Motivated by the need for pumped-storage planning under high renewable energy penetration, this study proposes a two-stage stochastic planning and layout model for pumped-storage power stations. The model considers hydrometeorological factors, source-load uncertainty, candidate-site heterogeneity, and multi-scenario operational verification. First, a meteorology-source-load–regulation-demand mapping mechanism is constructed to convert multi-source uncertainties associated with wind power, photovoltaic generation, and electric vehicles into regional regulation capacity requirements. Second, a candidate-site parameter database is established by considering reservoir capacity, hydraulic head, efficiency, cost, grid-connection conditions, and regional attributes. Furthermore, a compact reformulation and hierarchical warm-start solution strategy is proposed to improve the solution efficiency of the large-scale stochastic mixed-integer model. The strategy integrates single-mode variable reformulation, online-unit-number binding, safe upper-bound tightening, redundant-constraint reduction, and warm-start mechanisms. Case study results show that the proposed model can effectively characterize the planning layout and operational adaptability of pumped-storage power stations under uncertainty, while improving the solution efficiency and stability of large-scale planning models, thereby providing decision support for the scientific allocation of pumped-storage resources in new-type power systems.