https://ucl-lasp.github.io/tag/sustainability/SustainabilityHugo Blox Builder (https://hugoblox.com)en-usSun, 25 Jan 2026 00:00:00 +0000https://ucl-lasp.github.io/media/icon_hu488c70cfa50b07216f285734af4abcd1_22080_512x512_fill_lanczos_center_3.pnghttps://ucl-lasp.github.io/tag/sustainability/https://ucl-lasp.github.io/project/sustainable-grids/Sun, 25 Jan 2026 00:00:00 +0000https://ucl-lasp.github.io/project/sustainable-grids/<h2 id="overview">Overview</h2> <p>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.</p> <h2 id="active-projects">Active Projects</h2> <h3 id="1-neural-unit-commitment">1. Neural Unit Commitment</h3> <p><strong>Goal:</strong> Optimize power dispatch in real-time. <strong>Details:</strong> The Unit Commitment (UC) problem—deciding which power plants to turn on—is a hard combinatorial problem. We are designing <strong>Graph Neural Networks</strong> that can approximate optimal solutions for UC faster than classical solvers, facilitating the integration of fluctuating renewable sources like wind and solar.</p> <h3 id="2-resilient-infrastructure-monitoring">2. Resilient Infrastructure Monitoring</h3> <p><strong>Goal:</strong> Detect failures before they become disasters. <strong>Details:</strong> 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.</p> <h2 id="works-done">Works Done</h2>