基于改进遗传算法的含风电场电力系统无功优化

Reactive optimization of improved genetic algorithm based power system with wind farm

文中研究了目前分布式电源中发展最成熟的风电机组及其并网后对电力系统无功优化和有功网损的影响。引用基于概率发生的风机功率场景选择策略,考虑风电机组随机出力的场景模型。在电力系统潮流计算中同时考虑风电机组的特点,构造出含风电机组的电力系统无功优化模型。该模型以网损期望值最小为目标函数,并考虑控制变量和状态变量约束。在上述模型的基础上,使用改进遗传算法对含风电场的IEEE33节点系统进行无功优化,并改进了随着遗传自适应的交叉率和变异率计算公式,与传统遗传算法相比,得到了更好的网损和电压优化结果。算例表明,改进算法和模型具有更好的计算性能,证明了其有效性。

The most mature wind turbine generator in distributed generation(DG)and their effects on power system reactive optimization and active network loss after grid connection are studied.The selection strategy of fan power scenario based on probability is introduced.The scenario model of random output of wind turbine generator is taken into account.Meanwhile,the characteristics of wind turbine generator in power flow calculation of power system is taken into account to construct a reactive optimization model of power system with wind turbine.In the model,the minimum expected value of network loss are taken as the objective function,and the constraints of control variables and state variables are taken into account.On the basis of the above model,the improved genetic algorithm is used to perform reactive optimization of the IEEE33 node system with wind farm,and the calculation formula of crossover rate and mutation rate with genetic adaptation is improved.Contrastive experiment verifies that the system with improved genetic algorithm obtains better network loss and voltage optimization results than those with the traditional genetic algorithm.The example shows that the improved algorithm and model have better computational performances and prove their effectiveness.