• DocumentCode
    3105498
  • Title

    Optimal Distributed Generation allocation in distirbution systems employing ant colony to reduce losses

  • Author

    Sheidaei, Farnaz ; Shadkam, Majid ; Zarei, Mahdi

  • Author_Institution
    Eng. Dept., Univ. of Saveh, Saveh
  • fYear
    2008
  • fDate
    1-4 Sept. 2008
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    This paper presents a method for optimal allocation of distributed generation in distribution systems. In this paper, our aim would be optimal distributed generation allocation for loss reduction in distribution network. Ant colony search algorithm (ACSA) was used as solving tool. ACSA is inspired from the natural behaviour of the ant colonies on how they find the food source and bring them back to their nest by building the unique trail formation. This algorithm is used to minimize an objective function. For applying ACSA, a soft ware is programmed under Matlab software is prepared. This proposed ACSA method and genetic algorithm (GA) are implemented on IEEE 34 bus system, and the results show that the proposed method is better than the other two methods. Using the proper and optimal allocation of DG has many advantages, but the lack of it has disadvantages, such as: increasing losses, voltage flicker, and harmonic.
  • Keywords
    distributed power generation; distribution networks; genetic algorithms; search problems; IEEE 34 bus system; Matlab software; ant colony search algorithm; distribution network; distribution systems; genetic algorithm; loss reduction; optimal distributed generation allocation; Ant colony optimization; Costs; Distributed control; Distributed power generation; Drilling; Genetic algorithms; Petroleum; Power system planning; Technology planning; Voltage fluctuations; Allocation; Ant Colony Search Algorithm (ACSA); Distributed Generation (DG); Genetic Algorithm (GA); Losses;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Universities Power Engineering Conference, 2008. UPEC 2008. 43rd International
  • Conference_Location
    Padova
  • Print_ISBN
    978-1-4244-3294-3
  • Electronic_ISBN
    978-88-89884-09-6
  • Type

    conf

  • DOI
    10.1109/UPEC.2008.4651548
  • Filename
    4651548