Physics-Informed Neural Networks for Minimising Worst-Case Violations in DC Optimal Power Flow

28 June 2021

Papers citing "Physics-Informed Neural Networks for Minimising Worst-Case Violations in DC Optimal Power Flow"

20 / 20 papers shown

Title
Differentiable Projection-based Learn to Optimize in Wireless Network-Part I: Convex Constrained (Non-)Convex Programming Xiucheng Wang Xuan Zhao Nan Cheng 49 0 0 29 Jan 2025
Compact Optimality Verification for Optimization Proxies Wenbo Chen Haoruo Zhao Mathieu Tanneau Pascal Van Hentenryck 38 0 0 31 May 2024
Scalable Exact Verification of Optimization Proxies for Large-Scale Optimal Power Flow Rahul Nellikkath Mathieu Tanneau Pascal Van Hentenryck Spyros Chatzivasileiadis 47 0 0 09 May 2024
End-to-End Reinforcement Learning of Curative Curtailment with Partial Measurement Availability Hinrikus Wolf Luis Bottcher Sarra Bouchkati Philipp Lutat Jens Breitung ... Viktor Todosijević Jan Schiefelbein-Lach Oliver Pohl Andreas Ulbig Martin Grohe 21 0 0 06 May 2024
A Hybrid Approach of Transfer Learning and Physics-Informed Modeling: Improving Dissolved Oxygen Concentration Prediction in an Industrial Wastewater Treatment Plant Ece S. Koksal Erdal Aydin PINN AI4CE 31 0 0 20 Jan 2024
Robust Physics Informed Neural Networks Marcin Lo's Maciej Paszyñski PINN 14 0 0 04 Jan 2024
Dual Conic Proxies for AC Optimal Power Flow Guancheng Qiu Mathieu Tanneau Pascal Van Hentenryck 37 8 0 04 Oct 2023
AI Driven Near Real-time Locational Marginal Pricing Method: A Feasibility and Robustness Study Naga Venkata Sai Jitin Jami J. Kardoš Olaf Schenk Harald Kostler 23 0 0 16 Jun 2023
Self-supervised Equality Embedded Deep Lagrange Dual for Approximate Constrained Optimization Minsoo Kim Hongseok Kim 32 4 0 11 Jun 2023
End-to-End Feasible Optimization Proxies for Large-Scale Economic Dispatch Wenbo Chen Mathieu Tanneau Pascal Van Hentenryck 34 30 0 23 Apr 2023
A Physics-Informed Machine Learning for Electricity Markets: A NYISO Case Study Robert Ferrando Laurent Pagnier R. Mieth Zhirui Liang Y. Dvorkin D. Bienstock Michael Chertkov 26 7 0 31 Mar 2023
Enriching Neural Network Training Dataset to Improve Worst-Case Performance Guarantees Rahul Nellikkath Spyros Chatzivasileiadis 36 3 0 23 Mar 2023
Physics Informed Piecewise Linear Neural Networks for Process Optimization Ece S. Koksal E. Aydın PINN 24 11 0 02 Feb 2023
Minimizing Worst-Case Violations of Neural Networks Rahul Nellikkath Spyros Chatzivasileiadis 38 3 0 21 Dec 2022
Physics-informed neural networks for PDE-constrained optimization and control Jostein Barry-Straume A. Sarshar Andrey A. Popov Adrian Sandu PINN AI4CE 29 14 0 06 May 2022
Closing the Loop: A Framework for Trustworthy Machine Learning in Power Systems Jochen Stiasny Samuel C. Chevalier Rahul Nellikkath Brynjar Sævarsson Spyros Chatzivasileiadis 29 14 0 14 Mar 2022
Ensuring DNN Solution Feasibility for Optimization Problems with Convex Constraints and Its Application to DC Optimal Power Flow Problems Tianyu Zhao Xiang Pan Minghua Chen S. Low 27 10 0 15 Dec 2021
Physics-Informed Neural Networks for AC Optimal Power Flow Rahul Nellikkath Spyros Chatzivasileiadis PINN 34 88 0 06 Oct 2021
Neural Networks for Encoding Dynamic Security-Constrained Optimal Power Flow Daniel Timon Viola Andreas Venzke George S. Misyris Spyros Chatzivasileiadis 24 38 0 17 Mar 2020
Predicting AC Optimal Power Flows: Combining Deep Learning and Lagrangian Dual Methods Ferdinando Fioretto Terrence W.K. Mak Pascal Van Hentenryck AI4CE 81 199 0 19 Sep 2019