Parallel micro genetic algorithm based on merit order loading solutions for constrained dynamic economic dispatch

Ongsakul, Weerakorn; Tippayachai, J.

doi:10.1016/s0378-7796(01)00180-8

Cited by 20 publications

(6 citation statements)

References 9 publications

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“…This section aims to highlight how the DED problem has been tackled using different AI techniques, rather than providing a comprehensive description of each of them. These methods include: neural networks [7,8,9], simulated annealing [10], evolutionary algorithms [11,12,13,14,6,15], differential evolution [16,17,18], particle swarm optimization [19,20,21,14], Harmony Search [5,22], and Artificial Immune Systems [23]. Additional techniques have been reported in [14,5,24,25,26,27,28,29,30,31].…”

Section: Literature Reviewmentioning

confidence: 99%

A T-cell algorithm for solving dynamic economic power dispatch problems

Aragón

Coello

Leguizamón

2020

JC&ST

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This paper presents the artificial immune system IA_DED (Immune Algorithm Dynamic Economic Dispatch) to solve the Dynamic Economic Dispatch (DED) problem and the Dynamic Economic Emission Dispatch (DEED) problem. Our approach considers these as dynamic problems whose constraints change over time. IA\DED is inspired on the activation process that T cells suffer in order to find partial solutions. The proposed approach is validated using several DED problems taken from specialized literature and one DEED problem. The latter is addressed by transforming a multi-objective problem into a single-objective problem by using a linear aggregating function that combines the (weighted) values of the objectives into a single scalar value. Our results are compared with respect to those obtained by other approaches taken from the specialized literature. We also provide some statistical analysis in order to determine the sensitivity of the performance of our proposed approach to its parameters. Part of this work was presented at the XXV Argentine Congress of Computer Science (CACIC), 2019.

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Section: Literature Reviewmentioning

confidence: 99%

A T-cell algorithm for solving dynamic economic power dispatch problems

Aragón

Coello

Leguizamón

2020

JC&ST

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“…Also, a linear cost function [33,5] and piecewise linear cost function [34][35][36] have been employed. For smooth cost function it is usually assumed that its incremental cost function, i.e.…”

Section: Ded Formulationmentioning

confidence: 99%

“…In some power systems combined cycle units (CC) are used to supply the base load. For these units the cost function can be given as linear, piecewise or quadratic with decreasing incremental cost function [34,35]. For units with prohibited zones, the fuel cost function is discontinuous and nonconvex.…”

Section: Ded Formulationmentioning

confidence: 99%

“…when used to solve DED with nonsmooth or nonconvex cost functions, can fail to get global optimal solutions [31]. For non-monotonically increasing incremental cost functions, the lambda iterative method may not result in the optimal solution [35]. Linear programming usually faces poor computation efficiency [40].…”

Section: Literature Review On Dedmentioning

confidence: 99%

“…It is demonstrated that the ramping rate constraint does not put any additional burden on the genetic search but also helps in finding a better strategy to operate power systems. Ongsakul and Tippayachai [35] proposed a parallel micro genetic algorithm based on merit order loading solutions to solve DED problems for combined cycle units with linear decreasing and decreasing staircase incremental cost functions. The transmission line losses were taken into consideration.…”

Section: Genetic Algorithmmentioning

confidence: 99%

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