Multi-Objective Optimization of a Novel Photovoltaic-Thermoelectric Generator System Based on Hybrid Enhanced Algorithm

Abstract

The contradiction between the temperature demand of photovoltaic cells and thermoelectric generator modules leads to the low efficiency of solar energy utilization. The waste heat was transferred from PV cell’s back to hot side of TEG module by microchannel heat pipe (MHCP) to solve the contradiction. Phase change material (PCM) was used for heat storage to extend working time of TEG module without solar radiation. A novel PV-MCHP-PCM-TEG system was proposed to solve low efficiency of PV-TEG electrical generation system. The electrical efficiency and total life cycle costs were taken as multi-objective functions, and sensitivity factors analysis and system optimization were performed, to achieve the global optimization over the whole life cycle. The mathematical model of PV-MCHP-TEG-PCM system was established and verified by experiment in Wuhan, China. The factors were identified by Sobol global sensitivity analysis method. PV reference efficiency, quantity of TEG module, thickness of inner and outer PCM, melting temperatures of inner and outer PCM were selected as sensitive factors and optimized by Non-dominated Sorting Genetic Algorithms II coupled with Multi-Objective Particle Swarm Optimization (NSGA II-MOPSO) algorithm. After assigning values to the two objective functions by entropy weighting method, TOPSIS solution showed that the optimal efficiency was 25.6 %, and the lowest total life cycle cost was 335.4 CNY. Compared to before optimization, the system's electrical efficiency was increased by 52.4%, and total life cycle cost was decreased by 98.4% in typical year.

Keywords: Photovoltaic, Microchannel heat pipe, Phase change material, multi-objective optimization, NSGA II-MOPSO algorithm