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Optimizing distributed generation parameters through economic feasibility assessment

Journal Article


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Abstract


  • To meet the fast growth of electricity demand, the traditional network solution tends to expand existing substations, build more new substations, and build transmission lines. Distributed Generation (DG) is posed as an alternative method for the network providers not only to accommodate the load increase and relieve network overload, but also to offer other additional technical and economic benefits. This paper addresses the issue of DG planning and has proposed a technique for optimizing the DG size and location to minimize the overall investment and operational cost of the system. The proposed optimization methodology assesses the compatibility of different generation schemes in terms of their cost factors that can be significantly contributed by a DG. The direct and indirect costs of power supply quality, reliability, energy loss, total power operation, and DG investment are used as key cost components of the DG siting and sizing strategy. The Particle Swarm Optimization (PSO) method is applied to obtain the optimal DG planning solutions. Finally, the proposed approach is tested on a distribution feeder of an Australian power network. Simulation results are presented to illustrate the feasibility and effectiveness of the proposed method.

UOW Authors


  •   Muttaqi, Kashem
  •   Le, An D. T. (external author)
  •   Aghaei, Jamshid (external author)
  •   Mahboubi-Moghaddam, Esmaeil (external author)
  •   Negnevitsky, Michael (external author)
  •   Ledwich, Gerard F. (external author)

Publication Date


  • 2016

Citation


  • K. M. Muttaqi, A. D. T. Le, J. Aghaei, E. Mahboubi-Moghaddam, M. Negnevitsky & G. Ledwich, "Optimizing distributed generation parameters through economic feasibility assessment," Applied Energy, vol. 165, pp. 893-903, 2016.

Scopus Eid


  • 2-s2.0-84953911793

Ro Full-text Url


  • http://ro.uow.edu.au/context/eispapers/article/6177/type/native/viewcontent

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5150

Has Global Citation Frequency


Number Of Pages


  • 10

Start Page


  • 893

End Page


  • 903

Volume


  • 165

Place Of Publication


  • United Kingdom

Abstract


  • To meet the fast growth of electricity demand, the traditional network solution tends to expand existing substations, build more new substations, and build transmission lines. Distributed Generation (DG) is posed as an alternative method for the network providers not only to accommodate the load increase and relieve network overload, but also to offer other additional technical and economic benefits. This paper addresses the issue of DG planning and has proposed a technique for optimizing the DG size and location to minimize the overall investment and operational cost of the system. The proposed optimization methodology assesses the compatibility of different generation schemes in terms of their cost factors that can be significantly contributed by a DG. The direct and indirect costs of power supply quality, reliability, energy loss, total power operation, and DG investment are used as key cost components of the DG siting and sizing strategy. The Particle Swarm Optimization (PSO) method is applied to obtain the optimal DG planning solutions. Finally, the proposed approach is tested on a distribution feeder of an Australian power network. Simulation results are presented to illustrate the feasibility and effectiveness of the proposed method.

UOW Authors


  •   Muttaqi, Kashem
  •   Le, An D. T. (external author)
  •   Aghaei, Jamshid (external author)
  •   Mahboubi-Moghaddam, Esmaeil (external author)
  •   Negnevitsky, Michael (external author)
  •   Ledwich, Gerard F. (external author)

Publication Date


  • 2016

Citation


  • K. M. Muttaqi, A. D. T. Le, J. Aghaei, E. Mahboubi-Moghaddam, M. Negnevitsky & G. Ledwich, "Optimizing distributed generation parameters through economic feasibility assessment," Applied Energy, vol. 165, pp. 893-903, 2016.

Scopus Eid


  • 2-s2.0-84953911793

Ro Full-text Url


  • http://ro.uow.edu.au/context/eispapers/article/6177/type/native/viewcontent

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5150

Has Global Citation Frequency


Number Of Pages


  • 10

Start Page


  • 893

End Page


  • 903

Volume


  • 165

Place Of Publication


  • United Kingdom