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A Biased Random Key Genetic Algorithm Approach for Unit Commitment Problem

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Experimental Algorithms (SEA 2011)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6630))

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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 \(\left[0,1\right]\). 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 to 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, from the comparisons made it can be concluded that the results produced improve upon some of the best known solutions.

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Author information

Authors and Affiliations

  1. ISEP-DEMA/GECAD, Instituto Superior de Engenharia do Porto, Portugal

    Luís A. C. Roque

  2. FEP/LIAAD-INESC Porto L.A., Universidade do Porto, Portugal

    Dalila B. M. M. Fontes

  3. FEUP/ISR-Porto, Universidade do Porto, Portugal

    Fernando A. C. C. Fontes

Authors
  1. Luís A. C. Roque
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  2. Dalila B. M. M. Fontes
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  3. Fernando A. C. C. Fontes
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Editor information

Editors and Affiliations

  1. Center for Applied Optimization, Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

  2. Division of Economics and Business, Colorado School of Mines, Engineering Hall 310, 816 15th Street, 80401, Golden, CO, USA

    Steffen Rebennack

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© 2011 Springer-Verlag Berlin Heidelberg

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Roque, L.A.C., Fontes, D.B.M.M., Fontes, F.A.C.C. (2011). A Biased Random Key Genetic Algorithm Approach for Unit Commitment Problem. In: Pardalos, P.M., Rebennack, S. (eds) Experimental Algorithms. SEA 2011. Lecture Notes in Computer Science, vol 6630. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20662-7_28

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  • DOI: https://doi.org/10.1007/978-3-642-20662-7_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-20661-0

  • Online ISBN: 978-3-642-20662-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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