IDEAS home Printed from https://ideas.repec.org/a/epw/ejece0/v3y2019i3id19082.html

A Novel Optimal Distributed Generation Planning in Distribution Network using Cuckoo Optimization Algorithm

Author

Listed:
  • Ali Aranizadeh

    (Department of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran)

  • Iman Niazazari

    (Department of Electrical and Biomedical Engineering, University of Nevada, Reno, USA)

  • Mirpouya Mirmozaffari

    (Gainforte School of Computing, Montana State University, Bozeman, USA.)

Abstract

The optimal sizing and placement of distributed generators have recently drawn a considerable attention to itself. This paper proposes an evolutionary cuckoo optimization algorithm (COA) for optimal placement of distributed generation (DG) in a distribution system. The optimal DG placement problem is formulated as a cost function of network losses, voltage profile, and DG expenses. The proposed method is validated on a 13-bus distribution system. The results show that any variation in the parameter’s weight in the objective function leads to a significant change in the prediction of the DG’s location and capacity.

Suggested Citation

  • Ali Aranizadeh & Iman Niazazari & Mirpouya Mirmozaffari, 2019. "A Novel Optimal Distributed Generation Planning in Distribution Network using Cuckoo Optimization Algorithm," European Journal of Electrical Engineering and Computer Science, European Open Science, vol. 3(3), April.
    • Handle: RePEc:epw:ejece0:v:3:y:2019:i:3:id:19082
    DOI: 10.24018/ejece.2019.3.3.82
    as

    Download full text from publisher

    File URL: https://eu-opensci.org/index.php/ejece/article/view/19082
    File Function: Abstract page
    Download Restriction: no
    File URL: https://eu-opensci.org/index.php/ejece/article/download/19082/11033
    File Function: Full text
    Download Restriction: no
    File URL: https://libkey.io/10.24018/ejece.2019.3.3.82?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:epw:ejece0:v:3:y:2019:i:3:id:19082. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: support (email available below). General contact details of provider: https://eu-opensci.org/index.php/ejece .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.