Solving wind‐integrated unit commitment problem by a modified African vultures optimization algorithm

Unit commitment (UC) stands out as a significant challenge in electrical power systems. With the rapid growth demand and pressing issues of fossil fuel scarcity global warming, it has become crucial to enhance utilization renewable energy sources. This study focuses on addressing UC problem by incorporating wind farm proposes modified version metaheuristic African vultures optimization algorithm (AVOA) binary form, utilizing sigmoid transfer function. The AVOA employs multiple phase-shift tactics overcome premature local optima. By determining on/off status generating units, improves algorithm's effectiveness. Additionally, paper develops an auto-regressive moving average model (ARMA) forecast speeds, with assisting selecting optimal orders (q p) ARMA model. is done using historical speed data capture uncertainty speed. then calculated various models integrated into problem. effectiveness examined IEEE 30-bus system. (BAVOA) outperforms several algorithms presented case study, demonstrating its superiority. Furthermore, results indicate that BAVOA delivers superior outcomes within discrete search space when compared continuous space.