Optimal Configuration Framework of Hybrid Renewable Energy Technologies-Based Hydrogen Energy Storage System Assessment using Enhanced Artificial Rabbit Algorithm
Yazarlar (2)
Doç. Dr. Aykut Fatih GÜVEN
Yalova Üniversitesi, Türkiye
Faculty Of Engineering, Mısır
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale |
| Dergi Adı | Energy |
| Dergi ISSN | 0360-5442 Wos Dergi Scopus Dergi |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Dergi Grubu | Q1 |
| Makale Dili | İngilizce |
| Basım Tarihi | 07-2025 |
| Cilt No | 326 |
| Sayı | 1 |
| Sayfalar | 1 / 33 |
| DOI Numarası | 10.1016/j.energy.2025.135408 |
| Makale Linki | https://doi.org/10.1016/j.energy.2025.135408 |
| Özet |
| This study introduces the enhanced artificial rabbit optimization (EARO) algorithm, which was developed specifically to optimize hybrid renewable energy systems (HRES) for efficient electricity and hydrogen production. The EARO algorithm was designed to meet specific load demands and economic metrics to promote sustainable energy solutions. By integrating wind turbines, photovoltaic panels, fuel cells, biomass generators, and inverters into a comprehensive HRES, the EARO algorithm effectively manages and allocates power, thus ensuring both cost reduction and reliability. Performance tests conducted using HOMER software compared EARO's efficacy against nine contemporary algorithms, demonstrating EARO's superior ability to optimize system configurations. EARO achieved a photovoltaic output of 2806.3399 kW, a wind turbine output of 597.2655 kW, and integrated 548.8034H2 storage tanks, collectively leading to an annual system cost of $3.0926 million and a total net present cost of $25.119 million, with a levelized cost of energy of $1.2196 per kWh. The optimized HRES achieves a 100 % renewable energy fraction with a balanced energy mix that significantly reduces operational costs and enhances the sustainability of on-site power generation. This makes EARO a critical tool for stakeholders and policymakers in renewable energy, especially for off-grid or remote applications. This study underscores the potential of EARO to drive the transition toward sustainable and autonomous energy systems. |
| Anahtar Kelimeler |
| Optimization design | Hydrogen production | Renewable energy system | Annual system cost | Energy management | EARO algorithm |
Science Direct
Optimal Configuration Framework of Hybrid Renewable Energy Technologies-Based Hydrogen Energy Storage System Assessment using Enhanced Artificial Rabbit Algorithm
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| WoS | 7 |
| SCOPUS | 9 |