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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
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    References listed on IDEAS

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    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. 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.
    3. 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.
    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. 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.
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    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.

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