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)


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.


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