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Journal of Zhejiang University SCIENCE C
ISSN 1869-1951(Print), 1869-196x(Online), Monthly
2014 Vol.15 No.4 P.300-311
Optimal placement of distributed generation units in distribution systems via an enhanced multi-objective particle swarm optimization algorithm
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.
Key words: Distributed generation, Multi-objective particle swarm optimization, Optimal placement, Voltage stability index, Power loss
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。
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DOI:
10.1631/jzus.C1300250
CLC number:
TM715
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On-line Access:
2024-08-27
Received:
2023-10-17
Revision Accepted:
2024-05-08
Crosschecked:
2014-03-17