IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i6p1075-d215666.html
   My bibliography  Save this article

A Novel Social Spider Optimization Algorithm for Large-Scale Economic Load Dispatch Problem

Author

Listed:
  • Le Chi Kien

    (Faculty of Electrical and Electronics Engineering, Ho Chi Minh City University of Technology and Education, Ho Chi Minh City 700000, Vietnam)

  • Thang Trung Nguyen

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

  • Chiem Trong Hien

    (Faculty of Electrical and Electronic Technology, Ho Chi Minh City University of Food Industry, Ho Chi Minh City 700000, Vietnam)

  • Minh Quan Duong

    (Department of Electrical Engineering, The University of Da Nang, University of Science and Technology, Da Nang city 550000, Vietnam)

Abstract

The paper develops an improved social spider optimization algorithm (ISSO) for finding optimal solutions of economic load dispatch (ELD) problems. Different ELD problem study cases can bring huge challenges for testing the robustness and effectiveness of the proposed ISSO method since discontinuous objective functions as well as complicated constraints are taken into account. The improved method is different from original social spider optimization algorithm (SSSO) by performing several modifications directly related to three processes of new solution generation. Namely, the proposed method keeps one formula for the first and the second generations and modify them effectively while SSSO has two different formulas for each generation. In the third generation, the proposed method applies a new formula for determining the mating radius of dominant males and females with the intent to expand search space and avoid falling into local zones. The modifications can support the proposed ISSO method find better solutions with faster manner than SSSO while the number of control parameters and the number of computational processes can be reduced. As a result, the proposed method can find much less generation cost and achieve faster search speeds than SSSO for all considered systems. On the other hand, the search ability evaluation of the proposed method is also given by comparing results with other existing methods available in previous studies. The proposed method can obtain approximate or better results and faster convergence than nearly all compared methods excluding for the last system. Consequently, the proposed ISSO method can be recommended to be a strong method for ELD problem and it can be tried for other mathematical problems in engineering.

Suggested Citation

  • Le Chi Kien & Thang Trung Nguyen & Chiem Trong Hien & Minh Quan Duong, 2019. "A Novel Social Spider Optimization Algorithm for Large-Scale Economic Load Dispatch Problem," Energies, MDPI, vol. 12(6), pages 1-26, March.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:6:p:1075-:d:215666
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/6/1075/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/6/1075/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Thang Trung Nguyen & Nguyen Vu Quynh & Le Van Dai, 2018. "Improved Firefly Algorithm: A Novel Method for Optimal Operation of Thermal Generating Units," Complexity, Hindawi, vol. 2018, pages 1-23, July.
    2. Secui, Dinu Calin, 2016. "A modified Symbiotic Organisms Search algorithm for large scale economic dispatch problem with valve-point effects," Energy, Elsevier, vol. 113(C), pages 366-384.
    3. Vo, Dieu Ngoc & Ongsakul, Weerakorn, 2012. "Economic dispatch with multiple fuel types by enhanced augmented Lagrange Hopfield network," Applied Energy, Elsevier, vol. 91(1), pages 281-289.
    4. Fesanghary, M. & Ardehali, M.M., 2009. "A novel meta-heuristic optimization methodology for solving various types of economic dispatch problem," Energy, Elsevier, vol. 34(6), pages 757-766.
    5. Abdelaziz, A.Y. & Ali, E.S. & Abd Elazim, S.M., 2016. "Implementation of flower pollination algorithm for solving economic load dispatch and combined economic emission dispatch problems in power systems," Energy, Elsevier, vol. 101(C), pages 506-518.
    6. Adarsh, B.R. & Raghunathan, T. & Jayabarathi, T. & Yang, Xin-She, 2016. "Economic dispatch using chaotic bat algorithm," Energy, Elsevier, vol. 96(C), pages 666-675.
    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. Le Chi Kien & Thanh Long Duong & Van-Duc Phan & Thang Trung Nguyen, 2020. "Maximizing Total Profit of Thermal Generation Units in Competitive Electric Market by Using a Proposed Particle Swarm Optimization," Sustainability, MDPI, vol. 12(3), pages 1-35, February.
    2. 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.
    3. Ghulam Abbas & Irfan Ahmad Khan & Naveed Ashraf & Muhammad Taskeen Raza & Muhammad Rashad & Raheel Muzzammel, 2023. "On Employing a Constrained Nonlinear Optimizer to Constrained Economic Dispatch Problems," Sustainability, MDPI, vol. 15(13), pages 1-23, June.
    4. 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.

    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. Yang, Wenqiang & Zhu, Xinxin & Xiao, Qinge & Yang, Zhile, 2023. "Enhanced multi-objective marine predator algorithm for dynamic economic-grid fluctuation dispatch with plug-in electric vehicles," Energy, Elsevier, vol. 282(C).
    2. Naila & Shaikh Saaqib Haroon & Shahzad Hassan & Salman Amin & Intisar Ali Sajjad & Asad Waqar & Muhammad Aamir & Muneeb Yaqoob & Imtiaz Alam, 2018. "Multiple Fuel Machines Power Economic Dispatch Using Stud Differential Evolution," Energies, MDPI, vol. 11(6), pages 1-20, May.
    3. Modiri-Delshad, Mostafa & Aghay Kaboli, S. Hr. & Taslimi-Renani, Ehsan & Rahim, Nasrudin Abd, 2016. "Backtracking search algorithm for solving economic dispatch problems with valve-point effects and multiple fuel options," Energy, Elsevier, vol. 116(P1), pages 637-649.
    4. Guojiang Xiong & Jing Zhang & Xufeng Yuan & Dongyuan Shi & Yu He & Yao Yao & Gonggui Chen, 2018. "A Novel Method for Economic Dispatch with Across Neighborhood Search: A Case Study in a Provincial Power Grid, China," Complexity, Hindawi, vol. 2018, pages 1-18, November.
    5. Zhang, Xian & Wang, Huaizhi & Peng, Jian-chun & Liu, Yitao & Wang, Guibin & Jiang, Hui, 2018. "GPNBI inspired MOSDE for electric power dispatch considering wind energy penetration," Energy, Elsevier, vol. 144(C), pages 404-419.
    6. Al-Bahrani, Loau Tawfak & Chandra Patra, Jagdish, 2018. "Multi-gradient PSO algorithm for optimization of multimodal, discontinuous and non-convex fuel cost function of thermal generating units under various power constraints in smart power grid," Energy, Elsevier, vol. 147(C), pages 1070-1091.
    7. Kheshti, Mostafa & Kang, Xiaoning & Bie, Zhaohong & Jiao, Zaibin & Wang, Xiuli, 2017. "An effective Lightning Flash Algorithm solution to large scale non-convex economic dispatch with valve-point and multiple fuel options on generation units," Energy, Elsevier, vol. 129(C), pages 1-15.
    8. Elsakaan, Asmaa A. & El-Sehiemy, Ragab A. & Kaddah, Sahar S. & Elsaid, Mohammed I., 2018. "An enhanced moth-flame optimizer for solving non-smooth economic dispatch problems with emissions," Energy, Elsevier, vol. 157(C), pages 1063-1078.
    9. Xu, Shengping & Xiong, Guojiang & Mohamed, Ali Wagdy & Bouchekara, Houssem R.E.H., 2022. "Forgetting velocity based improved comprehensive learning particle swarm optimization for non-convex economic dispatch problems with valve-point effects and multi-fuel options," Energy, Elsevier, vol. 256(C).
    10. Mohammadian, M. & Lorestani, A. & Ardehali, M.M., 2018. "Optimization of single and multi-areas economic dispatch problems based on evolutionary particle swarm optimization algorithm," Energy, Elsevier, vol. 161(C), pages 710-724.
    11. Ly Huu Pham & Minh Quan Duong & Van-Duc Phan & Thang Trung Nguyen & Hoang-Nam Nguyen, 2019. "A High-Performance Stochastic Fractal Search Algorithm for Optimal Generation Dispatch Problem," Energies, MDPI, vol. 12(9), pages 1-25, May.
    12. Secui, Dinu Calin, 2016. "A modified Symbiotic Organisms Search algorithm for large scale economic dispatch problem with valve-point effects," Energy, Elsevier, vol. 113(C), pages 366-384.
    13. Alaa A. K. Ismaeel & Essam H. Houssein & Doaa Sami Khafaga & Eman Abdullah Aldakheel & Ahmed S. AbdElrazek & Mokhtar Said, 2023. "Performance of Osprey Optimization Algorithm for Solving Economic Load Dispatch Problem," Mathematics, MDPI, vol. 11(19), pages 1-19, September.
    14. Xiong, Guojiang & Shi, Dongyuan & Duan, Xianzhong, 2013. "Multi-strategy ensemble biogeography-based optimization for economic dispatch problems," Applied Energy, Elsevier, vol. 111(C), pages 801-811.
    15. 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.
    16. Meng, Anbo & Li, Jinbei & Yin, Hao, 2016. "An efficient crisscross optimization solution to large-scale non-convex economic load dispatch with multiple fuel types and valve-point effects," Energy, Elsevier, vol. 113(C), pages 1147-1161.
    17. Raheela Jamal & Baohui Men & Noor Habib Khan & Muhammad Asif Zahoor Raja, 2019. "Hybrid Bio-Inspired Computational Heuristic Paradigm for Integrated Load Dispatch Problems Involving Stochastic Wind," Energies, MDPI, vol. 12(13), pages 1-23, July.
    18. Tawhid, M.A. & Ibrahim, A.M., 2021. "Solving nonlinear systems and unconstrained optimization problems by hybridizing whale optimization algorithm and flower pollination algorithm," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 190(C), pages 1342-1369.
    19. Wei Sun & Junjian Zhang, 2020. "Carbon Price Prediction Based on Ensemble Empirical Mode Decomposition and Extreme Learning Machine Optimized by Improved Bat Algorithm Considering Energy Price Factors," Energies, MDPI, vol. 13(13), pages 1-22, July.
    20. Chun-Yao Lee & Maickel Tuegeh, 2020. "An Optimal Solution for Smooth and Non-Smooth Cost Functions-Based Economic Dispatch Problem," Energies, MDPI, vol. 13(14), pages 1-16, July.

    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:12:y:2019:i:6:p:1075-:d:215666. 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.