Enterprise AI Analysis
Multi-objective techno-economic and environmental optimization of hydrogen-based hybrid renewable energy system using osprey optimization algorithm
This study proposes a comprehensive multi-objective optimization framework for a grid-connected Hybrid Renewable Energy System (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. The PV/WT/FC configuration provides the most balanced Pareto-optimal solution, achieving the lowest environmental impact with an HHD of 0.419 DALY and a competitive COE of 0.238 $/kWh.
Executive Impact Summary
The intermittent nature of renewable energy sources poses significant challenges for continuous power supply. 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. Traditional metaheuristic algorithms often suffer from premature convergence and getting trapped in local optima when dealing with highly nonlinear, multi-dimensional search spaces typical of grid-connected PV/WT/FC configurations. This study introduces a novel approach by simultaneously evaluating economic (COE) and direct human health impacts (HHD) using a multi-objective optimization framework based on the Osprey Optimization Algorithm (OOA). The OOA's two-phase mechanism (searching and catching prey) ensures robust global search capabilities and highly focused local search, overcoming limitations of classical algorithms. It dynamically models a grid-connected HRES with PV, WT, FC, electrolyzer, and H2-tank components, ensuring LPSP=0.
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Optimal COE
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Lowest HHD
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Reliability
Deep Analysis & Enterprise Applications
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The paper details the mathematical models for PV, WT, electrolyzer, hydrogen tank, FC, and inverter, along with the objective functions (COE, HHD) and system constraints. It introduces the Osprey Optimization Algorithm (OOA) for multi-objective optimization.
HRES Operational Strategy
START
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Hourly Loop (t=1,...,8760)
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Compute Prenb=PV+WT
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Surplus (Prenb ≥ Pe)
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Store/Produce H2 (Up to H2,max)
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Is FC Sufficient?
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Supply from FC (PFC=Pdeficit)
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Use Max FC Power (PFC=PFC,max)
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Keep LPSP=0
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Convert/Invert PLoad
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Buy From Grid (Pbuy=Pdeficit-PFC)
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END
Osprey Optimization Algorithm Advantage
OOA Superior Convergence & ExplorationThe OOA effectively manages exploration and exploitation, preventing premature convergence and local optima traps, which is critical for complex HRES optimization problems.
This section compares different HRES configurations (PV/WT/FC, PV/FC, WT/FC) based on techno-economic and environmental performance, highlighting the PV/WT/FC scenario as the most balanced.
| Scenario | Optimal COE ($/kWh) | Optimal HHD (DALY) | Key Advantages |
|---|---|---|---|
| PV/WT/FC | 0.238 | 0.419 |
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| PV/FC | 0.248 | 0.471 |
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| WT/FC | 0.194 | 0.535 |
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PV/WT/FC Configuration
57.88% Renewable PenetrationThis configuration demonstrated the highest renewable penetration rate, validating the effectiveness of integrating hydrogen storage for sustainability.
The study concludes that OOA provides superior convergence stability and solution quality for HRES optimization. Future work will focus on integrating stochastic uncertainty modeling and AI-based predictive control strategies.
Future Potential: AI-Driven HRES Management
Stochastic Uncertainty Modeling & Predictive Control
Future work aims to integrate stochastic uncertainty modeling for renewable generation and develop AI-based predictive control strategies to further enhance real-time system management. This will lead to even more robust and adaptive hybrid power systems, capable of responding dynamically to fluctuating energy demands and supply, further optimizing COE and HHD.
This approach aims to build on the OOA's success, making HRES more resilient and economically viable.
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Phase 1: System Modeling & Data Integration
Develop mathematical models for all HRES components (PV, WT, FC, electrolyzer, H2-tank) and integrate one-year meteorological and load data from Central Anatolia, Türkiye.
Phase 2: OOA Algorithm Implementation & Multi-objective Optimization
Implement the Osprey Optimization Algorithm (OOA) within a multi-objective framework to concurrently minimize Cost of Energy (COE) and Human Health Damage (HHD), ensuring LPSP=0.
Phase 3: Scenario Analysis & Comparative Evaluation
Analyze system performance across different configurations (PV/WT/FC, PV/FC, WT/FC) and compare OOA's results with PSO, TLBO, and GWO algorithms.
Phase 4: Pareto Front Analysis & Optimal Solution Selection
Generate and interpret Pareto fronts to identify the most balanced techno-economic and environmental solutions, focusing on the 'knee point' for decision-makers.
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