IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i5p1222-d504708.html
   My bibliography  Save this article

Solving Single- and Multi-Objective Optimal Reactive Power Dispatch Problems Using an Improved Salp Swarm Algorithm

Author

Listed:
  • Andrei M. Tudose

    (Department of Electrical Power Systems, University “Politehnica” of Bucharest, 060042 Bucharest, Romania)

  • Irina I. Picioroaga

    (Department of Electrical Power Systems, University “Politehnica” of Bucharest, 060042 Bucharest, Romania)

  • Dorian O. Sidea

    (Department of Electrical Power Systems, University “Politehnica” of Bucharest, 060042 Bucharest, Romania)

  • Constantin Bulac

    (Department of Electrical Power Systems, University “Politehnica” of Bucharest, 060042 Bucharest, Romania)

Abstract

The optimal reactive power dispatch (ORPD) problem represents a fundamental concern in the efficient and reliable operation of power systems, based on the proper coordination of numerous devices. Therefore, the ORPD calculation is an elaborate nonlinear optimization problem that requires highly performing computational algorithms to identify the optimal solution. In this paper, the potential of metaheuristic methods is explored for solving complex optimization problems specific to power systems. In this regard, an improved salp swarm algorithm is proposed to solve the ORPD problem for the IEEE-14 and IEEE-30 bus systems, by approaching the reactive power planning as both a single- and a multi- objective problem and aiming at minimizing the real power losses and the bus voltage deviations. Multiple comparison studies are conducted based on the obtained results to assess the proposed approach performance with respect to other state-of-the-art techniques. In all cases, the results demonstrate the potential of the developed method and reflect its effectiveness in solving challenging problems.

Suggested Citation

  • Andrei M. Tudose & Irina I. Picioroaga & Dorian O. Sidea & Constantin Bulac, 2021. "Solving Single- and Multi-Objective Optimal Reactive Power Dispatch Problems Using an Improved Salp Swarm Algorithm," Energies, MDPI, vol. 14(5), pages 1-20, February.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:5:p:1222-:d:504708
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/5/1222/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/14/5/1222/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zahir Sahli & Abdellatif Hamouda & Abdelghani Bekrar & Damien Trentesaux, 2018. "Reactive Power Dispatch Optimization with Voltage Profile Improvement Using an Efficient Hybrid Algorithm †," Energies, MDPI, vol. 11(8), pages 1-21, August.
    2. Happy Aprillia & Hong-Tzer Yang & Chao-Ming Huang, 2020. "Short-Term Photovoltaic Power Forecasting Using a Convolutional Neural Network–Salp Swarm Algorithm," Energies, MDPI, vol. 13(8), pages 1-20, April.
    3. Mohamed Ebeed & Ayman Alhejji & Salah Kamel & Francisco Jurado, 2020. "Solving the Optimal Reactive Power Dispatch Using Marine Predators Algorithm Considering the Uncertainties in Load and Wind-Solar Generation Systems," Energies, MDPI, vol. 13(17), pages 1-19, August.
    4. Mini Vishnu & Sunil Kumar T. K., 2020. "An Improved Solution for Reactive Power Dispatch Problem Using Diversity-Enhanced Particle Swarm Optimization," Energies, MDPI, vol. 13(11), pages 1-21, June.
    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. Tawhid, Mohamed A. & Ibrahim, Abdelmonem M., 2022. "Improved salp swarm algorithm combined with chaos," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 202(C), pages 113-148.
    2. Lu Liu & Yun Zeng, 2023. "Intelligent ISSA-Based Non-Singular Terminal Sliding-Mode Control of DC–DC Boost Converter Feeding a Constant Power Load System," Energies, MDPI, vol. 16(13), pages 1-23, June.
    3. Lenin Kanagasabai, 2022. "Jerusalem artichoke algorithm for power loss reduction and power stability enhancement," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(4), pages 1788-1800, August.
    4. Mahmoud Hemeida & Tomonobu Senjyu & Salem Alkhalaf & Asmaa Fawzy & Mahrous Ahmed & Dina Osheba, 2022. "Reactive Power Management Based Hybrid GAEO," Sustainability, MDPI, vol. 14(11), pages 1-17, June.
    5. Lenin Kanagasabai, 2023. "Real power loss reduction by extreme learning machine based Panthera leo, chaotic based Jungle search and Quantum based Chipmunk search optimization algorithms," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(1), pages 55-78, March.
    6. Lenin Kanagasabai, 2022. "Real power loss dwindling and voltage reliability enrichment by gradient based optimization algorithm," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(5), pages 2727-2742, October.
    7. Rahmad Syah & Safoura Faghri & Mahyuddin KM Nasution & Afshin Davarpanah & Marek Jaszczur, 2021. "Modeling and Optimization of Wind Turbines in Wind Farms for Solving Multi-Objective Reactive Power Dispatch Using a New Hybrid Scheme," Energies, MDPI, vol. 14(18), pages 1-22, September.
    8. Lenin Kanagasabai, 2022. "Mathematics based calculation and stemonitis inspired optimization algorithms for loss reduction and power solidity augmentation," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(5), pages 2710-2726, October.
    9. Lenin Kanagasabai, 2021. "Real power loss reduction by enhanced Apple Maggot optimization algorithm," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 12(6), pages 1385-1396, December.

    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. Lenin Kanagasabai, 2022. "Real power loss dwindling and voltage reliability enrichment by gradient based optimization algorithm," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(5), pages 2727-2742, October.
    2. Lenin Kanagasabai, 2023. "Real power loss reduction by extreme learning machine based Panthera leo, chaotic based Jungle search and Quantum based Chipmunk search optimization algorithms," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(1), pages 55-78, March.
    3. Lenin Kanagasabai, 2022. "Mathematics based calculation and stemonitis inspired optimization algorithms for loss reduction and power solidity augmentation," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(5), pages 2710-2726, October.
    4. Lenin Kanagasabai, 2022. "Buoyancy based optimization algorithm for real power loss diminution," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(5), pages 2442-2457, October.
    5. Lenin Kanagasabai, 2023. "Legislative optimization algorithm for real power loss diminishing and voltage reliability escalation," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(4), pages 1197-1207, August.
    6. 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.
    7. Sulaiman Z. Almutairi & Emad A. Mohamed & Fayez F. M. El-Sousy, 2023. "A Novel Adaptive Manta-Ray Foraging Optimization for Stochastic ORPD Considering Uncertainties of Wind Power and Load Demand," Mathematics, MDPI, vol. 11(11), pages 1-35, June.
    8. Mahmoud Hemeida & Tomonobu Senjyu & Salem Alkhalaf & Asmaa Fawzy & Mahrous Ahmed & Dina Osheba, 2022. "Reactive Power Management Based Hybrid GAEO," Sustainability, MDPI, vol. 14(11), pages 1-17, June.
    9. Samson Ademola Adegoke & Yanxia Sun, 2023. "Diminishing Active Power Loss and Improving Voltage Profile Using an Improved Pathfinder Algorithm Based on Inertia Weight," Energies, MDPI, vol. 16(3), pages 1-14, January.
    10. Shahenda Sarhan & Ragab El-Sehiemy & Amlak Abaza & Mona Gafar, 2022. "Turbulent Flow of Water-Based Optimization for Solving Multi-Objective Technical and Economic Aspects of Optimal Power Flow Problems," Mathematics, MDPI, vol. 10(12), pages 1-22, June.
    11. Samson Ademola Adegoke & Yanxia Sun & Zenghui Wang, 2023. "Minimization of Active Power Loss Using Enhanced Particle Swarm Optimization," Mathematics, MDPI, vol. 11(17), pages 1-17, August.
    12. Shahenda Sarhan & Abdullah Shaheen & Ragab El-Sehiemy & Mona Gafar, 2023. "An Augmented Social Network Search Algorithm for Optimal Reactive Power Dispatch Problem," Mathematics, MDPI, vol. 11(5), pages 1-42, March.
    13. Cai Tao & Junjie Lu & Jianxun Lang & Xiaosheng Peng & Kai Cheng & Shanxu Duan, 2021. "Short-Term Forecasting of Photovoltaic Power Generation Based on Feature Selection and Bias Compensation–LSTM Network," Energies, MDPI, vol. 14(11), pages 1-16, May.
    14. Ashraf Ramadan & Mohamed Ebeed & Salah Kamel & Almoataz Y. Abdelaziz & Hassan Haes Alhelou, 2021. "Scenario-Based Stochastic Framework for Optimal Planning of Distribution Systems Including Renewable-Based DG Units," Sustainability, MDPI, vol. 13(6), pages 1-23, March.
    15. Andi A. H. Lateko & Hong-Tzer Yang & Chao-Ming Huang, 2022. "Short-Term PV Power Forecasting Using a Regression-Based Ensemble Method," Energies, MDPI, vol. 15(11), pages 1-21, June.
    16. Duan, Jikai & Zuo, Hongchao & Bai, Yulong & Chang, Mingheng & Chen, Xiangyue & Wang, Wenpeng & Ma, Lei & Chen, Bolong, 2023. "A multistep short-term solar radiation forecasting model using fully convolutional neural networks and chaotic aquila optimization combining WRF-Solar model results," Energy, Elsevier, vol. 271(C).
    17. Park, Sung-Won & Son, Sung-Yong, 2020. "Interaction-based virtual power plant operation methodology for distribution system operator’s voltage management," Applied Energy, Elsevier, vol. 271(C).
    18. Lenin Kanagasabai, 2022. "Real power loss reduction by quantum based Ptilonorhynchus violaceus optimization and Haliastur Indus algorithms," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(4), pages 1913-1931, August.
    19. Dukhwan Yu & Seowoo Lee & Sangwon Lee & Wonik Choi & Ling Liu, 2020. "Forecasting Photovoltaic Power Generation Using Satellite Images," Energies, MDPI, vol. 13(24), pages 1-15, December.
    20. Khizer Mehmood & Naveed Ishtiaq Chaudhary & Zeshan Aslam Khan & Khalid Mehmood Cheema & Muhammad Asif Zahoor Raja & Ahmad H. Milyani & Abdullah Ahmed Azhari, 2022. "Nonlinear Hammerstein System Identification: A Novel Application of Marine Predator Optimization Using the Key Term Separation Technique," Mathematics, MDPI, vol. 10(22), pages 1-22, 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:14:y:2021:i:5:p:1222-:d:504708. 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.