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基于改进加权Voronoi图算法的有源配电网变电站规划

刘洪 王博 李梅菊 郭力 刘伟 屈高强

电力系统自动化2017,Vol.41Issue(13):45-52,8.
电力系统自动化2017,Vol.41Issue(13):45-52,8.DOI:10.7500/AEPS20160927009

基于改进加权Voronoi图算法的有源配电网变电站规划

Substation Planning of Active Distribution Network Based on Improved Weighted Voronoi Diagram Method

刘洪 1王博 1李梅菊 2郭力 1刘伟 3屈高强4

作者信息

  • 1. 智能电网教育部重点实验室(天津大学),天津市 300072
  • 2. 青海民族大学物理与电子信息工程学院,青海省西宁市 810007
  • 3. 中国电力科学研究院,北京市 100192
  • 4. 国网宁夏电力公司经济技术研究院,宁夏回族自治区银川市 750011
  • 折叠

摘要

Abstract

Faced with the situation of large-scale access to distributed generator (DG) and various load characteristics, a substation planning method of active distribution network based on the improved weighted Voronoi diagram method is proposed.Firstly, load characteristics are introduced to the evaluation of DG capacity credit, and the influence of load and DG output characteristics on DG capacity credit is analyzed.Secondly, with the DG capacity credit taken into account, a substation planning model of the active distribution network is developed.Thirdly, two improved weighted Voronoi diagram methods are adopted to solve the model.By adjusting the weight according to the situation of power supply range expanding at each iteration, the hierarchical improvement of weighted Voronoi diagram method is archived.By steadily adjusting the weight according to the power supply range consistency of substation and DG, a directional improvement of the weighted Voronoi diagram method is archived.Finally, an example is used to show the scientific and practical merits of the proposed method.

关键词

变电站规划/有源配电网/负荷特性/置信容量/改进加权Voronoi图

Key words

substation planning/active distribution network/load characteristics/capacity credit/improved weighted Voronoi diagram

引用本文复制引用

刘洪,王博,李梅菊,郭力,刘伟,屈高强..基于改进加权Voronoi图算法的有源配电网变电站规划[J].电力系统自动化,2017,41(13):45-52,8.

基金项目

国家重点研发计划资助项目(2016YFB0900401) (2016YFB0900401)

国家自然科学基金资助项目(51477116) (51477116)

国家电网公司2016年总部研究项目"基于大数据的配电网投入产出效益分析与决策技术研究".This work is supported by National Key Research and Development Program of China (No.2016YFB0900401), National Natural Science Foundation of China (No.51477116) and State Grid Corporation of China. (No.2016YFB0900401)

电力系统自动化

OA北大核心CSCDCSTPCD

1000-1026

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