Numerical analysis and optimization of a novel photovoltaic thermal solar unit improved by Nano-PCM as an energy storage media and finned collector

An increment in temperature will deteriorate the efficiency of photovoltaic systems dramatically. The application of a cooling mechanism can reduce the operating temperature and perform a better operation. Therefore, in this research, a nanoparticle-based phase change material (PCM) layer is combined with finned collectors as the condenser for higher electricity generation in photovoltaic/thermal (PV/T) collectors. The temperature distri-bution of the PCM layer in both solid and fully melted phases are poked to identify the effect of the Nano-PCM layer on the mentioned system efficiency. Afterward, a big data collection is created and employed to introduce a deep learning model to tune and find the best network. Ultimately, the gilt-edged network is optimized using LINMAP, TOPSIS, as well as Shannon entropy decision makings and Gray Wolf Optimization (GWO), Bat algo-rithm (BA), Particle swarm optimization (PSO), and Biogeography-based optimization (BBO) techniques. The results indicated that the maximum thermal efficiency values are obtained at wind speed values less than 2 m/s and DNI higher than 950 W/m2. Considering their mentioned impacts on thermal efficiency, this variable of the renewable unit changes by 10-24%. Moreover, the optimal condition reveals the optimum melted PCM, the coolant outlet temperature, and the electrical efficiency of 6.497 kg, 37.72 degrees C, and 13.92%, respectively. It is concluded that compared with the traditional systems, enhancement in electrical efficiency can be achieved by Nano-PCM layer utilization as cooling media.

Automated summary

An increase in temperature decreases the efficiency of photovoltaic systems, but using a cooling mechanism can improve the operation. This study combines a nanoparticle-based phase change material layer with finned collectors to enhance electricity generation in PV/T collectors. The effect of the Nano-PCM layer on system efficiency is analyzed, and a deep learning model is used to optimize the network. Results show that the maximum thermal efficiency is achieved at wind speeds below 2 m/s and DNI above 950 W/m2. The utilization of the Nano-PCM layer as a cooling medium improves electrical efficiency compared to traditional systems.