IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i11p4577-d1403797.html
   My bibliography  Save this article

A Multi-Objective Approach for Optimal Sizing and Placement of Distributed Generators and Distribution Static Compensators in a Distribution Network Using the Black Widow Optimization Algorithm

Author

Listed:
  • Rameez Shaikh

    (School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne 3122, Australia)

  • Alex Stojcevski

    (School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne 3122, Australia)

  • Mehdi Seyedmahmoudian

    (School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne 3122, Australia)

  • Jaideep Chandran

    (School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne 3122, Australia)

Abstract

This paper presents a new optimization technique for the locations and sizes of Distributed Generators (DGs) and distribution static compensators (DSTATCOMs) in a radial system of a distribution network based on a multi-objective approach. It uses black widow optimization to improve voltage profile and power loss reduction. The black widow optimization simulates the mating behaviour of black widow spiders. The optimum size and placement of DGs and DSTATCOMs are deemed to be decision variables that are defined by using black widow optimization. The proposed technique is implemented in selected IEEE bus systems to evaluate its performance. The simulation results indicate reduced power losses and voltage profile enhancement as sizes and locations of integrated DGs and DSTATCOMs are adjusted based on optimization. The number of DGs and DSTATCOMs required to achieve the objectives is reduced. Furthermore, the results of the black widow algorithm are compared to existing techniques in the literature.

Suggested Citation

  • Rameez Shaikh & Alex Stojcevski & Mehdi Seyedmahmoudian & Jaideep Chandran, 2024. "A Multi-Objective Approach for Optimal Sizing and Placement of Distributed Generators and Distribution Static Compensators in a Distribution Network Using the Black Widow Optimization Algorithm," Sustainability, MDPI, vol. 16(11), pages 1-27, May.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:11:p:4577-:d:1403797
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/11/4577/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/11/4577/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Rajnish Kler & Roshan Gangurde & Samariddin Elmirzaev & Md Shamim Hossain & Nhut V. T. Vo & Tien V. T. Nguyen & P. Naveen Kumar & Peiman Ghasemi, 2022. "Optimization of Meat and Poultry Farm Inventory Stock Using Data Analytics for Green Supply Chain Network," Discrete Dynamics in Nature and Society, Hindawi, vol. 2022, pages 1-8, October.
    2. Kargarian, A. & Raoofat, M. & Mohammadi, M., 2011. "Reactive power market management considering voltage control area reserve and system security," Applied Energy, Elsevier, vol. 88(11), pages 3832-3840.
    3. Bayat, A. & Bagheri, A., 2019. "Optimal active and reactive power allocation in distribution networks using a novel heuristic approach," Applied Energy, Elsevier, vol. 233, pages 71-85.
    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. Chandrasekaran Venkatesan & Raju Kannadasan & Mohammed H. Alsharif & Mun-Kyeom Kim & Jamel Nebhen, 2021. "A Novel Multiobjective Hybrid Technique for Siting and Sizing of Distributed Generation and Capacitor Banks in Radial Distribution Systems," Sustainability, MDPI, vol. 13(6), pages 1-34, March.
    2. Peng Cheng & Zhiyu Xu & Ruiye Li & Chao Shi, 2022. "A Hybrid Taguchi Particle Swarm Optimization Algorithm for Reactive Power Optimization of Deep-Water Semi-Submersible Platforms with New Energy Sources," Energies, MDPI, vol. 15(13), pages 1-16, June.
    3. Aouss Gabash & Pu Li, 2016. "On Variable Reverse Power Flow-Part I: Active-Reactive Optimal Power Flow with Reactive Power of Wind Stations," Energies, MDPI, vol. 9(3), pages 1-12, February.
    4. Jerzy Andruszkiewicz & Józef Lorenc & Agnieszka Weychan, 2023. "Determination of the Optimal Level of Reactive Power Compensation That Minimizes the Costs of Losses in Distribution Networks," Energies, MDPI, vol. 17(1), pages 1-24, December.
    5. Bielecki, Sławomir & Skoczkowski, Tadeusz, 2018. "An enhanced concept of Q-power management," Energy, Elsevier, vol. 162(C), pages 335-353.
    6. Devabalaji Kaliaperumal Rukmani & Yuvaraj Thangaraj & Umashankar Subramaniam & Sitharthan Ramachandran & Rajvikram Madurai Elavarasan & Narottam Das & Luis Baringo & Mohamed Imran Abdul Rasheed, 2020. "A New Approach to Optimal Location and Sizing of DSTATCOM in Radial Distribution Networks Using Bio-Inspired Cuckoo Search Algorithm," Energies, MDPI, vol. 13(18), pages 1-21, September.
    7. Azeredo, Lucas F.S. & Yahyaoui, Imene & Fiorotti, Rodrigo & Fardin, Jussara F. & Garcia-Pereira, Hilel & Rocha, Helder R.O., 2023. "Study of reducing losses, short-circuit currents and harmonics by allocation of distributed generation, capacitor banks and fault current limiters in distribution grids," Applied Energy, Elsevier, vol. 350(C).
    8. Xiaosheng Peng & Kai Cheng & Jianxun Lang & Zuowei Zhang & Tao Cai & Shanxu Duan, 2021. "Short-Term Wind Power Prediction for Wind Farm Clusters Based on SFFS Feature Selection and BLSTM Deep Learning," Energies, MDPI, vol. 14(7), pages 1-18, March.
    9. Ji, Haoran & Wang, Chengshan & Li, Peng & Zhao, Jinli & Song, Guanyu & Ding, Fei & Wu, Jianzhong, 2018. "A centralized-based method to determine the local voltage control strategies of distributed generator operation in active distribution networks," Applied Energy, Elsevier, vol. 228(C), pages 2024-2036.
    10. Jay, Devika & Swarup, K.S., 2021. "A comprehensive survey on reactive power ancillary service markets," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    11. Sousa, Tiago & Morais, Hugo & Vale, Zita & Castro, Rui, 2015. "A multi-objective optimization of the active and reactive resource scheduling at a distribution level in a smart grid context," Energy, Elsevier, vol. 85(C), pages 236-250.
    12. Pereira, Luan D.L. & Yahyaoui, Imene & Fiorotti, Rodrigo & de Menezes, Luíza S. & Fardin, Jussara F. & Rocha, Helder R.O. & Tadeo, Fernando, 2022. "Optimal allocation of distributed generation and capacitor banks using probabilistic generation models with correlations," Applied Energy, Elsevier, vol. 307(C).
    13. James Deva Koresh Hezekiah & Karnam Chandrakumar Ramya & Mercy Paul Selvan & Vishnu Murthy Kumarasamy & Dipak Kumar Sah & Malathi Devendran & Sivakumar Sabapathy Arumugam & Rajagopal Maheswar, 2023. "Nature-Inspired Energy Enhancement Technique for Wireless Sensor Networks," Energies, MDPI, vol. 16(20), pages 1-19, October.
    14. Wang, Xiaoxue & Wang, Chengshan & Xu, Tao & Meng, He & Li, Peng & Yu, Li, 2018. "Distributed voltage control for active distribution networks based on distribution phasor measurement units," Applied Energy, Elsevier, vol. 229(C), pages 804-813.
    15. Ahmadimanesh, A. & Kalantar, M., 2017. "A novel cost reducing reactive power market structure for modifying mandatory generation regions of producers," Energy Policy, Elsevier, vol. 108(C), pages 702-711.
    16. Ahmed Al-Shafei & Hamidreza Zareipour & Yankai Cao, 2022. "High-Performance and Parallel Computing Techniques Review: Applications, Challenges and Potentials to Support Net-Zero Transition of Future Grids," Energies, MDPI, vol. 15(22), pages 1-58, November.
    17. Syed Ali Abbas Kazmi & Usama Ameer Khan & Hafiz Waleed Ahmad & Sajid Ali & Dong Ryeol Shin, 2020. "A Techno-Economic Centric Integrated Decision-Making Planning Approach for Optimal Assets Placement in Meshed Distribution Network Across the Load Growth," Energies, MDPI, vol. 13(6), pages 1-71, March.
    18. Naz, Muhammad Naveed & Mushtaq, Muhammad Irfan & Naeem, Muhammad & Iqbal, Muhammad & Altaf, Muhammad Waseem & Haneef, Muhammad, 2017. "Multicriteria decision making for resource management in renewable energy assisted microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 323-341.
    19. Ghazi M. Magableh, 2023. "Evaluating Wheat Suppliers Using Fuzzy MCDM Technique," Sustainability, MDPI, vol. 15(13), pages 1-23, July.
    20. Ayat Ali Saleh & Tomonobu Senjyu & Salem Alkhalaf & Majed A. Alotaibi & Ashraf M. Hemeida, 2020. "Water Cycle Algorithm for Probabilistic Planning of Renewable Energy Resource, Considering Different Load Models," Energies, MDPI, vol. 13(21), pages 1-24, 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:jsusta:v:16:y:2024:i:11:p:4577-:d:1403797. 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.