In their talk they explain how our Reinforcement Learning (RL) powered grid operation decision support system suggests non-costly, carbon free measures (e.g., topological corrections) as grid control actions to human operators. Our technology aims at optimizing existing infrastructure with the ultimate goal of increasing grid reliability and flexibility as well as the yield of renewable generation. This unlocks the potential of active topology control as a readily available, fast and economic measure for preventing upcoming congestions.
Our mobile mapping solution Detekt won the IÖB Award in the category 'Innovative Solutions for Infrastructure and Traffic Management'
We are excited to present our work on power grid congestion management at this year’s RL4RealLife Workshop @ NeurIPS