An economic/emission dispatch based on a new multi-objective artificial bee colony optimization algorithm and NSGA-II

The conventional energy resources have limited reserves and their utilization is adversely affecting the environment. Hence, it is necessary to generate electricity with the least cost and emission. The studies in the past reveal that the combined economic emission dispatch (CEED) problem has been s...

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Bibliographic Details
Published in:Evolutionary intelligence 2024-04, Vol.17 (2), p.1127-1162
Main Authors: Sutar, Maneesh, Jadhav, H. T.
Format: Article
Language:English
Subjects:
Applications of Mathematics
Artificial Intelligence
Bees
Bioinformatics
Control
Emission analysis
Energy sources
Engineering
Genetic algorithms
Mathematical and Computational Engineering
Mechatronics
Multiple objective analysis
Optimization
Optimization algorithms
Research Paper
Robotics
Search algorithms
Sorting algorithms
Statistical Physics and Dynamical Systems
Swarm intelligence
System effectiveness
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Summary:The conventional energy resources have limited reserves and their utilization is adversely affecting the environment. Hence, it is necessary to generate electricity with the least cost and emission. The studies in the past reveal that the combined economic emission dispatch (CEED) problem has been solved by evolutionary and swarm intelligence-based optimization algorithms. However, the methodology to identify the best compromising solution for the CEED problem has not been studied much in the literature. In this paper, a multi-objective optimization algorithm, which reduces fuel cost of power generation as well as emission simultaneously, is presented. The algorithm is a combination of an artificial bee colony algorithm (ABC) and a non-dominated sorting genetic algorithm (NSGA-II) with a new constraint handling feature. To validate the effectiveness of the proposed algorithm it is applied to three benchmark systems, commonly used to study the effectiveness of optimization algorithms. Moreover, the best compromising solution, obtained by the proposed algorithm, is identified by using sixteen multi-attribute decision-making (MADM) methods. The non-dominated solutions reported in the past literature, for different test systems, are also analysed using MADM methods. It has been shown that the proposed algorithm gives better results without violating the constraints and with the minimum number of iterations.
ISSN:1864-5909
1864-5917
DOI:10.1007/s12065-022-00796-x