Power Grid
Power grids are critical infrastructure facing increasing complexity due to the energy transition and the rise of renewable energy sources. Current research focuses on improving grid operation and resilience through advanced modeling and control techniques, employing graph neural networks, reinforcement learning, and large language models to optimize power flow, predict and mitigate cascading failures, and enhance cybersecurity. These efforts aim to improve grid stability, efficiency, and security, impacting both the scientific understanding of complex systems and the practical operation of power grids worldwide.
Papers
Robust N-1 secure HV Grid Flexibility Estimation for TSO-DSO coordinated Congestion Management with Deep Reinforcement Learning
Zhenqi Wang, Sebastian Wende-von Berg, Martin Braun
Power Grid Congestion Management via Topology Optimization with AlphaZero
Matthias Dorfer, Anton R. Fuxjäger, Kristian Kozak, Patrick M. Blies, Marcel Wasserer
Hybrid AI-based Anomaly Detection Model using Phasor Measurement Unit Data
Yuval Abraham Regev, Henrik Vassdal, Ugur Halden, Ferhat Ozgur Catak, Umit Cali
Evaluation of Look-ahead Economic Dispatch Using Reinforcement Learning
Zekuan Yu, Guangchun Ruan, Xinyue Wang, Guanglun Zhang, Yiliu He, Haiwang Zhong