Environmental Science and Pollution Research

, Volume 26, Issue 18, pp 17927–17938 | Cite as

Siting and sizing of distributed generators based on improved simulated annealing particle swarm optimization

  • Hongsheng SuEmail author
Environmental Pollution and Energy Management


Distributed power grids generally contain multiple diverse types of distributed generators (DGs). Traditional particle swarm optimization (PSO) and simulated annealing PSO (SA-PSO) algorithms have some deficiencies in site selection and capacity determination of DGs, such as slow convergence speed and easily falling into local trap. In this paper, an improved SA-PSO (ISA-PSO) algorithm is proposed by introducing crossover and mutation operators of genetic algorithm (GA) into SA-PSO, so that the capabilities of the algorithm are well embodied in global searching and local exploration. In addition, diverse types of DGs are made equivalent to four types of nodes in flow calculation by the backward or forward sweep method, and reactive power sharing principles and allocation theory are applied to determine initial reactive power value and execute subsequent correction, thus providing the algorithm a better start to speed up the convergence. Finally, a mathematical model of the minimum economic cost is established for the siting and sizing of DGs under the location and capacity uncertainties of each single DG. Its objective function considers investment and operation cost of DGs, grid loss cost, annual purchase electricity cost, and environmental pollution cost, and the constraints include power flow, bus voltage, conductor current, and DG capacity. Through applications in an IEEE33-node distributed system, it is found that the proposed method can achieve desirable economic efficiency and safer voltage level relative to traditional PSO and SA-PSO algorithms, and is a more effective planning method for the siting and sizing of DGs in distributed power grids.


Distributed generator (DG) Site Size Optimization Simulated annealing (SA) Particle swarm optimization (PSO) Crossover Mutation 


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  1. 1.School of Automation and Electrical EngineeringLanzhou Jiaotong UniversityLanzhouChina

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