Design of high-performance photovoltaic-thermoelectric hybrid systems using multi-objective genetic algorithm

Abstract

Hybrid systems which integrate photovoltaic (PV) and thermoelectric generator (TEG) can improve the utilization of solar energy. In this study, a multi-objective genetic algorithm (MOGA) is adopted to maximize the output power and conversion efficiency for the PV-TEG system, where the CFD model is coupled in the optimization procedure and the penalty function is employed to ensure the appropriate operating temperature. Meanwhile, two geometric parameters along with two boundary conditions are selected as the design variables, while the effects of three cooling conditions are also discussed. Compared with the PV only system, the output power of the PV-TEG system is improved by up to 11.52% with the conversion efficiency also improved by 44.97%. At the same solar concentration condition, the optimal PV-TEG system could increase the output power by 36%. Based on the analysis of optimal solutions, it is demonstrated that the adopted TEG contributes significantly to the output power, while a better cooling condition also has a positive effect on the comprehensive performances. Furthermore, a multiple-criteria decision-making approach is applied to balance output power and conversion efficiency. Compared with the maximum efficiency solution, the best compromise solution increases output power by 120.02% and decreases the efficiency only by 14.83%.