AI for Sustainable Power Grids
Overview
The transition to renewable energy requires a smarter, more resilient grid. We apply graph-based learning to manage the combinatorial complexity of power networks and critical infrastructure.
Active Projects
1. Neural Unit Commitment
Goal: Optimize power dispatch in real-time. Details: The Unit Commitment (UC) problem—deciding which power plants to turn on—is a hard combinatorial problem. We are designing Graph Neural Networks that can approximate optimal solutions for UC faster than classical solvers, facilitating the integration of fluctuating renewable sources like wind and solar.
2. Resilient Infrastructure Monitoring
Goal: Detect failures before they become disasters. Details: Building on our work in water distribution networks, we develop graph-based anomaly detection systems. These models learn the topology of the infrastructure to localize leaks, faults, or attacks in complex sensor networks.