Dynamic economic emission dispatch using whale optimization algorithm for multi-objective function

Introduction. Dynamic Economic Emission Dispatch is the extended version of the traditional economic emission dispatch problem in which ramp rate is taken into account for the limit of generators in a power network. Purpose. Dynamic Economic Emission Dispatch considered the treats of economy and emi...

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Vydané v:Electronics and electromechanics číslo 2; s. 64 - 69
Hlavní autori: Mehdi, M. F., Ahmad, A., Ul Haq, S. S., Saqib, M., Ullah, M. F.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Department of Electrical Apparatus of National Technical University, Kharkiv Polytechnic Institute 10.04.2021
National Technical University "Kharkiv Polytechnic Institute"
Predmet:
Algorithms
Analysis
Annealing
Cetacea
Conflict management
dynamic economic emission dispatch
Economic aspects
economic emission
Mathematical optimization
multi-objective problem
Neural networks
ramp rate
Testing equipment
whale optimization algorithm
ISSN:2074-272X, 2309-3404
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Abstract Introduction. Dynamic Economic Emission Dispatch is the extended version of the traditional economic emission dispatch problem in which ramp rate is taken into account for the limit of generators in a power network. Purpose. Dynamic Economic Emission Dispatch considered the treats of economy and emissions as competitive targets for optimal dispatch problems, and to reach a solution it requires some conflict resolution. Novelty. The decision-making method to solve the Dynamic Economic Emission Dispatch problem has a goal for each objective function, for this purpose, the multi-objective problem is transformed into single goal optimization by using the weighted sum method and then control/solve by Whale Optimization Algorithm. Methodology. This paper presents a newly developed metaheuristic technique based on Whale Optimization Algorithm to solve the Dynamic Economic Emission Dispatch problem. The main inspiration for this optimization technique is the fact that metaheuristic algorithms are becoming popular day by day because of their simplicity, no gradient information requirement, easily bypass local optima, and can be used for a variety of other problems. This algorithm includes all possible factors that will yield the minimum cost and emissions of a Dynamic Economic Emission Dispatch problem for the efficient operation of generators in a power network. The proposed approach performs well to perform in diverse problem and converge the solution to near best optimal solution. Results. The proposed strategy is validated by simulating on MATLAB® for 5 IEEE standard test system. Numerical results show the capabilities of the proposed algorithm to establish an optimal solution of the Dynamic Economic Emission Dispatch problem in a several runs. The proposed algorithm shows good performance over the recently proposed algorithms such as Multi-Objective Neural Network trained with Differential Evolution, Particle swarm optimization, evolutionary programming, simulated annealing, Pattern search, multi-objective differential evolution, and multi-objective hybrid differential evolution with simulated annealing technique.
AbstractList Introduction. Dynamic Economic Emission Dispatch is the extended version of the traditional economic emission dispatch problem in which ramp rate is taken into account for the limit of generators in a power network. Purpose. Dynamic Economic Emission Dispatch considered the treats of economy and emissions as competitive targets for optimal dispatch problems, and to reach a solution it requires some conflict resolution. Novelty. The decision-making method to solve the Dynamic Economic Emission Dispatch problem has a goal for each objective function, for this purpose, the multi- objective problem is transformed into single goal optimization by using the weighted sum method and then control/solve by Whale Optimization Algorithm. Methodology. This paper presents a newly developed metaheuristic technique based on Whale Optimization Algorithm to solve the Dynamic Economic Emission Dispatch problem. The main inspiration for this optimization technique is the fact that metaheuristic algorithms are becoming popular day by day because of their simplicity, no gradient information requirement, easily bypass local optima, and can be used for a variety of other problems. This algorithm includes all possible factors that will yield the minimum cost and emissions of a Dynamic Economic Emission Dispatch problem for the efficient operation of generators in a power network. The proposed approach performs well to perform in diverse problem and converge the solution to near best optimal solution. Results. The proposed strategy is validated by simulating on MATLAB[R] for 5 IEEE standard test system. Numerical results show the capabilities of the proposed algorithm to establish an optimal solution of the Dynamic Economic Emission Dispatch problem in a several runs. The proposed algorithm shows good performance over the recently proposed algorithms such as Multi-Objective Neural Network trained with Differential Evolution, Particle swarm optimization, evolutionary programming, simulated annealing, Pattern search, multi-objective differential evolution, and multi-objective hybrid differential evolution with simulated annealing technique. References 17, tables 3, figures 5. Key words: whale optimization algorithm, dynamic economic emission dispatch, ramp rate, multi-objective problem, economic emission.
Introduction. Dynamic Economic Emission Dispatch is the extended version of the traditional economic emission dispatch problem in which ramp rate is taken into account for the limit of generators in a power network. Purpose. Dynamic Economic Emission Dispatch considered the treats of economy and emissions as competitive targets for optimal dispatch problems, and to reach a solution it requires some conflict resolution. Novelty. The decision-making method to solve the Dynamic Economic Emission Dispatch problem has a goal for each objective function, for this purpose, the multi-objective problem is transformed into single goal optimization by using the weighted sum method and then control/solve by Whale Optimization Algorithm. Methodology. This paper presents a newly developed metaheuristic technique based on Whale Optimization Algorithm to solve the Dynamic Economic Emission Dispatch problem. The main inspiration for this optimization technique is the fact that metaheuristic algorithms are becoming popular day by day because of their simplicity, no gradient information requirement, easily bypass local optima, and can be used for a variety of other problems. This algorithm includes all possible factors that will yield the minimum cost and emissions of a Dynamic Economic Emission Dispatch problem for the efficient operation of generators in a power network. The proposed approach performs well to perform in diverse problem and converge the solution to near best optimal solution. Results. The proposed strategy is validated by simulating on MATLAB® for 5 IEEE standard test system. Numerical results show the capabilities of the proposed algorithm to establish an optimal solution of the Dynamic Economic Emission Dispatch problem in a several runs. The proposed algorithm shows good performance over the recently proposed algorithms such as Multi-Objective Neural Network trained with Differential Evolution, Particle swarm optimization, evolutionary programming, simulated annealing, Pattern search, multi-objective differential evolution, and multi-objective hybrid differential evolution with simulated annealing technique.
Audience Academic
Author Saqib, M.
Mehdi, M. F.
Ullah, M. F.
Ul Haq, S. S.
Ahmad, A.
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National Technical University "Kharkiv Polytechnic Institute"
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SubjectTerms Algorithms
Analysis
Annealing
Cetacea
Conflict management
dynamic economic emission dispatch
Economic aspects
economic emission
Mathematical optimization
multi-objective problem
Neural networks
ramp rate
Testing equipment
whale optimization algorithm
Title Dynamic economic emission dispatch using whale optimization algorithm for multi-objective function
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