IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v10y2017i2p150-d88625.html
   My bibliography  Save this article

Application of Meta-Heuristic Techniques for Optimal Load Shedding in Islanded Distribution Network with High Penetration of Solar PV Generation

Author

Listed:
  • Mohammad Dreidy

    (Department of Electrical Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia)

  • Hazlie Mokhlis

    (Department of Electrical Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia)

  • Saad Mekhilef

    (Power Electronics and Renewable Energy Research Laboratory (PEARL), Department of Electrical Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia)

Abstract

Recently, several environmental problems are beginning to affect all aspects of life. For this reason, many governments and international agencies have expressed great interest in using more renewable energy sources (RESs). However, integrating more RESs with distribution networks resulted in several critical problems vis-à-vis the frequency stability, which might lead to a complete blackout if not properly treated. Therefore, this paper proposed a new Under Frequency Load Shedding (UFLS) scheme for islanding distribution network. This scheme uses three meta-heuristics techniques, binary evolutionary programming (BEP), Binary genetic algorithm (BGA), and Binary particle swarm optimization (BPSO), to determine the optimal combination of loads that needs to be shed from the islanded distribution network. Compared with existing UFLS schemes using fixed priority loads, the proposed scheme has the ability to restore the network frequency without any overshooting. Furthermore, in terms of execution time, the simulation results show that the BEP technique is fast enough to shed the optimal combination of loads compared with BGA and BPSO techniques.

Suggested Citation

  • Mohammad Dreidy & Hazlie Mokhlis & Saad Mekhilef, 2017. "Application of Meta-Heuristic Techniques for Optimal Load Shedding in Islanded Distribution Network with High Penetration of Solar PV Generation," Energies, MDPI, vol. 10(2), pages 1-24, January.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:2:p:150-:d:88625
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/10/2/150/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/10/2/150/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Qi Wang & Yi Tang & Feng Li & Mengya Li & Yang Li & Ming Ni, 2016. "Coordinated Scheme of Under-Frequency Load Shedding with Intelligent Appliances in a Cyber Physical Power System," Energies, MDPI, vol. 9(8), pages 1-14, August.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Jafar Jallad & Saad Mekhilef & Hazlie Mokhlis & Javed Laghari & Ola Badran, 2018. "Application of Hybrid Meta-Heuristic Techniques for Optimal Load Shedding Planning and Operation in an Islanded Distribution Network Integrated with Distributed Generation," Energies, MDPI, vol. 11(5), pages 1-25, May.
    2. Hazlee Azil Illias & Wee Zhao Liang, 2018. "Identification of transformer fault based on dissolved gas analysis using hybrid support vector machine-modified evolutionary particle swarm optimisation," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-15, January.
    3. Shun Li & Fei Tang & Youguo Shao & Qingfen Liao, 2017. "Adaptive Under-Frequency Load Shedding Scheme in System Integrated with High Wind Power Penetration: Impacts and Improvements," Energies, MDPI, vol. 10(9), pages 1-16, September.
    4. Laura M. Cruz & David L. Alvarez & Ameena S. Al-Sumaiti & Sergio Rivera, 2020. "Load Curtailment Optimization Using the PSO Algorithm for Enhancing the Reliability of Distribution Networks," Energies, MDPI, vol. 13(12), pages 1-15, June.
    5. Lutfu Saribulut & Gorkem Ok & Arman Ameen, 2023. "A Case Study on National Electricity Blackout of Turkey," Energies, MDPI, vol. 16(11), pages 1-20, May.
    6. Robert Małkowski & Janusz Nieznański, 2020. "Underfrequency Load Shedding: An Innovative Algorithm Based on Fuzzy Logic," Energies, MDPI, vol. 13(6), pages 1-16, March.
    7. Skrjanc, T. & Mihalic, R. & Rudez, U., 2023. "A systematic literature review on under-frequency load shedding protection using clustering methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 180(C).
    8. Sohail Sarwar & Hazlie Mokhlis & Mohamadariff Othman & Munir Azam Muhammad & J. A. Laghari & Nurulafiqah Nadzirah Mansor & Hasmaini Mohamad & Alireza Pourdaryaei, 2020. "A Mixed Integer Linear Programming Based Load Shedding Technique for Improving the Sustainability of Islanded Distribution Systems," Sustainability, MDPI, vol. 12(15), pages 1-23, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Robert Małkowski & Janusz Nieznański, 2020. "Underfrequency Load Shedding: An Innovative Algorithm Based on Fuzzy Logic," Energies, MDPI, vol. 13(6), pages 1-16, March.
    2. Shun Li & Fei Tang & Youguo Shao & Qingfen Liao, 2017. "Adaptive Under-Frequency Load Shedding Scheme in System Integrated with High Wind Power Penetration: Impacts and Improvements," Energies, MDPI, vol. 10(9), pages 1-16, September.
    3. Skrjanc, T. & Mihalic, R. & Rudez, U., 2023. "A systematic literature review on under-frequency load shedding protection using clustering methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 180(C).

    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:gam:jeners:v:10:y:2017:i:2:p:150-:d:88625. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.