Renewable power generation systems are significantly affected by uncertainty due to intense variability often observed in energy sources. Uncertainty should be considered during design to enable optimum performance within constantly changing conditions. However, the resulting computational complexity and effort is high, especially in view of flowsheets integrating multiple subsystems. To address this challenge, the presented work proposes the partitioning of the space representing uncertain realizations to facilitate the development and continuous update of a surrogate model in the course of optimization. A wide exploration of this strategy reveals and addresses important issues in the implementation of the partitioning and model regression layers. Formal statistical associations are examined regarding the beneficial implications of partitioning to computational efficiency and surrogate model development. The proposed strategy is presented as part of a Simulated Annealing algorithm. This is tested in terms of computational efficiency and solution robustness against an adaptation of Stochastic Annealing, which addresses computational intensity through a different approach while depending entirely on a full system model. Results are illustrated through numerical examples and case studies on a stand-alone, hybrid system using renewable energy sources for power generation and storage.