IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v250y2022ics0360544222006983.html
   My bibliography  Save this article

An improved heap optimization algorithm for efficient energy management based optimal power flow model

Author

Listed:
  • Shaheen, Abdullah M.
  • El-Sehiemy, Ragab A.
  • Hasanien, Hany M.
  • Ginidi, Ahmed R.

Abstract

The optimal power flow is considered as a crucial tool in the power systems’ operation and planning. To demonstrate, it aims at minimizing the operational costs of energy production and transmission by adjusting control variables with maintaining economic, operational, and environmental constraints. This article proposes and scrutinizes an Improved Heap-based Optimization Algorithm as a novel technique that successfully enhances the performance of a recently algorithm, namely Heap-based Optimization algorithm to address the optimal power flow problem. In the improved optimizer, an effective exploitation feature is emerged with the conventional version to improve its performance by enhancing the searching around the leader position. This enhancement can avoid being trapped in a local optimum and increase its global search capabilities. For the sake of practicality, the optimizers are developed with diverse objectives of the optimal power flow problem with minimization of fuel cost, emission amount, and transmission power losses with additional restrictions in real power systems that include valve-point effect and security constraints. A proposed multi-objective, improved heap optimization algorithm is investigated to solve multiobjective cases studied. The proposed multi-objective is developed based on the Pareto concept. Three standard systems: IEEE 57-bus system, a large scale 118-bus system, and a practical System are utilized to reveal the suitability and performance of the proposed technique in solving the optimal power flow problem. To illustrate the effectiveness of the proposed optimizer in handling non-convex and diverse scale optimization problems, a comparative analysis has been illustrated with those in the literature.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:250:y:2022:i:c:s0360544222006983
    DOI: 10.1016/j.energy.2022.123795
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222006983
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.123795?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Chou, Jui-Sheng & Truong, Dinh-Nhat, 2020. "Multiobjective optimization inspired by behavior of jellyfish for solving structural design problems," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    2. Yuan, Xiaohui & Zhang, Binqiao & Wang, Pengtao & Liang, Ji & Yuan, Yanbin & Huang, Yuehua & Lei, Xiaohui, 2017. "Multi-objective optimal power flow based on improved strength Pareto evolutionary algorithm," Energy, Elsevier, vol. 122(C), pages 70-82.
    3. 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).
    4. 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.
    5. 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.
    6. 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).
    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.
    8. 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.
    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. Alencar, Marina Valença & da Silva, Diego Nunes & Nepomuceno, Leonardo & Martins, André Christóvão Pio & Balbo, Antonio Roberto & Soler, Edilaine Martins, 2024. "Discrete optimal power flow with prohibited zones, multiple-fuel options, and practical operational rules for control devices," Applied Energy, Elsevier, vol. 358(C).
    2. Lucas do Carmo Yamaguti & Juan Manuel Home-Ortiz & Mahdi Pourakbari-Kasmaei & José Roberto Sanches Mantovani, 2023. "Economic/Environmental Optimal Power Flow Using a Multiobjective Convex Formulation," Energies, MDPI, vol. 16(12), pages 1-21, June.
    3. Adedayo Owosuhi & Yskandar Hamam & Josiah Munda, 2023. "Maximizing the Integration of a Battery Energy Storage System–Photovoltaic Distributed Generation for Power System Harmonic Reduction: An Overview," Energies, MDPI, vol. 16(6), pages 1-22, March.
    4. Gengli Song & Hua Wei, 2022. "Distributionally Robust Multi-Energy Dynamic Optimal Power Flow Considering Water Spillage with Wasserstein Metric," Energies, MDPI, vol. 15(11), pages 1-18, May.
    5. Ragab El-Sehiemy & Abdullah Shaheen & Ahmed Ginidi & Mostafa Elhosseini, 2022. "A Honey Badger Optimization for Minimizing the Pollutant Environmental Emissions-Based Economic Dispatch Model Integrating Combined Heat and Power Units," Energies, MDPI, vol. 15(20), pages 1-22, October.
    6. Martín M. Sánchez-Mora & Walter M. Villa-Acevedo & Jesús M. López-Lezama, 2023. "Multi-Area and Multi-Period Optimal Reactive Power Dispatch in Electric Power Systems," Energies, MDPI, vol. 16(17), pages 1-24, September.
    7. Mohamed A. M. Shaheen & Zia Ullah & Mohammed H. Qais & Hany M. Hasanien & Kian J. Chua & Marcos Tostado-Véliz & Rania A. Turky & Francisco Jurado & Mohamed R. Elkadeem, 2022. "Solution of Probabilistic Optimal Power Flow Incorporating Renewable Energy Uncertainty Using a Novel Circle Search Algorithm," Energies, MDPI, vol. 15(21), pages 1-19, November.

    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. 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).
    2. 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).
    3. 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.
    4. Abdullah Shaheen & Ahmed Ginidi & Ragab El-Sehiemy & Abdallah Elsayed & Ehab Elattar & Hassen T. Dorrah, 2022. "Developed Gorilla Troops Technique for Optimal Power Flow Problem in Electrical Power Systems," Mathematics, MDPI, vol. 10(10), pages 1-29, May.
    5. 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.
    6. 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).
    7. 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).
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. Zhang, Jingrui & Li, Zhuoyun & Wang, Beibei, 2021. "Within-day rolling optimal scheduling problem for active distribution networks by multi-objective evolutionary algorithm based on decomposition integrating with thought of simulated annealing," Energy, Elsevier, vol. 223(C).
    15. 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.
    16. 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.
    17. Sheila Mahapatra & Nitin Malik & Saurav Raj & Mohan Krishna Srinivasan, 2022. "Constrained optimal power flow and optimal TCSC allocation using hybrid cuckoo search and ant lion optimizer," 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(2), pages 721-734, April.
    18. 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).
    19. Zelan Li & Yijia Cao & Le Van Dai & Xiaoliang Yang & Thang Trung Nguyen, 2019. "Optimal Power Flow for Transmission Power Networks Using a Novel Metaheuristic Algorithm," Energies, MDPI, vol. 12(22), pages 1-36, November.
    20. Qian, Tong & Tang, Wenhu & Wu, Qinghua, 2020. "A fully decentralized dual consensus method for carbon trading power dispatch with wind power," Energy, Elsevier, vol. 203(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:eee:energy:v:250:y:2022:i:c:s0360544222006983. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.