Optimal Sizing and Techno-economic Analysis of Combined Solar Wind Power System, Fuel Cell and Tidal Turbines Using Meta-heuristic Algorithms: A Case Study of Lavan Island
Abstract Combined renewable energy sources (RESs) are emerging as a competitive alternative to conventional energy production facilities due to their sustainability and zero-emission characteristics. However, determining the optimal system size is complicated by two major challenges: the cost of ene...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-01-01
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| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44196-025-00737-3 |
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| Summary: | Abstract Combined renewable energy sources (RESs) are emerging as a competitive alternative to conventional energy production facilities due to their sustainability and zero-emission characteristics. However, determining the optimal system size is complicated by two major challenges: the cost of energy (COE) and the intermittent nature of RESs. This study introduces a novel mathematical approach to optimize the sizing of photovoltaic (PV), wind, hydrogen, battery, and fuel cell systems with electrolyzers, specifically tailored for the remote area of Lavan Island. The proposed method aims to deliver electricity without reliance on the traditional electricity distribution grid, while offering a scalable solution applicable to other geographical regions. The primary objective is to achieve cost-effective electricity generation while ensuring a reliable energy supply through the evaluation of system reliability indices. A fuzzy logic system is employed to minimize the costs of a hybrid system incorporating hydroelectric, wind, solar, and battery technologies, while simultaneously calculating two key reliability metrics: the Loss of Power Supply Probability (LPSP) and the Dump Energy Probability (DEP). To optimize the objective function, this study applies three advanced algorithms: the Shuffled Frog Leaping Algorithm (SFLA), the Grasshopper Optimization Algorithm (GOA), and the Honey Badger Algorithm (HBA). These algorithms are used to determine the global optimum, with comparative analyses conducted to highlight the performance of the proposed approach. The results are evaluated based on statistical metrics, including consistency, execution time, convergence speed, and the minimization of the objective function. The findings demonstrate the superiority and the reliability of the proposed method over alternative approaches, paving the way for cost-efficient and sustainable energy solutions in isolated regions. |
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| ISSN: | 1875-6883 |