IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i6p3305-d769197.html
   My bibliography  Save this article

Optimal Location and Sizing of Distributed Generators in Power System Network with Power Quality Enhancement Using Fuzzy Logic Controlled D-STATCOM

Author

Listed:
  • Prashant

    (Department of Electrical Engineering, Faculty of Engineering & Technology, Jamia Millia Islamia, New Delhi 110025, India)

  • Anwar Shahzad Siddiqui

    (Department of Electrical Engineering, Faculty of Engineering & Technology, Jamia Millia Islamia, New Delhi 110025, India)

  • Md Sarwar

    (Department of Electrical Engineering, Faculty of Engineering & Technology, Jamia Millia Islamia, New Delhi 110025, India)

  • Ahmed Althobaiti

    (Electrical Engineering Department, College of Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia)

  • Sherif S. M. Ghoneim

    (Electrical Engineering Department, College of Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia)

Abstract

This article presents the selection of location and sizing of multiple distributed generators (DGs) for boosting performance of the radial distribution system in the case of constant power load flow and constant impedance load flow. The consideration of placing and sizing of DGs is to meet the load demand. This article tries to overcome the limitations of existing techniques for determining the appropriate location and size of DGs. The selection of DG location is decided in terms of real power losses, accuracy, and sensitivity. The size of DG is measured in terms of real and reactive power. Both positioning and sizing of DG are analyzed with the genetic algorithm and the heuristic probability distribution method. The results are compared with other existing methods such as ant-lion optimization algorithm, coyote optimizer, modified sine-cosine algorithm, and particle swarm optimization. Further, the power quality improvement of the network is assessed by positioning D-STATCOM, and its location is decided on the basis of the nearby bus having poor voltage profile and high total harmonic distortion (THD). The switching and controlling of D-STATCOM are assessed with fuzzy logic controller (FLC) for improving the performance parameters such as voltage profile and THD at that particular bus. The proposed analytical approach for the system is tested on the IEEE 33 bus system. It is observed that the performance of the system with the genetic algorithm gives a better solution in comparison to heuristic PDF and other existing methods for determining the optimal location and size of DG. The introduction of D-STATCOM into the system with FLC shows better performance in terms of improved voltage profile and THD in comparison to existing techniques.

Suggested Citation

  • Prashant & Anwar Shahzad Siddiqui & Md Sarwar & Ahmed Althobaiti & Sherif S. M. Ghoneim, 2022. "Optimal Location and Sizing of Distributed Generators in Power System Network with Power Quality Enhancement Using Fuzzy Logic Controlled D-STATCOM," Sustainability, MDPI, vol. 14(6), pages 1-31, March.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:6:p:3305-:d:769197
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/6/3305/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/6/3305/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ke-yan Liu & Wanxing Sheng & Yongmei Liu & Xiaoli Meng, 2017. "A Network Reconfiguration Method Considering Data Uncertainties in Smart Distribution Networks," Energies, MDPI, vol. 10(5), pages 1-17, May.
    2. Kalambe, Shilpa & Agnihotri, Ganga, 2014. "Loss minimization techniques used in distribution network: bibliographical survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 29(C), pages 184-200.
    3. Hossein Moayedi & Amir Mosavi, 2021. "Synthesizing Multi-Layer Perceptron Network with Ant Lion Biogeography-Based Dragonfly Algorithm Evolutionary Strategy Invasive Weed and League Champion Optimization Hybrid Algorithms in Predicting He," Sustainability, MDPI, vol. 13(6), pages 1-20, March.
    4. Hussein M. K. Al-Masri & Abed A. Al-Sharqi & Sharaf K. Magableh & Ali Q. Al-Shetwi & Maher G. M. Abdolrasol & Taha Selim Ustun, 2022. "Optimal Allocation of a Hybrid Photovoltaic Biogas Energy System Using Multi-Objective Feasibility Enhanced Particle Swarm Algorithm," Sustainability, MDPI, vol. 14(2), pages 1-20, January.
    5. 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.
    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. Sultana, U. & Khairuddin, Azhar B. & Aman, M.M. & Mokhtar, A.S. & Zareen, N., 2016. "A review of optimum DG placement based on minimization of power losses and voltage stability enhancement of distribution system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 63(C), pages 363-378.
    2. Syed Ali Abbas Kazmi & Muhammad Khuram Shahzad & Akif Zia Khan & Dong Ryeol Shin, 2017. "Smart Distribution Networks: A Review of Modern Distribution Concepts from a Planning Perspective," Energies, MDPI, vol. 10(4), pages 1-47, April.
    3. Mirna Fouad Abd El-salam & Eman Beshr & Magdy B. Eteiba, 2018. "A New Hybrid Technique for Minimizing Power Losses in a Distribution System by Optimal Sizing and Siting of Distributed Generators with Network Reconfiguration," Energies, MDPI, vol. 11(12), pages 1-26, November.
    4. Mohd Ikhwan Muhammad Ridzuan & Sasa Z. Djokic, 2019. "Energy Regulator Supply Restoration Time," Energies, MDPI, vol. 12(6), pages 1-16, March.
    5. Ma, Chenjie & Menke, Jan-Hendrik & Dasenbrock, Johannes & Braun, Martin & Haslbeck, Matthias & Schmid, Karl-Heinz, 2019. "Evaluation of energy losses in low voltage distribution grids with high penetration of distributed generation," Applied Energy, Elsevier, vol. 256(C).
    6. Rade Čađenović & Damir Jakus & Petar Sarajčev & Josip Vasilj, 2018. "Optimal Distribution Network Reconfiguration through Integration of Cycle-Break and Genetic Algorithms," Energies, MDPI, vol. 11(5), pages 1-19, May.
    7. Takele Ferede Agajie & Armand Fopah-Lele & Isaac Amoussou & Ahmed Ali & Baseem Khan & Emmanuel Tanyi, 2023. "Optimal Design and Mathematical Modeling of Hybrid Solar PV–Biogas Generator with Energy Storage Power Generation System in Multi-Objective Function Cases," Sustainability, MDPI, vol. 15(10), pages 1-26, May.
    8. S. Angalaeswari & P. Sanjeevikumar & K. Jamuna & Zbigniew Leonowicz, 2020. "Hybrid PIPSO-SQP Algorithm for Real Power Loss Minimization in Radial Distribution Systems with Optimal Placement of Distributed Generation," Sustainability, MDPI, vol. 12(14), pages 1-21, July.
    9. 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.
    10. Mahmoud M. Sayed & Mohamed Y. Mahdy & Shady H. E. Abdel Aleem & Hosam K. M. Youssef & Tarek A. Boghdady, 2022. "Simultaneous Distribution Network Reconfiguration and Optimal Allocation of Renewable-Based Distributed Generators and Shunt Capacitors under Uncertain Conditions," Energies, MDPI, vol. 15(6), pages 1-27, March.
    11. Sohrab Mirsaeidi & Subash Devkota & Xiaojun Wang & Dimitrios Tzelepis & Ghulam Abbas & Ahmed Alshahir & Jinghan He, 2022. "A Review on Optimization Objectives for Power System Operation Improvement Using FACTS Devices," Energies, MDPI, vol. 16(1), pages 1-24, December.
    12. Jordehi, A. Rezaee, 2015. "Optimisation of electric distribution systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 1088-1100.
    13. Jain, Sanjay & Kalambe, Shilpa & Agnihotri, Ganga & Mishra, Anuprita, 2017. "Distributed generation deployment: State-of-the-art of distribution system planning in sustainable era," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 363-385.
    14. Mahesh Kumar & Perumal Nallagownden & Irraivan Elamvazuthi, 2017. "Optimal Placement and Sizing of Renewable Distributed Generations and Capacitor Banks into Radial Distribution Systems," Energies, MDPI, vol. 10(6), pages 1-25, June.
    15. Khan, Babar Sattar & Qamar, Affaq & Ullah, Farman & Bilal, Muhammad, 2023. "Ingenuity of Shannon entropy-based fractional order hybrid swarming strategy to solve optimal power flows," Chaos, Solitons & Fractals, Elsevier, vol. 170(C).
    16. Mukhopadhyay, Bineeta & Das, Debapriya, 2020. "Multi-objective dynamic and static reconfiguration with optimized allocation of PV-DG and battery energy storage system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    17. Joisa Dutra, Flavio M. Menezes, and Xuemei Zheng, 2016. "Price Regulation and the Incentives to Pursue Energy Efficiency by Minimizing Network Losses," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4).
    18. Sultana, U. & Khairuddin, Azhar B. & Mokhtar, A.S. & Zareen, N. & Sultana, Beenish, 2016. "Grey wolf optimizer based placement and sizing of multiple distributed generation in the distribution system," Energy, Elsevier, vol. 111(C), pages 525-536.
    19. Mohamed A. Tolba & Hegazy Rezk & Vladimir Tulsky & Ahmed A. Zaki Diab & Almoataz Y. Abdelaziz & Artem Vanin, 2018. "Impact of Optimum Allocation of Renewable Distributed Generations on Distribution Networks Based on Different Optimization Algorithms," Energies, MDPI, vol. 11(1), pages 1-33, January.
    20. Syed Ali Abbas Kazmi & Dong Ryeol Shin, 2017. "DG Placement in Loop Distribution Network with New Voltage Stability Index and Loss Minimization Condition Based Planning Approach under Load Growth," Energies, MDPI, vol. 10(8), pages 1-28, August.

    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:14:y:2022:i:6:p:3305-:d:769197. 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.