基于改进多目标粒子群优化算法的分布式发电在配电网中的优化配置

Shan CHENG; Min-you CHEN; Rong-jong WAI; Fang-zong WANG

doi:10.1631/jzus.C1300250

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基于改进多目标粒子群优化算法的分布式发电在配电网中的优化配置

电气工程 | Updated：2022-05-19

- 基于改进多目标粒子群优化算法的分布式发电在配电网中的优化配置
- Optimal placement of distributed generation units in distribution systems via an enhanced multi-objective particle swarm optimization algorithm
- 信息与电子工程前沿（英文） 2014年15卷第4期页码：300-311
- Affiliations：
  
  1.College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China
  2.State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing 400044, China
  3.Department of Electrical Engineering and Fuel Cell Center, Yuan Ze University, Taiwan 32003, Chung Li
- Author bio：
  
  [ "Shan CHENG, E-mail: hpucquyzu@ctgu.edu.cn" ]
  [ "Min-you CHEN, E-mail: mchencqu@126.com" ]
  [ "Rong-jong WAI, E-mail: rjwai@saturn.yzu.edu.tw" ]
  [ "Fang-zong WANG, E-mail: fzwang@ctgu.edu.cn" ]
- Funds：
  
  Project supported by the Science & Technology Innovation Team of Outstanding Youth of Hubei Provincial Universities (No. T201319) and the Scientific Research Foundation for Talents of China Three Gorges University (No. 0620130076)
- DOI：10.1631/jzus.C1300250
  中图分类号： TM715
- 收稿：2013-09-11，
  
  修回：2014-;3-17，
  
  纸质出版：2014-04
- Accepted：
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基于改进多目标粒子群优化算法的分布式发电在配电网中的优化配置[J]. 信息与电子工程前沿（英文）, 2014,15(4):300-311.

Shan CHENG, Min-you CHEN, Rong-jong WAI, et al. Optimal placement of distributed generation units in distribution systems via an enhanced multi-objective particle swarm optimization algorithm[J]. Frontiers of Information Technology & Electronic Engineering, 2014, 15(4): 300-311.
基于改进多目标粒子群优化算法的分布式发电在配电网中的优化配置[J]. 信息与电子工程前沿（英文）, 2014,15(4):300-311. DOI： 10.1631/jzus.C1300250.

Shan CHENG, Min-you CHEN, Rong-jong WAI, et al. Optimal placement of distributed generation units in distribution systems via an enhanced multi-objective particle swarm optimization algorithm[J]. Frontiers of Information Technology & Electronic Engineering, 2014, 15(4): 300-311. DOI： 10.1631/jzus.C1300250.

摘要

研究目的

分布式发电装置的优化配置是保证各分布式电源充分发挥其积极作用的基础，是电力工作者规划分布式发电时面临的挑战性工作。本文旨在综合考虑系统运行的经济和技术指标，构建多种约束条件下的分布式发电在配电网中的多目标优化配置模型和优化求解算法，求得分布式发电的最佳安装位置和容量。

创新要点

综合考虑系统损耗和电压稳定性指标，构建了多种约束条件下的分布式发电多目标优化配置模型，并提出兼顾收敛性和多样性的改进的多目标粒子群优化算法，求得分布式发电在配电网中的最佳安装位置和容量。

方法提亮

该模型未将多目标优化问题简单地转化为单目标优化问题求解，而是采用改进的多目标粒子群优化算法求解；粒子群算法收敛性和多样性得到加强的关键是，提高种群的多样性（式（11）和（14））和提高非劣解的分布均匀性（图2）。

重要结论

（1）采用多目标优化方法对分布式发电的安装位置和容量同时进行优化求解，所得的优化方案带来的效益优于采用单目标优化方法以及仅对安装位置或容量进行优化求解；（2）分散安装于配电网所带来的效益，优于集中安置于一点；（3）改进的多目标粒子群优化算法兼顾了多目标优化算法的收敛性和多样性。

Abstract

This paper deals with the optimal placement of distributed generation (DG) units in distribution systems via an enhanced multi-objective particle swarm optimization (EMOPSO) algorithm. To pursue a better simulation of the reality and provide the designer with diverse alternative options

a multi-objective optimization model with technical and operational constraints is constructed to minimize the total power loss and the voltage fluctuation of the power system simultaneously. To enhance the convergence of MOPSO

special techniques including a dynamic inertia weight and acceleration coefficients have been integrated as well as a mutation operator. Besides

to promote the diversity of Pareto-optimal solutions

an improved non-dominated crowding distance sorting technique has been introduced and applied to the selection of particles for the next iteration. After verifying its effectiveness and competitiveness with a set of well-known benchmark functions

the EMOPSO algorithm is employed to achieve the optimal placement of DG units in the IEEE 33-bus system. Simulation results indicate that the EMOPSO algorithm enables the identification of a set of Pareto-optimal solutions with good tradeoff between power loss and voltage stability. Compared with other representative methods

the present results reveal the advantages of optimizing capacities and locations of DG units simultaneously

and exemplify the validity of the EMOPSO algorithm applied for optimally placing DG units.

关键词

Keywords

references

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