随着全球对清洁能源需求的不断攀升，风电在电力系统中的占比日益增加。提出了改进人工原生动物优化（improved artificial protozoa optimizer, iAPO）算法用于求解含风电的复杂电力系统经济调度问题，重点探究风电惩罚系数对系统经济性与风电消纳的协同影响机制。基于风电不确定性建模，构建6单元和10单元机组的风-火混合电力系统模型。实验结果显示，在负荷需求分别为600、800、1 000 MW时，iAPO算法在6单元系统中获得的最优燃料成本分别为29 812.276、39 390.485、49 626.111美元，综合表现优于APO等对比算法，具有更优异的收敛性能。在所建立的10单元系统模型中，当风电出力低估系数为5，高估系数低于70时，风电机组几乎全负荷出力；随着高估系数的增加，风电参与率逐渐降低。研究表明，iAPO为大规模风电集成系统的经济调度问题提供了新方法。

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.