IDEAS home Printed from https://ideas.repec.org/a/igg/jamc00/v4y2013i4p63-89.html
   My bibliography  Save this article

Optimal Photovoltaic System Design with Multi-Objective Optimization

Author

Listed:
  • Amin Ibrahim

    (University of Ontario Institute of Technology, Oshawa, Canada)

  • Farid Bourennani

    (University of Ontario Institute of Technology, Oshawa, Canada)

  • Shahryar Rahnamayan

    (University of Ontario Institute of Technology, Oshawa, Canada)

  • Greg F. Naterer

    (Memorial University of Newfoundland, St. John's, Canada)

Abstract

Recently, several parts of the world suffer from electrical black-outs due to high electrical demands during peak hours. Stationary photovoltaic (PV) collector arrays produce clean and sustainable energy especially during peak hours which are generally day time. In addition, PVs do not emit any waste or emissions, and are silent in operation. The incident energy collected by PVs is mainly dependent on the number of collector rows, distance between collector rows, dimension of collectors, collectors inclination angle and collectors azimuth, which all are involved in the proposed modeling in this article. The objective is to achieve optimal design of a PV farm yielding two conflicting objectives namely maximum field incident energy and minimum of the deployment cost. Two state-of-the-art multi-objective evolutionary algorithms (MOEAs) called Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and Generalized Differential Evolution Generation 3 (GDE3) are compared to design PV farms in Toronto, Canada area. The results are presented and discussed to illustrate the advantage of utilizing MOEA in PV farms design and other energy related real-world problems.

Suggested Citation

  • Amin Ibrahim & Farid Bourennani & Shahryar Rahnamayan & Greg F. Naterer, 2013. "Optimal Photovoltaic System Design with Multi-Objective Optimization," International Journal of Applied Metaheuristic Computing (IJAMC), IGI Global, vol. 4(4), pages 63-89, October.
    • Handle: RePEc:igg:jamc00:v:4:y:2013:i:4:p:63-89
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijamc.2013100104
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yin, Peng-Yeng & Wu, Tsai-Hung & Hsu, Ping-Yi, 2017. "Simulation based risk management for multi-objective optimal wind turbine placement using MOEA/D," Energy, Elsevier, vol. 141(C), pages 579-597.
    2. Yin, Peng-Yeng & Cheng, Chun-Ying & Chen, Hsin-Min & Wu, Tsai-Hung, 2020. "Risk-aware optimal planning for a hybrid wind-solar farm," Renewable Energy, Elsevier, vol. 157(C), pages 290-302.

    More about this item

    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:igg:jamc00:v:4:y:2013:i:4:p:63-89. 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    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.