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有源配电网宽适应性潮流灵敏度分析模型
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作者单位:

1.天津大学 教育部智能电网重点实验室;2.山东科技大学电气与自动化工程学院

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Power Flow Sensitivity Analysis Model with Wide Adaptability in Active Distribution Network
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Affiliation:

1.Key Laboratory of Smart Grid of Ministry of Education,Tianjin University;2.College of Electrical Engineering and Automation,Shandong University of Science and Technology

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    摘要:

    分布式发电、储能及柔性负荷在有源配电网的广泛接入引起配电网节点注入功率的频繁变化,准确、快速地评估节点注入功率变化对节点电压、线路损耗、支路潮流等运行参数的影响变得至关重要。传统的灵敏度模型在节点注入功率变化较大时精度降低,无法有效满足有源配电网运行分析的需要。为此,本文利用配电网支路方程,提出了一种具有宽适应性的潮流灵敏度分析模型,具体包含节点注入功率对线路损耗的二次灵敏度模型、节点注入功率对节点电压的线性灵敏度模型和节点注入功率对支路潮流的线性灵敏度模型及二次灵敏度模型。利用IEEE33节点配电系统和123节点配电系统进行仿真分析,仿真结果证明了所提灵敏度模型的精确性和有效性。

    Abstract:

    Due to high penetration of distributed generations, energy storage systems, and responsible loads in active distribution network (ADN), nodal power injections could vary frequently and significantly. Thus, it becomes crucial to assess the influence of power injection variations on key operation characters of ADN, such as nodal voltages, line losses, and branch power flows, accurately and quickly. The traditional sensitivity models can not satisfy the accuracy requirement of ADN analysis when the nodal power injections change greatly. In this paper, branch equation of distribution system is analyzed to derive the complete power flow sensitivity models with wide adaptability, which contain a quadratic sensitivity model of nodal power injections to line losses, a linear sensitivity model of nodal power injections to nodal voltages, a quadratic and a simplified linear sensitivity model of nodal power injections to branch powers. Simulation results of the IEEE33 distribution system and 123-bus distribution system illustrate the accuracy and efficiency of the proposed sensitivity models.

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引用本文

刘琪,王守相,吉兴全.有源配电网宽适应性潮流灵敏度分析模型[J/OL].电力系统自动化,http://doi.org/10.7500/AEPS20190509002.
LIU Qi,WANG Shouxiang,Ji Xingquan.Power Flow Sensitivity Analysis Model with Wide Adaptability in Active Distribution Network[J/OL].Automation of Electric Power Systems,http://doi.org/10.7500/AEPS20190509002.

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历史
  • 收稿日期:2019-05-09
  • 最后修改日期:2020-05-06
  • 录用日期:2019-11-24
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