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

Rajakumar Ramalingam, Dinesh Karunanidy, Sultan S. Alshamrani, Mamoon Rashid (), Swamidoss Mathumohan and Ankur Dumka
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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

Mathematics, 2022, vol. 10, issue 18, 1-24

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.

Keywords: economic load dispatch; pigeon-inspired optimizer; oppositional-based learning; swarm intelligence algorithm; oppositional-based pigeon-inspired optimizer (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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