Integrated Techno-Economic Optimization and Metaheuristic Benchmarking of Grid-Connected Hybrid Renewable Energy Systems Using Real-World Load and Climate Data    
Yazarlar (5)
Doç. Dr. Aykut Fatih GÜVEN Yalova Üniversitesi, Türkiye
Onur Özdal Mengi
Giresun Üniversitesi, Türkiye
Mohit Bajaj
Graphic Era Deemed To Be University, Hindistan
Ahmad Taher Azar
Prince Sultan University, Suudi Arabistan
Walid El-Shafai
Prince Sultan University, Suudi Arabistan
Makale Türü Açık Erişim Özgün Makale
Makale Alt Türü SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale
Dergi Adı E Prime Advances in Electrical Engineering Electronics and Energy
Dergi ISSN 2772-6711 Scopus Dergi
Dergi Tarandığı Indeksler SCI-Expanded
Dergi Grubu Q1
Makale Dili İngilizce
Basım Tarihi 09-2025
Cilt No 13
Sayı 1
Sayfalar 1 / 30
DOI Numarası 10.1016/j.prime.2025.101099
Makale Linki https://doi.org/10.1016/j.prime.2025.101099
Özet
This study proposes an integrated optimization framework for the techno-economic sizing and performance evaluation of a grid-connected hybrid renewable energy system (HRES) comprising photovoltaic (PV) panels, wind turbines (WT), battery storage (BTS), and a diesel generator (DG). A real-world case study is conducted on a university campus in Turkey using high-resolution hourly meteorological and load data over a full year (8760 h). The objective is to minimize the annualized cost of the system (ACS), levelized cost of energy (LCOE), and total net present cost (TNPC), while ensuring high reliability through a constraint on the loss of power supply probability (LPSP) at 0.5 %. The decision variables include the optimal capacities of PV, WT, DG, BT, and inverter components, bounded by technical, economic, and operational constraints, including a minimum renewable energy fraction (REF) requirement. The system's energy production, storage, and grid interactions are modeled using detailed mathematical formulations. Optimization is performed using the Moth-Flame Optimization Algorithm (MFOA) and benchmarked against the Whale Optimization Algorithm (WOA), Flower Pollination Algorithm (FPA), and Genetic Algorithm (GA). Simulation results identify the PV/WT/BT configuration as the most cost-effective and reliable, achieving an LCOE of $0.1342/kWh, a TNPC of $3.2542 × 10⁶, and an ACS of $2.9214 × 10⁵. These values reflect a 33 % cost reduction compared to the off-grid configuration. Additionally, the system enables annual grid electricity purchases of up to 4.4086 × 10⁵ kWh and sales of up to 1.2114 × 10⁶ kWh. Notably, the achieved LCOE is significantly lower than the prevailing commercial grid tariff of $0.35/kWh in Turkey, demonstrating the financial competitiveness of the proposed system for institutional and commercial users. In terms of algorithmic performance, MFOA outperforms the other methods by delivering the fastest convergence, highest optimization stability, and a fully renewable solution (REF = 100 %) without DG operation. This solution achieves an LCOE of $0.1443/kWh and a TNPC of $3.5085 × 10⁶, which is slightly higher than the absolute minimum cost but demonstrates the ability to reach 100 % renewable penetration without diesel usage. The system also reports the shortest execution time (336.5 s), confirming its suitability for real-time or iterative design tasks. Overall, the proposed HRES configuration offers a technically feasible, economically advantageous, and environmentally sustainable solution for campus electrification and broader smart grid applications, and serves as a replicable decision-support model for renewable energy planning in regions with high electricity tariffs.
Anahtar Kelimeler
Battery storage | Diesel generator | Grid-connected system | Hybrid renewable energy system | Metaheuristic algorithms | Microgrid optimization | Photovoltaic-wind integration | Techno-economic analysis