Optimizing Economic Load Dispatch Using a Hybrid PSO-SA Algorithm: A Novel Approach

Abdul Rafay; Uzair Khan; Muhammad Mujtaba; Abbas Javeed; Muhammad Tayyab Khalid; Zubair Ali

Authors

Abdul Rafay Department of Electrical Engineering (RCET), University of Engineering and Technology, Lahore, 39161, Pakistan
Uzair Khan Department of Electrical Engineering (RCET), University of Engineering and Technology, Lahore, 39161, Pakistan
Muhammad Mujtaba Department of Electrical Engineering (RCET), University of Engineering and Technology, Lahore, 39161, Pakistan
Abbas Javeed Department of Electrical Engineering (KSK), University of Engineering and Technology, Lahore, 39161, Pakistan
Muhammad Tayyab Khalid Department of Electrical Engineering (RCET), University of Engineering and Technology, Lahore, 39161, Pakistan
Zubair Ali Department of Electrical Engineering (RCET), University of Engineering and Technology, Lahore, 39161, Pakistan

Keywords:

Particle Swarm Optimization (PSO), Simulating Annealing (SA), Economic Load Dispatch (ELD), Hybrid Optimization, Power System Optimization

Abstract

Economic Load Dispatch (ELD) is a crucial power system optimization task. It aims to minimize the total cost of electricity generation by strategically allocating power output among available generating units to meet the system's demand while respecting operational limits. This paper investigates how soft computing methods can improve the effectiveness of Electronic Logging Device (ELD) solutions. Specifically, Particle Swarm Optimization (PSO) and Simulated Annealing (SA) algorithms are employed to minimize generation costs for a power system comprising three generating units. The optimization process considers loss coefficients, generation limits, and a predefined cost function. Initially, PSO is used to determine near-optimal solutions, which are further refined using SA to avoid local minima. A hybrid PSO-SA method integrates the global exploration of Particle Swarm Optimization (PSO) with the local refinement of Simulated Annealing (SA) to enhance convergence and solution quality. 1 This approach was implemented in MATLAB and validated through a case study. Simulation results demonstrate that the hybrid method consistently yields high-quality solutions with reduced computational effort, proving its robustness and reliability for solving ELD problems. Combining metaheuristic algorithms shows promise for real-world power system optimization.

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