IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i24p13709-d700562.html
   My bibliography  Save this article

Re-Allocation of Distributed Generations Using Available Renewable Potential Based Multi-Criterion-Multi-Objective Hybrid Technique

Author

Listed:
  • Chandrasekaran Venkatesan

    (Department of Electrical and Electronics Engineering, Sri Venkateswara College of Engineering, Sriperumbudur, Chennai 602117, India)

  • Raju Kannadasan

    (Department of Electrical and Electronics Engineering, Sri Venkateswara College of Engineering, Sriperumbudur, Chennai 602117, India)

  • Dhanasekar Ravikumar

    (Electrical and Electronics Engineering, Sri Sairam Institute of Technology, West Tambaram, Chennai 600044, India)

  • Vijayaraja Loganathan

    (Electrical and Electronics Engineering, Sri Sairam Institute of Technology, West Tambaram, Chennai 600044, India)

  • Mohammed H. Alsharif

    (Department of Electrical Engineering, College of Electronics and Information Engineering, Sejong University, Seoul 05006, Korea)

  • Daeyong Choi

    (School of Electrical Engineering, Chosun University, Gwangju 61452, Korea)

  • Junhee Hong

    (College of IT Convergence, Gachon University, Seongnam 13120, Korea)

  • Zong Woo Geem

    (College of IT Convergence, Gachon University, Seongnam 13120, Korea)

Abstract

Integration of Distributed generations (DGs) and capacitor banks (CBs) in distribution systems (DS) have the potential to enhance the system’s overall capabilities. This work demonstrates the application of a hybrid optimization technique the applies an available renewable energy potential (AREP)-based, hybrid-enhanced grey wolf optimizer–particle swarm optimization (AREP-EGWO-PSO) algorithm for the optimum location and sizing of DGs and CBs. EGWO is a metaheuristic optimization technique stimulated by grey wolves, and PSO is a swarm-based metaheuristic optimization algorithm. Hybridization of both algorithms finds the optimal solution to a problem through the movement of the particles. Using this hybrid method, multi-criterion solutions are obtained, such as technical, economic, and environmental, and these are enriched using multi-objective functions (MOF), namely minimizing active power losses, voltage deviation, the total cost of electrical energy, total emissions from generation sources and enhancing the voltage stability index (VSI). Five different operational cases were adapted to validate the efficacy of the proposed scheme and were performed on two standard distribution systems, namely, IEEE 33- and 69-bus radial distribution systems (RDSs). Notably, the proposed AREP-EGWO-PSO algorithm compared the AREP at the candidate locations and re-allocated the DGs with optimal re-sizing when the EGWO-PSO algorithm failed to meet the AREP constraints. Further, the simulated results were compared with existing optimization algorithms considered in recent studies. The obtained results and analysis show that the proposed AREP-EGWO-PSO re-allocates the DGs effectively and optimally, and that these objective functions offer better results, almost similar to EGWO-PSO results, but more significant than other existing optimization techniques.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:24:p:13709-:d:700562
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/24/13709/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/24/13709/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. Quadri, Imran Ahmad & Bhowmick, S. & Joshi, D., 2018. "A comprehensive technique for optimal allocation of distributed energy resources in radial distribution systems," Applied Energy, Elsevier, vol. 211(C), pages 1245-1260.
    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. Pesaran H.A., Mahmoud & Nazari-Heris, Morteza & Mohammadi-Ivatloo, Behnam & Seyedi, Heresh, 2020. "A hybrid genetic particle swarm optimization for distributed generation allocation in power distribution networks," Energy, Elsevier, vol. 209(C).
    5. 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.
    6. 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.
    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. Lakshmana Perumal Pattathurani & Subhransu S. Dash & Rajat K. Dwibedi & Mani Devesh Raj & Raju Kannadasan & Max F. Savio & Mohammed H. Alsharif & James Hyungkwan Kim, 2022. "Harmonics Minimisation in Non-Linear Grid System Using an Intelligent Hysteresis Current Controller Operated from a Solar Powered ZETA Converter," Sustainability, MDPI, vol. 14(12), pages 1-14, June.
    2. Siddik Shakul Hameed & Ramesh Ramadoss & Kannadasan Raju & GM Shafiullah, 2022. "A Framework-Based Wind Forecasting to Assess Wind Potential with Improved Grey Wolf Optimization and Support Vector Regression," Sustainability, MDPI, vol. 14(7), pages 1-29, April.

    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. 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.
    3. Shahid Nawaz Khan & Syed Ali Abbas Kazmi & Abdullah Altamimi & Zafar A. Khan & Mohammed A. Alghassab, 2022. "Smart Distribution Mechanisms—Part I: From the Perspectives of Planning," Sustainability, MDPI, vol. 14(23), pages 1-109, December.
    4. 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).
    5. Mahesh Kumar & Amir Mahmood Soomro & Waqar Uddin & Laveet Kumar, 2022. "Optimal Multi-Objective Placement and Sizing of Distributed Generation in Distribution System: A Comprehensive Review," Energies, MDPI, vol. 15(21), pages 1-48, October.
    6. Wang, Yi & Qiu, Dawei & Sun, Mingyang & Strbac, Goran & Gao, Zhiwei, 2023. "Secure energy management of multi-energy microgrid: A physical-informed safe reinforcement learning approach," Applied Energy, Elsevier, vol. 335(C).
    7. Singh, Pushpendra & Meena, Nand K. & Yang, Jin & Vega-Fuentes, Eduardo & Bishnoi, Shree Krishna, 2020. "Multi-criteria decision making monarch butterfly optimization for optimal distributed energy resources mix in distribution networks," Applied Energy, Elsevier, vol. 278(C).
    8. Waseem, Muhammad & Lin, Zhenzhi & Liu, Shengyuan & Zhang, Zhi & Aziz, Tarique & Khan, Danish, 2021. "Fuzzy compromised solution-based novel home appliances scheduling and demand response with optimal dispatch of distributed energy resources," Applied Energy, Elsevier, vol. 290(C).
    9. Khaled Fettah & Talal Guia & Ahmed Salhi & Souhil Mouassa & Alessandro Bosisio & Rouzbeh Shirvani, 2024. "Optimal Allocation of Capacitor Banks and Distributed Generation: A Comparison of Recently Developed Metaheuristic Optimization Techniques on the Real Distribution Networks of ALG-AB-Hassi Sida, Alger," Sustainability, MDPI, vol. 16(11), pages 1-23, May.
    10. Zhang, Rongda & Wei, Jing & Zhao, Xiaoli & Liu, Yang, 2022. "Economic and environmental benefits of the integration between carbon sequestration and underground gas storage," Energy, Elsevier, vol. 260(C).
    11. Luis A. Gallego Pareja & Jesús M. López-Lezama & Oscar Gómez Carmona, 2022. "A Mixed-Integer Linear Programming Model for the Simultaneous Optimal Distribution Network Reconfiguration and Optimal Placement of Distributed Generation," Energies, MDPI, vol. 15(9), pages 1-26, April.
    12. Elseify, Mohamed A. & Hashim, Fatma A. & Hussien, Abdelazim G. & Kamel, Salah, 2024. "Single and multi-objectives based on an improved golden jackal optimization algorithm for simultaneous integration of multiple capacitors and multi-type DGs in distribution systems," Applied Energy, Elsevier, vol. 353(PA).
    13. Fridgen, Gilbert & Halbrügge, Stephanie & Olenberger, Christian & Weibelzahl, Martin, 2020. "The insurance effect of renewable distributed energy resources against uncertain electricity price developments," Energy Economics, Elsevier, vol. 91(C).
    14. 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).
    15. Ahmed M. Mahmoud & Shady H. E. Abdel Aleem & Almoataz Y. Abdelaziz & Mohamed Ezzat, 2022. "Towards Maximizing Hosting Capacity by Optimal Planning of Active and Reactive Power Compensators and Voltage Regulators: Case Study," Sustainability, MDPI, vol. 14(20), pages 1-34, October.
    16. Naveed Ahmad Khan & Muhammad Sulaiman & Carlos Andrés Tavera Romero & Fawaz Khaled Alarfaj, 2021. "Numerical Analysis of Electrohydrodynamic Flow in a Circular Cylindrical Conduit by Using Neuro Evolutionary Technique," Energies, MDPI, vol. 14(22), pages 1-19, November.
    17. Suprava Chakraborty & Sumit Verma & Aprajita Salgotra & Rajvikram Madurai Elavarasan & Devaraj Elangovan & Lucian Mihet-Popa, 2021. "Solar-Based DG Allocation Using Harris Hawks Optimization While Considering Practical Aspects," Energies, MDPI, vol. 14(16), pages 1-26, August.
    18. Subrat Kumar Dash & Sivkumar Mishra & Almoataz Y. Abdelaziz & Mamdouh L. Alghaythi & Ahmed Allehyani, 2022. "Optimal Allocation of Distributed Generators in Active Distribution Networks Using a New Oppositional Hybrid Sine Cosine Muted Differential Evolution Algorithm," Energies, MDPI, vol. 15(6), pages 1-35, March.
    19. V, Kavitha & V, Malathi & Guerrero, Josep M. & Bazmohammadi, Najmeh, 2022. "Energy management system using Mimosa Pudica optimization technique for microgrid applications," Energy, Elsevier, vol. 244(PA).
    20. 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).

    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:13:y:2021:i:24:p:13709-:d:700562. 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.