Implementation and Comparison of Particle Swarm Optimization and Genetic Algorithm Techniques in Combined Economic Emission Dispatch of an Independent Power Plant

Shahbaz Hussain, Mohammed Al-Hitmi, Salman Khaliq, Asif Hussain and Muhammad Asghar Saqib
Additional contact information
Shahbaz Hussain: Department of Electrical Engineering, Qatar University, P.O. Box 2713 Doha, Qatar
Mohammed Al-Hitmi: Department of Electrical Engineering, Qatar University, P.O. Box 2713 Doha, Qatar
Salman Khaliq: Intelligent Mechatronics Research Center, Korea Electronics Technology Institute (KETI), Gyeonggi-do 13509, Korea
Asif Hussain: Department of Electrical Engineering, University of Management and Technology, Lahore 54792, Pakistan
Muhammad Asghar Saqib: Department of Electrical Engineering, University of Engineering and Technology, Lahore 54890, Pakistan

Energies, 2019, vol. 12, issue 11, 1-15

Abstract: This paper presents the optimization of fuel cost, emission of NO X , CO X, and SO X gases caused by the generators in a thermal power plant using penalty factor approach. Practical constraints such as generator limits and power balance were considered. Two contemporary metaheuristic techniques, particle swarm optimization (PSO) and genetic algorithm (GA), have were simultaneously implemented for combined economic emission dispatch (CEED) of an independent power plant (IPP) situated in Pakistan for different load demands. The results are of great significance as the real data of an IPP is used and imply that the performance of PSO is better than that of GA in case of CEED for finding the optimal solution concerning fuel cost, emission, convergence characteristics, and computational time. The novelty of this work is the parallel implementation of PSO and GA techniques in MATLAB environment employed for the same systems. They were then compared in terms of convergence characteristics using 3D plots corresponding to fuel cost and gas emissions. These results are further validated by comparing the performance of both algorithms for CEED on IEEE 30 bus test bed.

Keywords: economic load dispatch; emission dispatch; combined economic emission/environmental dispatch; particle swarm optimization; genetic algorithm; penalty factor approach (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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