Optimal placement of distributed generation units in distribution systems via an enhanced multi-objective particle swarm optimization algorithm

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

doi:10.1631/jzus.C1300250

Your Location：

Home >

Browse articles >

Optimal placement of distributed generation units in distribution systems via an enhanced multi-objective particle swarm optimization algorithm

Electrical Engineering | Updated：2022-05-19

- Optimal placement of distributed generation units in distribution systems via an enhanced multi-objective particle swarm optimization algorithm
- 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
- Frontiers of Information Technology & Electronic Engineering Vol. 15, Issue 4, Pages: 300-311(2014)
- 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：
- DOI：10.1631/jzus.C1300250
  CLC： TM715
- Received：11 September 2013，
  
  Revised：2014-;3-17，
  
  Published：2014-04
- Accepted：
Scan QR Code
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：

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

F.S. Abu-Mouti , , , M.E. El-Hawary . . Optimal distributed generation allocation and sizing in distribution systems via artificial bee colony algorithm . . IEEE Trans.Power Del. , , 2011 . . 26 ( ( 4 ): ): 2090 - - 2101 . . DOI: 10.1109/TPWRD.2011.2158246 http://doi.org/10.1109/TPWRD.2011.2158246 . .

M.F. Akorede , , , H. Hizam , , , I. Aris , , , 等 . . Effective method for optimal allocation of distributed generation units in meshed electric power systems . . IET Gener.Transm.Distr. , , 2011 . . 5 ( ( 2 ): ): 276 - - 287 . . DOI: 10.1049/iet-gtd.2010.0199 http://doi.org/10.1049/iet-gtd.2010.0199 . .

Y.M. Atwa , , , E.F. El-Saadany , , , M.M.A. Salama , , , 等 . . Optimal renewable resources mix for distribution system energy loss minimization . . IEEE Trans.Power Syst. , , 2010 . . 25 ( ( 1 ): ): 360 - - 370 . . DOI: 10.1109/TPWRS.2009.2030276 http://doi.org/10.1109/TPWRS.2009.2030276 . .

H.M. Ayres , , , W. Freitas , , , Almeida de , , , 等 . . Method for determining the maximum allowable penetration level of distributed generation without steady-state voltage violations . . IET Gener.Transm.Distr. , , 2010 . . 4 ( ( 4 ): ): 495 - - 508 . . DOI: 10.1049/iet-gtd.2009.0317 http://doi.org/10.1049/iet-gtd.2009.0317 . .

M.E. Baran , , , F.F. Wu . . Network reconfiguration in distribution systems for loss reduction and load balancing . . IEEE Trans.Power Del. , , 1989 . . 4 ( ( 2 ): ): 1401 - - 1407 . . DOI: 10.1109/61.25627 http://doi.org/10.1109/61.25627 . .

M.Y. Chen , , , S. Cheng . . Multi-objective optimization of the allocation of DG units considering technical, economical and environmental attributes . . Przeglad Elektrotechnizny , , 2012 . . 88 ( ( 12A ): ): 233 - - 237 . . . .

M.Y. Chen , , , C.Y. Zhang , , , C.Y. Luo . . Adaptive evolutionary multi-objective particle swarm optimization algorithm . . Contr.Dec. , , 2009 . . 24 ( ( 12 ): ): 1851 - - 1855 . . . .

C.A.C. Coello , , , G.T. Pulido , , , M.S. Lechuga . . Handling multiple objectives with particle swarm optimization . . IEEE Trans.Evol.Comput. , , 2004 . . 8 ( ( 3 ): ): 256 - - 279 . . DOI: 10.1109/TEVC.2004.826067 http://doi.org/10.1109/TEVC.2004.826067 . .

K. Deb . . Multi-objective Optimization Using Evolutionary Algorithms , , : : New York, USA Wiley , , 2001 . . p.7 . .

K. Deb , , , A. Pratap , , , S. Agarwal , , , 等 . . A fast and elitist multiobjective genetic algorithm: NSGA-Ⅱ . . IEEE Trans.Evol.Comput. , , 2002 . . 6 ( ( 2 ): ): 182 - - 197 . . DOI: 10.1109/4235.996017 http://doi.org/10.1109/4235.996017 . .

P. Dehghanian , , , S.H. Hosseini , , , M. Moeini-Aghtaie , , , 等 . . Optimal siting of DG Units in power systems from a probabilistic multi-objective optimization perspective . . Int.J.Electr.Power Energy Syst. , , 2013 . . 51 ( ( 10 ): ): 14 - - 26 . . DOI: 10.1016/j.ijepes.2013.02.014 http://doi.org/10.1016/j.ijepes.2013.02.014 . .

S. Devi , , , M. Geethanjali . . Application of modified bacterial foraging optimization algorithm for optimal placement and sizing of distributed generation . . Expert Syst.Appl. , , 2013 . . 41 ( ( 6 ): ): 2772 - - 2781 . . DOI: 10.1016/j.eswa.2013.10.010 http://doi.org/10.1016/j.eswa.2013.10.010 . .

Naik S. Gopiya , , , D.K. Khatod , , , M.P. Sharma . . Optimal allocation of combined DG and capacitor for real power loss minimization in distribution networks . . Int.J.Electr.Power Energy Syst. , , 2013 . . 53 ( ( 12 ): ): 967 - - 973 . . DOI: 10.1016/j.ijepes.2013.06.008 http://doi.org/10.1016/j.ijepes.2013.06.008 . .

G.H. Hu , , , W. He , , , S. Cheng , , , 等 . . Optimal allocation of distributed generation units considering environmental effects . . J.Inf.Comput.Sci. , , 2013 . . 10 ( ( 11 ): ): 3353 - - 3362 . . DOI: 10.12733/jics20101937 http://doi.org/10.12733/jics20101937 . .

S.J. Jia , , , B. Du , , , H. Yue . . Local search and hybrid diversity strategy based multi-objective particle swarm optimization algorithm . . Contr.Dec. , , 2012 . . 27 ( ( 6 ): ): 813 - - 818 . . . .

K.V. Kumar , , , M.P. Selvan . . Planning and operation of distributed generations in distribution systems for improved voltage profile . . Power Systems Conf.and Exposition , , 2009 . . p.1 - - 7 . . DOI: 10.1109/PSCE.2009.4840152 http://doi.org/10.1109/PSCE.2009.4840152 . .

S.H. Lee , , , J.W. Park . . Selection of optimal location and size of multiple distributed generations by using Kalman filter algorithm . . IEEE Trans.Power Syst. , , 2009 . . 24 ( ( 3 ): ): 1393 - - 1400 . . DOI: 10.1109/TPWRS.2009.2016540 http://doi.org/10.1109/TPWRS.2009.2016540 . .

X.D. Li . . A non-dominated sorting particle swarm optimizer for multiobjective optimization . . LNCS , , 2003 . . 2723 27 - - 48 . . DOI: 10.1007/3-540-45105-6_4 http://doi.org/10.1007/3-540-45105-6_4 . .

Y. Li , , , B.X. Zhou , , , N. Lin , , , 等 . . Application of improved clonal genetic algorithm in distributed generation planning . . Proc.CSU-EPSA , , 2013 . . 25 ( ( 4 ): ): 128 - - 132 . . . .

J. Liu , , , P.X. Bi , , , H.P. Dong . . Analysis and Optimization of Complex Distribution Networks , , : : Beijing, China China Electric Power Press , , 2002 . . p.140 . .

K.D. Mistry , , , R. Roy . . Enhancement of loading capacity of distribution system through distributed generator placement considering techno-economic benefits with load growth . . Int.J.Electr.Power Energy Syst. , , 2014 . . 54 ( ( 1 ): ): 505 - - 515 . . DOI: 10.1016/j.ijepes.2013.07.032 http://doi.org/10.1016/j.ijepes.2013.07.032 . .

M.H. Moradi , , , M. Abedini . . A combination of genetic algorithm and particle swarm optimization for optimal DG location and sizing in distribution systems . . Int.J.Electr.Power Energy Syst. , , 2012 . . 34 ( ( 1 ): ): 66 - - 74 . . DOI: 10.1016/j.ijepes.2011.08.023 http://doi.org/10.1016/j.ijepes.2011.08.023 . .

A. Ratnaweera , , , S.K. Halgamuge , , , H.C. Watson . . Self- organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients . . IEEE Trans.Evol.Comput. , , 2004 . . 8 ( ( 3 ): ): 240 - - 255 . . DOI: 10.1109/TEVC.2004.826071 http://doi.org/10.1109/TEVC.2004.826071 . .

W.X. Sheng , , , Y.M. Liu , , , X.L. Meng , , , 等 . . An improved strength Pareto evolutionary algorithm 2 with application to the optimization of distributed generations . . Comput.Math. Appl. , , 2012 . . 64 ( ( 5 ): ): 944 - - 955 . . DOI: 10.1016/j.camwa.2012.01.063 http://doi.org/10.1016/j.camwa.2012.01.063 . .

M.R. Sierra , , , C.A.C. Coello . . Multi-objective particle swarm optimizers: a survey of the state-of-the-art . . Int.J.Comput.Intell.Res. , , 2006 . . 2 ( ( 3 ): ): 287 - - 308 . . . .

K. Tanaka , , , M. Oshiro , , , S. Toma , , , 等 . . Decentralised control of voltage in distribution systems by distributed generators . . IET Gener.Transm.Distr. , , 2010 . . 4 ( ( 11 ): ): 1251 - - 1260 . . DOI: 10.1049/iet-gtd.2010.0003 http://doi.org/10.1049/iet-gtd.2010.0003 . .

Q. Yu , , , G. Liu , , , Z.F. Liu , , , 等 . . Multi-objective optimal planning of distributed generation based on quantum differential evolution algorithm . . Power Syst.Protect.Contr. , , 2013 . . 41 ( ( 14 ): ): 66 - - 72 . . . .

Views

Downloads

CSCD

Alert me when the article has been cited

Submit

Tools

Publicity Resources

No data

Related Author

No data

Related Institution

No data

Chat

Address：Zhejiang University Press, 148 Tianmushan Road, Hangzhou, China Postal code：310028
Tel：+86-571-88273162 Email：fitee@zju.edu.cn
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰