<?xml version="1.0" encoding="UTF-8"?><oembed><type>video</type><version>1.0</version><html>&lt;iframe src=&quot;https://www.loom.com/embed/b4a26fd8b8d44f6782832e2aca1476a9&quot; frameborder=&quot;0&quot; width=&quot;1920&quot; height=&quot;1440&quot; webkitallowfullscreen mozallowfullscreen allowfullscreen&gt;&lt;/iframe&gt;</html><height>1440</height><width>1920</width><provider_name>Loom</provider_name><provider_url>https://www.loom.com</provider_url><thumbnail_height>1440</thumbnail_height><thumbnail_width>1920</thumbnail_width><thumbnail_url>https://cdn.loom.com/sessions/thumbnails/b4a26fd8b8d44f6782832e2aca1476a9-2585291f745c137f.gif</thumbnail_url><duration>109.185</duration><title>Transforming Energy Management with AI-Driven Digital Twins</title><description>In this video, I discuss how we are transforming our approach to managing energy grids by utilizing ERCOT&apos;s live data APIs to create a living digital twin. Our intervention sandbox allows planners to simulate heatwaves and EV surges, helping us identify risk zones and prevent potential failures. By deploying a one-megawatt battery downtown, we can improve our risk-resilient index significantly. I also highlight how a $500,000 solar incentive could reduce evening peak demand by 16 megawatts. I encourage you to explore these insights and consider how we can implement proactive measures in our energy management strategies.</description></oembed>