Keyword(s):Unit commitment, Genetic algorithm, Optimization, Electrical power generation.

Related
Ontology
Subjects/Areas/Topics:Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computing
;
Evolutionary Multiobjective Optimization
;
Genetic Algorithms
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Soft Computing

Abstract: A Biased Random Key Genetic Algorithm (BRKGA) is proposed to find solutions for the unit commitment problem. In this problem, one wishes to schedule energy production on a given set of thermal generation units in order to meet energy demands at minimum cost, while satisfying a set of technological and spinning reserve constraints. In the BRKGA, solutions are encoded by using random keys, which are represented as vectors of real numbers in the interval [0,1]. The GA proposed is a variant of the random key genetic algorithm, since bias is introduced in the parent selection procedure, as well as in the crossover strategy. Tests have been performed on benchmark large-scale power systems of up 100 units for a 24 hours period. The results obtained have shown the proposed methodology to be an effective and efficient tool for finding solutions to large-scale unit commitment problems. Furthermore, form the comparisons made it can be concluded that the results produced improve upon the best known solutions.(More)

A Biased Random Key Genetic Algorithm (BRKGA) is proposed to find solutions for the unit commitment problem. In this problem, one wishes to schedule energy production on a given set of thermal generation units in order to meet energy demands at minimum cost, while satisfying a set of technological and spinning reserve constraints. In the BRKGA, solutions are encoded by using random keys, which are represented as vectors of real numbers in the interval [0,1]. The GA proposed is a variant of the random key genetic algorithm, since bias is introduced in the parent selection procedure, as well as in the crossover strategy. Tests have been performed on benchmark large-scale power systems of up 100 units for a 24 hours period. The results obtained have shown the proposed methodology to be an effective and efficient tool for finding solutions to large-scale unit commitment problems. Furthermore, form the comparisons made it can be concluded that the results produced improve upon the best known solutions.

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A. C. Roque, L.; B. M. M. Fontes, D. and A. C. C. Fontes, F. (2010). A BIASED RANDOM KEY GENETIC ALGORITHM APPROACH FOR UNIT COMMITMENT PROBLEM.In Proceedings of the International Conference on Evolutionary Computation - Volume 1: ICEC, (IJCCI 2010) ISBN 978-989-8425-31-7, pages 332-339. DOI: 10.5220/0003076703320339

@conference{icec10, author={Luís A. C. Roque. and Dalila B. M. M. Fontes. and Fernando A. C. C. Fontes.}, title={A BIASED RANDOM KEY GENETIC ALGORITHM APPROACH FOR UNIT COMMITMENT PROBLEM}, booktitle={Proceedings of the International Conference on Evolutionary Computation - Volume 1: ICEC, (IJCCI 2010)}, year={2010}, pages={332-339}, publisher={SciTePress}, organization={INSTICC}, doi={10.5220/0003076703320339}, isbn={978-989-8425-31-7}, }

TY - CONF

JO - Proceedings of the International Conference on Evolutionary Computation - Volume 1: ICEC, (IJCCI 2010) TI - A BIASED RANDOM KEY GENETIC ALGORITHM APPROACH FOR UNIT COMMITMENT PROBLEM SN - 978-989-8425-31-7 AU - A. C. Roque, L. AU - B. M. M. Fontes, D. AU - A. C. C. Fontes, F. PY - 2010 SP - 332 EP - 339 DO - 10.5220/0003076703320339