IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i22p8328-d966191.html
   My bibliography  Save this article

Single and Multi-Objective Optimal Power Flow Based on Hunger Games Search with Pareto Concept Optimization

Author

Listed:
  • Murtadha Al-Kaabi

    (Department of Power Systems, Faculty of Energy, University Politehnica of Bucharest, 060029 Bucharest, Romania)

  • Virgil Dumbrava

    (Department of Power Systems, Faculty of Energy, University Politehnica of Bucharest, 060029 Bucharest, Romania)

  • Mircea Eremia

    (Department of Power Systems, Faculty of Energy, University Politehnica of Bucharest, 060029 Bucharest, Romania)

Abstract

In this study, a new meta-heuristic optimization method inspired by the behavioral choices of animals and hunger-driven activities, called hunger games search (HGS), is suggested to solve and formulate the single- and multi-objective optimal power flow problem in power systems. The main aim of this study is to optimize the objective functions, which are total fuel cost of generator, active power losses in transmission lines, total emission issued by fossil-fueled thermal units, voltage deviation at PQ bus, and voltage stability index. The proposed HGS approach is optimal and easy, avoids stagnation in local optima, and can solve multi-constrained objectives. Various single-and multi-objective (conflicting) functions were proposed simultaneously to solve OPF problems. The proposed algorithm (HGS) was developed to solve the multi-objective function, called the multi-objective hunger game search (MOHGS), by incorporating the proposed optimization (HGS) with Pareto optimization. The fuzzy membership theory is the function responsible to extract the best compromise solution from non-dominated solutions. The crowding distance is the strategies carried out to determine and ordering the Pareto non-dominated set. Two standard tests (IEEE 30 bus and IEEE 57 bus systems) are the power systems that were applied to investigate the performance of the proposed approaches (HGS and MOHGS) for solving single and multiple objective functions with 25 studied cases using MATLAB software. The numerical results obtained by the proposed approaches (HGS and MOHGS) were compared to other optimization algorithms in the literature. The numerical results confirmed the efficiency and superiority of the proposed approaches by achieving an optimal solution and giving the faster convergence characteristics in single objective functions and extracting the best compromise solution and well-distributed Pareto front solutions in multi-objective functions.

Suggested Citation

  • Murtadha Al-Kaabi & Virgil Dumbrava & Mircea Eremia, 2022. "Single and Multi-Objective Optimal Power Flow Based on Hunger Games Search with Pareto Concept Optimization," Energies, MDPI, vol. 15(22), pages 1-31, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:22:p:8328-:d:966191
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/22/8328/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/22/8328/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Meng, Anbo & Zeng, Cong & Wang, Peng & Chen, De & Zhou, Tianmin & Zheng, Xiaoying & Yin, Hao, 2021. "A high-performance crisscross search based grey wolf optimizer for solving optimal power flow problem," Energy, Elsevier, vol. 225(C).
    2. Ghasemi, Mojtaba & Ghavidel, Sahand & Ghanbarian, Mohammad Mehdi & Gharibzadeh, Masihallah & Azizi Vahed, Ali, 2014. "Multi-objective optimal power flow considering the cost, emission, voltage deviation and power losses using multi-objective modified imperialist competitive algorithm," Energy, Elsevier, vol. 78(C), pages 276-289.
    3. Nguyen, Thang Trung, 2019. "A high performance social spider optimization algorithm for optimal power flow solution with single objective optimization," Energy, Elsevier, vol. 171(C), pages 218-240.
    4. Sirote Khunkitti & Apirat Siritaratiwat & Suttichai Premrudeepreechacharn, 2021. "Multi-Objective Optimal Power Flow Problems Based on Slime Mould Algorithm," Sustainability, MDPI, vol. 13(13), pages 1-21, July.
    5. 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.
    6. Abdullah Khan & Hashim Hizam & Noor Izzri bin Abdul Wahab & Mohammad Lutfi Othman, 2020. "Optimal power flow using hybrid firefly and particle swarm optimization algorithm," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-21, August.
    7. David H. Wolpert & William G. Macready, 1995. "No Free Lunch Theorems for Search," Working Papers 95-02-010, Santa Fe Institute.
    8. 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.
    9. Xuanhu He & Wei Wang & Jiuchun Jiang & Lijie Xu, 2015. "An Improved Artificial Bee Colony Algorithm and Its Application to Multi-Objective Optimal Power Flow," Energies, MDPI, vol. 8(4), pages 1-26, March.
    10. Li, Shuijia & Gong, Wenyin & Wang, Ling & Yan, Xuesong & Hu, Chengyu, 2020. "Optimal power flow by means of improved adaptive differential evolution," Energy, Elsevier, vol. 198(C).
    11. Li, Shuijia & Gong, Wenyin & Hu, Chengyu & Yan, Xuesong & Wang, Ling & Gu, Qiong, 2021. "Adaptive constraint differential evolution for optimal power flow," Energy, Elsevier, vol. 235(C).
    12. Niknam, Taher & Narimani, Mohammad rasoul & Jabbari, Masoud & Malekpour, Ahmad Reza, 2011. "A modified shuffle frog leaping algorithm for multi-objective optimal power flow," Energy, Elsevier, vol. 36(11), pages 6420-6432.
    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. Fangfang Zheng & Xiaofang Meng & Tiefeng Xu & Yongchang Sun & Hui Wang, 2023. "Optimization Method of Energy Storage Configuration for Distribution Network with High Proportion of Photovoltaic Based on Source–Load Imbalance," Sustainability, MDPI, vol. 15(13), pages 1-17, July.
    2. 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).

    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. 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.
    2. 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).
    3. Ali S. Alghamdi, 2022. "Optimal Power Flow in Wind–Photovoltaic Energy Regulation Systems Using a Modified Turbulent Water Flow-Based Optimization," Sustainability, MDPI, vol. 14(24), pages 1-27, December.
    4. 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.
    5. Sherif S. M. Ghoneim & Mohamed F. Kotb & Hany M. Hasanien & Mosleh M. Alharthi & Attia A. El-Fergany, 2021. "Cost Minimizations and Performance Enhancements of Power Systems Using Spherical Prune Differential Evolution Algorithm Including Modal Analysis," Sustainability, MDPI, vol. 13(14), pages 1-15, July.
    6. Hao Su & Qun Niu & Zhile Yang, 2023. "Optimal Power Flow Using Improved Cross-Entropy Method," Energies, MDPI, vol. 16(14), pages 1-33, July.
    7. Weng, Xuemeng & Xuan, Ping & Heidari, Ali Asghar & Cai, Zhennao & Chen, Huiling & Mansour, Romany F. & Ragab, Mahmoud, 2023. "A vertical and horizontal crossover sine cosine algorithm with pattern search for optimal power flow in power systems," Energy, Elsevier, vol. 271(C).
    8. Li, Shuijia & Gong, Wenyin & Hu, Chengyu & Yan, Xuesong & Wang, Ling & Gu, Qiong, 2021. "Adaptive constraint differential evolution for optimal power flow," Energy, Elsevier, vol. 235(C).
    9. Shaheen, Abdullah M. & El-Sehiemy, Ragab A. & Alharthi, Mosleh M. & Ghoneim, Sherif S.M. & Ginidi, Ahmed R., 2021. "Multi-objective jellyfish search optimizer for efficient power system operation based on multi-dimensional OPF framework," Energy, Elsevier, vol. 237(C).
    10. Gonggui Chen & Xingting Yi & Zhizhong Zhang & Hangtian Lei, 2018. "Solving the Multi-Objective Optimal Power Flow Problem Using the Multi-Objective Firefly Algorithm with a Constraints-Prior Pareto-Domination Approach," Energies, MDPI, vol. 11(12), pages 1-18, December.
    11. Amr Khaled Khamees & Almoataz Y. Abdelaziz & Makram R. Eskaros & Mahmoud A. Attia & Mariam A. Sameh, 2022. "Optimal Power Flow with Stochastic Renewable Energy Using Three Mixture Component Distribution Functions," Sustainability, MDPI, vol. 15(1), pages 1-21, December.
    12. Ragab El-Sehiemy & Abdallah Elsayed & Abdullah Shaheen & Ehab Elattar & Ahmed Ginidi, 2021. "Scheduling of Generation Stations, OLTC Substation Transformers and VAR Sources for Sustainable Power System Operation Using SNS Optimizer," Sustainability, MDPI, vol. 13(21), pages 1-24, October.
    13. Zhang, Jingrui & Wang, Silu & Tang, Qinghui & Zhou, Yulu & Zeng, Tao, 2019. "An improved NSGA-III integrating adaptive elimination strategy to solution of many-objective optimal power flow problems," Energy, Elsevier, vol. 172(C), pages 945-957.
    14. Li, Shuijia & Gong, Wenyin & Wang, Ling & Yan, Xuesong & Hu, Chengyu, 2020. "Optimal power flow by means of improved adaptive differential evolution," Energy, Elsevier, vol. 198(C).
    15. Mahmoud A. Ali & Salah Kamel & Mohamed H. Hassan & Emad M. Ahmed & Mohana Alanazi, 2022. "Optimal Power Flow Solution of Power Systems with Renewable Energy Sources Using White Sharks Algorithm," Sustainability, MDPI, vol. 14(10), pages 1-21, May.
    16. Shaheen, Abdullah M. & El-Sehiemy, Ragab A. & Hasanien, Hany M. & Ginidi, Ahmed R., 2022. "An improved heap optimization algorithm for efficient energy management based optimal power flow model," Energy, Elsevier, vol. 250(C).
    17. El Sehiemy, Ragab A. & Selim, F. & Bentouati, Bachir & Abido, M.A., 2020. "A novel multi-objective hybrid particle swarm and salp optimization algorithm for technical-economical-environmental operation in power systems," Energy, Elsevier, vol. 193(C).
    18. Zhang, Xiao & Wu, Zhi & Sun, Qirun & Gu, Wei & Zheng, Shu & Zhao, Jingtao, 2024. "Application and progress of artificial intelligence technology in the field of distribution network voltage Control：A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    19. Fitiwi, Desta Z. & Olmos, L. & Rivier, M. & de Cuadra, F. & Pérez-Arriaga, I.J., 2016. "Finding a representative network losses model for large-scale transmission expansion planning with renewable energy sources," Energy, Elsevier, vol. 101(C), pages 343-358.
    20. 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.

    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:15:y:2022:i:22:p:8328-:d:966191. 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.