• DocumentCode
    648021
  • Title

    Optimal planning of distributed generations with the combination of genetic algorithm and interval numbers TOPSIS

  • Author

    Liang Han ; Shouxiang Wang ; Dong Wang ; Xiguang Fan

  • Author_Institution
    Key Lab. of Smart Grid of Minist. of Educ., Tianjin Univ., Tianjin, China
  • fYear
    2013
  • fDate
    21-25 July 2013
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    In order to solve optimal technical and economic allocation of distributed generation in distribution network, a multi-objective nonlinear optimization model is built considering the uncertainty of the type, location and capacity of distributed generation. The sub-objectives in this model include the least investment cost, the highest earning, the highest environment benefits and the least loss in distribution network. Interval numbers are used to deal with the uncertainty of the property values and their weights in the actual decision-making of DG optimal allocation. A method combined by TOPSIS algorithm based on interval mathematics and genetic algorithm is proposed in this paper to solve that model. Taking a typical distribution network as a test example, the result shows the validity and practicability of the proposed method.
  • Keywords
    distributed power generation; distribution networks; genetic algorithms; power generation economics; power generation planning; DG optimal allocation; TOPSIS algorithm; Technique for Order of Preference by Similarity to Ideal Solution; actual decision making; distributed generation; distribution network; genetic algorithm; interval mathematics; interval numbers TOPSIS; least investment cost; multiobjective nonlinear optimization model; optimal planning; Economics; Genetics; Ice; Integrated optics; Photovoltaic systems; Reliability; distributed generation; distribution network; genetic algorithm; interval TOPSIS algorithm; multi-objective optimization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Power and Energy Society General Meeting (PES), 2013 IEEE
  • Conference_Location
    Vancouver, BC
  • ISSN
    1944-9925
  • Type

    conf

  • DOI
    10.1109/PESMG.2013.6672576
  • Filename
    6672576