基于微分进化算法的电力系统最优潮流

将微分进化算法（Differential Evolution，DE）应用到电力系统最优潮流（Optimal Power Flow,OPF）问题中，以系统总发电成本为目标函数，除平衡节点外发电机节点有功功率、发电机节点电压幅值和可调变压器变比作为控制变量，建立了DE-OPF的数学模型．基于增广拉格朗日函数法，将状态变量约束考虑入优化的目标函数中．以IEEE30节点测试系统进行了测试，仿真结果表明，与两种遗传算法和一种改进的粒子群算法：传统遗传算法（canonical genetic algorithm，CGA）、自适应遗传算法（adapive genetic algorithm，AGA）和全面学习粒子群算法（CLPSO）相比，DE算法具有较好的全局寻优能力和较快的收敛速度，能有效地解决最优潮流问题．

Optimal power flow based on differential evolution

Differential Evolution （DE） is used in Optimal Power Flow（OPF） problem in electric power system in this paper. The model of OPF is established by taking the minimum total system generation cost as the objective. The control variables consist of generator real power output, generator bus voltag magniude and transformer tap position in OPF model, This paper presents a new OPF solution algorithm, based on the combination of an evolutionary computation technique （known as the differential evolution method） and the augmented Lagrangian approach （used to handle constriaints）. In order to account for possible violations to their corresponding feasible limits, state vairables must be considered in the OPF augmented Lagrangian function. Numerical results on the IEEE 30-bus system validate the effectiveness of the algorithm for finding accurate OPF solutions compared to those reported recenly in the lierature.