Wind power prediction interval (WPPI) models in the literature have predominantly been developed for and tested on specific case studies. However, wind behavior and characteristics can vary significantly across regions. Thus, a prediction model that performs well in one case might underperform in another. To address this shortcoming, this paper proposes an ensemble WPPI framework that integrates multiple WPPI models with distinct characteristics to improve robustness. Another important and often overlooked factor is the role of probabilistic wind power prediction (WPP) in quantifying wind power uncertainty, which should be handled by operating reserve. Operating reserve in WPPI frameworks enhances the efficacy of WPP. In this regard, the proposed framework employs a novel bi-layer optimization approach that takes both WPPI quality and reserve requirements into account. Comprehensive analysis with different real-world datasets and various benchmark models validates the quality of the obtained WPPIs while resulting in more optimal reserve requirements.