Verified physics-aware machine learning to transform non-linear power system stability and optimization

Project Information

VeriPhIED

Grant agreement ID: 949899

DOI

10.3030/949899

EC signature date 25 August 2020

Start date 1 May 2022

End date 30 April 2027

Funded under

• EXCELLENT SCIENCE - European Research Council (ERC)

Total cost

€ 1 499 183,00

EU contribution

€ 1 499 183,00

Coordinated by

DANMARKS TEKNISKE UNIVERSITET
Denmark

CORDIS provides links to public deliverables and publications of HORIZON projects.

Links to deliverables and publications from FP7 projects, as well as links to some specific result types such as dataset and software, are dynamically retrieved from OpenAIRE .

Deliverables

Open Research Data Pilot (1)

Data Management Plan (DMP)

Participation in the Open Research Data Pilot

Publications

Peer reviewed articles (10)

Bayesian Physics-informed Neural Networks for system identification of inverter-dominated power systems

Author(s): Simon Stock, Davood Babazadeh, Christian Becker, Spyros Chatzivasileiadis
Published in: Electric Power Systems Research, Issue 235, 2024, Page(s) 110860, ISSN 0378-7796
Publisher: Elsevier BV
DOI: 10.1016/j.epsr.2024.110860

Learning Active Constraints to Efficiently Solve Linear Bilevel Problems: Application to the Generator Strategic Bidding Problem

Author(s): Eléa Prat, Spyros Chatzivasileiadis
Published in: IEEE Transactions on Power Systems, Issue 38, 2024, Page(s) 2376-2387, ISSN 0885-8950
Publisher: Institute of Electrical and Electronics Engineers
DOI: 10.1109/tpwrs.2022.3188432

Machine Learning in Power Systems: Is It Time to Trust It?

Author(s): Spyros Chatzivasileiadis, Andreas Venzke, Jochen Stiasny, Georgios Misyris
Published in: IEEE Power and Energy Magazine, Issue 20, 2022, Page(s) 32-41, ISSN 1540-7977
Publisher: Institute of Electrical and Electronics Engineers
DOI: 10.1109/mpe.2022.3150810

Modeling the AC power flow equations with optimally compact neural networks: Application to unit commitment

Author(s): Alyssa Kody, Samuel Chevalier, Spyros Chatzivasileiadis, Daniel Molzahn
Published in: Electric Power Systems Research, Issue 213, 2024, Page(s) 108282, ISSN 0378-7796
Publisher: Elsevier BV
DOI: 10.1016/j.epsr.2022.108282

PINNSim: A simulator for power system dynamics based on Physics-Informed Neural Networks

Author(s): Jochen Stiasny, Baosen Zhang, Spyros Chatzivasileiadis
Published in: Electric Power Systems Research, Issue 235, 2024, Page(s) 110796, ISSN 0378-7796
Publisher: Elsevier BV
DOI: 10.1016/j.epsr.2024.110796

Can Machine Learning Help Keep the System Secure?: Power Systems and Change Addressing the Increasing Complexity and Uncertainty During the Energy Transition

Author(s): Panagiotis N. Papadopoulos, Spyros Chatzivasileiadis, Antoine Marot
Published in: IEEE Power and Energy Magazine, Issue 22, 2024, Page(s) 100-111, ISSN 1540-7977
Publisher: Institute of Electrical and Electronics Engineers
DOI: 10.1109/mpe.2024.3421388

On Machine Learning-Based Techniques for Future Sustainable and Resilient Energy Systems

Author(s): Jiawei Wang, Pierre Pinson, Spyros Chatzivasileiadis, Mathaios Panteli, Goran Strbac, Vladimir Terzija
Published in: IEEE Transactions on Sustainable Energy, Issue 14, 2024, Page(s) 1230-1243, ISSN 1949-3029
Publisher: Institute of Electrical and Electronics Engineers
DOI: 10.1109/tste.2022.3194728

Physics-informed neural networks for phase locked loop transient stability assessment

Author(s): Rahul Nellikkath, Ilgiz Murzakhanov, Spyros Chatzivasileiadis, Andreas Venzke, Mohammad Kazem Bakhshizadeh
Published in: Electric Power Systems Research, Issue 236, 2024, Page(s) 110790, ISSN 0378-7796
Publisher: Elsevier BV
DOI: 10.1016/j.epsr.2024.110790

Physics-Informed Neural Networks for AC Optimal Power Flow

Author(s): Rahul Nellikkath, Spyros Chatzivasileiadis
Published in: Electric Power Systems Research, Issue 212, 2024, Page(s) 108412, ISSN 0378-7796
Publisher: Elsevier BV
DOI: 10.1016/j.epsr.2022.108412

Physics-informed neural networks for time-domain simulations: Accuracy, computational cost, and flexibility

Author(s): Jochen Stiasny, Spyros Chatzivasileiadis
Published in: Electric Power Systems Research, Issue 224, 2024, Page(s) 109748, ISSN 0378-7796
Publisher: Elsevier BV
DOI: 10.1016/j.epsr.2023.109748

Other (3)

A Dataset Generation Toolbox for Dynamic Security Assessment: On the Role of the Security Boundary

Author(s): Bastien Giraud, Lola Charles, Agnes Marjorie Nakiganda, Johanna Vorwerk, Spyros Chatzivasileiadis
Published in: 2025
Publisher: Working Paper
DOI: 10.48550/arxiv.2501.09513

Minimizing Worst-Case Violations of Neural Networks

Author(s): Nellikkath, Rahul; Chatzivasileiadis, Spyros
Published in: Issue 2, 2022
Publisher: Working Paper
DOI: 10.48550/arxiv.2212.10930

Correctness Verification of Neural Networks Approximating Differential Equations

Author(s): Petros Ellinas, Rahul Nellikath, Ignasi Ventura, Jochen Stiasny, Spyros Chatzivasileiadis
Published in: Working Paper, 2024
Publisher: ArXiV
DOI: 10.48550/arxiv.2402.07621

Conference proceedings (8)

Enriching Neural Network Training Dataset to Improve Worst-Case Performance Guarantees

Author(s): Rahul Nellikkath, Spyros Chatzivasileiadis
Published in: 2023 IEEE Belgrade PowerTech, 2023, Page(s) 1-6
Publisher: IEEE
DOI: 10.1109/powertech55446.2023.10202770

GPU-Accelerated Verification of Machine Learning Models for Power Systems

Author(s): Samuel Chevalier, Ilgiz Murzakhanov, Spyros Chatzivasileiadis
Published in: Proceedings of the 57th Hawaii International Conference on System Sciences, 2024
Publisher: IEEE

Bayesian Physics-Informed Neural Networks for Robust System Identification of Power Systems

Author(s): Simon Stock, Jochen Stiasny, Davood Babazadeh, Christian Becker, Spyros Chatzivasileiadis
Published in: 2023 IEEE Belgrade PowerTech, 2023, Page(s) 1-6
Publisher: IEEE
DOI: 10.1109/powertech55446.2023.10202692

Emission-Constrained Optimization of Gas Networks: Input-Convex Neural Network Approach

Author(s): Vladimir Dvorkin, Samuel Chevalier, Spyros Chatzivasileiadis
Published in: 2023 62nd IEEE Conference on Decision and Control (CDC), 2024, Page(s) 1575-1579
Publisher: IEEE
DOI: 10.1109/cdc49753.2023.10383948

Closing the Loop: A Framework for Trustworthy Machine Learning in Power Systems

Author(s): Stiasny, Jochen; Chevalier, Samuel; Nellikkath, Rahul; Sævarsson, Brynjar; Chatzivasileiadis, Spyros
Published in: 2022 iREP Symposium - Bulk Power System Dynamics and Control - XI (iREP), Issue 4, 2022
Publisher: 11th Bulk Power Systems Dynamics and Control Symposium (IREP 2022)
DOI: 10.48550/arxiv.2203.07505

Neural Networks for Encoding Dynamic Security-Constrained Optimal Power Flow

Author(s): Murzakhanov, Ilgiz; Venzke, Andreas; Misyris, George S.; Chatzivasileiadis, Spyros
Published in: Proceedings of 11th Bulk Power Systems Dynamics and Control Symposium, Issue 5, 2022
Publisher: IEEE
DOI: 10.48550/arxiv.2003.07939

Interpretable Machine Learning for Power Systems: Establishing Confidence in SHapley Additive exPlanations

Author(s): Robert I. Hamilton, Jochen Stiasny, Tabia Ahmad, Samuel Chevalier, Rahul Nellikkath, Ilgiz Murzakhanov, Spyros Chatzivasileiadis, Panagiotis N. Papadopoulos
Published in: Climate Change Workshop of the International Conference on Learning Representations (ICLR), 2024
Publisher: ICLR
DOI: 10.48550/arxiv.2209.05793

Accelerating Dynamical System Simulations with Contracting and Physics-Projected Neural-Newton Solvers

Author(s): Samuel Chevalier, Jochen Stiasny, Spyros Chatzivasileiadis
Published in: Proceedings of Machine Learning Research, Issue vol 168, 2022, Page(s) 1-14
Publisher: 4th Annual Conference on Learning for Dynamics and Control