Due to the increasing global demand for renewable energy, the proportion of wind energy in the power grid is growing. iAPO (improved artificial protozoa optimizer) algorithm was proposed to be applied to solve the economic dispatch problem in complex power systems integrated with wind power. The focus is on investigating the synergistic impact of penalty coefficients on system economy and wind power accommodation. A wind-thermal hybrid power system model consisting of 6-unit and 10-unit configurations was developed based on stochastic wind power uncertainty modeling. Experimental results demonstrate that under load demands of 600 MW, 800 MW, and 1 000 MW, the iAPO algorithm achieves optimal fuel costs of $ 29 812.276、$ 39 390.485, and $ 49 626.111, respectively, in the 6-unit system, outperforming benchmark algorithms such as the original APO in comprehensive performance and exhibiting superior convergence efficiency. Wind turbines are designed to run at almost full capacity in the 10-unit system model when the underestimation coefficient is 5 and the overestimation coefficient stays below 70. However, the wind power participation rate gradually declines as the overestimation coefficient increases. The study demonstrates that the iAPO algorithm offers a new approach to the economic dispatch problem of large-scale wind power integration systems.