Abstract
Recently the concern about energy consumption across the globe has become more severe due to global warming. One essential way to address this problem is to maximize the efficiency of existing renewable energy resources and effectively eliminate their power losses. The previous studies on energy harvesting of photovoltaic (PV) modules try to cope with this problem using gradient-based control techniques and pay little attention to the significant loss of solar energy in the form of waste heat. To reconcile these waste-heat problems, this paper investigates hybrid photovoltaic-thermoelectric generation (PV-TEG) systems. We implement the generalized particle swarm optimization (GEPSO) technique to maximize the power of PV systems under dynamic conditions by utilizing the waste heat to produce electricity through embedding the thermoelectric generator (TEG) with the PV module. The removal of waste heat increases the efficiency of PV systems and also adds significant electrical power. As a control method, the proposed GEPSO can maximize the output power. Simulations confirm that GEPSO outperforms some state-of-the-art methods, e.g., the perturb and observe (PO), cuckoo search (CS), incremental conductance (INC), and particle swarm optimization (PSO), in terms of accuracy and tracking speed.
Highlights
To reconcile these waste-heat problems, this paper investigates hybrid photovoltaic-thermoelectric generation (PV-thermoelectric generator (TEG)) systems
The simulation results of generalized particle swarm optimization (GEPSO) on a hybrid PV-TEG system are explained in Case 5 and Case 6
This paper proposes a GEPSO based energy harvesting technique for PV and hybrid PV-TEG systems under various operating conditions
Summary
The control techniques to harvest the energy are playing a significant role in improving the energy conversion efficiency of hybrid PV-TEG systems. These techniques extract the optimal energy from the source and minimize power loss of the system through fixing the duty cycle of the dc-dc converter. Further improvements have been suggested in the literature by implementing soft computing techniques such as memetic reinforcement learning [33], FLC-based techniques [34], and artificial neural network (ANN) [35] These approaches can effectively deal with the nonlinear properties of the PV-curves but needs substantial computing resources, and enormous amounts of data for training, which rely on the previous knowledge of the systems. Irradiation change only affects the current of PV module while keeping the voltage almost invariable
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