation of intermediate results, and iterative planning. Diagnosing and resolving non-convergent power flow cases is a promising yet largely unexplored application, as it requires engine…

• Scalable Self-Supervised Learning for Multiphase AC-OPF in Distribution Systems with Topology Reconfiguration

  Hoang T. Nguyen, Shaohui Liu, Reetam Sen Biswas, Varsha Pendyala, Nurali Virani, Deepjyoti Deka, Priya L. Donti · 27 August 2026

  The proliferation of distributed energy resources (DERs) in distribution grids enables the active coordination of these assets to reduce costs and enable cleaner operations. Realizing this potential requires solving multiphase AC optimal power flow (AC-OPF) quickly across varying loads, DER availabi…

• One Request, Multiple Experts: LLM Orchestrates Domain Specific Models via Adaptive Task Routing

  Xu Yang, Chenhui Lin, Haotian Liu, Qi Wang, Yue Yang, Wenchuan Wu · 25 August 2026

  With the integration of massive distributed energy resources and the widespread participation of novel market entities, the operation of active distribution networks (ADNs) is progressively evolving into a complex, multi-scenario, and multi-objective problem. Although expert engineers have developed…

• Learning to Run Power Networks: Effective AlphaZero-inspired Topological Control

  Lukas Zetto, Benjamin Sch\"afer, Qiong Huang · 17 August 2026

  As the integration of volatile renewable energy sources increases the strain on modern power grids, the use of Reinforcement Learning (RL) for autonomous topological reconfiguration has emerged as a promising research field to keep strained grids stable and operational. Compared to traditional redis…

• TANGCO: Learning Topology-Aware Capacity Allocation for Overload-driven Cascading Failures

  Orkun Irsoy, Leman Akoglu, Osman Yagan · 14 August 2026

  Networked systems, from power grids to traffic networks and cloud clusters, carry loads across nodes with limited capacity. A node whose load exceeds its capacity fails and sheds its load onto its neighbors, which can trigger a system-wide cascade. We study how to allocate a fixed capacity budget ac…

• GENCO - A Unified Neural Solver Embedded in a Development Framework for Steady-State Grid Analysis

  Alban Puech, Matteo Mazzonelli, Tamara R. Govindasamy, Mangaliso Mngomezulu, H\'ector Maeso-Garc\'ia, Thomas Tolhurst, Javad Bayazi, Ali Moeini, Naomi Simumba, Celia Cintas, David Nelischer, Romeo Kienzler, Jonas Weiss, Anna Varbella, Florian D\"orfler, Gabriela Hug, Martin Mevissen, Juan Bernab\'e-Moreno, Fran\c{c}ois Mirall\`es, Hendrik F. Hamann, Etienne Vos, Thomas Brunschwiler · 11 August 2026

  Foundation models are transforming business workflows and boosting productivity, yet they remain largely absent from engineering domains such as power system analysis, where strict physical consistency must be enforced. We present GENCO (GEometric Neural Corrective Optimizer), a unified neural sol…

• Operationally Feasible Synthetic Power-Grid Scenarios via Learning the AC-Operable Joint Distribution

  Chenhan Xiao, Xinyu He, Haoran Li, Hanghang Tong, Yang Weng · 5 August 2026

  Synthetic power-grid scenarios are essential for planning, resilience assessment, contingency analysis, and data-driven power-system applications. Recent synthetic grid generation methods have improved structural realism and operational feasibility by incorporating engineering knowledge through post…

• Bridging Artificial Intelligence and Power Systems Education Using a Hands-On Executable Framework

  Junjie Yin (Fran), Buxin She (Fran), Xinyu Feng (Fran), Fangxing (Fran), Li · 4 August 2026

  Artificial intelligence (AI) is increasingly central to power and energy systems, supporting modeling, forecasting, optimization, and control. Yet most existing works emphasize specialized applications and offer little reusable material for newcomers or interdisciplinary learners, who increasingly r…

• HetGPS: Scalable Graph Multi-Agent Reinforcement Learning with Physics-Anchored Adaptive Safety for EV Charging

  Xiangwei Wang, Nanduni Nimalsiri, Yu Xia, Peng Wang, Saman Halgamuge · 4 August 2026

  Safety interventions for large populations of network-coupled agents must protect shared constraints without unnecessarily overriding task-oriented policy decisions. We present HetGPS, a hybrid graph-control framework synergizing learned graph risk with physics-anchored correction by separating inte…

• Physics-Informed Graph Neural Networks for Robust AC-Optimal Power Flow

  Anna Varbella, Damien Briens, Blazhe Gjorgiev, Giuseppe Alessio D'Inverno, Priya L. Donti, Giovanni Sansavini · 30 July 2026

  We present PINCO, an unsupervised learning framework that integrates Graph Neural Networks with physics-informed neural networks for AC optimal power flow (AC-OPF) solutions. Unlike state-of-the-art unsupervised methods that require prescreened datasets containing only feasible instances, our approa…

• PowerAtlas: Towards Electricity-Computing Co-Scheduling for Power Systems

  Kaiwen Jiang, Siya Xu, Ziyue Zhu, Chao Yang, Anh Tuan Luu, Haoran Luo · 30 July 2026

  The rapid growth of AI workloads is turning data centers into large-scale, volatile, yet spatiotemporally flexible grid loads, creating an urgent need for coordinated electricity-computing scheduling. Under stringent grid constraints, schedules from general-purpose large language models (LLMs) are o…

• FMOPF: Latent Flow Matching with Constraint-Aware Interaction Priors for AC Optimal Power Flow

  Zhilin Huang · 28 July 2026

  AC optimal power flow determines the minimum-cost generation dispatch under nonlinear power balance constraints and is solved thousands of times daily in electricity market operations. Learning a direct mapping from load conditions to OPF solutions can accelerate this computation, yet with deepening…

• Gradient-Free Topology Adaptation for Power Flow Surrogates via In-Context Whitening

  Ayushi Jolotia, Parikshit Pareek · 15 July 2026

  Machine-learned surrogates for the AC power flow (ACPF) problem amortize the cost of repeated solves on a fixed network, but lose one to two orders of magnitude of accuracy when a line outage changes the topology. This degradation is an operator shift. The altered admittance matrix changes the input…

• Power Flow Feasibility Assessment Using Variational Graph Autoencoders

  Ferran Bohigas-Daranas, Hamid Latif-Martinez, Eduardo Prieto-Araujo, Pere Barlet-Ros, Oriol Gomis-Bellmunt · 13 July 2026

  Data-driven methods, including graph neural networks, have been studied for accelerating power flow calculations in recent years, but very little attention has been paid to the solution feasibility, which can be obtained by traditional solvers. This paper presents a Variational Graph Autoencoder (VG…

• A Machine Learning Surrogate for Component Criticality Ranking in Interdependent Power-Communication Networks

  Sohini Roy, Xheni Hylviu · 13 July 2026

  Cyber-physical power systems are vulnerable to cascading failures caused by tight interdependencies between power and communication infrastructures. Evaluating these failures over large N-k contingency sets with a high-fidelity simulator is computationally prohibitive for resilience planning. Using …

• Creating Power Distribution Network Layouts Using Generative Adversarial Networks and Image-Based Representations

  Juan Manuel Garcia-Perez, Carlos Mateo · 9 July 2026

  Utilities increasingly rely on planning and operational tools to cope with the increased penetrations of distributed energy resources, yet the lack of realistic, openly available datasets remains a major barrier for benchmarking and comparison. Traditional test feeders, and recently proposed large-s…