Framework design and empirical analysis of intelligent scheduling system for high-altitude photovoltaic power generation based on mixed optimization of long-nosed raccoon optimization algorithm and black winged kite optimization algorithm (COA-BKA)

This study proposes an intelligent scheduling system for high-altitude photovoltaic power generation, utilizing a hybrid optimization approach that combines the Long-nosed Raccoon Optimization Algorithm (COA) and the Black-winged Kite Optimization Algorithm (BKA) (COA-BKA). The goal is to enhance scheduling accuracy, stability, and response speed under the unique environmental conditions of high-altitude regions, such as fluctuating light intensity, extreme temperatures, and dynamic load demands. In experimental comparisons with traditional algorithms like Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), COA-BKA achieved a scheduling accuracy of 0.98, outperforming PSO (0.92) and GA (0.90). COA-BKA also demonstrated superior convergence speed, reaching the optimal solution by the 50th iteration, while PSO and GA required more iterations (80 and 100, respectively). Additionally, COA-BKA completed scheduling in just 4.5 s, significantly faster than PSO (6.3 s) and GA (7.2 s). The system effectively handled fluctuating light intensity and load demand changes, showcasing its robust adaptability. These results suggest that COA-BKA provides a highly efficient and stable solution for intelligent scheduling in high-altitude photovoltaic power systems, improving operational efficiency and reducing costs, while offering significant advancements for real-time optimization in smart grids.