Multi-objective optimization of concentrated Photovoltaic-Thermoelectric hybrid system via non-dominated sorting genetic algorithm (NSGA II)

Thermoelectric generators harvest additional electrical power when used in combination with concentrated photovoltaic cells given rise to a hybrid system. Overall cost of the system is high; therefore, the parameters of the system need to be optimized to obtain high output performance. This study determines the output performances of four sets of equations (models) used in the hybrid system, using the performance of recently developed nanostructured thermoelectric materials. Seven parameters of the system were optimized through these models using non-dominated genetic algorithm. Models 1 and 2 have the highest performance chosen by TOPSIS decision-making method. The power output and conversion efficiencies of the hybrid system in models 1 and 2 are 426.5 W, 11.45% and 461.12 W, 10.77%, respectively. Likewise, the highest TOPSIS solution for power output of one TEG module operating in the hybrid system and its corresponding efficiency is obtained in model 4 and are 1.97 W and 0.078%, respectively. This validates the fact that TEG operating in a hybrid system has optimum performance at a point when the load resistance is less than its internal resistance.

Automated summary

This study examines the output performances of different equations in a hybrid system combining thermoelectric generators with concentrated photovoltaic cells, utilizing nanostructured thermoelectric materials. By optimizing parameters and selecting appropriate models, it was found that the system performs best when the load resistance is less than the internal resistance of the TEG.