LAPSE:2023.27845
Published Article
LAPSE:2023.27845
Solving Renewables-Integrated Economic Load Dispatch Problem by Variant of Metaheuristic Bat-Inspired Algorithm
April 11, 2023
One of the most important concerns in the planning and operation of an electric power generation system is the effective scheduling of all power generation facilities to meet growing power demand. Economic load dispatch (ELD) is a phenomenon where an optimal combination of power generating units is selected in such a way as to minimize the total fuel cost while satisfying the load demand, subject to operational constraints. Different numerical and metaheuristic optimization techniques have gained prominent importance and are widely used to solve the nonlinear problem. Although metaheuristic techniques have a good convergence rate than numerical techniques, however, their implementation seems difficult in the presence of nonlinear and dynamic parameters. This work is devoted to solving the ELD problem with the integration of variable energy resources using a modified directional bat algorithm (dBA). Then the proposed technique is validated via different realistic test cases consisting of thermal and renewable energy sources (RESs). From simulation results, it is observed that dBA reduces the operational cost with less computational time and has better convergence characteristics than that of standard BA and other popular techniques like particle swarm optimization (PSO) and genetic algorithm (GA).
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Keywords
convergence characteristics, directional bat algorithm (dBA), operational cost, renewables incorporated ELD problem
Subject
Suggested Citation
Tariq F, Alelyani S, Abbas G, Qahmash A, Hussain MR. Solving Renewables-Integrated Economic Load Dispatch Problem by Variant of Metaheuristic Bat-Inspired Algorithm. (2023). LAPSE:2023.27845
Author Affiliations
Tariq F: Department of Electrical Engineering, The University of Lahore, Lahore 54000, Pakistan
Alelyani S: Center for Artificial Intelligence (CAI), King Khalid University, Abha 61421, Saudi Arabia; College of Computer Science, King Khalid University, Abha 61421, Saudi Arabia
Abbas G: Department of Electrical Engineering, The University of Lahore, Lahore 54000, Pakistan [ORCID]
Qahmash A: Center for Artificial Intelligence (CAI), King Khalid University, Abha 61421, Saudi Arabia; College of Computer Science, King Khalid University, Abha 61421, Saudi Arabia [ORCID]
Hussain MR: Center for Artificial Intelligence (CAI), King Khalid University, Abha 61421, Saudi Arabia; College of Computer Science, King Khalid University, Abha 61421, Saudi Arabia [ORCID]
Alelyani S: Center for Artificial Intelligence (CAI), King Khalid University, Abha 61421, Saudi Arabia; College of Computer Science, King Khalid University, Abha 61421, Saudi Arabia
Abbas G: Department of Electrical Engineering, The University of Lahore, Lahore 54000, Pakistan [ORCID]
Qahmash A: Center for Artificial Intelligence (CAI), King Khalid University, Abha 61421, Saudi Arabia; College of Computer Science, King Khalid University, Abha 61421, Saudi Arabia [ORCID]
Hussain MR: Center for Artificial Intelligence (CAI), King Khalid University, Abha 61421, Saudi Arabia; College of Computer Science, King Khalid University, Abha 61421, Saudi Arabia [ORCID]
Journal Name
Energies
Volume
13
Issue
23
Article Number
E6225
Year
2020
Publication Date
2020-11-26
ISSN
1996-1073
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Original Submission
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PII: en13236225, Publication Type: Journal Article
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LAPSE:2023.27845
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https://doi.org/10.3390/en13236225
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Apr 11, 2023
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Apr 11, 2023
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