一种基于改进遗传算法的机组优化策略

An Optimization Strategy for Unit Commitment Based on the Improved Genetic Algorithm

阐述了AGC机组优化组合的数学模型,在理论分析的基础上列出了其目标函数及约束条件.为了得到最优解,在标准遗传算法的基础上,对算法进行了改进,应用了趋同和异化概念,且算法实现了多子交叉和特殊变异,并引入精英选择的概念完成对解的筛选.最后利用文中所提改进遗传算法对机组优化仿真,与标准遗传算法进行对比.结果表明,改进遗传算法要优于标准算法11.33%,具有较大优势,可应用于大、中型系统实现机组组合优化.

The mathematical model of optimal combination of AGC units is discussed and the objective function and constraint conditions are listed on the basis of theoretical analysis. In order to get the optimal solution, the algorithm was improved based on standard genetic algorithm with the application of the concept of convergence and alienation. The multi cross and special variation were realized, and the concept of the complete solution of the elite selection was introduced. Finally, the improved genetic algorithm is used to optimize the units, compared with the standard genetic algorithm. The results show that the improved genetic algorithm is better than the standard algorithm at 11.33% with a great advantage, which can be applied to the optimization of combination of AGG units for big nad medium systems.