Resilience Evaluation of Smart Distribution Network in Extreme Weather

Zhenkun LI; Fashun WANG; Weiyi GUO; Yang MI; Liang JI

doi:10.7500/AEPS20190418008

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Resilience Evaluation of Smart Distribution Network in Extreme Weather

Basic Research | 更新时间：2025-06-16

- Resilience Evaluation of Smart Distribution Network in Extreme Weather
- Automation of Electric Power Systems Vol. 44, Issue 9, Pages: 60-68(2020)
- 作者机构：
  
  上海电力大学电气工程学院，上海市 200090
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China
- DOI：10.7500/AEPS20190418008
  CLC：
- Received：18 April 2019，
  
  Revised：2019-08-04，
  
  Published Online：24 March 2020，
  
  Published：10 May 2020
- 稿件说明：
移动端阅览
李振坤,王法顺,郭维一等.极端天气下智能配电网的弹性评估[J].电力系统自动化,2020,44(9):60-68.

LI Zhenkun,WANG Fashun,GUO Weiyi,et al.Resilience Evaluation of Smart Distribution Network in Extreme Weather[J].Automation of Electric Power Systems,2020,44(9):60-68.
李振坤,王法顺,郭维一等.极端天气下智能配电网的弹性评估[J].电力系统自动化,2020,44(9):60-68. DOI： 10.7500/AEPS20190418008.

LI Zhenkun,WANG Fashun,GUO Weiyi,et al.Resilience Evaluation of Smart Distribution Network in Extreme Weather[J].Automation of Electric Power Systems,2020,44(9):60-68. DOI： 10.7500/AEPS20190418008.

摘要

近年来受极端天气的影响，配电网大范围停电事故率不断上升，能够防御自然灾害、减小停电事故影响的弹性配电网引起了人们的重视。为了更好地评估配电网应对极端灾害的弹性，文中提出了一种智能配电网弹性评估方法。首先，建立了极端天气下的故障模型用以量化极端天气对配电网的影响。其次，针对极端天气的随机性，文中通过蒙特卡洛法模拟极端天气场景并采用

-means聚类算法对场景进行缩减，根据脆弱性曲线可以得到配电网各支路的时变故障率。考虑到负荷、可再生能源出力的不确定性，采用拉丁超立方抽样抽取负荷、可再生能源出力和配电网故障场景对配电网进行弹性评估。然后，为了全面准确地反映含分布式电源的配电网弹性，将配电网遭受自然灾害时分为防灾和减灾2个阶段，并基于此构建了包括配电网防御时间、弹性恢复系数、孤岛可持续时间覆盖率和重要负荷平均中断时间在内的弹性评估指标体系。最后，对一包含2条馈线的实际配电系统的弹性进行仿真评估，并考虑了电力线路类型、光伏渗透率和联络线对配电网弹性的影响，验证了所提评估方法的有效性。

Abstract

In recent years

due to the influence of extreme weather

the rate of large-scale blackouts in distribution network continues to rise

and the resilient distribution network that can resist natural disasters and reduce the impact of power failure accidents has attracted more attention. In order to better evaluate the resilience of distribution network to extreme disasters

a method for evaluating the resilience of smart distribution network is proposed. Firstly

a fault model in extreme weather is established to quantify the influence of extreme weather on the distribution network. Secondly

in view of the randomness of extreme weather

Monte Carlo simulation of extreme weather scenarios is adopted and

-means clustering algorithm is applied for scenarios reduction. Time-varying failure rate of each branch of distribution network can be obtained according to the vulnerability curve. Considering the uncertainty of load and renewable energy

Latin hypercube sampling is adopted to extract load

renewable energy and distribution network fault scenarios to evaluate the resilience of distribution network. Then

in order to fully and accurately reflect the resilience of distribution network with distribution generator

the distribution network is divided into two stages of disaster prevention and reduction while suffering from natural disaster. Based on this

the index system of resilience evaluation is constructed including defense time of distribution network

coefficient of resilience recovery

coverage rate of island sustainable time and mean interruption time of critical load. Finally

the simulation evaluation of the resilience of an actual distribution system with two feeders is carried out

and the influence of power line type

photovoltaic permeability rate and tie lines on the resilience of the distribution network is considered

which verifies the effectiveness of the proposed evaluation method.

关键词

Keywords

references

MA S , CHEN B , WANG Z . Resilience enhancement strategy for distribution systems under extreme weather events [J]. IEEE Transactions on Smart Grid , 2018 , 9 ( 2 ): 1442 - 1451 .

别朝红，林雁翎，邱爱慈 . 弹性电网及其恢复力的基本概念与研究展望 [J]. 电力系统自动化， 2015 ， 39 ( 22 )： 1 - 9 .

BIE Zhaohong , LIN Yanling , QIU Aici . Concept and research prospects of power system resilience [J]. Automation of Electric Power Systems , 2015 , 39 ( 22 ): 1 - 9 .

ALVEHAG K , SODER L . A reliability model for distribution systems incorporating seasonal variations in severe weather [J]. IEEE Transactions on Power Delivery , 2011 , 26 ( 2 ): 910 - 919 .

LI G , ZHANG P , LUH P , et al . Risk analysis for distribution systems in the Northeast US under wind storms [J]. IEEE Transactions on Power Systems , 2014 , 29 ( 2 ): 889 - 898 .

高海翔，陈颖，黄少伟，等 . 配电网韧性及其相关研究进展 [J]. 电力系统自动化， 2015 ， 39 ( 23 )： 1 - 8 .

GAO Haixiang , CHEN Ying , HUANG Shaowei , et al . Distribution systems resilience: an overview of research progress [J]. Automation of Electric Power Systems , 2015 , 39 ( 23 ): 1 - 8 .

陈碧云，李翠珍，覃鸿，等 . 考虑网架重构和灾区复电过程的配电网抗台风韧性评估 [J]. 电力系统自动化， 2018 ， 42 ( 6 )： 47 - 52 .

CHEN Biyun , LI Cuizhen , QIN Hong , et al . Evaluation of typhoon resilience of distribution network considering grid reconstruction and disaster recovery [J]. Automation of Electric Power Systems , 2018 , 42 ( 6 ): 47 - 52 .

OUYANG M , DUEÑAS-OSORIO L , MIN X . A three-stage resilience analysis framework for urban infrastructure systems [J]. Structural Safety , 2012 , 36 : 23 - 31 .

周晓敏，葛少云，李腾，等 . 极端天气条件下的配电网韧性分析方法及提升措施研究 [J]. 中国电机工程学报， 2018 ， 38 ( 2 )： 505 - 513 .

ZHOU Xiaomin , GE Shaoyun , LI Teng , et al . Assessing and boosting resilience of distribution system under extreme weather [J]. Proceedings of the CSEE , 2018 , 38 ( 2 ): 505 - 513 .

HUANG G , WANG J H , CHEN C , et al . Integration of preventive and emergency responses for power grid resilience enhancement [J]. IEEE Transactions on Power Systems , 2017 , 32 ( 6 ): 4451 - 4463 .

杨火明，徐潇源，严正 . 考虑配电网韧性的储能系统选址定容优化方法 [J]. 电力建设， 2018 ， 39 ( 1 ): 30 - 39 .

YANG Huoming , XU Xiaoyuan , YAN Zheng . Optimization approach of energy storage system locating and sizing considering distribution system resilience [J]. Electric Power Construction , 2018 , 39 ( 1 ): 30 - 39 .

李天友，徐丙垠 . 智能配电网自愈功能与评价指标 [J]. 电力系统保护与控制， 2010 ， 38 ( 22 )： 105 - 108 .

LI Tianyou ， XU Bingyin . Self-healing and its benchmarking of smart distribution grid [J]. Power System Protection and Control ， 2010 ， 38 ( 22 )： 105 - 108 .

颜伟，任洲洋，赵霞，等 . 光伏电源输出功率的非参数核密度估计模型 [J]. 电力系统自动化， 2013 ， 37 ( 10 )： 35 - 40 .

YAN Wei , REN Zhouyang , ZHAO Xia , et al . Probabilistic photovoltaic power modeling based on nonparametric kernel density estimation [J]. Automation of Electric Power Systems , 2013 , 37 ( 10 ): 35 - 40 .

PANTELI M , PICKERING C , WILKINSON S , et al . Power system resilience to extreme weather: fragility modeling, probabilistic impact assessment, and adaptation measures [J]. IEEE Transactions on Power Systems , 2017 , 32 ( 5 ): 3747 - 3757 .

OUYANG M , DUEÑAS-OSORIO L . Multi-dimensional hurricane resilience assessment of electric power systems [J]. Structural Safety , 2014 , 48 : 15 - 24 .

黄伟，葛良军，华亮亮，等 . 基于概率潮流的主动配电网日前-实时两级优化调度 [J]. 电力系统自动化， 2018 ， 42 ( 12 )： 51 - 57 .

HUANG Wei , GE Liangjun , HUA Liangliang , et al . Day-ahead and real-time optimal scheduling for active distribution network based on probabilistic power flow [J]. Automation of Electric Power Systems , 2018 , 42 ( 12 ): 51 - 57 .

张巍峰，车延博，刘阳升 . 电力系统可靠性评估中的改进拉丁超立方抽样方法 [J]. 电力系统自动化， 2015 ， 39 ( 4 )： 52 - 57 .

ZHANG Weifeng , CHE Yanbo , LIU Yangsheng . Improved Latin hypercube sampling method for reliability evaluation of power systems [J]. Automation of Electric Power Systems , 2015 , 39 ( 4 ): 52 - 57 .

PANTELI M , TRAKAS D N , MANCARELLA P , et al . Boosting the power grid resilience to extreme weather events using defensive islanding [J]. IEEE Transactions on Smart Grid , 2016 , 7 ( 6 ): 2913 - 2922 .

YANG Y , TANG W , LIU Y , et al . Quantitative resilience assessment for power transmission systems under typhoon weather [J]. IEEE Access , 2018 , 6 : 40747 - 40756 .

DONG J , ZHU L , SU Y , et al . Battery and backup generator sizing for a resilient microgrid under stochastic extreme events [J]. IET Generation , Transmission & Distribution , 2018 , 12 ( 20 ): 4443 - 4450 .

李振坤，周伟杰，钱啸，等 . 有源配电网孤岛恢复供电及黑启动策略研究 [J]. 电工技术学报， 2015 ， 30 ( 21 )： 67 - 75 .

LI Zhenkun , ZHOU Weijie , QIAN Xiao , et al . Distribution network restoration and black start based on distributed generators [J]. Transactions of China Electrotechnical Society , 2015 , 30 ( 21 ): 67 - 75 .

李振坤，岳美，胡荣，等 . 计及分布式电源与可平移负荷的变电站优化规划 [J]. 中国电机工程学报， 2016 ， 36 ( 18 )： 4883 - 4893 .

LI Zhenkun , YUE Mei , HU Rong , et al . Optimal planning of substation considering distributed generation and shiftable loads [J]. Proceedings of the CSEE , 2016 , 36 ( 18 ): 4883 - 4893 .

杨泳昕，葛耀君，项海帆 . 风速风向联合分布的平均风统计分析 [J]. 结构工程师， 2002 , 18 ( 3 )： 29 - 36 .

YANG Yongxin , GE Yaojun , XIANG Haifan . Statistic analysis of wind speed based on the joint distribution of wind speed and wind direction [J]. Structural Engineers , 2002 , 18 ( 3 ): 29 - 36 .

张余得，商少平，谢燕双，等 . 基于强风圈半径的台风风场模型 [J]. 厦门大学学报(自然科学版) ， 2014 ， 53 ( 2 )： 252 - 256 .

ZHANG Yude , SHANG Shaoping , XIE Yanshuang , et al . Typhoon wind field model based on the radii of wind circle [J]. Journal of Xiamen University (Natural Science) , 2014 , 53 ( 2 ): 252 - 256 .

VICKERY P , TWISDALE L . Prediction of hurricane wind speeds in the United States [J]. Journal of Structural Engineering , 1995 , 121 ( 11 ): 1691 - 1699 .

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