基于POA-GWO-CSO 算法的新能源电力系统精准切负荷控制多目标优化方法

A multi-objective optimization method for precise load sheddingcontrol of power system based on POA-GWO-CSO algorithm

为解决新能源电力系统因功率缺额引发系统频率、电压偏移等一系列安全问题,文章提出了一种基于POA-GWO-CSO算法的电力系统精准切负荷控制多目标优化方法。首先,从电力系统的安全性和经济性两个方面综合考虑电力系统稳定运行和分布式电源出力特性等各项约束条件,提出一种基于负荷分类的精准切负荷控制多目标优化模型;然后,为了增强传统鹈鹕优化算法(POA)全局与局部搜索能力之间的协调关系,克服优化算法在处理复杂问题时出现收敛过早、寻优范围不够、求解精度不高等问题,引入非线性惯性权重因子、灰狼优化算法(GWO)中狼群领导者策略以及纵横交叉法(CSO),对鹈鹕新的个体的位置进行更新;最后,基于改进后的IEEE33节点进行实证分析。分析结果表明,利用改进的POA-GWO算法对紧急切负荷模型进行求解,实现了系统经济性及稳定性的协调控制。

In order to solve a series of security problems such as system frequency and voltage offset caused by power shortage in power system,this paper proposes a multi-objective optimization method for precise load shedding control based on POA-GWO-CSO algorithm.Firstly,a multi-objective optimization model of precise load shedding control based on load classification is proposed from the aspects of safety and economy of power system,considering the constraints of stable operation of power system and output characteristics of distributed generation.In order to enhance the coordination relationship between global and local search in the traditional pelican optimization algorithm(POA),and to overcome the problems of premature convergence,insufficient optimization range and low accuracy of the optimization algorithm in dealing with complex problems.In this paper,the nonlinear inertia weight factor,the wolf group leader strategy in the grey wolf optimization algorithm(GWO)and the crisscross optimization(CSO)are introduced to update the position of the new individual of the pelican.Finally,based on the empirical analysis of the modified IEEE33 node,the improved POA-GWO-CSO algorithm proposed in this paper is used to solve the emergency load shedding model,and the system coordinated control of economy and stability is realized.