A Social Beetle Swarm Algorithm Based on Grey Target Decision-Making for a Multiobjective Distribution Network Reconfiguration Considering Partition of Time Intervals

被引：19

作者：

Chen, Qian ^{[1
]}

Wang, Weiqing ^{[1
]}

Wang, Haiyun ^{[1
]}

Wu, Jiahui ^{[1
]}

Li, Xiaozhu ^{[1
]}

Lan, Jiongfeng ^{[2
]}

机构：

[1] Xinjiang Univ, Educ Minist Renewable Energy Power Generat & Grid, Engn Res Ctr, Urumqi 830047, Peoples R China

[2] Newcastle Univ, Business Sch, Newcastle NE1 4SE, England

来源：

IEEE ACCESS | 2020年 / 8卷

基金：

中国国家自然科学基金;

关键词：

Distribution network reconfiguration; multiobjective optimization; grey target decision-making strategy; grey relation projection; SBSO algorithm; ACTIVE DISTRIBUTION NETWORKS; DISTRIBUTION-SYSTEMS; LOAD; OPTIMIZATION; INTEGRATION; ALLOCATION; GRIDS;

D O I：

10.1109/ACCESS.2020.3036898

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

With the increased distributed generation (DG) and the combination of residential, commercial, and industrial loads connected to the distribution networks, it is more difficult to ensure the safe and economic operation of the distribution networks because of the great volatility and randomness of DG and complex loads. In this paper, with the aim of minimizing network loss, load balance, and maximum voltage deviation, a multiobjective reconfiguration model of the distribution network is established under the condition of satisfying network constraints. Moreover, a new social beetle swarm optimization algorithm (SBSO) considering two social behaviors is adopted to solve the complex problem according to the characteristics of the distribution network reconfiguration (DNRC). Based on the SBSO algorithm, grey target decision-making (GTDM) strategy is used to choose the best beetle in the process of solving the multiobjective problem. Additionally, The grey relation projection (GRP) method is used to divide the time period of DNRC according to the change of DG and loads in a day, in order to reduce the number of switching operations. Finally, the effectiveness of the proposed multiobjective model and algorithm are verified on the standard IEEE-33 system and IEEE-69 system.

引用

页码：204987 / 205013

页数：27

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