Feasibility study and techno-economic optimization of an efficient renewable-based system for round-the-clock energy harvesting using machine learning approaches: A case study in Khaf city

A novel multi-generation solar-wind system is proposed in this study, uniting solar and wind renewable energy sources to enhance system reliability. The system incorporates hydrogen energy storage for continuous day and night energy harvesting, offering novel advancements in both reliability and uninterrupted energy supply. The system consists of parabolic through solar collectors (PTSCs), wind turbines (WTs), a steam Rankine cycle-thermoelectric generator (SRC-TEG), a reverse osmosis (RO) desalination unit, a proton exchange membrane (PEM) electrolyzer, hydrogen compression unit and a PEM fuel cell (PEMFC). During the day, the system pro-duces power, fresh water and hydrogen, and at night, when the solar cycle is inactive, the system produces power using a fuel cell that consumes the hydrogen stored in the hydrogen tank. Also, the multi-generation system is fully evaluated from the point of view of energy, exergy and economy in both day and night modes. The proposed system is capable of generating 1624.7 kW of power during the day and 1290.4 kW of power at night. In addition, fresh water can be produced at a rate of 24.29 kg/s and hydrogen at a rate of 8.39 kg/h during the day. Moreover, meteorological data identifies Khaf city as highly suitable for harnessing solar and wind energy on an hourly and yearly basis. Furthermore, artificial neural network (ANN) and multi-objective optimization are used to optimize the system performance. In optimal conditions, the exergy round trip efficiency and overall cost rate of the proposed system are 42.75% and 158.93 $/h, respectively.