An adaptive column-level smart power optimizer with hybrid sensing and black-winged kite algorithm for rooftop PV systems under non-uniform irradiance

Non-uniform irradiances (NUIs) significantly affect the performance of rooftop PV systems, causing losses, distorted characteristics, and substantial energy inefficiencies in consumer energy systems. Existing solutions encounter limitations in adaptability, increased hardware complexity, slower convergence, and higher costs, rendering them less suitable for real-time rooftop applications. This paper proposes a smart power optimizer (SPO) featuring a low-switch adaptive column-level architecture combined with a fast-converging black-winged kite algorithm to reconfigure modules and maximize output power under NUI conditions. A hybrid sensing framework that combines module-level voltage measurements and array-current sensing is employed to detect electrical mismatch caused by NUI, without requiring per-module irradiance sensors, thereby reducing sensing complexity and system cost. The SPO is validated on 3 × 3, 9 × 9, and 9 × 5 arrays in MATLAB, OPAL-RT, and field experiments across numerous scenarios, and compared with 22 existing techniques. Results show that SPO enhances array power output by 33.83%, 19.35%, and 15.73% compared to conventional, static, and dynamic techniques, while reducing system size and cost by 20-43% and 71-73%, making it highly suitable for consumer-level smart energy optimization in rooftop PV applications. Later, the economic evaluation reveals that SPO can deliver 17-34% revenue improvements over existing techniques with a short payback period, accounting for 2.17% of the total lifetime financial gains.