Improving the Efficiency of Photovoltaic-Thermoelectric Generator System Using Novel Flying Squirrel Search Optimization Algorithm: Hybrid Renewable and Thermal Energy System (RTES) for Electricity Generation

The world is moving towards cleaner energy to cater to the effects of global warming, the existing renewable energy resources need to be hybridized with other resources for better output using the same input. Photovoltaic-thermoelectric generator (PV-TEG) energy system is one example of a hybrid renewable and thermal energy system (RTES) for electricity generation, the waste heat which if accumulated on the PV Panel causes an efficiency dip, the heat can be converted into useful energy using a TEG module resulting in PV Panel cooling as well as added energy at the output. For the PV-TEG energy system the controllability aspect is crucial as the main problem lies in the optimization and harvesting of energy from these two sources, the non-linear energy generation nature of the PV and TEG energy systems due to changing conditions i.e., partial shading (PS) and dynamic temperature spread (DTS), makes it hard to attain the full potential of PV and TEG systems using classical/analog techniques. To solve this problem, a novel implementation of Flying Squirrel Search Optimization (FSSO) is used for the Maximum Power Point Tracking (MPPT) for the PV-TEG energy system. The proposed FSSO MPPT algorithm is proven effective through a comparison with Particle Swarm Optimization (PSO), Fruit Fly Optimization (FFO), Perturb and Observe (P&O), and Incremental Conductance (InC) algorithms, demonstrating its superiority. The FSSO-based MPPT algorithm exhibits rapid and accurate Global Maxima (GM) tracking in real-time, minimizing power oscillations with a tracking efficiency of 99.56% and a tracking time of under 0.3s.