Work

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

Year: 2025

Type: article

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 × 1 0 5 ± 7 . 97 × 1 0 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 × 1 0 4 ; Max–Min spread = 3 . 82 × 1 0 5 ), 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.

Source: Sustainable Computing Informatics and Systems

DOI: 10.1016/j.suscom.2025.101207

Author: Ercan Erkalkan

Institution: Marmara University

Language: English

Topic: Electric Power System Optimization

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $3,290