电力系统节能优化控制过程仿真分析

Simulation Analysis of Energy Saving Optimization Control Process of Power System

研究电力系统节能优化控制问题。传统的PSO节能控制方法以电力系统总发电成本消耗最小为寻优目标,但是在线路阻抗角较小等特殊情况下,输电线路传输的功率与成本之间呈现弱耦合性,此时把功率与成本关联计算,会引起控制过程的不收敛,控制结果效果差。提出采用多重自适应粒子群的电力系统节能控制算法。算法首先生成大量随机粒子,然后根据当前成本与能耗的最优位置和全局最优位置更新粒子的位置和速度,同时在迭代过程中不断调整调度优化的惯性权重和学习因子,使得所有粒子不断逼近节能调度的全局最优值。仿真结果表明,新的粒子群算法在函数最优值上的搜索精度高于同时期的两种粒子群算法。将改进算法用于电力系统优化仿真中,可以有效的优化电力能耗,实用性高。

Energy saving optimization control problem of power system was researched in this paper. An energy saving control algorithm of power system was proposed based on multiple adaptive particle swarm. Firstly, a large number of random particles were generated in this algorithm and then based on the current cost and the optimal location of energy consumption as well as the global optimal position, the particle＇s position and speed were updated. At the same time, in the iteration process, inertia weight and learning factor of scheduling optimization were constantly adjusted to make all particles to close to the global optimal value of the energy saving scheduling. Simulation experi- ments show that the search accuracy of the function＇s optimal value using new particle swarm optimization （ pso）, is higher than the two kinds of particle swarm optimization （pso） algorithms at the same period. The application of im- proved algorithm on power system＇s optimizing simulation analysis can effectively optimize the power energy consump- tion and the practicability is high.