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Energy and Exergy Analysis of a Proton Exchange Membrane Electrolyzer-Based Wind Power Plant
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Ghafar Karimi1   , Ehsan Daroune2 , Ehsanolah Assareh *3 |
1- Department of Chemical Engineering, Dez.C., Islamic Azad University, Dezful, Iran, Ghafarkarimi7@gmail.com
2- Department of Chemical Engineering, Dez.C., Islamic Azad University, Dezful, Iran
3- Department of Mechanical Engineering, Dez.C., Islamic Azad University, Dezful, Iran , assareh@iaud.ac.ir |
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Abstract: (25 Views) |
In the current research, thermodynamic, economic modeling and multi-objective optimization of the wind energy-based electricity generation system consisting of a wind farm and a gas turbine to supply electricity to the city of Melbourne in Australia were discussed. In order to model the investigated system and also to obtain the results of the system analysis, engineering equation solving software was used. In this study, a multi-objective optimization was carried out to find the most optimal state of the system with respect to the changes of 8 parameters affecting the system performance, including the number of wind turbines, gas turbine efficiency, gas turbine inlet temperature, compressor efficiency, aftercooler efficiency, intercooler efficiency, input pressure to the compressed air energy storage tank and pressure ratio using a combination of neural network method and genetic algorithm. The optimal results showed that the system in its most optimal state can reach an exergy efficiency of 47.88% and a cost rate of 2369.29 $/h, and the optimal number of wind turbines to form a powerful wind farm in the optimal state was 155. The results of exergy analysis showed that the total rate of exergy destruction of the system (total exergy destruction of equipment and units) is 1733 kWh. The results showed that the system can produce 115743600 kWh of electricity during the year, with a cost rate of 11539944 $/year. The environmental results showed that by producing 115,743 MWh of electricity, it could prevent the emission of 23,611 tons of carbon dioxide per year, reduce $566,680 in environmental costs, and help expand 114 hectares of green space. |
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| Keywords: Renewable system, neural network, multi-objective optimization, genetic algorithm, wind energy, wind turbine |
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Full-Text [PDF 2294 kb]
(10 Downloads)
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Type of Study: Applicable |
Subject:
Thermodynamics
Received: 2026/06/23 | Accepted: 2026/08/19 | Published: 2026/09/1
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| * Corresponding Author Address: assarehehsanolah960@gmail.com |
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