Journal of Combinatorial Optimization

, Volume 28, Issue 1, pp 140–166 | Cite as

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

  • L. A. C. Roque
  • D. B. M. M. Fontes
  • F. A. C. C. Fontes


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.


Unit commitment Genetic algorithms Hybrid metaheuristics Electrical power generation 



We acknowledge the support of the ERDF (FEDER), the COMPETE through the FCT as part of projects PTDC/EGE-GES/099741/2008 and PTDC/EEA-CRO/116014/2009 and the North Portugal Regional Operational Programme (ON.2 O Novo Norte), under the National Strategic Reference Framework (NSRF), through the European Regional Development Fund (ERDF).


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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • L. A. C. Roque
    • 1
  • D. B. M. M. Fontes
    • 2
  • F. A. C. C. Fontes
    • 3
  1. 1.DEMAInstituto Superior de Engenharia do PortoPortoPortugal
  2. 2.LIAAD-INESC-TEC, Faculdade de EconomiaUniversidade do PortoPortoPortugal
  3. 3.ISR-Porto, Faculdade de EngenhariaUniversidade do PortoPortoPortugal

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