Enhancing Power Grid Resilience to Extreme Weather by Deploying Machine Learning Models

Mehrnaz Anvari – Fraunhofer SCAI

Societies are undergoing rapid changes in energy generation and consumption. ENTSO-E predicts that electric energy will constitute up to 50% of total energy use by mid-century, up from the current 20%. This increase in electrification will necessitate significant advancements in sectors like transportation and heating. To ensure a resilient society, a robust power system is essential due to emerging dependencies. The sixth IPCC Assessment Report underscores the intensification of weather extremes, such as severe wind conditions, which may exploit vulnerabilities in the power system. In this talk, I will present our co-evolution model that integrates the dynamics of extreme events with structural changes in the power grid to identify critical transmission lines. Additionally, I will discuss the use of Graph Neural Networks (GNN) and physics-informed loss to simulate power outages caused by extreme weather and identify critical components in the power grid.