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文章摘要
配电网分布式电源最优配置研究*
Research on the Optimal Allocation of Distributed Generation in Distribution Network
Received:January 14, 2017  Revised:January 14, 2017
DOI:
中文关键词: 分布式电源  选址与定容  配电网规划  多目标优化  改进果蝇优化算法
英文关键词: distributed generation  locating and sizing  distribution network planning  multi-objective optimization  improved fruit fly optimization algorithm
基金项目:
Author NameAffiliationE-mail
YAN Chao* 1. School of Electrical and Power Engineering,China University of Mining Technology yanjunyi@cumt.edu.cn 
LIU Jiayu 1. School of Electrical and Power Engineering,China University of Mining Technology 1695150659@qq.com 
HE Shiming 1. School of Electrical and Power Engineering,China University of Mining Technology 1906724337@qq.com 
GAO Zhenyuan 1. School of Electrical and Power Engineering,China University of Mining Technology 2585816630@qq.com 
CHENG Menghan 1. School of Electrical and Power Engineering,China University of Mining Technology 1074886421@qq.com 
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中文摘要:
      针对配电网中分布式电源的选址与定容问题,本文以分布式电源的渗透率最高、配电网的网损改善率最大及电压质量改善率最大三个目标构成目标函数,利用判断矩阵确定权重建立多目标优化模型。提出一种改进的果蝇优化算法对配电网中分布式电源的接入位置和接入容量进行优化,IEEE33节点配电网络的仿真结果表明:与遗传算法和基本果蝇优化算法相比,改进的果蝇优化算法在收敛速度和求解精度两个方面都具有较为明显的优势,验证了上述优化配置模型及改进算法的实用性和有效性。
英文摘要:
      Facing the problem of locating and sizing of distributed generation (DG) in distribution network, a multi-objective optimization model is proposed in this paper. The multi-objective function includes the maximum penetration of DG, maximum system network loss improvement rate and the maximum voltage quality improvement rate and its weight determined by estimation-matrix method. An improved fruit fly optimization algorithm (IFOA) is proposed and employed to optimize the location and sizing of the distributed generation. The simulation results of IEEE 33 node system show that, compared with the genetic algorithm (GA) and traditional fruit fly optimization algorithm (FOA), the improved fruit fly optimization algorithm has a great advantage in search speed and accuracy, which verifies the practicability and validity of the proposed optimization model and improved algorithm.
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