Physics-informed machine learning with optimization-based guarantees: Applications to AC power flow

Jalving, Jordan; Eydenberg, Michael; Blakely, Logan; Castillo, Anya; Kilwein, Zachary; Skolfield, J. Kyle; Boukouvala, Fani; Laird, Carl

doi:10.1016/j.ijepes.2023.109741

Physics-informed machine learning with optimization-based guarantees: Applications to AC power flow

Journal Article · Sat Jun 01 00:00:00 EDT 2024 · International Journal of Electrical Power and Energy Systems

DOI:https://doi.org/10.1016/j.ijepes.2023.109741· OSTI ID:2283480

; Eydenberg, Michael; Blakely, Logan; ; ; Skolfield, J. Kyle; Boukouvala, Fani; Laird, Carl

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Research Organization:: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: NA0003525

OSTI ID:: 2283480

Alternate ID(s):: OSTI ID: 2311399

Report Number(s):: SAND--2024-00708J; S0142061523007986; 109741; PII: S0142061523007986

Journal Information:: International Journal of Electrical Power and Energy Systems, Journal Name: International Journal of Electrical Power and Energy Systems Journal Issue: C Vol. 157; ISSN 0142-0615

Publisher:: ElsevierCopyright Statement

Country of Publication:: United Kingdom

Language:: English

References (25)

Constraint-Based Lagrangian Relaxation Fontaine, Daniel; Michel, Laurent; Van Hentenryck, Pascal Principles and Practice of Constraint Programming https://doi.org/10.1007/978-3-319-10428-7_25	book	January 2014
Deep neural networks and mixed integer linear optimization Fischetti, Matteo; Jo, Jason Constraints, Vol. 23, Issue 3 https://doi.org/10.1007/s10601-018-9285-6	journal	April 2018
ReLU networks as surrogate models in mixed-integer linear programs Grimstad, Bjarne; Andersson, Henrik Computers & Chemical Engineering, Vol. 131 https://doi.org/10.1016/j.compchemeng.2019.106580	journal	December 2019
State-of-the-art, challenges, and future trends in security constrained optimal power flow Capitanescu, F.; Martinez Ramos, J. L.; Panciatici, P. Electric Power Systems Research, Vol. 81, Issue 8 https://doi.org/10.1016/j.epsr.2011.04.003	journal	August 2011
Modeling the AC power flow equations with optimally compact neural networks: Application to unit commitment Kody, Alyssa; Chevalier, Samuel; Chatzivasileiadis, Spyros Electric Power Systems Research, Vol. 213 https://doi.org/10.1016/j.epsr.2022.108282	journal	December 2022
Semidefinite programming for optimal power flow problems Bai, Xiaoqing; Wei, Hua; Fujisawa, Katsuki International Journal of Electrical Power & Energy Systems, Vol. 30, Issue 6-7, p. 383-392 https://doi.org/10.1016/j.ijepes.2007.12.003	journal	July 2008
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations Raissi, M.; Perdikaris, P.; Karniadakis, G. E. Journal of Computational Physics, Vol. 378 https://doi.org/10.1016/j.jcp.2018.10.045	journal	February 2019
Deep Kronecker neural networks: A general framework for neural networks with adaptive activation functions Jagtap, Ameya D.; Shin, Yeonjong; Kawaguchi, Kenji Neurocomputing, Vol. 468 https://doi.org/10.1016/j.neucom.2021.10.036	journal	January 2022
Estimates on the generalization error of physics-informed neural networks for approximating a class of inverse problems for PDEs Mishra, Siddhartha; Molinaro, Roberto IMA Journal of Numerical Analysis, Vol. 42, Issue 2 https://doi.org/10.1093/imanum/drab032	journal	June 2021
Reliable load flow technique for radial distribution networks Gomez Exposito, A.; Romero Ramos, E. IEEE Transactions on Power Systems, Vol. 14, Issue 3 https://doi.org/10.1109/59.780924	journal	August 1999
Recent Developments in Machine Learning for Energy Systems Reliability Management Duchesne, Laurine; Karangelos, Efthymios; Wehenkel, Louis Proceedings of the IEEE, Vol. 108, Issue 9 https://doi.org/10.1109/JPROC.2020.2988715	journal	September 2020
DeepOPF: A Feasibility-Optimized Deep Neural Network Approach for AC Optimal Power Flow Problems Pan, Xiang; Chen, Minghua; Zhao, Tianyu IEEE Systems Journal, Vol. 17, Issue 1 https://doi.org/10.1109/JSYST.2022.3201041	journal	March 2023
A Physics-Informed Neural Network for Quantifying the Microstructural Properties of Polycrystalline Nickel Using Ultrasound Data: A promising approach for solving inverse problems Shukla, Khemraj; Jagtap, Ameya D.; Blackshire, James L. IEEE Signal Processing Magazine, Vol. 39, Issue 1 https://doi.org/10.1109/MSP.2021.3118904	journal	January 2022
Learning Optimal Power Flow: Worst-Case Guarantees for Neural Networks Venzke, Andreas; Qu, Guannan; Low, Steven 2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) https://doi.org/10.1109/SmartGridComm47815.2020.9302963	conference	November 2020
An Optimal Power-Flow Approach to Improve Power System Voltage Stability Using Demand Response Yao, Mengqi; Molzahn, Daniel K.; Mathieu, Johanna L. IEEE Transactions on Control of Network Systems, Vol. 6, Issue 3 https://doi.org/10.1109/TCNS.2019.2910455	journal	September 2019
Radial Distribution Load Flow Using Conic Programming Jabr, R.A. IEEE Transactions on Power Systems, Vol. 21, Issue 3, p. 1458-1459 https://doi.org/10.1109/TPWRS.2006.879234	journal	August 2006
New Formulation and Strong MISOCP Relaxations for AC Optimal Transmission Switching Problem Kocuk, Burak; Dey, Santanu S.; Sun, Xu Andy IEEE Transactions on Power Systems, Vol. 32, Issue 6 https://doi.org/10.1109/TPWRS.2017.2666718	journal	November 2017
Global Solution Strategies for the Network-Constrained Unit Commitment Problem With AC Transmission Constraints Liu, Jianfeng; Laird, Carl D.; Scott, Joseph K. IEEE Transactions on Power Systems, Vol. 34, Issue 2 https://doi.org/10.1109/TPWRS.2018.2876127	journal	March 2019
Efficient Database Generation for Data-Driven Security Assessment of Power Systems Thams, Florian; Venzke, Andreas; Eriksson, Robert IEEE Transactions on Power Systems, Vol. 35, Issue 1 https://doi.org/10.1109/TPWRS.2018.2890769	journal	January 2020
Physics-Guided Deep Neural Networks for Power Flow Analysis Hu, Xinyue; Hu, Haoji; Verma, Saurabh IEEE Transactions on Power Systems, Vol. 36, Issue 3 https://doi.org/10.1109/TPWRS.2020.3029557	journal	May 2021
Combining Deep Learning and Optimization for Preventive Security-Constrained DC Optimal Power Flow Velloso, Alexandre; Van Hentenryck, Pascal IEEE Transactions on Power Systems, Vol. 36, Issue 4 https://doi.org/10.1109/TPWRS.2021.3054341	journal	July 2021
Verification of Neural Network Behaviour: Formal Guarantees for Power System Applications Venzke, Andreas; Chatzivasileiadis, Spyros IEEE Transactions on Smart Grid, Vol. 12, Issue 1 https://doi.org/10.1109/TSG.2020.3009401	journal	January 2021
When Do Extended Physics-Informed Neural Networks (XPINNs) Improve Generalization? Hu, Zheyuan; Jagtap, Ameya D.; Karniadakis, George Em SIAM Journal on Scientific Computing, Vol. 44, Issue 5 https://doi.org/10.1137/21M1447039	journal	September 2022
Predicting AC Optimal Power Flows: Combining Deep Learning and Lagrangian Dual Methods Fioretto, Ferdinando; Mak, Terrence W. K.; Van Hentenryck, Pascal Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34, Issue 01 https://doi.org/10.1609/aaai.v34i01.5403	journal	April 2020
Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations Karniadakis, Ameya D. Jagtap & George Em Communications in Computational Physics, Vol. 28, Issue 5 https://doi.org/10.4208/cicp.OA-2020-0164	journal	June 2020

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