Locating and Sizing of Distributed Generation Sources and Parallel Capacitors Using Multiple Objective Particle Swarm Optimization Algorithm

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Kamarposhti M. A., Lorenzini G., Solyman A. A. A.

Mathematical Modelling of Engineering Problems, vol.8, no.1, pp.10-24, 2021 (Scopus) identifier

  • Publication Type: Article / Article
  • Volume: 8 Issue: 1
  • Publication Date: 2021
  • Doi Number: 10.18280/mmep.080102
  • Journal Name: Mathematical Modelling of Engineering Problems
  • Journal Indexes: Scopus
  • Page Numbers: pp.10-24
  • Keywords: distributed generation, loss reduction, MOPSO algorithm, parallel capacitors, voltage profile
  • Istanbul Gelisim University Affiliated: Yes


© 2021, Mathematical Modelling of Engineering Problems.All Rights Reserved.In this paper, the MOPSO algorithm has been used to locate and determine the capacity of distributed generation sources and capacitor banks in the distribution system. The intended objective function is a combination of different objective functions. The first goal is to reduce losses, and the second goal is to improve the voltage profile and the third goal is to reduce costs, which has been used by placing weight coefficients in the form of an objective function in the algorithms. For this purpose, the standard 33-bus system has been used to conduct studies. Studies have been repeated in three scenarios. In the first scenario, the locating and determination of the capacity of active and reactive resources has been accomplished with the approach of reducing losses and improving the voltage profile. However, in the second scenario, the locating and determining the capacity of these resources has been accomplished with loss and cost reduction approach and it was considered as constraint in voltage profile. In the third scenario, the simultaneous reduction of all three objective functions has been performed simultaneously. To validate the results obtained by the MOPSO algorithm, its results were compared with genetic and particle swarm algorithms. The results indicate better and more accurate performance of MOPSO algorithm in minimizing objective functions relative to other two algorithms.