A Fuzzy C-Means-Based Mathematical Framework for the Storage-Oriented Evaluation of Hybrid Energy Systems

This study develops a Fuzzy C-Means-based mathematical framework for the storage-oriented evaluation and classification of hybrid energy system alternatives. The analysis considers fifteen hybrid configurations generated through pairwise combinations of solar, wind, biomass, geothermal, hydropower, and fossil-based energy sources. These alternatives are evaluated with respect to fourteen storage-related criteria, namely energy efficiency, exergy efficiency, entropy, lifetime, cost, CO2 emissions, recyclability, decarbonization potential, discharge duration, charge duration, power capacity, energy capacity, sustainability, and environmental impact. After constructing and normalizing the decision matrix, the Fuzzy C-Means algorithm is employed to identify latent similarity structures and to determine the degree of membership of each hybrid alternative to multiple clusters. To support the selection of an analytically meaningful partition, alternative cluster structures are compared in terms of partition quality and interpretability. The results indicate that the considered hybrid configurations can be grouped into distinct yet partially overlapping storage-oriented profiles, reflecting differences in technical performance, environmental burden, and sustainability characteristics. In particular, hydropower-supported systems are associated with more stable and infrastructure-compatible profiles, while biomass- and geothermal-related combinations occupy more balanced transitional positions. By extending fuzzy clustering to the storage-oriented analysis of hybrid energy systems, the study provides a mathematically transparent basis for comparative assessment, exploratory classification, and preliminary decision support.