风电高渗透率电网优化GA-PSO调度及算例分析

GA-PSO Scheduling and Examplesfor Wind Power Network with High Permeability

为了弥补传统粒子群(PSO)算法风电出力鲁棒调度较低的问题,通过遗传算法(GA)优化PSO的方式设计了一种风电高渗透率电网优化GA-PSO调度方法。通过PSO算法与交叉变异相融合形式来PSO快速收敛的效果,避免粒子产生局部最优的情况,计算获得更可靠的不确定集。开展算法研究结果表明,选择优化GA-PSO算法处理能够实现标准粒子群快速收敛的效果,防止引起局部最优现象。优化GA-PSO算法可以实现交叉变异功能,可以消除粒子出现局部最优的情况,更好地适应电网调度要求。该研究有助于提高电网调度效率,为后续的电网性能强化奠定一定的理论基础。

In order to make up for the low robust scheduling of wind power output based on the traditional particle swarm optimization(PSO)algorithm,a GA-PSO scheduling method for wind power network optimization with high permeability was designed by optimizing PSO using genetic algorithm(GA).By combining PSO algorithm with cross variation,the PSO convergence is fast,so as to avoid the local optimal of particles and obtain a more reliable uncertain set.The results show that the GA-PSO algorithm can achieve rapid convergence of standard particle swarm and prevent local optimization.The optimized GA-PSO algorithm can realize the cross-mutation function,eliminate the local optimal situation of particles,and better adapt to the requirements of power grid scheduling.This study is helpful to improve the dispatching efficiency of power grid and lay a theoretical foundation for the subsequent strengthening of power grid performance.