•Mixed-integer linear programming model for optimizing renewable energy storage.•A clustering algorithm to approximate the optimal levelized cost of electricity.•Case study on providing power under different demand profiles in New York City.•Motivation for including backup storage options in addition to battery. Intermittent solar and wind availabilities pose design and operational challenges for renewable power systems because they are asynchronous with consumer demand. To align this supply-demand mismatch, optimization-based design and scheduling models have been developed to minimize the capital and operational costs associated with power production and energy storage. However, hourly time discretization and large time horizons used to describe short- and long-term solar and wind dynamics, demand fluctuations, & price changes significantly increase the computational burden of solving these models. A decomposition algorithm based on agglomerative hierarchical clustering (AHC) is developed to alleviate the model complexity and optimize the system over representative time periods, instead of every hour. An advantage for AHC compared to other clustering methods is the preservation of time chronology, which is important for energy storage applications. The algorithm is applied to investigate a renewable power system with battery storage in New York City. Results show that a few representative time periods (5–15 days) sufficiently capture the system performance within 5% of the true optimal solution. The decomposition algorithm is suitable for investigating any optimization problem with time series data.