Risk-Aware Allocation of Transmission Capacity for AI Data Centers
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| _version_ | 1866910118377947136 |
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| author | Li, Shaoze Fang, Bohang Chen, Cong |
| author_facet | Li, Shaoze Fang, Bohang Chen, Cong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_08854 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Risk-Aware Allocation of Transmission Capacity for AI Data Centers Li, Shaoze Fang, Bohang Chen, Cong Systems and Control Computer Science and Game Theory 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. |
| title | Risk-Aware Allocation of Transmission Capacity for AI Data Centers |
| topic | Systems and Control Computer Science and Game Theory |
| url | https://arxiv.org/abs/2604.08854 |