IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i9p3148-d802102.html
   My bibliography  Save this article

Heuristic Optimization Approaches for Capacitor Sizing and Placement: A Case Study in Kazakhstan

Author

Listed:
  • Olzhas Baimakhanov

    (Electrical Power Systems Department, Almaty University of Power Engineering & Telecommunications Named after G. Daukeyev, Almaty 480013, Kazakhstan)

  • Hande Şenyüz

    (Management Information Systems Department, Kadir Has University, Istanbul 34083, Turkey)

  • Almaz Saukhimov

    (Electrical Power Systems Department, Almaty University of Power Engineering & Telecommunications Named after G. Daukeyev, Almaty 480013, Kazakhstan)

  • Oğuzhan Ceylan

    (Electrical and Electronics Engineering Department, Marmara University, Istanbul 34854, Turkey)

Abstract

Two methods for estimating the near-optimal positions and sizes of capacitors in radial distribution networks are presented. The first model assumes fixed-size capacitors, while the second model assumes controllable variable-size capacitors by changing the tap positions. In the second model, we limit the number of changes in capacitor size. In both approaches, the models consider many load scenarios and aim to obtain better voltage profiles by minimizing voltage deviations and active power losses. We use two recently developed heuristic algorithms called Salp Swarm Optimization algorithm (SSA) and Dragonfly algorithm (DA) to solve the proposed optimization models. We performed numerical simulations using data by modifying an actual distribution network in Almaty, Kazakhstan. To mimic various load scenarios, we start with the baseline load values and produce random variations. For the first model, the optimization algorithms identify the near-optimal positioning and sizes of fixed-size capacitors. Since the second model assumes variable-size capacitors, the algorithms also decide the tap positions for this case. Comparative analysis of the heuristic algorithms shows that the DA and SSA algorithms give similar results in solving the two optimization models: the former gives a slightly better voltage profile and lower active power losses.

Suggested Citation

  • Olzhas Baimakhanov & Hande Şenyüz & Almaz Saukhimov & Oğuzhan Ceylan, 2022. "Heuristic Optimization Approaches for Capacitor Sizing and Placement: A Case Study in Kazakhstan," Energies, MDPI, vol. 15(9), pages 1-16, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:3148-:d:802102
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/9/3148/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/9/3148/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Das, Sangeeta & Das, Debapriya & Patra, Amit, 2019. "Operation of distribution network with optimal placement and sizing of dispatchable DGs and shunt capacitors," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    Full references (including those not matched with items on IDEAS)

    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. J. Rajalakshmi & S. Durairaj, 2021. "Application of multi-objective optimization algorithm for siting and sizing of distributed generations in distribution networks," Journal of Combinatorial Optimization, Springer, vol. 41(2), pages 267-289, February.
    2. Papadimitrakis, M. & Giamarelos, N. & Stogiannos, M. & Zois, E.N. & Livanos, N.A.-I. & Alexandridis, A., 2021. "Metaheuristic search in smart grid: A review with emphasis on planning, scheduling and power flow optimization applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    3. Chandrasekaran Venkatesan & Raju Kannadasan & Dhanasekar Ravikumar & Vijayaraja Loganathan & Mohammed H. Alsharif & Daeyong Choi & Junhee Hong & Zong Woo Geem, 2021. "Re-Allocation of Distributed Generations Using Available Renewable Potential Based Multi-Criterion-Multi-Objective Hybrid Technique," Sustainability, MDPI, vol. 13(24), pages 1-28, December.
    4. Dhivya Swaminathan & Arul Rajagopalan, 2022. "Optimized Network Reconfiguration with Integrated Generation Using Tangent Golden Flower Algorithm," Energies, MDPI, vol. 15(21), pages 1-19, November.

    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:15:y:2022:i:9:p:3148-:d:802102. 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.