A multidisciplinary approach to select wind turbines for power-hydrogen production: Energy, exergy, economic, environmental under uncertainty prediction by artificial intelligence

Abstract

A promising solution for carbon reduction in energy systems is the use of hydrogen formed by low-carbon sources. Hydrogen production from wind energy not only addresses industrial hydrogen demands but also mitigates environmental and intermittent challenges. In the present study, a novel framework is proposed in which the wind to hydrogen system is evaluated from energy, exergy, economic, environmental (4E), uncertainty aspects, and the best wind turbine is determined using a multi-criteria approach. At first, eight wind turbines are selected for the case study, and the system performance is analyzed from 4E aspects. Furthermore, to assess the uncertainty, the system’s performance is predicted for 3000 years using artificial intelligence, and the project successful probability and economic risk is evaluated using Monte Carlo simulation. The results showed that system’s energy (exergy) efficiency ranges 22–29 % (36–46 %). Additionally, the Levelized Cost of Hydrogen (Payback period) ranges 3.55–3.36 $/kg (6.3 to 9.8 years). Moreover, the proposed system saved 463,872–611,661 m3/yr of natural gas. The Monte Carlo simulation results also showed that the project successful probability ranges 80–93.8 %. Finally, the present framework identified the Gamsa G90 as the best wind turbine for hydrogen production. Overall, the present approach is comprehensive and can be applied for evaluation in other renewable applications.