| Reactive power markets: A review T Wolgast, S Ferenz, A Nieße IEEE access 10, 28397-28410, 2022 | 44 | 2022 |
| Towards reinforcement learning for vulnerability analysis in power-economic systems T Wolgast, EMSP Veith, A Nieße Energy Informatics 4, 1-20, 2021 | 12 | 2021 |
| Analyzing power grid, ICT, and market without domain knowledge using distributed artificial intelligence E Veith, S Balduin, N Wenninghoff, M Tröschel, L Fischer, A Nieße, ... arXiv preprint arXiv:2006.06074, 2020 | 12 | 2020 |
| A sketch of unwanted gaming strategies in flexibility provision for the energy system S Buchholz, PH Tiemann, T Wolgast, A Scheunert, J Gerlach, N Majumdar, ... 16th International Conference on Wirtschaftsinformatik, Pre-Conference …, 2021 | 4 | 2021 |
| Towards modular composition of agent-based voltage control concepts T Wolgast, A Nieße Energy Informatics 2 (Suppl 1), 26, 2019 | 4 | 2019 |
| ANALYSE — Learning to attack cyber–physical energy systems with intelligent agents T Wolgast, N Wenninghoff, S Balduin, E Veith, B Fraune, T Woltjen, ... SoftwareX 23, 101484, 2023 | 3 | 2023 |
| ANALYSE--Learning to Attack Cyber-Physical Energy Systems With Intelligent Agents T Wolgast, N Wenninghoff, S Balduin, E Veith, B Fraune, T Woltjen, ... arXiv preprint arXiv:2305.09476, 2023 | 3 | 2023 |
| Design and evaluation of a multi-level reactive power market J Bozionek, T Wolgast, A Nieße Energy Informatics 5 (1), 6, 2022 | 3 | 2022 |
| Dynamic inspection interval determination for efficient distribution grid asset-management T Neugebauer, T Wolgast, A Nieße Energies 13 (15), 3875, 2020 | 3 | 2020 |
| Approximating energy market clearing and bidding with model-based reinforcement learning T Wolgast, A Nieße arXiv preprint arXiv:2303.01772, 2023 | 2 | 2023 |
| palaestrAI: A training ground for autonomous agents E Veith, S Balduin, N Wenninghoff, T Wolgast, M Baumann, D Winkler, ... Proceedings of the 37th annual European Simulation and Modelling Conference …, 2023 | 2 | 2023 |
| Learning to Attack Powergrids with DERs E Veith, N Wenninghoff, S Balduin, T Wolgast, S Lehnhoff arXiv preprint arXiv:2204.11352, 2022 | 1 | 2022 |
| Learning the Optimal Power Flow: Environment Design Matters T Wolgast, A Nieße arXiv preprint arXiv:2403.17831, 2024 | | 2024 |
| Publisher Correction: Towards reinforcement learning for vulnerability analysis in power-economic systems. T Wolgast, EMSP Veith, A Nieße Energy Inform. 6 (1), 11, 2023 | | 2023 |
| Towards Reinforcement Learning for Vulnerability Detection in Power Systems and Markets: Poster T Wolgast, EMSP Veith, A Nieße Proceedings of the Twelfth ACM International Conference on Future Energy …, 2021 | | 2021 |
Astrid NießeUniversität Oldenburg在 uni-oldenburg.de 的电子邮件经过验证
