Aggregations and data centers: If a resource shows up when the grid is straining, make it count.
Applies the lessons that earned DERs a place on the grid to AI data centers, in six policy moves.
Utility Dive · 5 min read
In brief
Arushi argues that the planning, operational and system-design principles that helped distributed energy earn its place on the grid should now be applied to AI data centers. She calls for interconnection studies that accept enforceable flexibility declarations, co-location pathways for storage, ride-through and grid-services standards for non-traditional facilities, cost allocation tied to controllability, and stability studies that model real compute load shapes. Her closing principle is tech-agnostic: whether flexibility comes from a hyperscale campus or a thousand home batteries, what matters is the reliability contribution.
Key points
- 01Plan interconnection around enforceable flexibility, not a flat 24/7 draw.
- 02Allocate upgrade costs by controllability and contribution to system reliability.
- 03Define the performance that helps, then open participation to anyone who can meet it.
“Energy for AI must be judged not by how much it uses, but by how well it performs.”
