Multi-objective optimization of a solar photovoltaic thermal water collector using Grey Wolf Optimizer

Abstract

Solar photovoltaic thermal (PV-T) collectors offer enhanced overall efficiency owing to reduced heat losses at the aperture plane and the potential for increased irradiance per unit area in comparison to larger prototypes. In spite of its promise, investigations into optimizing PV-T collector performance remain limited. This paper introduces a novel approach: the multi-objective optimization of a hybrid PV-T water system with a channel-type absorber design, utilizing the Grey Wolf Optimizer. The proposed approach employs regression models to depict key performance metrics—average thermal and electrical efficiencies. Optimal parameters, including rate of flow of cooling fluid, temperature at inlet, and slope of the solar panel, are determined and reported. In this paper, Grey Wolf Optimization algorithm, has been used to systematically explore the interaction of various PV-T parameter values. Optimal values, in line with the defined objective function(s) were obtained. This approach enabled the researchers to concurrently ascertain the comprehensive performance of the collector. Remarkably, the optimization process revealed a unique insight. Despite the inherently conflicting nature of thermal and electrical efficiencies as observed in single-objective optimization outcomes, the multi-objective MOGWO approach unveiled a solution where compromise was attained.