Optimizing the efficiency of photovoltaic-thermoelectric systems equipped with hybrid nanofluid channels: Environmental and economic considerations

Abstract

This study aims to optimize a solar Photovoltaic (PV) and thermoelectric (TE) unit utilizing the Non-dominated Sorting Genetic Algorithm II (NSGA-II). The system incorporates a hybrid nanofluid jet, composed of water and ND-Co3O4 nanoparticles. Optimization, conducted in Python, utilizes data from an extensive 3D numerical model. Key factors under consideration include solar irradiation, the jet’s injection location, tube and jet inlet velocities, and the proportion of hybrid nanoparticles. The primary goals are to reduce pumping power (Ep), maximize the system’s overall gain over a 10-year span, and improve CO2 reduction. This research is significant for its comprehensive approach to enhancing solar energy technology, boosting system performance and efficiency, while addressing environmental concerns by lowering CO2 emissions. By combining advanced numerical simulations with NSGA-II optimization, this work advances sustainable energy solutions, providing valuable insights for the design of well-organized and environmentally friendly solar energy units. The optimization successfully balanced system gain, CO2 reduction, and pumping power, achieving optimal results of $12,508.8 for system gain, 431.59 tons for CO2 reduction, and 0.2097 for pumping power. The Mean Squared Error (MSE) percentages for the training data are under 1 % for system gain, approximately 1.6 % for CO2 reduction, and around 1.1 % for pumping power, underscoring the effectiveness of the optimization process.