基于改进NSGA-II算法的含分布式电源配电网无功优化

张晓英; 张艺; 王琨; 张蜡宝; 陈伟; 王晓兰

引用本文:	张晓英,张艺,王琨,等.基于改进NSGA-II算法的含分布式电源配电网无功优化[J].电力系统保护与控制,2020,48(1):55-64.
	ZHANG Xiaoying,ZHANG Yi,WANG Kun,et al.Reactive power optimization of distribution network with distributed generations based on improved NSGA-II algorithm[J].Power System Protection and Control,2020,48(1):55-64

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基于改进NSGA-II算法的含分布式电源配电网无功优化
张晓英¹,张艺¹,王琨²,张蜡宝³,陈伟¹,王晓兰¹
(1.兰州理工大学电气工程与信息工程学院，甘肃兰州 730050;2.国网甘肃省电力公司电力科学研究院，甘肃兰州 730050;3.南京大学电子科学与工程学院，江苏南京 210093)

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。

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

DOI：10.19783/j.cnki.pspc.190089

分类号:

基金项目: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)

Reactive power optimization of distribution network with distributed generations based on improved NSGA-II algorithm

ZHANG Xiaoying¹,ZHANG Yi¹,WANG Kun²,ZHANG Labao³,CHEN Wei¹,WANG Xiaolan¹

(1. College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China;2. State Grid Gansu Electric Power Company Electric Power Research Institute, Lanzhou 730050, China;3. School of Electronic Science and Engineering, Nanjing University, Nanjing 210093, China)

Abstract:

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. This work is supported by National Natural Science Foundation of China (No. 51867015 and No. 51767017).

Key words: distributed generation reactive power optimization of distribution network weighted Gauss mixture distribution slice sampling algorithm improved NSGA-II algorithm

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