Akademik Veri Yönetim Sistemi
5th International Thales Congress on Life, Engineering, Architecture and Mathematics, Cairo, Mısır, 16 - 18 Ekim 2025, cilt.1, sa.1, ss.1-10, (Tam Metin Bildiri)
The integration of energy storage systems (ESS) into renewable-based microgrids is increasingly constrained by the lifetime limitations of batteries, making degradation-aware modeling a vital component of optimization frameworks. This study systematically evaluates the applicability of multiple cycle and calendar aging formulations derived from experimental findings by embedding them into a Python–Gurobi optimization framework for prosumer-based microgrid operation. Three representative models of each aging type are cross-combined to generate nine scenarios, of which five were retained for detailed analysis after excluding those that distorted the optimization results or conflicted with the operational logic of the developed energy management algorithm. The findings demonstrate that degradation model selection substantially influences both projected lifetime capacity fade and techno-economic outcomes, including cost of electricity (COE), renewable fraction (RF), and carbon emissions. Notably, sophisticated calendar aging models that account for state-of-charge effects amplify degradation under partial or high state of charge (SoC) conditions, yielding up to threefold higher capacity fade compared to simplified approaches. Meanwhile, the economic impact of degradation emerges indirectly through altered charging–discharging dynamics, as replacement costs are not explicitly monetized in the objective function. The results highlight that overlooking degradation pathways may lead to overly optimistic projections of system sustainability and renewable penetration. By confirming that degradation-aware optimization alters the operational feasibility and environmental footprint of microgrids, this work underscores the necessity of carefully selecting aging formulations in decision-making processes. The study contributes methodological insights into balancing cost minimization and battery health preservation, offering a foundation for future research on robust, degradation-conscious energy management in renewable-based microgrids.