High-efficiency hybrid PV-TEG system with intelligent control to harvest maximum energy under various non-static operating conditions

Abstract

Recently the cleaner energy production is in high demand. Renewable energy systems such as photovoltaic (PV), thermoelectric generators (TEG), concentrated solar, geothermal, wind and hybrid PV-TEG systems achieved cost parity with nonrenewable fossil fuels. The power density of hybrid PV-TEG systems is much higher as compared to the standard photovoltaic (PV) or concentrated solar thermoelectric generator systems. However, the nonlinear nature of the composite hybrid power system makes it impossible to attain maximum available power using classical optimization techniques. The energy from the system is sensitive to stochastic operating conditions. Non-uniform temperature distribution (NUTD) further compromises efficiency for the available power. As a solution, a novel implementation of an arithmetic optimization algorithm (AOA) is utilized as an active maximum power point tracking (MPPT) controller for hybrid PV-TEG system power control. A comprehensive case study is made using five scenarios including real-world atmospheric data and experimental setup to validate the feasibility and effectiveness of the proposed AOA technique in real-world applications. Results are compared with highly effective intelligent techniques including grey wolf optimization (GWO), cuckoo search algorithm (CSA), particles swarm optimization (PSO), and optimized perturb and observe (P&O). Quantitative and statistical analyses confirm the robustness of the proposed technique. AOA achieves 8% higher energy and 99.86% power tracking efficiency. The tracking time in transitioning operating conditions is well within 100 ms with an average settling time of 280 ms. The results, comparative analysis, and statistical indices indicate a superior performance achieved by the proposed AOA optimization making PV-TEG a cleaner source of electrical power generation.