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.

翻译：人工智能驱动数据中心的负荷快速增长给输电系统并网带来重大挑战。本文提出鲁棒性和风险感知框架，将输电容量量化为固定容量与灵活容量。通过高效求解鲁棒优化问题，我们确定了在最小化数据中心未服务需求前提下的固定容量。在此基础上，提出灵活容量的风险感知配置方案，证明容忍极小概率的服务中断与停电风险可释放输电网络的显著灵活容量，并加速数据中心并网。为在竞争性数据中心之间高效分配稀缺输电容量，我们采用同步升价拍卖机制，将产品特征定义为容量、风险等级与地理位置。在加性对称凹估值函数条件下，该拍卖收敛至竞争均衡并实现有效配置。