2004
DOI: 10.1109/tpwrs.2003.821625
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A Solution to the Unit-Commitment Problem Using Integer-Coded Genetic Algorithm

Abstract: This paper presents a new solution to the thermal unit-commitment (UC) problem based on an integer-coded genetic algorithm (GA). The GA chromosome consists of a sequence of alternating sign integer numbers representing the sequence of operation/reservation times of the generating units. The proposed coding achieves significant chromosome size reduction compared to the usual binary coding. As a result, algorithm robustness and execution time are improved. In addition, generating unit minimum up and minimum down… Show more

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Cited by 364 publications
(206 citation statements)
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“…The UC simulation results of the proposed SSAS are compared against several UC solution methods taken from the previous publications in the following: Lagrangian Relaxation (LR) [13], Genetic Algorithms [13], Evolutionary Programming (EP) [6], Improved Cooperative Genetic Algorithms (ICGA) [14], Genetic Algorithms for Unit Commitment (GAUC) [15], Fast Genetic Algorithms (FGA) [16], and Enhanced Lagrangian Relaxation (ELR) [17]. For confirming the performance, the proposed SSAS is also compared to the results of AS based the diversity control approach (DAS) [9].…”
Section: Simulation Resultsmentioning
confidence: 99%
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“…The UC simulation results of the proposed SSAS are compared against several UC solution methods taken from the previous publications in the following: Lagrangian Relaxation (LR) [13], Genetic Algorithms [13], Evolutionary Programming (EP) [6], Improved Cooperative Genetic Algorithms (ICGA) [14], Genetic Algorithms for Unit Commitment (GAUC) [15], Fast Genetic Algorithms (FGA) [16], and Enhanced Lagrangian Relaxation (ELR) [17]. For confirming the performance, the proposed SSAS is also compared to the results of AS based the diversity control approach (DAS) [9].…”
Section: Simulation Resultsmentioning
confidence: 99%
“…In this section, we also propose a new method to adjust transition probability parameters of trail decision formula in (14) adaptively. These parameters, represented as alpha (α t,u  ) and beta (β t,u ), control the relative importance of the pheromone trail and the visibility, respectively.…”
Section: Selective Self-adaptive Transition Probability Parametersmentioning
confidence: 99%
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“…In (30), it is assumed that all p objective functions should be minimized. In order to properly apply the ε-constraint method, the ranges of at least p-1 objective functions are needed that will be used as the additional objective function constraints.…”
Section: Multiobjective Mathematical Programming (Mmp)mentioning
confidence: 99%
“…It can characterize the on/off status of units and optimally handle the UC control variables. In this method, the binary variables are replaced by integer operation cycles without previous restrictions in [13]. This leads to a substantial drop down of the number of decision variables and better numerical results.…”
Section: Introductionmentioning
confidence: 99%