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计及多阶段抗灾性能的骨干网架多目标优化
作者:
作者单位:

1.浙江大学电气工程学院;2.国网浙江省电力有限公司台州供电公司

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基金项目:

国家重点研发计划 (2016YFB0900100); 国家自然科学基金资助项目 (51777185); 浙江省自然科学基金(LY17E070003)


Multi-objective Optimization of Backbone Power Grid Considering Multi-stage Disaster Resistance
Author:
Affiliation:

1.School of Electrical Engineering,Zhejiang University;2.Taizhou Power Supply Company, State Grid Zhejiang Electric Power Co., Ltd.

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    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 anti-disaster backbone power grids and differentially strengthening them can guarantee the power grid security and power supply for important loads in extreme disaster. This paper proposes a multi-objective optimization model of backbone power grid considering multi-stage disaster resistance. In this model, the topology and operation importance of nodes and lines in the power grid are evaluated quantitatively, and a comprehensive evaluation method based on kernel principal component analysis (KPCA) is proposed. Then, the anti-disaster backbone power grid is optimized for maximizing the survivability, invulnerability and restorability of the power grids with investment constraints satisfied. The graph repair strategy and the files learning strategy are embedded in the improved comprehensive learning particle swarm optimization (ICLPSO) algorithm for solving 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.

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引用本文

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/OL].电力系统自动化,http://doi.org/10.7500/AEPS20191015009.
JIN Weichao,HAN Chang,YANG Li,et al.Multi-objective Optimization of Backbone Power Grid Considering Multi-stage Disaster Resistance[J/OL].Automation of Electric Power Systems,http://doi.org/10.7500/AEPS20191015009.

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历史
  • 收稿日期:2019-10-15
  • 最后修改日期:2020-05-01
  • 录用日期:2020-03-15
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