Akademik Veri Yönetim Sistemi
Sustainable Computing: Informatics and Systems, cilt.48, 2025 (SCI-Expanded)
This study addresses renewable-energy storage scheduling — a high-dimensional, multimodal optimization task — by proposing an enhanced Grey Wolf–Whale Optimization Algorithm (EGW–WOA). The method fuses GWO's hierarchical leadership with WOA's spiral exploitation and augments them with Lévy flights and progress-triggered chaotic re-initialization. Across 100 Monte-Carlo trials, EGW–WOAreduced 24 h operating cost to 2.94×105±7.97×104, improving over WOA by 16.62%, GA by 10.15%, FPA by 63.6%, and HS by 80.76%, with a 100% feasibility rate. It achieved the lowest dispersion (Std =7.97×104; Max–Min spread =3.82×105), shaved peak-demand charges by ≈9%, and limited depth-of-discharge swings to <35%, projecting a 12%–18% life extension. A 50-iteration run completed in 38.6 s on a 3.4 GHz CPU — over 20× faster than a comparable MILP baseline — demonstrating suitability for near-real-time PV–wind microgrid control. Within the scope of Sustainable Computing: Informatics and Systems, this work delivers a reproducible, open-source optimization engine with non-parametric statistical validation and edge-suitable runtimes, linking algorithmic advances to system-level sustainability metrics (LCOS, demand charges). The results show how algorithm–system co-design can lower operating cost and risk while preserving battery health in cyber–physical energy systems.