基于改进粒子群算法的储能优化配置  

Optimization configuration of energy storage based on the improved particle swarm optimization

作　　者：温春雪[1] 赵天赐 于赓 王鹏[1] 李建林 WEN Chunxue;ZHAO Tianci;YU Geng;WANG Peng;LI Jianlin(Frequency Conversion Technology Engineering Research Center,North China University of Technology,Beijing 100144)

机构地区：[1]北方工业大学北京市变频技术工程研究中心,北京100144

出　　处：《电气技术》2022年第10期1-9,58,共10页Electrical Engineering

基　　金：国家重点研发计划(2018YFB1503005);国家自然科学基金(51777002)。

摘　　要：储能将功率在时间维度上进行转移,可以抑制电压波动,减小网络损耗。为了合理地配置储能,以改进多目标粒子群算法为基础,建立一种以电压波动率、网络损耗和配置成本为优化目标的储能优化模型。在粒子初始化阶段,通过增加初始粒子个数,挑选出分散的非支配优势粒子作为初始种群,来提高初始种群的随机性;在速度更新阶段,采用节点电压指导粒子的进化方向,提高算法的计算速度。采用IEEE-33节点算例的仿真结果表明,对粒子群算法的改进提高了算法的稳定性、计算速度和精度;采用改进算法的储能配置方案,降低了系统的节点电压波动,减小了电能损耗。Energy storage transfers power in the time dimension, which can suppress voltage fluctuations and reduce network loss. In order to reasonably configure energy storage, an energy storage optimization model is established, which is based on voltage volatility, network loss and configuration cost. The particle swarm optimization is improved by increasing the number of initial particles and selecting the scattered non-dominant particles as the initial particles to improve the randomness of the initial particles. In the speed update stage, the node voltage is used to guide the particle evolution direction and improve the calculation speed of the algorithm. Simulation results of using IEEE-33 node examples show that the improvement of the algorithm improves the stability, computational speed and accuracy. The improved energy storage configuration scheme reduces the node voltage fluctuation of the system and reduces the power loss.

关 键 词：配电网 改进粒子群算法 储能配置 多目标优化 

分 类 号：TK02[动力工程及工程热物理] TP18[自动化与计算机技术—控制理论与控制工程]