Large-scale renewable energy bases are increasingly deployed in arid regions, which offer favorable conditions for wind and PV generation supported by energy storage systems and long-distance transmission lines. However, the planning of such bases is complicated by the high variability of renewable generation, limited flexibility resources, and complex multi-objective trade-offs. To address these issues, this study proposes a capacity planning model for wind-PV-thermal-storage renewable energy bases, minimizing construction and operational costs while accounting for uncertainty and explicitly quantifying the value of flexibility resources. Compared with existing capacity planning models that rely on deterministic formulations or simplified two-stage stochastic representations, the proposed model explicitly embeds intraday operational flexibility and forecast-error costs into life-cycle planning. Operational costs are assessed through sequential production simulations, in which intraday forecast errors are incorporated via deviation costs and flexibility requirements. A hybrid sampling strategy combining Latin hypercube sampling and importance sampling is used for scenario generation, followed by scenario reduction to improve computational efficiency. To solve the optimization model, a nested generalized Benders decomposition framework is developed, decomposing the model into a master problem and multiple production simulation subproblems, which are further divided into mixed-integer and continuous-variable layers to enhance computational tractability and solution accuracy. Case studies demonstrate that the proposed model and algorithm demonstrate the role of flexibility resources, resulting in economically viable and practically implementable capacity under high renewable penetration. By explicitly accounting for intraday forecast deviations, the resulting plans ensure reserve adequacy for over 95% of uncertainty realizations while remaining economically viable and practically implementable. Moreover, the impact of carbon emission penalties on capacity allocation and renewable utilization is quantified, highlighting implications for system design and planning strategies for wind-PV-thermal-storage renewable energy bases.