The large-scale development of wind power and photovoltaic (PV) in China's desert and Gobi regions has become a key strategy for achieving carbon neutrality, while the mismatch between renewable energy resources (RES) and load centers continues to challenge system integration. In this case, ultra-high voltage direct current (UHVDC) transmission enables long-distance delivery of bundled multi-energy output for renewable energy accommodation. This study proposes a day-ahead scheduling model for a wind-PV-thermal-pumped storage renewable energy base (WPTP-EB) based on distributionally robust chance-constrained (DRCC), with the objective of maximizing RES accommodation. Stable operational requests of UHVDC consisting of short-circuit ratio (SCR) constraints and stepwise transmission requirements are considered in the proposed model. An ambiguity set baed on Wasserstein-metric is employed to characterize the uncertainty of RES availability. Then, the proposed DRCC formulation is further transformed into a tractable mixed-integer linear programming (MILP) problem through conditional value-at-risk (CVaR) approximation and duality theory. Case studies in Qinghai Province demonstrate that: (i) incorporating pumped storage (PS) increases RES utilization by 12.51%; (ii) higher minimum SCR thresholds reduce RES transmission due to stability requirements, while phase modulation operation of PS effectively mitigates this effect; and (iii) compared with stochastic optimization based on assumed distributions, the proposed DRCC framework yields more conservative but robust scheduling decisions, ensuring secure operation under adverse scenarios. The findings highlight the critical role of PS and DRCC-based optimization in enhancing the reliability and flexibility of large-scale renewable energy bases integrated with UHVDC transmission.