IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i23p6087-d1535883.html
   My bibliography  Save this article

Hyper-FDB-INFO Algorithm for Optimal Placement and Sizing of FACTS Devices in Wind Power-Integrated Optimal Power Flow Problem

Author

Listed:
  • Bekir Emre Altun

    (Department of Electricity and Energy, Technical Sciences Vocational School, Amasya University, Amasya 05100, Türkiye)

  • Enes Kaymaz

    (Electrical and Electronics Engineering, Engineering Faculty, Duzce University, Duzce 81620, Türkiye)

  • Mustafa Dursun

    (Electrical and Electronics Engineering, Engineering Faculty, Duzce University, Duzce 81620, Türkiye)

  • Ugur Guvenc

    (Electrical and Electronics Engineering, Engineering Faculty, Duzce University, Duzce 81620, Türkiye)

Abstract

In this study, firstly, the balance between the exploration and exploitation capabilities of the weighted mean of vectors (INFO) algorithm was developed using the fitness–distance balance (FDB) method. Then, the FDB-INFO algorithm was developed with a hyper-heuristic method to create the beginning optimal population by using Linear Population Reduction Success History-based Adaptive Differential Evolution (LSHADE) and a novel Hyper-FDB-INFO algorithm was presented. Finally, the developed Hyper-FDB-INFO algorithm was applied to solve the optimal placement and sizing of FACTS devices for the optimal power flow (OPF) problem incorporating wind energy sources. Moreover, determining the placement and sizing of FACTS devices is an additional problem to minimize the total cost of generation and reducing the power losses of the power system. The experimental results showed that the Hyper-FDB-INFO algorithm is a more effective solver than the SHADE-SF, INFO, FDB-INFO and Hyper-INFO algorithms for wind power and FACTS devices integrating the OPF problem.

Suggested Citation

  • Bekir Emre Altun & Enes Kaymaz & Mustafa Dursun & Ugur Guvenc, 2024. "Hyper-FDB-INFO Algorithm for Optimal Placement and Sizing of FACTS Devices in Wind Power-Integrated Optimal Power Flow Problem," Energies, MDPI, vol. 17(23), pages 1-25, December.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:23:p:6087-:d:1535883
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/23/6087/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/23/6087/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Panda, Ambarish & Tripathy, M., 2015. "Security constrained optimal power flow solution of wind-thermal generation system using modified bacteria foraging algorithm," Energy, Elsevier, vol. 93(P1), pages 816-827.
    2. Khaled Nusair & Feras Alasali, 2020. "Optimal Power Flow Management System for a Power Network with Stochastic Renewable Energy Resources Using Golden Ratio Optimization Method," Energies, MDPI, vol. 13(14), pages 1-46, July.
    3. Amal Amin Mohamed & Salah Kamel & Mohamed H. Hassan & Mohamed I. Mosaad & Mansour Aljohani, 2022. "Optimal Power Flow Analysis Based on Hybrid Gradient-Based Optimizer with Moth–Flame Optimization Algorithm Considering Optimal Placement and Sizing of FACTS/Wind Power," Mathematics, MDPI, vol. 10(3), pages 1-31, January.
    4. Ozkaya, Burcin, 2024. "Enhanced growth optimizer algorithm with dynamic fitness-distance balance method for solution of security-constrained optimal power flow problem in the presence of stochastic wind and solar energy," Applied Energy, Elsevier, vol. 368(C).
    5. Hasanien, Hany M. & Alsaleh, Ibrahim & Alassaf, Abdullah & Alateeq, Ayoob, 2023. "Enhanced coati optimization algorithm-based optimal power flow including renewable energy uncertainties and electric vehicles," Energy, Elsevier, vol. 283(C).
    Full references (including those not matched with items on IDEAS)

    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. Ozkaya, Burcin, 2024. "Enhanced growth optimizer algorithm with dynamic fitness-distance balance method for solution of security-constrained optimal power flow problem in the presence of stochastic wind and solar energy," Applied Energy, Elsevier, vol. 368(C).
    2. Feras Alasali & Mohammad Salameh & Ali Semrin & Khaled Nusair & Naser El-Naily & William Holderbaum, 2022. "Optimal Controllers and Configurations of 100% PV and Energy Storage Systems for a Microgrid: The Case Study of a Small Town in Jordan," Sustainability, MDPI, vol. 14(13), pages 1-20, July.
    3. Rafiq Asghar & Francesco Riganti Fulginei & Hamid Wadood & Sarmad Saeed, 2023. "A Review of Load Frequency Control Schemes Deployed for Wind-Integrated Power Systems," Sustainability, MDPI, vol. 15(10), pages 1-29, May.
    4. Rahmani, Shima & Amjady, Nima, 2017. "A new optimal power flow approach for wind energy integrated power systems," Energy, Elsevier, vol. 134(C), pages 349-359.
    5. Khaled Nusair & Lina Alhmoud, 2020. "Application of Equilibrium Optimizer Algorithm for Optimal Power Flow with High Penetration of Renewable Energy," Energies, MDPI, vol. 13(22), pages 1-35, November.
    6. Yuan Liu & Qinliang Tan & Jian Han & Mingxin Guo, 2021. "Energy-Water-Carbon Nexus Optimization for the Path of Achieving Carbon Emission Peak in China Considering Multiple Uncertainties: A Case Study in Inner Mongolia," Energies, MDPI, vol. 14(4), pages 1-21, February.
    7. Rambabu Muppidi & Ramakrishna S. S. Nuvvula & S. M. Muyeen & SK. A. Shezan & Md. Fatin Ishraque, 2022. "Optimization of a Fuel Cost and Enrichment of Line Loadability for a Transmission System by Using Rapid Voltage Stability Index and Grey Wolf Algorithm Technique," Sustainability, MDPI, vol. 14(7), pages 1-19, April.
    8. Yaçine Merrad & Mohamed Hadi Habaebi & Siti Fauziah Toha & Md. Rafiqul Islam & Teddy Surya Gunawan & Mokhtaria Mesri, 2022. "Fully Decentralized, Cost-Effective Energy Demand Response Management System with a Smart Contracts-Based Optimal Power Flow Solution for Smart Grids," Energies, MDPI, vol. 15(12), pages 1-27, June.
    9. Lei Zhang & Rui Tang, 2023. "Dispatch for a Continuous-Time Microgrid Based on a Modified Differential Evolution Algorithm," Mathematics, MDPI, vol. 11(2), pages 1-21, January.
    10. Ahmed I. Omar & Ziad M. Ali & Mostafa Al-Gabalawy & Shady H. E. Abdel Aleem & Mujahed Al-Dhaifallah, 2020. "Multi-Objective Environmental Economic Dispatch of an Electricity System Considering Integrated Natural Gas Units and Variable Renewable Energy Sources," Mathematics, MDPI, vol. 8(7), pages 1-37, July.
    11. Ghareeb Moustafa & Mostafa Elshahed & Ahmed R. Ginidi & Abdullah M. Shaheen & Hany S. E. Mansour, 2023. "A Gradient-Based Optimizer with a Crossover Operator for Distribution Static VAR Compensator (D-SVC) Sizing and Placement in Electrical Systems," Mathematics, MDPI, vol. 11(5), pages 1-30, February.
    12. 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).
    13. 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).
    14. Khaled Nusair & Feras Alasali, 2020. "Optimal Power Flow Management System for a Power Network with Stochastic Renewable Energy Resources Using Golden Ratio Optimization Method," Energies, MDPI, vol. 13(14), pages 1-46, July.
    15. Mohammad H. Nadimi-Shahraki & Ali Fatahi & Hoda Zamani & Seyedali Mirjalili, 2022. "Binary Approaches of Quantum-Based Avian Navigation Optimizer to Select Effective Features from High-Dimensional Medical Data," Mathematics, MDPI, vol. 10(15), pages 1-30, August.
    16. Zhu, Jun-Hua & Wang, Jie-Sheng & Zheng, Yue & Zhang, Xing-Yue & Liu, Xun & Wang, Xiao-Tian & Zhang, Song-Bo, 2024. "Two-stage coevolutionary constrained multi-objective optimization algorithm for solving optimal power flow problems with wind power and FACTS devices," Renewable Energy, Elsevier, vol. 232(C).
    17. 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.
    18. Feras Alasali & Husam Foudeh & Esraa Mousa Ali & Khaled Nusair & William Holderbaum, 2021. "Forecasting and Modelling the Uncertainty of Low Voltage Network Demand and the Effect of Renewable Energy Sources," Energies, MDPI, vol. 14(8), pages 1-31, April.
    19. Qun Niu & Ming You & Zhile Yang & Yang Zhang, 2021. "Economic Emission Dispatch Considering Renewable Energy Resources—A Multi-Objective Cross Entropy Optimization Approach," Sustainability, MDPI, vol. 13(10), pages 1-33, May.
    20. Hasanien, Hany M. & Alsaleh, Ibrahim & Tostado-Véliz, Marcos & Zhang, Miao & Alateeq, Ayoob & Jurado, Francisco & Alassaf, Abdullah, 2024. "Hybrid particle swarm and sea horse optimization algorithm-based optimal reactive power dispatch of power systems comprising electric vehicles," Energy, Elsevier, vol. 286(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:17:y:2024:i:23:p:6087-:d:1535883. 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.