IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i6p1328-d148583.html
   My bibliography  Save this article

Modified Cuckoo Search Algorithm: A Novel Method to Minimize the Fuel Cost

Author

Listed:
  • Thang Trung Nguyen

    (Power System Optimization Research Group, Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam)

  • Dieu Ngoc Vo

    (Department of Power Systems, Ho Chi Minh City University of Technology, Ho Chi Minh City 700000, Vietnam)

  • Nguyen Vu Quynh

    (Department of Electrical Engineering, Lac Hong University, Bien Hoa 810000, Vietnam)

  • Le Van Dai

    (Institute of Research and Development, Duy Tan University, Danang 550000, Vietnam
    Office of Science Research and Development, Lac Hong University, Bien Hoa 810000, Vietnam)

Abstract

Economic load dispatch (ELD) is an important optimization problem for operating and controlling modern power systems, and if ELD is effectively executed, power systems work stably and economically. The main objective of this paper is to develop a novel method to solve the ELD with the purpose of minimizing the total fuel cost of all available generating units while requirements are to satisfy all constraints regarding thermal units, generators, and transmission power networks. The proposed high performance cuckoo search algorithm (HPCSA) is developed from the efficient technique for the second new solution generation of conventional cuckoo search algorithm (CCSA), called adaptive mutation technique. This proposed technique diversifies the local search ability based on a new comparison criterion. The HPCSA is verified on difference systems under special conditions, namely the 10-unit system with multi fuels, 15-unit system considering prohibited operating zones, and three IEEE systems with 30, 57, and 118 buses considering transmission power network constraints. The specific evaluation of the HPCSA is compared to that of Lagrange optimization-based methods (LMS), neural network-based methods (NNMS), CCSA, and other popular methods such as Particle swarm optimization (PSO) variants, Differential evolution (DE) variants, Genetic Algorithm (GA) variants, and state-of-the-art methods. In comparison with CCSA, the proposed method is always more effective and more robust since the proposed method can find most solutions with better quality and faster convergence speed. In comparison with LMS and NNMS, the proposed method can also find solutions with approximate or equal quality. In comparison with popular methods and state-of-the-art methods, the proposed method has more potential since it can reach faster convergence to valid solutions with approximate or better quality. Consequently, it can be concluded that the proposed HPCSA is an effective optimization tool for dealing with ELD problems.

Suggested Citation

  • Thang Trung Nguyen & Dieu Ngoc Vo & Nguyen Vu Quynh & Le Van Dai, 2018. "Modified Cuckoo Search Algorithm: A Novel Method to Minimize the Fuel Cost," Energies, MDPI, vol. 11(6), pages 1-27, May.
  • Handle: RePEc:gam:jeners:v:11:y:2018:i:6:p:1328-:d:148583
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/6/1328/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/11/6/1328/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    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. 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.
    2. Ayman Alhejji & Alban Kuriqi & Jakub Jurasz & Farag K. Abo-Elyousr, 2021. "Energy Harvesting and Water Saving in Arid Regions via Solar PV Accommodation in Irrigation Canals," Energies, MDPI, vol. 14(9), pages 1-24, May.
    3. Thanh Long Duong & Phuong Duy Nguyen & Van-Duc Phan & Dieu Ngoc Vo & Thang Trung Nguyen, 2019. "Optimal Load Dispatch in Competitive Electricity Market by Using Different Models of Hopfield Lagrange Network," Energies, MDPI, vol. 12(15), pages 1-24, July.
    4. Oludamilare Bode Adewuyi & Ayooluwa Peter Adeagbo & Isaiah Gbadegesin Adebayo & Harun Or Rashid Howlader & Yanxia Sun, 2021. "Modified Analytical Approach for PV-DGs Integration into a Radial Distribution Network Considering Loss Sensitivity and Voltage Stability," Energies, MDPI, vol. 14(22), pages 1-20, November.
    5. Thang Trung Nguyen & Dieu Ngoc Vo & Hai Van Tran & Le Van Dai, 2019. "Optimal Dispatch of Reactive Power Using Modified Stochastic Fractal Search Algorithm," Complexity, Hindawi, vol. 2019, pages 1-28, May.

    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. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. Jonathan Oesterle & Lionel Amodeo & Farouk Yalaoui, 2019. "A comparative study of Multi-Objective Algorithms for the Assembly Line Balancing and Equipment Selection Problem under consideration of Product Design Alternatives," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 1021-1046, March.
    7. 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).
    8. 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.
    9. 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.
    10. 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.
    11. Muhammad Bachtiar Nappu & Ardiaty Arief & Willy Akbar Ajami, 2023. "Energy Efficiency in Modern Power Systems Utilizing Advanced Incremental Particle Swarm Optimization–Based OPF," Energies, MDPI, vol. 16(4), pages 1-13, February.
    12. Panda, Mitali & Nayak, Yogesh Kumar, 2022. "Impact analysis of renewable energy Distributed Generation in deregulated electricity markets: A Context of Transmission Congestion Problem," Energy, Elsevier, vol. 254(PC).
    13. 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.
    14. 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).
    15. Ali, Ehab S., 2015. "Speed control of induction motor supplied by wind turbine via Imperialist Competitive Algorithm," Energy, Elsevier, vol. 89(C), pages 593-600.
    16. 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.
    17. Ali S. Alghamdi, 2022. "Greedy Sine-Cosine Non-Hierarchical Grey Wolf Optimizer for Solving Non-Convex Economic Load Dispatch Problems," Energies, MDPI, vol. 15(11), pages 1-19, May.
    18. 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.
    19. Samal, Rajat Kanti & Tripathy, M., 2019. "A novel distance metric for evaluating impact of wind integration on power systems," Renewable Energy, Elsevier, vol. 140(C), pages 722-736.
    20. Morshed, Mohammad Javad & Hmida, Jalel Ben & Fekih, Afef, 2018. "A probabilistic multi-objective approach for power flow optimization in hybrid wind-PV-PEV systems," Applied Energy, Elsevier, vol. 211(C), pages 1136-1149.

    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:11:y:2018:i:6:p:1328-:d:148583. 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.