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基于合作博弈的电网支路脆弱性评估方法
作者:
作者单位:

1.清华大学电机工程与应用电子技术系;2.电力系统及发电设备控制和仿真国家重点实验室,清华大学;3.国网江苏省电力有限公司,江苏省南京市 210024

作者简介:

通讯作者:

基金项目:

国家电网公司总部科技项目(SGJS0000DKJS1900265)。


Vulnerability Assessment of Transmission Branch Lines Based on Cooperative Game Theory
Author:
Affiliation:

1.Department of Electrical Engineering, Tsinghua University, Beijing 100084, China;2.State Key Laboratory of Control and Simulation of Power System and Generation Equipments,Tsinghua University, Beijing 100084, China;3.State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210024, China

Fund Project:

This work is supported by State Grid Corporation of China(No. SGJS0000DKJS1900265).

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    摘要:

    电网支路脆弱性评估是连锁故障分析和防治的重要步骤。现有的脆弱性指标存在物理意义不明、未充分考虑多重故障对脆弱性的影响等问题。为此,首先构建电网支路的合作博弈框架,根据博弈结果将故障链的后果合理分配到每个支路,并以此作为电网支路的脆弱性指标。该指标能反映电网支路故障对停电事故导致电网损失的贡献,物理意义明确。为解决多重故障链的损失不满足超可加性的问题,将合作博弈的多目标优化问题转换为集体理性与个体理性2个阶段的优化模型,并分别使用遗传算法、线性规划求解。IEEE 39节点系统的算例分析验证了该指标的合理性。

    Abstract:

    The vulnerability assessment of transmission branch lines is an important step in the subsequent analysis and prevention of cascading outages. Existing vulnerability indices have problems including unclear physical meaning, lacking consideration of the impact of multiple outages on vulnerability. To address the issues, this paper constructs a cooperative game framework of transmission branch lines. According to the game result, the loss caused by a fault chain is distributed properly to each branch line as their vulnerability indices. The indices can reflect the contribution of the fault on branch lines to the power grid loss caused by the outage accidents, and its physical meaning is clear. To solve the problem of the non-superposition of the loss of mutiple fault chains, the corresponding multi-objective optimal programming is transformed into a two-stage optimization model with collective and individual rationality. The two-stage optimization model uses genetic algorithm and linear programming respectively. The case study of IEEE 39-bus system verifies the validity of the proposed index.

    表 11 Table 11
    表 12 Table 12
    表 2 故障的叠加作用Table 2 Superposition effect of faults
    表 7 含L14, L33, L35和L3的故障链和脆弱性Table 7 Fault chains and vulnerabilities including L14, L33, L35, L3
    表 1 合作博弈的要素Table 1 Elements of cooperative game
    表 13 Table 13
    表 10 Table 10
    表 6 部分输电线路的脆弱性,间接脆弱性和K核值排名Table 6 Ranking of vulnerability, indirect vulnerability and K-core indices of partial transmission lines
    表 9 Table 9
    表 3 遗传算法的参数Table 3 Parameters of the genetic algorithm
    表 8 L33和L3故障后其他线路的脆弱性Table 8 Vulnerabilities of other lines after the outage of L33 and L3
    图1 线路脆弱性Fig.1 Vulnerability of lines
    图2 个体理性与集体理性的一致性Fig.2 Accordance of individual rationality with collective rationality
    图 IEEE 39节点系统接线图Fig. Topology map of IEEE 39-node system (New England)
    表 4 部分线路的脆弱性及排名Table 4 Vulnerabilities and ranking of partial lines
    表 5 本文脆弱性指标和其他方法对比Table 5 Vulnerability indices in this paper compared with other mehods
    图 遗传算法的收敛性Fig. Convergence of the genetic algorithm
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引用本文

张至美,黄少伟,梅生伟,等.基于合作博弈的电网支路脆弱性评估方法[J].电力系统自动化. DOI:10.7500/AEPS20190626005.

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  • 收稿日期:2019-06-26
  • 最后修改日期:2020-02-08
  • 录用日期:2019-09-06
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