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Oppositional Pigeon-Inspired Optimizer for Solving the Non-Convex Economic Load Dispatch Problem in Power Systems

Author

Listed:
  • Rajakumar Ramalingam

    (Department of Computer Science and Technology, Madanapalle Institute of Technology & Science, Madanapalle 517325, Andhra Pradesh, India)

  • Dinesh Karunanidy

    (Department of Computer Science and Technology, Madanapalle Institute of Technology & Science, Madanapalle 517325, Andhra Pradesh, India)

  • Sultan S. Alshamrani

    (Department of Information Technology, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia)

  • Mamoon Rashid

    (Department of Computer Engineering, Faculty of Science and Technology, Vishwakarma University, Pune 411048, Maharashtra, India)

  • Swamidoss Mathumohan

    (Department of CSE, Unnamalai Institute of Technology, Kovilpatti 628502, Tamil Nadu, India)

  • Ankur Dumka

    (Department of Computer Science and Engineering, Women Institute of Technology, Dehradun 248007, Uttarakhand, India
    Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun 248007, Uttarakhand, India)

Abstract

Economic Load Dispatch (ELD) belongs to a non-convex optimization problem that aims to reduce total power generation cost by satisfying demand constraints. However, solving the ELD problem is a challenging task, because of its parity and disparity constraints. The Pigeon-Inspired Optimizer (PIO) is a recently proposed optimization algorithm, which belongs to the family of swarm intelligence algorithms. The PIO algorithm has the benefit of conceptual simplicity, and provides better outcomes for various real-world problems. However, this algorithm has the drawback of premature convergence and local stagnation. Therefore, we propose an Oppositional Pigeon-Inspired Optimizer (OPIO) algorithm—to overcome these deficiencies. The proposed algorithm employs Oppositional-Based Learning (OBL) to enhance the quality of the individual, by exploring the global search space. The proposed algorithm would be used to determine the load demand of a power system, by sustaining the various equality and inequality constraints, to diminish the overall generation cost. In this work, the OPIO algorithm was applied to solve the ELD problem of small- (13-unit, 40-unit), medium- (140-unit, 160-unit) and large-scale (320-unit, 640-unit) test systems. The experimental results of the proposed OPIO algorithm demonstrate its efficiency over the conventional PIO algorithm, and other state-of-the-art approaches in the literature. The comparative results demonstrate that the proposed algorithm provides better results—in terms of improved accuracy, higher convergence rate, less computation time, and reduced fuel cost—than the other approaches.

Suggested Citation

  • Rajakumar Ramalingam & Dinesh Karunanidy & Sultan S. Alshamrani & Mamoon Rashid & Swamidoss Mathumohan & Ankur Dumka, 2022. "Oppositional Pigeon-Inspired Optimizer for Solving the Non-Convex Economic Load Dispatch Problem in Power Systems," Mathematics, MDPI, vol. 10(18), pages 1-24, September.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:18:p:3315-:d:913410
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    References listed on IDEAS

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    1. 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.
    2. Zou, Dexuan & Li, Steven & Wang, Gai-Ge & Li, Zongyan & Ouyang, Haibin, 2016. "An improved differential evolution algorithm for the economic load dispatch problems with or without valve-point effects," Applied Energy, Elsevier, vol. 181(C), pages 375-390.
    3. Jayabarathi, T. & Raghunathan, T. & Adarsh, B.R. & Suganthan, Ponnuthurai Nagaratnam, 2016. "Economic dispatch using hybrid grey wolf optimizer," Energy, Elsevier, vol. 111(C), pages 630-641.
    4. Alsumait, J.S. & Sykulski, J.K. & Al-Othman, A.K., 2010. "A hybrid GA-PS-SQP method to solve power system valve-point economic dispatch problems," Applied Energy, Elsevier, vol. 87(5), pages 1773-1781, May.
    5. 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. 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.

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