Reliable methodology for performance enhancement of hybrid photovoltaic-thermoelectric system via dynamic reconfiguration

The operation of a hybrid photovoltaic (PV)-thermoelectric generation (TEG) unit is affected by real-world environmental factors like partial shade conditions (PSCs) and nonhomogeneous heat distribution (NHD). They have negative impact on the system’s output characteristics which results in reducing operating reliability and power loss. In order to optimize the system harvested generation, this research suggests a new dynamic reconfiguration of PV-TEG generating unit using the most recent blood-sucking leach optimizer (BSLO). The problem is presented as restricted optimization problem with the purpose of improving the generated power from PV-TEG unit. Many benefits come with the suggested methodology including strong local solution avoidance ability, rapid convergence speed, and outstanding exploration/exploitation balance. The symmetric 6 × 6 and asymmetric 6 × 10 arrays under ten PSCs including uneven column and row, long and short wide, long and short narrows, outer, diagonal, random, and center patterns are the two case studies that are investigated. The suggested BSLO is validated through conducting comparison to reported approaches and other programed Puma optimization algorithm (POA), artificial gorilla troops optimizer (GTO), and Beluga whale optimization (BWO). The suggested BSLO yields better generation boost of 27.04% for symmetric 6 × 6 array compared to the POA that obtained 23.86% during short wide shade pattern. However, the best enhancement that the suggested BSLO achieved for asymmetric 6 × 10 array during short wide pattern is 15.29%. The obtained findings validated the superiority of the suggested BSLO in accurately resolving the optimal reconfiguration of PV-TEG generating unit under PSCs and NHD.