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Application of Evolutionary Neural Network to Power System Unit Commitment

  • Po-Hung Chen
  • Hung-Cheng Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)

Abstract

This paper presents an evolutionary neural network (ENN) approach for solving the power system unit commitment problem. The proposed ENN approach combines a genetic algorithm (GA) with a back-propagation neural network (BPNN). The BPNN is first used as a dispatch tool to generate raw unit combinations for each hour temporarily ignoring time-dependent constraints. Then, the proposed decoding algorithm decodes the raw committed schedule of each unit into a feasible one. The GA is then used to find the finally optimal schedule. The most difficult time-dependent minimal uptime/downtime constraints are satisfied throughout the proposed encoding and decoding algorithm. Numerical results from a 10-unit example system indicate the attractive properties of the proposed ENN approach, which are a highly optimal solution and faster rate of computation.

Keywords

Unit Commitment Operation Schedule Genetic Algorithm Method Thermal Unit Unit Commitment Problem 
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.

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References

  1. 1.
    Wood, A.J., Wollenberg, B.F.: Power Generation, Operation, and Control, 2nd edn. John Wiley & Sons, New York (1996)Google Scholar
  2. 2.
    Padhy, N.P.: Unit Commitment-A Bibliographical Survey. IEEE Trans. on Power Systems 19, 1196–1205 (2004)CrossRefGoogle Scholar
  3. 3.
    Sasaki, H., Watanabe, M., Yokoyama, R.: A Solution Method of Unit Commitment by Artificial Neural Network. IEEE Trans. on Power Systems 7, 974–981 (1992)CrossRefGoogle Scholar
  4. 4.
    Wang, C., Shahidehpour, S.M.: Effects of Ramp-rate Limits on Unit Commitment and Economic Dispatch. IEEE Trans. on Power Systems 8, 1341–1350 (1993)CrossRefGoogle Scholar
  5. 5.
    Chang, H.C., Chen, P.H.: A Genetic Algorithm for Solving the Unit Commitment Problem. In: Proceedings of the International Power Engineering Conference, pp. 831–836 (1997)Google Scholar
  6. 6.
    Weerasooriya, S., El-Sharkawi, M.A.: Identification and Control of a DC Motor using Back-propagation Neural Networks. IEEE Trans. on Energy Conversion 6, 663–669 (1991)CrossRefGoogle Scholar
  7. 7.
    Riedmiller, M., Braun, H.: A Direct Adaptive Method for Faster Back Propagation Learning- The RPROP Algorithm. In: IEEE Int’l Conference on Neural Networks, vol. 1, pp. 586–591 (1993)Google Scholar
  8. 8.
    Davis, L.: Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York (1991)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Po-Hung Chen
    • 1
  • Hung-Cheng Chen
    • 2
  1. 1.Department of Electrical EngineeringSt. John’s UniversityTaipeiTaiwan
  2. 2.Institute of Information and Electrical EnergyNational Chin-Yi Institute of TechnologyTaichungTaiwan

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