Performance Improvement of Hybrid Photovoltaic/Thermal Systems: A Metaheuristic Artificial Intelligence Approach to Select the Best Model Using 10E Analysis

Abstract

Photovoltaic/thermal (PV/T) hybrid systems have until now encountered a real problem of sustainability-energy-cost concordance. Faced with this situation, new types of designs are in full expansion aimed at filling the limits of some. This therefore involves a very appropriate decision-making process. The energy, exergy, economic, environmental, energo-environmental, exergo-environmental, enviro-economic, energy-enviro-economic, exergo-enviro-economic and ergonomic analysis is carried out on seven PV/T configurations and therefore the simplified models are presented for a better interpretation of the mechanisms from different perspectives and the integration of a selection algorithm. Thus, an optimal selection methodology using the hybridization of genetic algorithms and multi-objective optimization by particle swarms based on ten performance indicators is proposed. The results obtained with good convergence and precision allow us to observe that the Air PV/T model is better. However, the study shows good viability of PV/T models with a cost of energy and a return on investment time all lower than 0.1$/kWh and 3 years respectively. Models with phase change materials (PCM) minimize thermal losses better than those with air, nanofluids or thermoelectric generator (TEG). The bifacial model stands out with a good energy-environmental balance compared to the water model which has a better durability index greater than 2.0 and a good ergonomic factor.