Advertisement

Genetic Algorithms for Scheduling Generation and Maintenance in Power Systems

  • C. J. Aldridge
  • K. P. Dahal
  • J. R. McDonald
Chapter
Part of the International Series on Microprocessor-Based and Intelligent Systems Engineering book series (ISCA, volume 20)

Abstract

Genetic algorithms (GAs) are search and optimisation methods based on a model of evolutionary adaptation in nature. Unlike traditional ‘hill-climbing’ methods involving iterative changes to a single solution, GAs work with a population of solutions, which is ‘evolved’ in a manner analogous to natural selection. Candidate solutions to an optimisation problem are represented by chromosomes, which for example encode the solution parameters as a numeric string. The ‘fitness’ of each solution is calculated using an evaluation function which measures its worth with respect to the objective and constraints of the optimisation problem.

Keywords

Genetic Algorithm Power System Lagrangian Relaxation Solution String Unit Commitment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Holland, J.H (1975) Adaptation in Natural and Artificial Systems,University of Michigan Press.Google Scholar
  2. 2.
    Davis, L. (1991) Handbook of Genetic Algorithms,Van Nostrand Reinhold.Google Scholar
  3. 3.
    Goldberg, D.E. (1989) Genetic Algorithms in Search, Optimisation, and Machine Learning,Addison-Wesley.Google Scholar
  4. 4.
    Michalewicz, Z. (1994) Genetic Algorithms + Data Structures =Evolution Programs,Springer-Verlag.Google Scholar
  5. 5.
    Mitchell, M. (1996) An Introduction to Genetic Algorithms. MIT Press.Google Scholar
  6. 6.
    GA Archive, web site http://www.aic.nrl.navy.mil/galist/
  7. 7.
    Fonseca, C.M. and Fleming, P.J. (1993) Genetic algorithms for multiobjective optimization: formulation, discussion and generalization, Proceeding of the 5`“ International Conference on Genetic Algorithms (ICGA’93), Morgan Kaufmann Publishers, 416–423.Google Scholar
  8. 8.
    Dahal, K.P., Aldridge, C.J. and McDonald, J.R. (in press) Generator maintenance scheduling using a genetic algorithm with a fuzzy evaluation function, Fuzzy Sets and Systems.Google Scholar
  9. 9.
    Quadstone Limited (1997) Reproductive Plan Language (RPL2), User manual.Google Scholar
  10. 10.
    Kim, H., Hayashi, Y. and Nara, K. (1997) An algorithm for thermal unit maintenance scheduling through combined use of GA, SA and TS, IEEE Transactions on Power Systems 12, 329–335.CrossRefGoogle Scholar
  11. 11.
    Kim, H., Nara, K. and Gen, M. (1994) A method for maintenance scheduling using GA combined with SA, Computers and Industrial Engineering 27, 477480.Google Scholar
  12. 12.
    Grefenstette, J. (1990) A user’s guide to GENESIS. ftp.aic.nrl.navy.mil/pub/galist/src/ga.
  13. 13.
    Whitley, D.L. (1990) GENITOR. Ftp.cs.colostate.edu/pub/GENITOR.tar.Google Scholar
  14. 14.
    Alander, J.T. (1996) An indexed bibliography of genetic algorithms in power engineering. Report 94–1-POWER, University of Vaasa, http://ftp.uwasa.fi,/cs/report94–1/gaPOWERbib.ps.Z.
  15. 15.
    Miranda, V., Srinivasan, D. and Proença, L.M. (1998) Evolutionary computation in power systems, Electrical Power & Energy Systems 19, 45–55.Google Scholar
  16. 16.
    Sheble, G.B. and Fand, G.N. (1994) Unit commitment literature synopsis, IEEE Transactions on Power Systems 9, 128–135.CrossRefGoogle Scholar
  17. 17.
    Garver, L. (1963) Power generation scheduling by integer programming -development of theory, AIEE Transactions 81, 1212–1218.Google Scholar
  18. 18.
    Oliveira, P., McKee, S., and Coles, C. (1992) Lagrangian relaxation and its application to the unit-commitment-economic-dispatch problem, IMA Journal of Mathematics Applied in Business and Industry 4, 261–272.zbMATHGoogle Scholar
  19. 19.
    Oliveira, P., Blair-Fish, J., McKee, S., and Coles, C. (1992) Parallel Lagrangian relaxation in power scheduling, Computer Systems in Engineering 3, 609–612.CrossRefGoogle Scholar
  20. 20.
    Aldridge, C.J., McKee, S. and McDonald, J.R. (1997) Genetic algorithm methodologies for scheduling electricity distribution, in M. Brt ns, M.P. Bendsoe and M.P. Sorensen (eds.), Progress in Industrial Mathematics at ECMI’96, Teubner, 364–371.Google Scholar
  21. 21.
    Aldridge, C.J., McDonald, J.R. and McKee, S. (1997) Unit commitment for power systems using a heuristically augmented genetic algorithm, Proceedings of 2nd International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications (GALESIA’97), IEE Conference Publication 446, 433–438.Google Scholar
  22. 22.
    Cai, X.-Q. and Lo, K.-M. (1997) Unit commitment by a genetic algorithm, Nonlinear Analysis, Theory, Methods & Applications 30, 4289–4299.MathSciNetzbMATHCrossRefGoogle Scholar
  23. 23.
    Hassoun, M.H. and Watta, P. (1994) Optimization of the unit commitment problem by a coupled gradient network and by a genetic algorithm, report no. TR-103697, Electric Power Research Institute.Google Scholar
  24. 24.
    Kazarlis, S.A., Bakirtzis, A.G. and Petridis, V. (1996) A genetic algorithm solution to the unit commitment problem, IEEE Transactions on Power Systems 11, 83–90.CrossRefGoogle Scholar
  25. 25.
    Ma, X., El-Keib, A.A., Smith, R.E. and Ma, H. (1995) A genetic algorithm based approach to thermal unit commitment of electrical power systems, Electrical Power Systems Research 34, 29–36.CrossRefGoogle Scholar
  26. 26.
    Maifeld, T.T. and Sheble, G.B. (1996) Genetic-based unit commitment algorithm, IEEE Transactions on Power Systems 11, 1359–1370.CrossRefGoogle Scholar
  27. 27.
    Numnonda, T., Annakkage, U.D. and Pahalawaththa, N.C. (1996) Unit commitment using stochastic optimisation, Proceedings of Intelligent Systems Applications in Power Systems (ISAP’96), 429–433.Google Scholar
  28. 28.
    Orero, S.O. and Irving, M.R. (1997) A combination of the genetic algorithm and Lagrangian relaxation decomposition techniques for the generation unit commitment problem, Electrical Power Systems Research 43, 149–156.CrossRefGoogle Scholar
  29. 29.
    Sheble, G.B. and Maifeld, T.T. (1994) Unit commitment by geneticalgorithm and expert system, Electrical Power Systems Research 30, 115–121.CrossRefGoogle Scholar
  30. 30.
    Shebleé, G.B., Maifeld, T.T., Brittig, K., Fand, G. and Fukurozaki-Coppinger, S. (1996) Unit commitment by genetic algorithm with penalty methods and a comparison of Lagrangian search and genetic algorithm–economic dispatch example, Electrical Power & Energy Systems 18, 339–346.CrossRefGoogle Scholar
  31. 31.
    Saitoh, H., Inoue, K. and Toyoda, J. (1994) Genetic algorithm approach to unit commitment, in A. Hertz, A.T. Holen and J.C. Rault (eds.) Proceedings of the International Conference on Intelligent System Application to Power Systems, 583–589.Google Scholar
  32. 32.
    Yang, P.-C., Yang, H.-T. and Huang, C.-L. (1996) Solving the unit commitment problem with a genetic algorithm through a constraint satisfaction technique, Electrical Power Systems Research 37, 55–65.CrossRefGoogle Scholar
  33. 33.
    Yang, H.-T., Yang, P.-C. and Huang, C.-L. (1997) A parallel genetic algorithm approach to solving the unit commitment problem: implementation on the transputer networks, IEEE Transactions on Power Systems 12, 661–668.CrossRefGoogle Scholar
  34. 34.
    Oliveira, P., McKee, S., and Coles, C. (1994) Genetic algorithms and optimising large nonlinear systems, in J.H. Johnson, S. McKee and A. Vella (eds.) Artificial Intelligence in Mathematics, OUP, 305–312.Google Scholar
  35. 35.
    Dasguptar, D. and McGregor, D.R. (1994) Thermal unit commitment using genetic algorithms, IEE Proceedings C–Generation, Transmission and Distribution 141, 459–465.CrossRefGoogle Scholar
  36. 36.
    Orero, S.O. and Irving, M.R. (1995) Scheduling of generators with a hybrid genetic algorithm, Proceedings of 1st International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications (GALESIA’95), IEE Conference Publication no. 414, 200–206.Google Scholar
  37. 37.
    Orero, S.O. and Irving, M.R. (1996) A genetic algorithm for generator scheduling in power system, International Journal of Electrical Power and Energy Systems 18, 19–26.CrossRefGoogle Scholar
  38. 38.
    Orero, S.O. and Irving, M.R. (1997) Large scale unit commitment using a hybrid genetic algorithm, International Journal of Electrical Power and Energy Systems 19, 45–55.CrossRefGoogle Scholar
  39. 39.
    IBM (1992) Optimisation Subroutine Library (OSL) Guide and Reference,Release 2.Google Scholar
  40. 40.
    Wielinga, B.J., Schreiber, A.Th. and Breuker J.A. (1992) KADS: A modeling approach to knowledge engineering, Knowledge Acquisition 4, 5–53.CrossRefGoogle Scholar
  41. 41.
    Fourer, R., Gay, D.M. and Kernighan, B.W. (1993) AMPL - A Modeling Language for Mathematical Programming,Boyd & Fraser.Google Scholar
  42. 42.
    Dahal, K.P., and McDonald, J.R. (1998) Generational and steady state genetic algorithms for generator maintenance scheduling problems, in G.D. Smith, N.C. Steele and R. Albrecht (eds.), Artificial Neural Nets and Genetic Algorithms, Proceedings of Third International Conference in Norwich (Icannga’97), Springer-Verlag, Vienna, 260–264.Google Scholar
  43. 43.
    Dahal, K.P., and McDonald, J.R. (1997) A review of generator maintenance scheduling using artificial intelligence techniques, Proceedings of 32nd Universities Power Engineering Conference (UPEC’97), 787–790.Google Scholar
  44. 44.
    Dahal, K.P. and McDonald, J.R. (1997) Generator maintenance scheduling of electric power systems using genetic algorithms with integer representation, Proceedings of 2nd International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications (GALESIA’97), IEE Conference Publication 446, 456–461.Google Scholar
  45. 45.
    Dopazo, J.F. and Merrill, H.M. (1975) Optimal generator maintenance scheduling using integer programming, IEEE Transactions on Power Apparatus and Systems 94, 1537–1545.CrossRefGoogle Scholar
  46. 46.
    Egan, G.T., Dillon, T.S. and Morsztyn, K. (1976) An experimental method of determination of optimal maintenance schedules in power systems using branch-and-bound technique, IEEE Transactions on Systems, Man and Cybernetics 6, 538–547.CrossRefGoogle Scholar
  47. 47.
    Yamayee, Z. and Sidenblad, K. (1983) A computationally efficient optimal maintenance scheduling method, IEEE Transactions on Power Apparatus and Systems 102, 330–338.CrossRefGoogle Scholar
  48. 48.
    Yang, S. (1994) Maintenance scheduling of generating units in a power system, in X. Wang and J.R. McDonald (eds.), Modern Power System Planning, McGraw-Hill, London, 247–307.Google Scholar
  49. 49.
    Bretthauer, G., Gamaleja, T., Handschin, E., Neumann U. and Hoffmann, W. (1998) Integrated maintenance scheduling system for electrical energy systems, IEEE Transactions on Power Delivery 13, 655–660.CrossRefGoogle Scholar
  50. 50.
    Burke, K.B., Clarke, J.A. and Smith, A.J. (1998) Four methods for maintenance scheduling, in G.D. Smith, N.C. Steele and R. Albrecht (eds.), Artificial Neural Nets and Genetic Algorithms, Proceedings of Third International Conference in Norwich (ICANNGA’97), Springer-Verlag, Vienna, 265–270.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 1999

Authors and Affiliations

  • C. J. Aldridge
    • 1
  • K. P. Dahal
    • 1
  • J. R. McDonald
    • 1
  1. 1.Centre for Electrical Power EngineeringUniversity of StrathclydeGlasgowUK

Personalised recommendations