Multiobjective Optimization of Photovoltaic–Thermal Collectors Using Nanofluids: Integrating Taguchi Method and Genetic Algorithm

Abstract

Photovoltaic (PV) systems suffer from significant efficiency losses due to temperature rise during operation, while existing cooling solutions often require excessive pumping power (PP) that reduces the net system output. Traditional optimization approaches have typically focused on either thermal efficiency or PP independently, creating a critical gap in achieving optimal overall system performance. This study presents an innovative optimization framework that uniquely combines the Taguchi method with a multigenetic algorithm to simultaneously maximize thermal efficiency and minimize PP in PV–thermal (PVT) collectors using nanofluids—a combination not previously explored in the literature. In this research, a standard flat plate collector, including copper circular cooling tubes, is attached to the backside of the PV panel. The present study focuses on developing a three-dimensional (3-D) solar PVT collector using copper oxide (CuO) nanofluid. Influential parameters such as fluid flow rate, inlet temperature, nanofluid volume fraction, number of tubes, and tube diameter were investigated. The effects of the parameters on thermal efficiency and PP were studied with an experimental design using the Taguchi method. Optimal results based on a multigenetic algorithm show that the optimal model leads to an increase in cooling efficiency of about 80% and an 86% reduction in PP compared to the initial state. Moreover, the most significant effect on cooling efficiency is related to volumetric flow rate and tube diameter. Compared to the initial state, the result of single objective optimization indicated that the pressure drop reduces and the efficiency increases by about 98% and about 140%, respectively.