Closed-Loop Enumeration–Surrogate Optimization for Source-Storage Capacity Planning in Large-Scale Renewable Energy Export Bases

Large-scale renewable energy export bases require coordinated source-storage capacity planning under curtailment, utilization, export, and reliability constraints. This study develops a closed-loop enumeration-surrogate optimization workflow for wind–photovoltaic–dispatchable baseload generation-storage planning. The method uses representative capacity samples evaluated by chronological production simulation, fits valid-domain surrogate models for curtailment and utilization indicators, embeds these diagnostics in a bounded nonlinear optimization problem, and then back tests the selected portfolio through production simulation. The Ordos case is now presented as a proof of concept based on limited disclosed simulation samples rather than as a universally validated planning rule. The recommended portfolio consists of 4000 MW wind power, 5500 MW photovoltaic capacity, 5300 MW supporting baseload capacity, and 1000 MWh energy storage. It keeps simulated maximum renewable curtailment at 4.21%, maintains utilization at 4736 h, and reduces annualized cost by 8.4% and 15.7% compared with two higher-capacity reference schemes. The results indicate that the workflow can identify a credible local planning region, while broader validation requires additional samples and multi-year meteorological scenarios.