Optimizing solar panel performance with new algorithm incorporating duct with helical tape and hybrid nanofluid

Abstract

Background

This work introduces an innovative configuration for intensifying the productivity of solar photovoltaic-thermal units (PVT) through the incorporation of a cooling system. Notably, a thermoelectric module is strategically added to further intensification of produced electricity.

Methods

This unit has a duct where the hybrid nanofluid passes through which a turbulator is placed. Furthermore, this system has a jet impingement component. In a departure from traditional methodologies, this investigation optimizes the PVT unit's overall effectiveness by employing an algorithm based on machine learning. Three critical goal functions are considered in this optimization process: Ep (pumping power), CO₂ − m (CO₂ mitigation), and Profit of the system, each of which respectively represents the generated electrical energy for energy analysis, the reduction of produced carbon for environmental evaluation and the financial gain from employing the present system for economic assessment. This innovative approach not only contributes to advancing the field of solar photovoltaic-thermal systems but also underscores the importance of optimizing these units for increased energy efficiency, reduced environmental impact, and enhanced economic viability in the context of renewable energy technologies.

Significant findings

The connections between the PVT's variable mappings, comprising the input parameters of the fluid velocity (V_Tube), solar radiation (G), jet impingement velocity (V_J), and helical tape ratio (R) and the outputs of the E_p, Profit, CO₂ − m, are established through the implementation of various models. The findings suggest that the GPR (Gaussian Process Regression) model is the most appropriate, as evidenced by its R² values of 0.9987, 1, and 1 for E_p, Profit, and CO₂ − m, correspondingly. The NSGA-II technique is utilized in this study. This procedure is used to ascertain the Pareto optimal solutions with respect to all three conflicting objectives. The outcome illustrates the Pareto graphs, and each of them in provides a suitable compromise between all objectives without degrading any of them.