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<p>The intermittent nature of renewable energy sources poses significant challenges for continuous power supply, necessitating the integration of robust energy storage and optimization systems. While hybrid renewable energy systems (HRES) offer a sustainable solution, achieving an optimal sizing that simultaneously minimizes economic costs and health-damaging carbon emissions remains a complex nonlinear challenge. This study proposes a comprehensive multi-objective optimization framework for a grid-connected HRES integrating photovoltaic (PV), wind turbine (WT), fuel cell (FC), electrolyzer, and hydrogen storage components. The Osprey Optimization Algorithm (OOA) is applied to optimize system sizing and power management. The primary objectives are to concurrently minimize the Cost of Energy (COE) and the Human Health Damage (HHD) due to lifecycle emissions, while ensuring strict system reliability (Loss of Power Supply Probability, LPSP = 0) using one-year meteorological data from Central Anatolia, Türkiye. Comparative analyses demonstrate that OOA exhibits superior performance in terms of solution quality and computational efficiency compared to PSO, TLBO, and GWO algorithms. Among the simulated scenarios (PV/WT/FC, PV/FC, and WT/FC), the PV/WT/FC configuration provides the most balanced Pareto-optimal solution. It achieves the lowest environmental impact with an HHD of 0.419 DALY and a highly competitive COE of 0.238 $/kWh. The integration of hydrogen production and storage effectively mitigates renewable intermittency, reducing grid dependency and emissions. The OOA is confirmed as a highly robust optimization tool, providing decision-makers with a cost-effective and environmentally sustainable framework for designing modern hybrid power systems.</p>

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Multi-objective techno-economic and environmental optimization of hydrogen-based hybrid renewable energy system using osprey optimization algorithm

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月2日

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  • Salih Ermiş,
  • Oğuz Taşdemir,
  • Rami Al-Hajj

摘要

The intermittent nature of renewable energy sources poses significant challenges for continuous power supply, necessitating the integration of robust energy storage and optimization systems. While hybrid renewable energy systems (HRES) offer a sustainable solution, achieving an optimal sizing that simultaneously minimizes economic costs and health-damaging carbon emissions remains a complex nonlinear challenge. This study proposes a comprehensive multi-objective optimization framework for a grid-connected HRES integrating photovoltaic (PV), wind turbine (WT), fuel cell (FC), electrolyzer, and hydrogen storage components. The Osprey Optimization Algorithm (OOA) is applied to optimize system sizing and power management. The primary objectives are to concurrently minimize the Cost of Energy (COE) and the Human Health Damage (HHD) due to lifecycle emissions, while ensuring strict system reliability (Loss of Power Supply Probability, LPSP = 0) using one-year meteorological data from Central Anatolia, Türkiye. Comparative analyses demonstrate that OOA exhibits superior performance in terms of solution quality and computational efficiency compared to PSO, TLBO, and GWO algorithms. Among the simulated scenarios (PV/WT/FC, PV/FC, and WT/FC), the PV/WT/FC configuration provides the most balanced Pareto-optimal solution. It achieves the lowest environmental impact with an HHD of 0.419 DALY and a highly competitive COE of 0.238 $/kWh. The integration of hydrogen production and storage effectively mitigates renewable intermittency, reducing grid dependency and emissions. The OOA is confirmed as a highly robust optimization tool, providing decision-makers with a cost-effective and environmentally sustainable framework for designing modern hybrid power systems.