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考虑区域特征的跨区域电力系统空间复杂网络模型构建*
引用本文:金成浩,荣莉莉.考虑区域特征的跨区域电力系统空间复杂网络模型构建*[J].科技管理研究,2021,41(20):204-215.
作者姓名:金成浩  荣莉莉
作者单位:大连理工大学经济管理学院,辽宁大连 116024
基金项目:国家自然科学基金资助项目(71371039; 71871039)
摘    要:为对区域电力网络脆弱性分析等相关研究以及不同区域电力网络采取相对应的保护措施提供支持,基于对电力系统的区域特征分析,考虑电力设施的供给能力和空间位置、电力设施密度与区域人口密度的分布关系等因素,提出一种基于复杂网络的电力空间网络模型(MProposed)构建方法,可为制订和实施具有区域针对性的保护措施提供相应支持.以华中地区某城市电力系统为对象,采用MProposed方法进行局域和全局区域的网络构建,运用仿真分析与已有的基于实际数据的方法和基于随机生成的方法及其改进算法进行系统结构比较,结果验证了MProposed方法的有效性和区域适应性.

关 键 词:空间复杂网络  电力系统  区域特征  区域适应性
收稿时间:2021/2/9 0:00:00
修稿时间:2021/10/19 0:00:00

A Spatial Complex Networks Model of Power System Considering Regional Characteristics
Jin Chenghao,Rong Lili.A Spatial Complex Networks Model of Power System Considering Regional Characteristics[J].Science and Technology Management Research,2021,41(20):204-215.
Authors:Jin Chenghao  Rong Lili
Abstract:In order to provide support for related research such as the vulnerability analysis of regional power networks, and the corresponding protection measures taken for power networks in different regional, considering the regional characteristics of the power system, and considering factors such as power capacity and spatial location of power facilities, distribution relationship of power facility density and density of regional population, this paper proposes a method of constructing a power spaced network model based on a complex network, which is called MProposed method, it can provide corresponding support for the formulation and implementation of regionally targeted protection measures. Taking the power system of a city in central China as the object, the paper constructs local and global networks by using the MProposed method, compares the system structure with the existing methods which include the method based on actual data, the method based on random generation and their improved algorithms by simulation analysis, finally, the results verify the effectiveness and regional adaptability of the MProposed method.
Keywords:spatial complex network  power system  regional characteristics  regional adaptability
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