IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i2p846-d1032472.html
   My bibliography  Save this article

Techno-Economic Strategy for the Load Dispatch and Power Flow in Power Grids Using Peafowl Optimization Algorithm

Author

Listed:
  • Mohammed Hamouda Ali

    (Department of Electrical Engineering, Faculty of Engineering, Al-Azhar University, Cairo 11651, Egypt)

  • Ali M. El-Rifaie

    (College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait)

  • Ahmed A. F. Youssef

    (College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait)

  • Vladimir N. Tulsky

    (Electrical Power Systems Department, National Research University “MPEI”, Moscow 111250, Russia)

  • Mohamed A. Tolba

    (Reactors Department, Nuclear Research Center, Egyptian Atomic Energy Authority (EAEA), Cairo 11787, Egypt)

Abstract

The purpose of this paper is to address an urgent operational issue referring to optimal power flow (OPF), which is associated with a number of technical and financial aspects relating to issues of environmental concern. In the last few decades, OPF has become one of the most significant issues in nonlinear optimization research. OPF generally improves the performance of electric power distribution, transmission, and production within the constraints of the control system. It is the purpose of an OPF to determine the most optimal way to run a power system. For the power system, OPFs can be created with a variety of financial and technical objectives. Based on these findings, this paper proposes the peafowl optimization algorithm (POA). A powerful meta-heuristic optimization algorithm inspired by collective foraging activities among peafowl swarms. By balancing local exploitation with worldwide exploration, the OPF is able to strike a balance between exploration and exploitation. In order to solve optimization problems involving OPF, using the standard IEEE 14-bus and 57-bus electrical network, a POA has been employed to find the optimal values of the control variables. Further, there are five study cases, namely, reducing fuel costs, real energy losses, voltage skew, fuel cost as well as reducing energy loss and voltage skew, and reducing fuel costs as well as reducing energy loss and voltage deviation, as well as reducing emissions costs. The use of these cases facilitates a fair and comprehensive evaluation of the superiority and effectiveness of POA in comparison with the coot optimization algorithm (COOT), golden jackal optimization algorithm (GJO), heap-based optimizer (HPO), leader slime mold algorithm (LSMA), reptile search algorithm (RSA), sand cat optimization algorithm (SCSO), and the skills optimization algorithm (SOA). Based on simulations, POA has been demonstrated to outperform its rivals, including COOT, GJO, HPO, LSMA, RSA, SCSO, and SOA. In addition, the results indicate that POA is capable of identifying the most appropriate worldwide solutions. It is also successfully investigating preferred search locations, ensuring a fast convergence speed and enhancing the search engine’s capabilities.

Suggested Citation

  • Mohammed Hamouda Ali & Ali M. El-Rifaie & Ahmed A. F. Youssef & Vladimir N. Tulsky & Mohamed A. Tolba, 2023. "Techno-Economic Strategy for the Load Dispatch and Power Flow in Power Grids Using Peafowl Optimization Algorithm," Energies, MDPI, vol. 16(2), pages 1-29, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:2:p:846-:d:1032472
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/2/846/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/2/846/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mohammed Hamouda Ali & Ahmed Tijani Salawudeen & Salah Kamel & Habeeb Bello Salau & Monier Habil & Mokhtar Shouran, 2022. "Single- and Multi-Objective Modified Aquila Optimizer for Optimal Multiple Renewable Energy Resources in Distribution Network," Mathematics, MDPI, vol. 10(12), pages 1-39, June.
    2. Mohamed Farhat & Salah Kamel & Ahmed M. Atallah & Mohamed H. Hassan & Ahmed M. Agwa, 2022. "ESMA-OPF: Enhanced Slime Mould Algorithm for Solving Optimal Power Flow Problem," Sustainability, MDPI, vol. 14(4), pages 1-33, February.
    3. Saket Gupta & Narendra Kumar & Laxmi Srivastava & Hasmat Malik & Amjad Anvari-Moghaddam & Fausto Pedro García Márquez, 2021. "A Robust Optimization Approach for Optimal Power Flow Solutions Using Rao Algorithms," Energies, MDPI, vol. 14(17), pages 1-28, September.
    4. Warid Warid & Hashim Hizam & Norman Mariun & Noor Izzri Abdul-Wahab, 2016. "Optimal Power Flow Using the Jaya Algorithm," Energies, MDPI, vol. 9(9), pages 1-18, August.
    5. Usama Khaled & Ali M. Eltamaly & Abderrahmane Beroual, 2017. "Optimal Power Flow Using Particle Swarm Optimization of Renewable Hybrid Distributed Generation," Energies, MDPI, vol. 10(7), pages 1-14, July.
    6. Kingsuk Majumdar & Puja Das & Provas Kumar Roy & Subrata Banerjee, 2017. "Solving OPF Problems using Biogeography Based and Grey Wolf Optimization Techniques," International Journal of Energy Optimization and Engineering (IJEOE), IGI Global, vol. 6(3), pages 55-77, July.
    7. Elattar, Ehab E. & ElSayed, Salah K., 2019. "Modified JAYA algorithm for optimal power flow incorporating renewable energy sources considering the cost, emission, power loss and voltage profile improvement," Energy, Elsevier, vol. 178(C), pages 598-609.
    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. Liang, Hejun & Pirouzi, Sasan, 2024. "Energy management system based on economic Flexi-reliable operation for the smart distribution network including integrated energy system of hydrogen storage and renewable sources," Energy, Elsevier, vol. 293(C).
    2. Zbigniew Kłosowski & Łukasz Mazur, 2023. "Influence of the Type of Receiver on Electrical Energy Losses in Power Grids," Energies, MDPI, vol. 16(15), pages 1-22, July.

    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. Salah K. ElSayed & Ehab E. Elattar, 2021. "Slime Mold Algorithm for Optimal Reactive Power Dispatch Combining with Renewable Energy Sources," Sustainability, MDPI, vol. 13(11), pages 1-25, May.
    2. Gracita Batista Rosas & Elizete Maria Lourenço & Djalma Mosqueira Falcão & Thelma Solange Piazza Fernandes, 2019. "An Expeditious Methodology to Assess the Effects of Intermittent Generation on Power Systems," Energies, MDPI, vol. 12(6), pages 1-18, March.
    3. Abdullah Khan & Hashim Hizam & Noor Izzri Abdul-Wahab & Mohammad Lutfi Othman, 2020. "Solution of Optimal Power Flow Using Non-Dominated Sorting Multi Objective Based Hybrid Firefly and Particle Swarm Optimization Algorithm," Energies, MDPI, vol. 13(16), pages 1-24, August.
    4. Saket Gupta & Narendra Kumar & Laxmi Srivastava & Hasmat Malik & Alberto Pliego Marugán & Fausto Pedro García Márquez, 2021. "A Hybrid Jaya–Powell’s Pattern Search Algorithm for Multi-Objective Optimal Power Flow Incorporating Distributed Generation," Energies, MDPI, vol. 14(10), pages 1-24, May.
    5. 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.
    6. Mohamed H. Hassan & Salah Kamel & Ali Selim & Tahir Khurshaid & José Luis Domínguez-García, 2021. "A Modified Rao-2 Algorithm for Optimal Power Flow Incorporating Renewable Energy Sources," Mathematics, MDPI, vol. 9(13), pages 1-22, June.
    7. Gonggui Chen & Zhengmei Lu & Zhizhong Zhang, 2018. "Improved Krill Herd Algorithm with Novel Constraint Handling Method for Solving Optimal Power Flow Problems," Energies, MDPI, vol. 11(1), pages 1-27, January.
    8. 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.
    9. Omaji Samuel & Nadeem Javaid & Mahmood Ashraf & Farruh Ishmanov & Muhammad Khalil Afzal & Zahoor Ali Khan, 2018. "Jaya based Optimization Method with High Dispatchable Distributed Generation for Residential Microgrid," Energies, MDPI, vol. 11(6), pages 1-29, June.
    10. Ahmed Al Ameri & Aouchenni Ounissa & Cristian Nichita & Aouzellag Djamal, 2017. "Power Loss Analysis for Wind Power Grid Integration Based on Weibull Distribution," Energies, MDPI, vol. 10(4), pages 1-16, April.
    11. Skolfield, J. Kyle & Escobedo, Adolfo R., 2022. "Operations research in optimal power flow: A guide to recent and emerging methodologies and applications," European Journal of Operational Research, Elsevier, vol. 300(2), pages 387-404.
    12. Anurag Gautam & Ibraheem & Gulshan Sharma & Mohammad F. Ahmer & Narayanan Krishnan, 2023. "Methods and Methodologies for Congestion Alleviation in the DPS: A Comprehensive Review," Energies, MDPI, vol. 16(4), pages 1-28, February.
    13. Brandon Cortés-Caicedo & Luis Fernando Grisales-Noreña & Oscar Danilo Montoya & Miguel Angel Rodriguez-Cabal & Javier Alveiro Rosero, 2022. "Energy Management System for the Optimal Operation of PV Generators in Distribution Systems Using the Antlion Optimizer: A Colombian Urban and Rural Case Study," Sustainability, MDPI, vol. 14(23), pages 1-35, December.
    14. Murtadha Al-Kaabi & Virgil Dumbrava & Mircea Eremia, 2022. "A Slime Mould Algorithm Programming for Solving Single and Multi-Objective Optimal Power Flow Problems with Pareto Front Approach: A Case Study of the Iraqi Super Grid High Voltage," Energies, MDPI, vol. 15(20), pages 1-33, October.
    15. José Adriano da Costa & David Alves Castelo Branco & Max Chianca Pimentel Filho & Manoel Firmino de Medeiros Júnior & Neilton Fidelis da Silva, 2019. "Optimal Sizing of Photovoltaic Generation in Radial Distribution Systems Using Lagrange Multipliers," Energies, MDPI, vol. 12(9), pages 1-19, May.
    16. 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).
    17. Mengqi Zhao & Xiaoling Wang & Jia Yu & Lei Bi & Yao Xiao & Jun Zhang, 2020. "Optimization of Construction Duration and Schedule Robustness Based on Hybrid Grey Wolf Optimizer with Sine Cosine Algorithm," Energies, MDPI, vol. 13(1), pages 1-17, January.
    18. Elattar, Ehab E. & ElSayed, Salah K., 2019. "Modified JAYA algorithm for optimal power flow incorporating renewable energy sources considering the cost, emission, power loss and voltage profile improvement," Energy, Elsevier, vol. 178(C), pages 598-609.
    19. Mohamed A. M. Shaheen & Hany M. Hasanien & Said F. Mekhamer & Mohammed H. Qais & Saad Alghuwainem & Zia Ullah & Marcos Tostado-Véliz & Rania A. Turky & Francisco Jurado & Mohamed R. Elkadeem, 2022. "Probabilistic Optimal Power Flow Solution Using a Novel Hybrid Metaheuristic and Machine Learning Algorithm," Mathematics, MDPI, vol. 10(17), pages 1-23, August.
    20. Jamal, Raheela & Zhang, Junzhe & Men, Baohui & Khan, Noor Habib & Ebeed, Mohamed & Jamal, Tanzeela & Mohamed, Emad A., 2024. "Chaotic-quasi-oppositional-phasor based multi populations gorilla troop optimizer for optimal power flow solution," Energy, Elsevier, vol. 301(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:jeners:v:16:y:2023:i:2:p:846-:d:1032472. 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.