基于遗传算法的随机机组组合问题求解

摘要

为考虑不确定性负荷对机组组合问题的影响,通过情景分析法引入一系列的情景对不确定性负荷进行建模,建立了随机机组组合问题的数学模型.采用遗传算法求解该优化问题,可自行满足情景簇约束.通过改进初始种群产生方式和变异算子,引进局部搜索算子对遗传算法进行改进,增强了算法的搜索能力.计算结果显示了随机机组组合问题的数学模型和改进遗传算法求解方法的有效性.%In order to consider the effects of uncertain electric power demand on unit commitment, the uncertainty of electric power demand is modeled by using a set of scenarios, which are introduced by scenario analysis. A mathematical formulation of the expected value model of the stochastic unit commitment (SUC) problem is established. This optimization problem is solved by using a genetic algorithm (GA) , which can automatically satisfy the bundle constraints. The performance of the algorithm is improved by introducing a new method to generate the initial population, a new mutation operator, and a local search operator. Based on numerical examples, test results show the feasibility of the mathematical model of the SUC problem and its improved GA-based solution method.