Intelligent Maximization of Eco-friendly Output Energy Based on Internal Photovoltaic Structure

Ashraf Saeed Al Bardawil, Nehad A. Zidan, Noha H. El-Amary, W. Abbas, Mostafa Fedawy

Abstract


This paper studies the effect of photovoltaic internal cell structure on the maximization of the overall panel output power. Photovoltaic (PV) power generation is one of the eco-friendly and sustainable electrical energy sources. On the other hand, due to the climate change harmful phenomena, the use of renewable energy resources is mandatory. Henceforward, the manuscript contribution is studying the influence of absorber layer thickness , buffer layer thickness , absorber layer doping  , and buffer layer doping  on the PV electrical energy management through examining their effect on cell efficiency and panel output power. The simulation is carried out using the SCAPS program. The maximum efficiency and output power are reached by applying three strategies of cell parameters variation ( , ,   and ). The simulation results are compared to a market-existing module. A Convolutional Neural Network (CNN) is used to model the PV behavior utilizing the SCAPS results. The obtained results indicate that the module area can be reduced by 22.39 % while maintaining the same power, thereby reducing the overall cost of energy production by the proportion of the land capital cost and maintenance. It indicates satisfying results

Keywords


Renewable energy; short circuit current; Maximum output power; solar cell efficiency; Convolutional Neural Network

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DOI (PDF): https://doi.org/10.20508/ijrer.v13i4.14174.g8823

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