Modelling and Analysis of a Hybrid Photovoltaic-Thermoelectric Generator System using a Relative Humidity Adaptive Maximum Power Point Tracking Algorithm

As the need for green energy intensifies, hybrid energy systems have emerged as promising solutions to improve power generation efficiency and reliability. Among these, Photovoltaic Thermoelectric Generator (PV-TEG) hybrid systems have attracted significant attention due to their ability to harness both solar irradiance and waste heat. Despite the progress in PV-TEG hybrid technologies, environmental factors such as relative humidity (RH) are often overlooked in system modelling and performance analysis. The present research develops a novel maximum power point tracking (MPPT) algorithm for a hybrid PV-TEG system integrating a humidity derating factor (HDF) and examines it in SIMULINK under diverse scenarios of solar irradiance, ambient temperature, and RH. Linear Regression is used to analyse corelations among the performance parameters of the hybrid system. Simulation results show that at 90% RH, the efficiency of the standalone PV system drops by 7%, while the same is improved by 12% in the PV-TEG hybrid model. The proposed humidity-adaptive MPPT algorithm based on Perturb & Observe framework achieves convergence within 0.03 seconds per iteration with a total simulation runtime of 12.4 seconds for a full 24-hour environmental profile. The findings highlight the ability of the proposed PV-TEG system to sustain stable voltage and power output during environmental fluctuations; and underscore the importance of incorporating HDF in MPPT control.