An enhanced hybrid optimization model for renewable energy storage: Integrating GWO and WOA, with Lévy mechanisms

Abstract

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 $2.94\times 10^5\pm 7.97\times 10^4$, 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 $=7.97\times 10^4$; Max–Min spread $=3.82\times 10^5$), shaved peak-demand charges by $\approx$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 $20\times$ 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.