CLC number: TM715
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2014-03-17
Cited: 10
Clicked: 10493
Shan Cheng, Min-you Chen, Rong-jong Wai, Fang-zong Wang. Optimal placement of distributed generation units in distribution systems via an enhanced multi-objective particle swarm optimization algorithm[J]. Journal of Zhejiang University Science C, 2014, 15(4): 300-311.
@article{title="Optimal placement of distributed generation units in distribution systems via an enhanced multi-objective particle swarm optimization algorithm",
author="Shan Cheng, Min-you Chen, Rong-jong Wai, Fang-zong Wang",
journal="Journal of Zhejiang University Science C",
volume="15",
number="4",
pages="300-311",
year="2014",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.C1300250"
}
%0 Journal Article
%T Optimal placement of distributed generation units in distribution systems via an enhanced multi-objective particle swarm optimization algorithm
%A Shan Cheng
%A Min-you Chen
%A Rong-jong Wai
%A Fang-zong Wang
%J Journal of Zhejiang University SCIENCE C
%V 15
%N 4
%P 300-311
%@ 1869-1951
%D 2014
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1300250
TY - JOUR
T1 - Optimal placement of distributed generation units in distribution systems via an enhanced multi-objective particle swarm optimization algorithm
A1 - Shan Cheng
A1 - Min-you Chen
A1 - Rong-jong Wai
A1 - Fang-zong Wang
J0 - Journal of Zhejiang University Science C
VL - 15
IS - 4
SP - 300
EP - 311
%@ 1869-1951
Y1 - 2014
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.C1300250
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.
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