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A hybrid biased random key genetic algorithm approach for the unit commitment problem

Author

Listed:
  • L. A. C. Roque

    (Instituto Superior de Engenharia do Porto)

  • D. B. M. M. Fontes

    (Universidade do Porto)

  • F. A. C. C. Fontes

    (Universidade do Porto)

Abstract

This work proposes a hybrid genetic algorithm (GA) to address the unit commitment (UC) problem. In the UC problem, the goal is to schedule a subset of a given group of electrical power generating units and also to determine their production output in order to meet energy demands at minimum cost. In addition, the solution must satisfy a set of technological and operational constraints. The algorithm developed is a hybrid biased random key genetic algorithm (HBRKGA). It uses random keys to encode the solutions and introduces bias both in the parent selection procedure and in the crossover strategy. To intensify the search close to good solutions, the GA is hybridized with local search. Tests have been performed on benchmark large-scale power systems. The computational results demonstrate that the HBRKGA is effective and efficient. In addition, it is also shown that it improves the solutions obtained by current state-of-the-art methodologies.

Suggested Citation

  • L. A. C. Roque & D. B. M. M. Fontes & F. A. C. C. Fontes, 2014. "A hybrid biased random key genetic algorithm approach for the unit commitment problem," Journal of Combinatorial Optimization, Springer, vol. 28(1), pages 140-166, July.
    • Handle: RePEc:spr:jcomop:v:28:y:2014:i:1:d:10.1007_s10878-014-9710-8
    DOI: 10.1007/s10878-014-9710-8
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    References listed on IDEAS

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    Cited by:

    1. Luís A. C. Roque & Dalila B. M. M. Fontes & Fernando A. C. C. Fontes, 2017. "A Metaheuristic Approach to the Multi-Objective Unit Commitment Problem Combining Economic and Environmental Criteria," Energies, MDPI, vol. 10(12), pages 1-25, December.
    2. Dalila B. M. M. Fontes & S. Mahdi Homayouni, 2023. "A bi-objective multi-population biased random key genetic algorithm for joint scheduling quay cranes and speed adjustable vehicles in container terminals," Flexible Services and Manufacturing Journal, Springer, vol. 35(1), pages 241-268, March.
    3. Julliany S. Brandão & Thiago F. Noronha & Celso C. Ribeiro, 2016. "A biased random-key genetic algorithm to maximize the number of accepted lightpaths in WDM optical networks," Journal of Global Optimization, Springer, vol. 65(4), pages 813-835, August.
    4. H. Faria & M. G. C. Resende & D. Ernst, 2017. "A biased random key genetic algorithm applied to the electric distribution network reconfiguration problem," Journal of Heuristics, Springer, vol. 23(6), pages 533-550, December.

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