arXiv:2611.05958v1 Announce Type: new Abstract: Large data center projects introduce a distinct transmission planning uncertainty because of their large scale, discrete project realization nature, and strong locational impacts on the transmission network. On the one hand, conventional deterministic treatments can either overbuild the network by assuming that all candidate projects materialize; on the other, they can leave the system underprepared by prematurely screening out projects. This paper develops a project-realization-based framework for systematically modeling, planning, and evaluating transmission expansion under uncertain data center realization. Candidate data center projects are represented through alternative realization scenarios and associated probabilities, enabling different planning strategies to be modeled and quantitatively assessed under a common framework. Within this framework, numerical case studies demonstrate the effectiveness of the proposed framework and the benefits of stochastic planning approach. The results show that stochastic planning can use realization probabilities to hedge multiple plausible futures, providing a more balanced tradeoff between transmission investment and reliability exposure than deterministic approaches under correctly specified and misspecified realization probabilities.