Risk-Aware Allocation of Transmission Capacity for AI Data Centers
2026-04-10 • Computer Science and Game Theory
Computer Science and Game Theory
AI summaryⓘ
The authors address the problem of connecting more AI data centers to the power grid, which is difficult because the grid can handle only so much electricity. They create methods to measure and allocate how much transmission capacity is guaranteed (firm) and how much can be flexible while accepting small risks of outages. They also design an auction system that helps fairly distribute limited grid capacity among multiple data centers based on capacity, risk, and location. Their approach ensures efficient and stable outcomes when data centers value capacity in certain predictable ways.
Transmission capacityData center interconnectionRobust optimizationRisk-aware allocationFlexible capacityFirm capacitySimultaneous ascending auctionCompetitive equilibriumValuation functions
Authors
Shaoze Li, Bohang Fang, Cong Chen
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.