Thomas Wolgast
Contact
Secretary:
Head
Thomas Wolgast
- Attacks on Power Systems
- Vulnerability Analysis
- Ancillary Service Markets
- Machine Learning / Reinforcement Learning
- Real-Time Optimal Power Flow
- Multi-Agent Control Systems
Department of Computing Science (» Postal address)
Curriculum vitae
Thomas Wolgast is doctoral student in the field of energy informatics at the University of Oldenburg.
He received his Master of Science in Power Engineering from the Leibniz University Hannover. His master thesis was the implementation and evaluation of multi-agent based voltage control concepts in the distribution grid.
Currently, he is using Machine Learning and especially Reinforcement Learning, to learn attack vectors and market manipulations on power systems and to identify systemic vulnerabilities this way. That in turn allows to evaluate countermeasures. Especially vulnerable for such attacks are ancillary service markets. Thomas is responsible for the PYRATE project.
He received his Master of Science in Power Engineering from the Leibniz University Hannover. His master thesis was the implementation and evaluation of multi-agent based voltage control concepts in the distribution grid.
Currently, he is using Machine Learning and especially Reinforcement Learning, to learn attack vectors and market manipulations on power systems and to identify systemic vulnerabilities this way. That in turn allows to evaluate countermeasures. Especially vulnerable for such attacks are ancillary service markets. Thomas is responsible for the PYRATE project.
Lectures
Winter term 2022 / 2023
Publications
- Wolgast, T., Nieße, A. Towards modular composition of agent-based voltage control concepts. Energy Inform 2, 26 (2019). https://doi.org/10.1186/s42162-019-0079-x
- Neugebauer, T.; Wolgast, T.; Nieße, A. Dynamic Inspection Interval Determination for Efficient Distribution Grid Asset-Management. Energies 2020, 13, 3875. https://doi.org/10.3390/en13153875
- Veith, Eric; Balduin, Stephan; Wenninghoff, Nils; Tröschel, Martin; Fischer, Lars; Nieße, Astrid et al. (2020): Analyzing Power Grid, ICT, and Market Without Domain Knowledge Using Distributed Artificial Intelligence. In:. CYBER 2020, The Fifth International Conference on Cyber-Technologies and Cyber-Systems, S. 86–93.
- Wolgast, Thomas (2020): Real-Time Capable Optimal Power Flow With Artificial Neural Networks. Abstracts from the 9th DACH+ Conference on Energy Informatics, Volume 3 Supplement 2, Sierre, Switzerland. 29-30 October 2020. https://doi.org/10.1186/s42162-020-00113-9
- Buchholz S, Tiemann PH, Wolgast T, Scheunert A, Gerlach J, Majumdar N, Breitner M, Hofmann L, Nieße A, Weyer H (2021) A sketch of unwanted gaming strategies in flexibility provision for the energy system. In: 16th International Conference on Wirtschaftsinformatik, Pre-Conference Community Workshop Energy Informatics and Electro Mobility ICT
- Wolgast, Thomas; Veith, Eric MSP; Nieße, Astrid (2021): Towards Reinforcement Learning for Vulnerability Detection in Power Systems and Markets. In: Proceedings of the Twelfth ACM International Conference on Future Energy Systems. e-Energy '21: The Twelfth ACM International Conference on Future Energy Systems. Virtual Event Italy, 28 06 2021 02 07 2021. New York,NY,United States: Association for Computing Machinery (ACM Digital Library), S. 292–293.
- Wolgast, Thomas; Veith, Eric MSP; Nieße, Astrid (2021): Towards reinforcement learning for vulnerability analysis in power-economic systems. In: Energy Informatics 4 (S3). DOI: 10.1186/s42162-021-00181-5 .