Hybrid Energy Storage Systems have attracted attention in recent years, promising to outperform single batteries in some applications. This can be in decreasing total cost of ownership, extend combined lifetime, higher versatility in providing multiservice, and reducing the combined size. Nevertheless, the sizing of these systems, in such a way that proves to outperform a single battery, is a challenging task. This is particularly true, if such a tool is expected to be a practical one, applicable to different inputs and which can provide a range of optimal solutions as a support for decision makers. This article provides exactly that, presenting a technology-independent sizing model for Hybrid Energy Storage Systems. The model introduces a three-step algorithm: the first block employs a clustering of time series using Dynamic Time Warping (DTW) for recurring pattern identification. The second block optimizes a battery dispatch using Linear Programming (LP) to the identified recurring load profile. Lastly, the third block identifies an optimal hybridization area for battery size configuration, and offers practical insights for commercial technology selection, particularly focusing on the hybridization potential of different storage solutions. In the webinar the model will be applied to a real dataset from an office building to show the model working, providing viable and non-viable hybridization sizing examples.