Energy storage systems (ESSs), such as lithium-ion batteries, are being used today in renewable grid systems to provide the capacity, power, and quick response required for operation in grid applications, including peak shaving, frequency regulation, back-up power, and voltage support. Each application imposes a different duty cycle on the ESS. This represents the charge/discharge profile associated with energy generation and demand. Different duty cycle characteristics can have different effects on performance, life, and duration of ESSs. Within lithium-ion batteries, various chemistries exist that own different features in terms of specific energy, power and cycle life, that ultimately determine their usability and performance. Therefore, characterization of duty cycles is key to determine how to properly design lithium-ion battery system for grid applications. Given the usage-dependent degradation trajectories, this research task is a critical step to study the unique aging behaviors of grid batteries. Significant energy and cost savings can be achieved by the optimal application of lithium-ion batteries for grid energy storage, enabling greater utilization of renewable grid systems. In this paper, we identify a systematic approach, based on the adoption of both unsupervised learning and frequency domain techniques, to characterizing grid-specific duty cycles for the grid-specific peak shaving application.