摘要
基于主动配电网的发展潮流,提出了一种基于改进遗传算法的主动配电网多目标重构模型。首先,将功率损耗、电压偏移和系统稳定裕度等指标融合到目标函数中,并引入判断矩阵法和线性加权法确定各个指标的权重和实现多目标函数的转化。其次,针对传统遗传算法的弊端,提出了基于"门当户对"原则的多种染色体交叉策略,丰富了种群进化方式的多样性,算法计算效率和寻优能力得到大大加强。最后通过设定临界值,实现主动配电网的自我感知和主动重构,提高配电网安全性和稳定性。仿真算例表明,所提出的模型和算法以及主动重构的理念符合当前主动配电网主动运行和主动控制的思路,具有较为广泛的应用前景。
With the developmental trend of active distribution network, this paper presents a multi-objective reconfiguration model of active distribution network based on the improved genetic algorithm. First of all, the power loss, voltage offset and system stability margin are combined with the objective function, in which the weight of each index can be acquired through the judgment matrix method and the multiple problems will be transformed to single problem by using the linearity weighted method. Secondly, aiming at reducing the drawbacks of traditional genetic algorithm, it puts forward several methods for swapping chromosomes based on the principle of "properly matched marriage". In this case, the several methods can enrich the evolutionary ways of population, and the computational efficiency and the searching ability of the algorithm are greatly enhanced. Finally, the active distribution network will gain the ability of active reconfiguration and self-perception to improve the security and stability of the distribution network by setting the threshold. The results of simulation show that the model, algorithm and the concepts of active reconfiguration proposed in this paper are in accord with the new ideas of active distribution network, which have wide application prospects.
作者
李锰
王利利
刘向实
杨德昌
李鹏
LI Meng;WANG Lili;LIU Xiangshi;YANG Dechang;LI Peng(State Grid Henan Economic Research Institute,Zhengzhou 450052,China;State Grid Henan Electric Power Company,Zhengzhou 450000,China;College of Information and Electrical Engineering,China Agriculture University,Beijing 100083,China)
出处
《电力系统保护与控制》
EI
CSCD
北大核心
2019年第7期30-38,共9页
Power System Protection and Control
基金
国家自然科学基金项目资助(51407186)~~
关键词
主动配电网重构
多目标
分布式电源
改进遗传算法
active distribution network reconfiguration
multi-objective
distributed generation
improved genetic algorithm