Bi-objective optimization of photovoltaic-thermal (PV/T) solar collectors according to various weather conditions using genetic algorithm: A numerical modeling

Electrical and thermal performance of hybrid photovoltaic-thermal solar collectors depend on design parameters such as tube diameter, spacing and mass flow rate. In this study, a computational finite volume code is developed to calculate transient two-dimensional temperature distribution in each layer of a PV/T collector. Hence, the temperature dependent performance can be obtained accurately for a system of arbitrary configuration. Genetic Algorithm is then used to find a set of design parameters that maximize the electrical and thermal efficiencies by single and bi-objective optimizations. In order to account for variable ambient conditions during a year, this procedure is repeated for a number of different ambient conditions and an appropriate design is selected in an innovative manner. Comparison of the results reveal that the system with maximum electrical efficiency has also very good thermal performance in different weather conditions. The optimizations are also performed for collectors of different sizes and aspect ratios. It is shown that the present procedure is capable of further improving the previously optimized designs to achieve 8.6% and 11.5% extra electrical power and thermal energy, respectively, in a day. Yearly electrical performance of the proposed system is also 25% better than a conventional PV module without cooling. (C) 2019 Elsevier Ltd. All rights reserved.