Cross-regional Hydrogen Energy Storage System (HESS) effectively addresses the uneven spatial and temporal distribution of renewable energy sources by facilitating energy storage, transfer, and scheduling across regions. However, the intermittency of renewable energy sources, dynamic changes in demand, and the high costs pose complex challenges for the planning and scheduling of cross-regional HESSs. Although hierarchical modeling, which is divided into upper-level planning and lower-level scheduling, provides flexibility and robustness in handling uncertainties, the difficulties of high model complexity and low solution efficiency still exist. To this end, this paper improves hierarchical modeling by introducing the concept of dynamic transmission price into HESS, which quantifies the difficulty of transferring stored energy to neighboring cities. This concept then guides the energy trading decisions and the power distribution strategies among HESS, eliminating the need to consider the discrete variable of cross-regional connections in the lower-level scheduling for the significant simplification of the solution process. In addition, this paper synchronously integrates simulated annealing and linear programming algorithms as an efficient optimization approach for this novel hierarchical modeling. A case study considering 50 cities demonstrates the feasibility of the hierarchical model and the effectiveness of the optimization approach, providing strong support for the large-scale consumption and absorption of renewable energy across regions.