摘要
针对含分布式电源(DG)的配电网无功优化的问题,为更准确地描述DG出力的不确定性,基于加权高斯混合分布(WGMD)和Beta分布分别构建风电DG和光伏DG的出力模型。采用结合切片采样算法的马尔科夫链蒙特卡洛模拟法进行潮流计算。建立以系统有功网损最小、节点电压总偏差最小为目标函数的多目标无功优化模型,并采用改进的非支配排序遗传算法(NSGA-II)对该优化模型进行求解。通过改进的IEEE 33节点系统的仿真验证了所提方法的可行性和有效性。
To more accurately describe the uncertainty of the DG power,this paper respectively establishes the output models of wind power DG and photovoltaic DG based on the Weighted Gauss Mixed Distribution(WGMD)and Beta distribution to solve the problem of reactive power optimization in the distribution network with Distributed Generations(DG).In the meantime,the Markov Chain Monte Carlo method combined with slice sampling algorithm is used for power flow calculation.The multi-objective reactive power optimization model which takes the minimum loss of the active power and minimum total voltage deviation of bus as objective function is established.And the improved Non-Dominated Sorting Genetic Algorithm(NSGA-II)is used to solve the optimization model.The feasibility and effectiveness of the proposed method is verified by the simulation of an improved IEEE33 node system.
作者
张晓英
张艺
王琨
张蜡宝
陈伟
王晓兰
ZHANG Xiaoying;ZHANG Yi;WANG Kun;ZHANG Labao;CHEN Wei;WANG Xiaolan(College of Electrical and Information Engineering,Lanzhou University of Technology,Lanzhou 730050,China;State Grid Gansu Electric Power Company Electric Power Research Institute,Lanzhou 730050,China;School of Electronic Science and Engineering,Nanjing University,Nanjing 210093,China)
出处
《电力系统保护与控制》
EI
CSCD
北大核心
2020年第1期55-64,共10页
Power System Protection and Control
基金
国家自然科学基金项目资助(51867015,51767017)
甘肃省基础研究创新群体项目资助(18JR3RA133)
甘肃省高校协同创新团队项目资助(2018C-09)~~
关键词
分布式电源
配电网无功优化
加权高斯混合分布
切片采样算法
改进的NSGA-II算法
distributed generation
reactive power optimization of distribution network
weighted Gauss mixture distribution
slice sampling algorithm
improved NSGA-II algorithm
