Research

GNN4GC (graph neural networks for grid control) aims to use GNNs and Reinforcement Learning for congestion management in power grids. It is a joint project between the research institutions Fraunhofer IEE and the University of Kassel, and the three transmission system operators TenneT TSO GmbH, 50Hertz and TenneT TSO BV.

Transmission grids are the backbone of energy distribution, but the integration of renewables, distributed generation and increasing electrification make operation more complex and lead to bottlenecks in the grid. To solve these problems, grid operators instruct generation plants to increase or reduce their output. A cost-effective alternative is to change the grid topology to alter the power flow in the grid. As the grid operator can do this themselves and the costs are very low, such topological actions have great potential. However, they have not been widely used because the large number of switching options is impossible for humans to keep track of. GNN4GC proposes GNN-based solutions to accelerate grid calculations and approximate load flow, enabling efficient evaluation of different topology actions for effective grid operation. Another key objective is to integrate GNNs with Reinforcement Learning to create self-learning grid control agents that learn grid control strategies by interacting with a simulated power grid. The agents will be evaluated in real use cases through continuous collaboration with grid operators. Ultimately, the project aims at a synergistic partnership between research and industry to provide practical methods to address the challenges of real-world energy operations and contribute to the success of the energy transition.

• Power flow forecasts at transmission grid nodes using Graph Neural Networks
• Graph reinforcement learning for power grids: A comprehensive survey