Technoeconomic Analysis of Microgrids for AI Data Centers in the Continental United States

Rapid growth of artificial intelligence demands data centers of unprecedented scale and deployment speed. To power these facilities, firms typically connect to the commercial electric grid or purchase power directly from existing or proposed generators. However, these plans overlook likely delays from grid interconnection and natural gas turbine availability. We compare grid-connected and off-grid data center energy infrastructure (AC-coupled PV+storage, DC-coupled PV+storage, islanded natural gas, and grid baseline) across the continental U.S. We combine models in the literature for location- specific cooling energy use, solar production, and battery degradation and develop data center component-level power and microgrid performance models. To estimate oppor- tunity costs of construction delays, we quantify deployment speed value using GPU spot market prices. We find that DC-coupled microgrids reduce costs 17% (7¢/kWh) on average versus AC counterparts by eliminating inverters and improving efficiency. When valuing deployment speed, all-DC microgrids are lowest-cost in 70%-89% of con- tinental U.S. locations depending on assumptions about natural gas plant construction timelines. These findings suggest that islanded microgrids can serve large data center loads without ratepayer-funded grid expansion, meaning expedited permitting for self- supplied data center power could accelerate AI deployment while preventing residential ratepayers from cross-subsidizing private infrastructure.