Genetic algorithm based unit commitment with energy contracts

Xing, Weiguo; Wu, F.F.

doi:10.1016/s0142-0615(01)00048-5

Cited by 29 publications

(13 citation statements)

References 20 publications

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“…Several GAs have been proposed for the unit commitment problem, see e.g. [14,5,26,2,29,7,1], the main differences being the representation scheme, the decoding procedure, and the solution evaluation procedure (i.e. fitness function).…”

Section: Methodsmentioning

confidence: 99%

A Biased Random Key Genetic Algorithm Approach for Unit Commitment Problem

Roque¹,

Fontes

2011

Experimental Algorithms

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Section: Methodsmentioning

confidence: 99%

A Biased Random Key Genetic Algorithm Approach for Unit Commitment Problem

Roque¹,

Fontes

2011

Experimental Algorithms

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“…The idea of this technique is to convert any infeasible individuals to a feasible solution by repairing the sequential possible violations constraints in the NSUCP problem. The following four repairing mechanisms taking from literature [16,55,[57][58][59] are incorporated in the proposed algorithm.…”

Section: • Repairing Technique • Penalizing Techniquementioning

confidence: 99%

Solving Nonlinear Single-Unit Commitment Problem by Genetic Algorithm Based Clustering Technique

El-Shorbagy

Mousa

Farag

2017

Review of Computer Engineering Research

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Article HistoryNonlinear single-unit commitment problem (NSUCP) is a NP-hard nonlinear mixedinteger optimization problem, encountered as one of the toughest problems in power systems. This paper presents a new algorithm for solving NSUCP using genetic algorithm (GA) based clustering technique. The proposed algorithm integrates the main features of binary-real coded GA and K-means clustering technique. Clustering technique divides population into a specific number of subpopulations. In this way, different operators of GA can be used instead of using one operator to the whole population to avoid the local minima and introduce diversity. The effectiveness of the proposed algorithm is validated by comparison with other well-known techniques. By comparison with the previously reported results, it is found that the performance of the proposed algorithm quite satisfactory. Contribution/ Originality:This study presents a new algorithm for solving nonlinear single-unit commitment problem using genetic algorithm based clustering technique; where it integrates the main features of binary-real coded genetic algorithm and K-means clustering technique. The tests demonstrated that the proposed approach has a satisfactory performance compared to previous studies.

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“…The repairing technique means to take an infeasible chromosome and generate a feasible one through some repairing procedure [55]. For many combinatorial optimization problems, it is relatively easy to create a repairing procedure [56], there for a repairing technique is used in this paper to handle with infeasible solution.…”

Section: Handling Constraintsmentioning

confidence: 99%

Binary-Real Coded Genetic Algorithm Based <i>k</i>-Means Clustering for Unit Commitment Problem

Farag¹,

El-Shorbagy²,

Eldesoky³

et al. 2015

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This paper presents a new algorithm for solving unit commitment (UC) problems using a binaryreal coded genetic algorithm based on k-means clustering technique. UC is a NP-hard nonlinear mixed-integer optimization problem, encountered as one of the toughest problems in power systems, in which some power generating units are to be scheduled in such a way that the forecasted demand is met at minimum production cost over a time horizon. In the proposed algorithm, the algorithm integrates the main features of a binary-real coded genetic algorithm (GA) and k-means clustering technique. The binary coded GA is used to obtain a feasible commitment schedule for each generating unit; while the power amounts generated by committed units are determined by using real coded GA for the feasible commitment obtained in each interval. k-means clustering algorithm divides population into a specific number of subpopulations with dynamic size. In this way, using k-means clustering algorithm allows the use of different GA operators with the whole population and avoids the local problem minima. The effectiveness of the proposed technique is validated on a test power system available in the literature. The proposed algorithm performance is found quite satisfactory in comparison with the previously reported results. KeywordsUnit Commitment (UC), Genetic Algorithm (GA), k-Means Clustering Technique M. A. Farag et al.

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Genetic algorithm based unit commitment with energy contracts

Cited by 29 publications

References 20 publications

A Biased Random Key Genetic Algorithm Approach for Unit Commitment Problem

A Biased Random Key Genetic Algorithm Approach for Unit Commitment Problem

Solving Nonlinear Single-Unit Commitment Problem by Genetic Algorithm Based Clustering Technique

Binary-Real Coded Genetic Algorithm Based <i>k</i>-Means Clustering for Unit Commitment Problem

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Genetic algorithm based unit commitment with energy contracts

Cited by 29 publications

References 20 publications

A Biased Random Key Genetic Algorithm Approach for Unit Commitment Problem

A Biased Random Key Genetic Algorithm Approach for Unit Commitment Problem

Solving Nonlinear Single-Unit Commitment Problem by Genetic Algorithm Based Clustering Technique

Binary-Real Coded Genetic Algorithm Based &lt;i&gt;k&lt;/i&gt;-Means Clustering for Unit Commitment Problem

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Binary-Real Coded Genetic Algorithm Based <i>k</i>-Means Clustering for Unit Commitment Problem