WEBINAR | Optimizing Distributed Energy Infrastructure for AI Factories Using GT-SUITE

The rise of AI data centers (AI factories) has stressed the ability of energy grids to reliably supply sufficient energy. Recent reports project data center energy usage to increase to between 325-580 TWh by 2028, or anywhere from 6.7-12% of the annual electrical energy consumption in the United States. Coupled with stringent requirements for reliability, cost control, and sustainability and net zero initiatives, long-term facilities planning and commissioning of the energy infrastructure for AI data centers is increasingly complex.

In this second webinar of a series, a deeper dive into the energy infrastructure surrounding AI factories is discussed. In particular, we will show how a modular, fast-running, multi-fidelity, multi-physics model of a microgrid can be created in GT-SUITE. We will explore how fungible designs with various behind-the-meter energy sources can be created virtually and analyzed to allow energy systems to be scaled and swapped, under a wide variety of locations and operating conditions. Lastly, we will discuss how economic factors can be added to the analysis, allowing for a comprehensive approach to long-term facilities planning.

What you will learn:

• Common components of a distributed energy infrastructure, and how to model them in different levels of fidelity using GT-SUITE software
• Why using GT-SUITE for system simulation empowers a modular, flexible, and fungible distributed energy infrastructure design
• How a techno-economic analysis (TEA) can be performed in GT-SUITE, using physics-based and/or data driven models to determine an optimal location-specific energy supply.

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