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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
Article

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.

Keywords

Unit commitment Genetic algorithms Hybrid metaheuristics Electrical power generation 

Notes

Acknowledgments

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).

References

  1. Abookazemi K, Mustafa M, Ahmad H (2009) Structured genetic algorithm technique for unit commitment problem. Int J Recent Trends Eng 1(3):135–139Google Scholar
  2. Arroyo J, Conejo A (2002) A parallel repair genetic algorithm to solve the unit commitment problem. IEEE Trans Power Syst 17:1216–1224CrossRefGoogle Scholar
  3. Bard J (1988) Short-term scheduling of thermal electric generators using Lagragian relaxation. Oper Res 36(5):756–766CrossRefzbMATHMathSciNetGoogle Scholar
  4. Bean J (1994) Genetic algorithms and random keys for sequencing and optimization. Oper Res Soc Am J Comput 6(2):154–160zbMATHGoogle Scholar
  5. Carrion M, Arroyo J (2006) A computationally efficient mixed-integer linear formulation for the thermal unit commitment problem. IEEE Trans Power Syst 21(3):1371–1378CrossRefGoogle Scholar
  6. Cohen AI, Yoshimura M (1983) A branch-and-bound algorithm for unit commitment. IEEE Trans Power Appar Syst 102(2):444–451CrossRefGoogle Scholar
  7. Dang C, Li M (2007) A floating-point genetic algorithm for solving the unit commitment problem. Eur J Oper Res 181(4):1370–1395CrossRefzbMATHGoogle Scholar
  8. Ericsson M, Resende M, Pardalos P (2002) A genetic algorithm for the weight setting problem in OSPF routing. J Comb Optim 6(3):299–333CrossRefzbMATHMathSciNetGoogle Scholar
  9. Fan W, Liao Y, Lee J, Kim Y (2012) Evaluation of two Lagrangian dual optimization algorithms for large-scale unit commitment problems. J Electr Eng Technol 7(1):17–22CrossRefGoogle Scholar
  10. Fontes DBMM, Gonçalves JF (2007) Heuristic solutions for general concave minimum cost network flow problems. Networks 50(1):67–76Google Scholar
  11. Fontes DBMM, Gonçalves JF (2012) A multi-population hybrid biased random key genetic algorithm for hop-constrained trees in nonlinear cost flow networks. Optim Lett 7(6):1–22Google Scholar
  12. Frangioni A, Gentile C (2006) Solving nonlinear single-unit commitment problems with ramping constraints. Oper Res 54(4):767–775CrossRefzbMATHGoogle Scholar
  13. Frangioni A, Gentile C, Lacalandra F (2008) Solving unit commitment problems with general ramp constraints. Electr Power Energy Syst 30(5):316–326CrossRefGoogle Scholar
  14. Frangioni A, Gentile C, Lacalandra F (2009) Tighter approximated MILP formulations for unit commitment problems. IEEE Trans Power Syst 24(1):105–113CrossRefGoogle Scholar
  15. Michalewicz Z, Janikow C (1991) Semidefinite programming: a practical application to hydro-thermal coordination. In: Proceedings of the fourteenth international power systems computation conference (PSCC), Seville, SpainGoogle Scholar
  16. Gonçalves J, Resende M (2010) Biased random-key genetic algorithms for combinatorial optimization. J Heuristics 17(5):487–525CrossRefGoogle Scholar
  17. Gonçalves J, Resende M (2011) A parallel multi-population genetic algorithm for a constrained two-dimensional orthogonal packing problem. J Comb Optim 22(2):180–201CrossRefMathSciNetGoogle Scholar
  18. Gonçalves J, Mendes JM, Resende M (2008) A genetic algorithm for the resource constrained multi-project scheduling problem. Eur J Oper Res 189(3):1171–1190CrossRefzbMATHGoogle Scholar
  19. Hadji M, Vahidi B (2012) A solution to the unit commitment problem using imperialistic competition algorithm. IEEE Trans Power Syst 27(1):117–124CrossRefGoogle Scholar
  20. Huang K, Yang H, Yang C (1998) A new thermal unit commitment approach using constraint logic programming. IEEE Trans Power Syst 13(3):936–945CrossRefGoogle Scholar
  21. Jeong Y, Park J, Shin J, Lee K (2009) A thermal unit commitment approach using an improved quantum evolutionary algorithm. Electr Power Compon Syst 37(7):770–786CrossRefGoogle Scholar
  22. Jiang R, Wang J, Guan Y (2012) Robust unit commitment with wind power and pumped storage hydro. IEEE Trans Power Syst 27(2):800–810CrossRefGoogle Scholar
  23. Juste K, Kita H, Tanaka E, Hasegawa J (1999) An evolutionary programming solution to the unit commitment problem. IEEE Trans Power Syst 14(4):1452–1459CrossRefGoogle Scholar
  24. Kallrath J, Pardalos P, Rebennack S, Scheidt M (2009) Optimization in the energy industry. Energy systems, Springer, BerlinGoogle Scholar
  25. Kazarlis S, Bakirtzis A, Petridis V (1996) A genetic algorithm solution to the unit commitment problem. IEEE Trans Power Syst 11:83–92CrossRefGoogle Scholar
  26. Kotsireas I, Koukouvinos C, Pardalos P, Simos D (2012) Competent genetic algorithms for weighing matrices. J Comb Optim 24(4):508–525CrossRefzbMATHMathSciNetGoogle Scholar
  27. Lau T, Chung C, Wong K, Chung T, Ho S (2009) Quantum-inspired evolutionary algorithm approach for unit commitment. IEEE Trans Power Syst 24(3):1503–1512CrossRefGoogle Scholar
  28. Lauer G, Sandell N, Bertsekas D, Posbergh T (1982) Solution of large scale optimal unit commitment problems. IEEE Trans Power Appar Syst PAS 101(1):79–96CrossRefGoogle Scholar
  29. Michalewicz Z, Janikow C (1991) Handling constraints in genetic algorithms. In: Belew RK, Booker LB (eds) Proceedings of the fourth international conference on genetic algorithms (ICGA-91). Morgan Kaufmann Publishers, San Mateo, California, University of California, San Diego, 151–157Google Scholar
  30. Muckstadt J, Koenig S (1977) An application of Lagrangian relaxation to scheduling in power-generation systems. Oper Res 25(3):387–403CrossRefzbMATHGoogle Scholar
  31. Ostrowski J, Anjos MF, Vannelli A (2012) Tight mixed integer linear programming formulations for the unit commitment problem. IEEE Trans Power Syst 27(1):39–46CrossRefGoogle Scholar
  32. Padhy N (2000) Unit commitment using hybrid models: a comparative study for dynamic programming, expert system, fuzzy system and genetic algorithms. Int J Electr Power Energy Syst 23(8):827–836CrossRefGoogle Scholar
  33. Padhy N (2004) Unit commitment: a bibliographical survey. IEEE Trans Power Syst 19(2):1196–1205CrossRefMathSciNetGoogle Scholar
  34. Patra S, Goswami S, Goswami B (2009) Fuzzy and simulated annealing based dynamic programming for the unit commitment problem. Expert Syst Appl 36(3):5081–5086CrossRefGoogle Scholar
  35. Rebennack S, Pardalos P, Pereira MV, Iliadis N (2010a) Handbook of power systems I. Energy systems, Springer, BerlinGoogle Scholar
  36. Rebennack S, Pardalos P, Pereira MV, Iliadis N (2010b) Handbook of power systems II. Energy systems, Springer, BerlinGoogle Scholar
  37. Reeves CR (1993) Modern heuristic techniques for combinatorial problems. Genetic algorithms, Blackwell Scientific Publications, OxfordGoogle Scholar
  38. Rong A, Hakonen H, Lahdelma R (2008) A variant of the dynamic programming algorithm for unit commitment of combined heat and power systems. Eur J Oper Res 190:741–755CrossRefzbMATHGoogle Scholar
  39. Roque L, Fontes DBMM, Fontes FACC (2011) A biased random key genetic algorithm approach for unit commitment problem. Lect Notes Comput Sci 6630(1):327–339CrossRefGoogle Scholar
  40. Roque L, Fontes DBMM, Fontes FACC (2012) BRKGA adapted to multiobjective unit commitment: solving Pareto frontier for the UC multiobjective problem, ICORES 2012. In: Proceedings of the 1st international conference on operations research and enterprise systems, 64–72Google Scholar
  41. Salam S (2007) Unit commitment solution methods. Proc World Acad Sci Eng Technol 26:600–605Google Scholar
  42. Schneider F, Klabjan D, Thonemann U (2013) Incorporating demand response with load shifting into stochastic unit commitment. doi: 10.2139/ssrn.2245548
  43. Sen S, Kothari D (1998) Optimal thermal generating unit commitment: a review. Electr Power Energy Syst 20:443–451CrossRefGoogle Scholar
  44. Simoglou CK, Biskas PN, Bakirtzis AG (2010) Optimal self-scheduling of a thermal producer in short-term electricity markets by MILP. IEEE Trans Power Syst 25(4):1965–1977CrossRefGoogle Scholar
  45. Sourirajan K, Ozsen L, Uzsoy R (2009) A genetic algorithm for a single product network design model with lead time and safety stock considerations. Eur J Oper Res 197(2):38–53CrossRefGoogle Scholar
  46. Sun L, Zhang Y, Jiang C (2006) A matrix real-coded genetic algorithm to the unit commitment problem. Electr Power Syst Res 76:716–728CrossRefGoogle Scholar
  47. Turgeon A (1978) Optimal scheduling of thermal generating units. IEEE Trans Autom Control 23:1000–1005CrossRefzbMATHGoogle Scholar
  48. Valenzuela J, Smith A (2002) A seeded memetic algorithm for large unit commitment problems. J Heuristics 8(2):173–195CrossRefGoogle Scholar
  49. Venkatesh B, Jamtsho T, Gooi H (2007) Unit commitment: a fuzzy mixed integer linear programming solution. IET Gener Transm Distrib 1(5):836–846CrossRefGoogle Scholar
  50. Viana A, Pedroso J (2013) A new MILP-based approach for unit commitment in power production planning. Electr Power Energy Syst 44(1):997–1005CrossRefGoogle Scholar
  51. Wang Q, Guan Y, Wang J (2012) A chance-constrained two-stage stochastic program for unit commitment with uncertain wind power output. IEEE Trans Power Syst 27(1):206–215CrossRefGoogle Scholar
  52. Zhao B, Guo C, Bai B, Cao Y (2006) An improved particle swarm optimization algorithm for unit commitment. Int J Electr Power Energy Syst 28(7):482–490CrossRefGoogle Scholar
  53. Zheng Q, Wang J, Pardalos P, Guan Y (2012) A decomposition approach to the two-stage stochastic unit commitment problem. Ann Oper Res 1–24Google Scholar

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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