A Biased Random Key Genetic Algorithm Approach for Unit Commitment Problem

  • Luís A. C. Roque
  • Dalila B. M. M. Fontes
  • Fernando A. C. C. Fontes
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6630)


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.


Unit Commitment Genetic Algorithm Optimization Electrical Power Generation 


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  1. 1.
    Abookazemi, K., Mustafa, M.W., Ahmad, H.: Structured Genetic Algorithm Technique for Unit Commitment Problem. International Journal of Recent Trends in Engineering 1(3), 135–139 (2009)Google Scholar
  2. 2.
    Arroyo, J.M., Conejo, A.J.: A parallel repair genetic algorithm to solve the unit commitment problem. IEEE Transactions on Power Systems 17, 1216–1224 (2002)CrossRefGoogle Scholar
  3. 3.
    Bean, J.C.: Genetic Algorithms and Random Keys for Sequencing and Optimization. ORSA Journal on Computing 6(2) (1994)Google Scholar
  4. 4.
    Chen, Y.M., Wang, W.S.: Fast solution technique for unit commitment by particle swarm optimisation and genetic algorithm. International Journal of Energy Technology and Policy 5(4), 440–456 (2007)CrossRefGoogle Scholar
  5. 5.
    Cheng, C.P., Liu, C.W., Liu, G.C.: Unit commitment by Lagrangian relaxation and genetic algorithms. IEEE Transactions on Power Systems 15, 707–714 (2000)CrossRefGoogle Scholar
  6. 6.
    Cohen, A.I., Yoshimura, M.: A Branch-and-Bound Algorithm for Unit Commitment. IEEE Transactions on Power Apparatus and Systems 102, 444–451 (1983)CrossRefGoogle Scholar
  7. 7.
    Dudek, G.: Unit commitment by genetic algorithm with specialized search operators. Electric Power Systems Research 72(3), 299–308 (2004)CrossRefGoogle Scholar
  8. 8.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, New York (1989)zbMATHGoogle Scholar
  9. 9.
    Gonçalves, J.F., Resende, M.G.C.: Biased random-key genetic algorithms for combinatorial optimization. Journal of Heuristics (2010), Published online (August 27, 2010). DOI: 10.1007/s10732-010-9143-1Google Scholar
  10. 10.
    Holland, J.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)Google Scholar
  11. 11.
    Jenkins, L., Purushothama, G.K.: Simulated annealing with local search-a hybrid algorithm for unit commitment. IEEE Transactions on Power Systems 18(1), 1218–1225 (2003)Google Scholar
  12. 12.
    Juste, K.A., Kita, H., Tanaka, E., Hasegawa, J.: An evolutionary programming solution to the unit commitment problem. IEEE Transactions on Power Systems 14(4), 1452–1459 (1999)CrossRefGoogle Scholar
  13. 13.
    Kazarlis, S.A., Bakirtzis, A.G., Petridis, V.: A Genetic Algorithm Solution to the Unit Commitment Problem. IEEE Transactions on Power Systems 11, 83–92 (1996)CrossRefGoogle Scholar
  14. 14.
    Lee, F.N.: Short-term unit commitment-a new method. IEEE Transactions on Power Systems 3(2), 421–428 (1998)CrossRefGoogle Scholar
  15. 15.
    Merlin, A., Sandrin, P.: A new method for unit commitment at Electricit de France. IEEE Transactions on Power Apparatus Systems 2(3), 1218–1225 (1983)CrossRefGoogle Scholar
  16. 16.
    Padhy, N.P.: Unit commitment using hybrid models: a comparative study for dynamic programming, expert system, fuzzy system and genetic algorithms. International Journal of Electrical Power & Energy Systems 23(8), 827–836 (2001)CrossRefGoogle Scholar
  17. 17.
    Raglend, I.J., Padhy, N.P.: Comparison of Practical Unit Commitment Solutions. Electric Power Components and Systems 36(8), 844–863 (2008)CrossRefGoogle Scholar
  18. 18.
    Rajan, C.C.A., Mohan, M.R.: An evolutionary programming-based tabu search method for solving the unit commitment problem. IEEE Transactions on Power Systems 19(1), 577–585 (2004)CrossRefGoogle Scholar
  19. 19.
    Rajan, C.C.A., Mohan, M.R., Manivannan, K.: Refined simulated annealing method for solving unit commitment problem. In: Proceedings of the 2002 International Joint Conference on Neural Networks, IJCNN 2002, vol. 1, pp. 333–338. IEEE, Los Alamitos (2002)Google Scholar
  20. 20.
    Salam, S.: Unit commitment solution methods. Proceedings of World Academy of Science, Engineering and Technology 26, 600–605 (2007)Google Scholar
  21. 21.
    Senjyu, T., Yamashiro, H., Uezato, K., Funabashi, T.: A unit commitment problem by using genetic algorithm based on unit characteristic classification. IEEE Power Engineering Society Winter Meeting 1 (2002)Google Scholar
  22. 22.
    Simopoulos, D.N., Kavatza, S.D., Vournas, C.D.: Unit commitment by an enhanced simulated annealing algorithm. IEEE Transactions on Power Systems 21(1), 68–76 (2006)CrossRefGoogle Scholar
  23. 23.
    Sriyanyong, P., Song, Y.H.: Unit commitment using particle swarm optimization combined with Lagrange relaxation. In: Power Engineering Society General Meeting, pp. 2752–2759. IEEE, Los Alamitos (2005)Google Scholar
  24. 24.
    Swarup, K.S., Yamashiro, S.: Unit Commitment Solution Methodology Using Genetic Algorithm. IEEE Transactions on Power Systems 17, 87–91 (2002)CrossRefGoogle Scholar
  25. 25.
    Valenzuela, J., Smith, A.E.: A seeded memetic algorithm for large unit commitment problems. Journal of Heuristics 8(2), 173–195 (2002)CrossRefGoogle Scholar
  26. 26.
    Viana, A., Sousa, J.P., Matos, M.A.: Fast solutions for UC problems by a new metaheuristic approach. IEEE Electric Power Systems Research 78, 1385–1389 (2008)CrossRefGoogle Scholar
  27. 27.
    Xing, W., Wu, F.F.: Genetic algorithm based unit commitment with energy contracts. International Journal of Electrical Power & Energy Systems 24(5), 329–336 (2002)CrossRefGoogle Scholar
  28. 28.
    Zhao, B., Guo, C.X., Bai, B.R., Cao, Y.J.: An improved particle swarm optimization algorithm for unit commitment. International Journal of Electrical Power & Energy Systems 28(7), 482–490 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Luís A. C. Roque
    • 1
  • Dalila B. M. M. Fontes
    • 2
  • Fernando A. C. C. Fontes
    • 3
  1. 1.ISEP-DEMA/GECADInstituto Superior de Engenharia do PortoPortugal
  2. 2.FEP/LIAAD-INESC Porto L.A.Universidade do PortoPortugal
  3. 3.FEUP/ISR-PortoUniversidade do PortoPortugal

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