IDEAS home Printed from https://ideas.repec.org/a/igg/jsir00/v10y2019i1p38-57.html
   My bibliography  Save this article

Renewable Energy Based Economic Emission Load Dispatch Using Grasshopper Optimization Algorithm

Author

Listed:
  • Sunanda Hazra

    (Department of Electrical Engineering, Central Institute of Plastics Engineering and Technology, Haldia, West Bengal, India)

  • Tapas Pal

    (Department of Electrical Engineering, Kalyani Government Engineering College, Kalyani, West Bengal, India)

  • Provas Kumar Roy

    (Department of Electrical Engineering, Kalyani Government Engineering College, Kalyani, West Bengal, India)

Abstract

This article presents an integrated approach towards the economical operation of a hybrid system which consists of conventional thermal generators and renewable energy sources like windmills using a grasshopper optimization algorithm (GOA). This is based on the social interaction nature of the grasshopper, considering a carbon tax on the emissions from the thermal unit and uncertainty in wind power availability. The Weibull distribution is used for nonlinearity of wind power availability. A standard system, containing six thermal units and two wind farms, is used for testing the dispatch model of three different loads. The GOA results are compared with those obtained using a recently developed quantum-inspired particle swarm optimization (QPSO) optimization technique available in the literature. The simulation results demonstrate the efficacy and ability of GOA over the QPSO algorithm in terms of convergence rate and minimum fitness value. Performance analysis under wind power integration and emission minimization further confirms the supremacy of the GOA algorithm.

Suggested Citation

  • Sunanda Hazra & Tapas Pal & Provas Kumar Roy, 2019. "Renewable Energy Based Economic Emission Load Dispatch Using Grasshopper Optimization Algorithm," International Journal of Swarm Intelligence Research (IJSIR), IGI Global, vol. 10(1), pages 38-57, January.
    • Handle: RePEc:igg:jsir00:v:10:y:2019:i:1:p:38-57
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJSIR.2019010103
    Download Restriction: no
    ---><---

    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:jsir00:v:10:y:2019:i:1:p:38-57. 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.