Risk-Aware Allocation of Transmission Capacity for AI Data Centers

TL;DR

This paper develops risk-aware frameworks for allocating transmission capacity to AI data centers, balancing reliability and flexibility to optimize interconnection and utilization.

Contribution

It introduces robust and risk-aware optimization methods and an auction-based allocation scheme for transmission capacity in AI data centers.

Findings

Risk-aware flexible capacity can significantly increase transmission network utilization.

The auction mechanism converges to a competitive equilibrium under certain valuation functions.

Tolerating minimal service interruption can unlock substantial flexible capacity.

Abstract

Rapid growth in AI-driven data center loads is creating significant challenges for transmission grid interconnection. This paper proposes robust and risk-aware frameworks to quantify transmission capacity as firm and flexible capacities. We efficiently solve the robust optimization problem to determine firm capacity when minimizing unserved data center demand. Building upon this, we introduce a risk-aware allocation for flexible capacity, showing that tolerating a minimal probability of service interruption and blackout can unlock substantial flexible capacity of transmission networks and accelerate data center interconnection. To efficiently allocate scarce transmission capacities among competing data centers, we adopt the simultaneous ascending auction, characterizing products by capacity, risk level, and location. Under additive or symmetric concave valuation functions, the auction converges to a competitive equilibrium and achieves efficient allocation.