Evolutionary Algorithms for Dynamic Economic Dispatch Problems

The dynamic economic dispatch problem is a high-dimensional complex constrained optimization problem that determines the optimal generation from a number of generating units by minimizing the fuel cost. Over the last few decades, a number of solution approaches, including evolutionary algorithms, ha...

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Vydané v:	IEEE transactions on power systems Ročník 31; číslo 2; s. 1486 - 1495
Hlavní autori:	Zaman, M. F., Elsayed, Saber M., Ray, Tapabrata, Sarker, Ruhul A.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.03.2016 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Constrained optimization constraint handling Cost engineering Design engineering differential evolution dynamic economic dispatch Dynamics Economics Evolutionary algorithms genetic algorithm Genetic algorithms Heuristic algorithms non-uniform mutation Optimization Power dispatch Power system dynamics Production scheduling Sociology State of the art Statistics
ISSN:	0885-8950, 1558-0679
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Abstract	The dynamic economic dispatch problem is a high-dimensional complex constrained optimization problem that determines the optimal generation from a number of generating units by minimizing the fuel cost. Over the last few decades, a number of solution approaches, including evolutionary algorithms, have been developed to solve this problem. However, the performance of evolutionary algorithms is highly dependent on a number of factors, such as the control parameters, diversity of the population, and constraint-handling procedure used. In this paper, a self-adaptive differential evolution and a real-coded genetic algorithm are proposed to solve the dynamic dispatch problem. In the algorithm design, a new heuristic technique is introduced to guide infeasible solutions towards the feasible space. Moreover, a constraint-handling mechanism, a dynamic relaxation for equality constraints, and a diversity mechanism are applied to improve the performance of the algorithms. The effectiveness of the proposed approaches is demonstrated on a number of dynamic economic dispatch problems for a cycle of 24 h. Their simulation results are compared with each other and state-of-the-art algorithms, which reveals that the proposed method has merit in terms of solution quality and reliability.
AbstractList	The dynamic economic dispatch problem is a high-dimensional complex constrained optimization problem that determines the optimal generation from a number of generating units by minimizing the fuel cost. Over the last few decades, a number of solution approaches, including evolutionary algorithms, have been developed to solve this problem. However, the performance of evolutionary algorithms is highly dependent on a number of factors, such as the control parameters, diversity of the population, and constraint-handling procedure used. In this paper, a self-adaptive differential evolution and a real-coded genetic algorithm are proposed to solve the dynamic dispatch problem. In the algorithm design, a new heuristic technique is introduced to guide infeasible solutions towards the feasible space. Moreover, a constraint-handling mechanism, a dynamic relaxation for equality constraints, and a diversity mechanism are applied to improve the performance of the algorithms. The effectiveness of the proposed approaches is demonstrated on a number of dynamic economic dispatch problems for a cycle of 24 h. Their simulation results are compared with each other and state-of-the-art algorithms, which reveals that the proposed method has merit in terms of solution quality and reliability.
Author	Zaman, M. F. Sarker, Ruhul A. Ray, Tapabrata Elsayed, Saber M.
Author_xml	– sequence: 1 givenname: M. F. surname: Zaman fullname: Zaman, M. F. email: md.zaman@student.adfa.edu.au organization: Sch. of Eng. & Inf. Technol., Univ. of New South Wales, Canberra, ACT, Australia – sequence: 2 givenname: Saber M. surname: Elsayed fullname: Elsayed, Saber M. email: s.elsayed@adfa.edu.au organization: Sch. of Eng. & Inf. Technol., Univ. of New South Wales, Canberra, ACT, Australia – sequence: 3 givenname: Tapabrata surname: Ray fullname: Ray, Tapabrata email: t.ray@adfa.edu.au organization: Sch. of Eng. & Inf. Technol., Univ. of New South Wales, Canberra, ACT, Australia – sequence: 4 givenname: Ruhul A. surname: Sarker fullname: Sarker, Ruhul A. email: r.sarker@adfa.edu.au organization: Sch. of Eng. & Inf. Technol., Univ. of New South Wales, Canberra, ACT, Australia
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SubjectTerms	Algorithms Constrained optimization constraint handling Cost engineering Design engineering differential evolution dynamic economic dispatch Dynamics Economics Evolutionary algorithms genetic algorithm Genetic algorithms Heuristic algorithms non-uniform mutation Optimization Power dispatch Power system dynamics Production scheduling Sociology State of the art Statistics
Title	Evolutionary Algorithms for Dynamic Economic Dispatch Problems
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