计及多阶段抗灾性能的骨干网架多目标优化

金伟超; 韩畅; 杨莉; 林振智; 高强; 应国德

doi:10.7500/AEPS20191015009

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计及多阶段抗灾性能的骨干网架多目标优化

学术研究 | 更新时间：2025-06-16

- 计及多阶段抗灾性能的骨干网架多目标优化
- Multi-objective Optimization of Backbone Power Grid Considering Multi-stage Disaster Resilience
- 电力系统自动化-方正平台 2020年44卷第15期页码：52-61
- 作者机构：
  
  1. 浙江大学电气工程学院，浙江省杭州市 310027
  2. 国网浙江省电力有限公司台州供电公司，浙江省台州市 318000
- 作者简介：
  
  金伟超(1996—)，男，硕士研究生，主要研究方向：电力应急与电力大数据。E-mail：eejwc@zju.edu.cn
  韩畅(1994—)，女，硕士研究生，主要研究方向：电力应急与电力恢复。E-mail：hc2399@163.com
  杨莉(1974—)，女，通信作者，副教授，硕士生导师，主要研究方向：电力市场、电力系统优化调度、分布式发电技术。E-mail：eeyangli@zju.edu.cn
- 基金信息：
  
  国家重点研发计划资助项目(2016YFB0900100);国家自然科学基金资助项目(51777185);浙江省自然科学基金资助项目(LY17E070003)
- DOI：10.7500/AEPS20191015009
  中图分类号：
- 收稿日期：2019-10-15，
  
  修回日期：2020-03-15，
  
  网络出版日期：2020-05-13，
  
  纸质出版日期：2020-08-10
- 稿件说明：
移动端阅览
金伟超,韩畅,杨莉等.计及多阶段抗灾性能的骨干网架多目标优化[J].电力系统自动化,2020,44(15):52-61.

JIN Weichao,HAN Chang,YANG Li,et al.Multi-objective Optimization of Backbone Power Grid Considering Multi-stage Disaster Resilience[J].Automation of Electric Power Systems,2020,44(15):52-61.
金伟超,韩畅,杨莉等.计及多阶段抗灾性能的骨干网架多目标优化[J].电力系统自动化,2020,44(15):52-61. DOI： 10.7500/AEPS20191015009.

JIN Weichao,HAN Chang,YANG Li,et al.Multi-objective Optimization of Backbone Power Grid Considering Multi-stage Disaster Resilience[J].Automation of Electric Power Systems,2020,44(15):52-61. DOI： 10.7500/AEPS20191015009.

摘要

构建抗灾型骨干网架并对其进行差异化加固，可以保障极端灾害下电网安全运行和重要负荷供电。在此背景下，提出一种计及多阶段抗灾性能的骨干网架多目标优化模型。该模型定量评估了电网中节点和线路的拓扑和运行重要度，并提出基于核主元分析的综合评估方法。在满足投资限制的基础上，以最大化网架生存性、抗毁性和系统可恢复性为目标对抗灾型骨干网架进行优化。然后，采用嵌入图论修复策略和档案学习策略的改进全面学习粒子群优化算法求解优化模型，以扩大可行解空间。最后，某区域电网仿真算例验证了该模型的有效性。

Abstract

Constructing and differentially strengthening anti-disaster backbone power grids can guarantee the power grid security operation and power supply for important loads in extreme disasters. This paper proposes a multi-objective optimization model of backbone power grid considering multi-stage disaster resilience. In this model

the topology and operation importance of nodes and lines in power grids are evaluated quantitatively

and a comprehensive evaluation method based on kernel principal component analysis (KPCA) is proposed. The anti-disaster backbone power grid is optimized for maximizing the survivability

invulnerability and restorability of the power grids within investment constraints. Then

the graph repair strategy and the files learning strategy are embedded in the improved comprehensive learning particle swarm optimization (ICLPSO) algorithm to solve the optimization model

which expands the feasible solution space. Finally

the effectiveness of the proposed model is verified by the simulation results of a regional power grid.

关键词

Keywords

references

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