To enhance the transmission capabilities in power system scheduling, this paper develops a unit commitment model that incorporates dynamic transmission line capacities and proposes an efficient solving algorithm. A multi-scenario unit commitment model that integrates dynamic transmission line capacities is introduced, using quantile regression to construct a data-driven capacity increase model based on historical environmental data. The model is solved using Lagrangian relaxation and the Alternating Direction Method of Multipliers (ADMM) to decouple dynamic constraints, allowing the dual problem to be decomposed into sub-problems and solved iteratively. The proposed model and algorithm are validated using the IEEE-118 and IEEE-300 test cases, demonstrating their effectiveness in handling dynamic transmission line capacities and improving scheduling performance.The approach provides a robust and flexible solution for power system scheduling, enhancing reliability and economic efficiency.