• DocumentCode
    1797636
  • Title

    A heuristic to generate initial feasible solutions for the Unit Commitment problem

  • Author

    Yi Sun ; Lam, Albert Y. S. ; Li, Victor O. K.

  • Author_Institution
    Dept. of Electr. & Electron. Eng., Univ. of Hong Kong, Hong Kong, China
  • fYear
    2014
  • fDate
    6-11 July 2014
  • Firstpage
    913
  • Lastpage
    920
  • Abstract
    This paper presents a heuristic approach to generate initial feasible solutions for the Unit Commitment (UC) problem in electric power generation. The Chemical Reaction Optimization (CRO) algorithm is implemented to solve this problem. Multiple generator constraints and system constraints are considered. We also program the binary PSO and the Elite PSO (EPSO) for comparison. The proposed heuristic approach is combined with the three optimization algorithms to form H-CRO, H-PSO and H-EPSO. We test the performance of all algorithms on the standard 10-unit system. Simulation results show that the heuristic can improve the performance and CRO provides better convergence than the two PSO algorithms. H-CRO is also tested on a 20-unit and 100-unit system to show its capability. The results provided in this paper suggest that the proposed heuristic approach is a better alternative for solving the UC problem. CRO also has its advantage in optimizing UC problems.
  • Keywords
    convergence of numerical methods; heuristic programming; particle swarm optimisation; power generation dispatch; power generation scheduling; CRO algorithm; EPSO; H-CRO; H-EPSO; H-PSO; UC problem; binary PSO; chemical reaction optimization; electric power generation; elite PSO; heuristic approach; initial feasible solution generation; multiple generator constraint; particle swarm optimization; system constraint; unit commitment problem; Algorithm design and analysis; Chemicals; Economics; Heuristic algorithms; Optimization; Power generation; Schedules; Chemical reaction optimization; heuristic; power grid; unit commitment;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks (IJCNN), 2014 International Joint Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    978-1-4799-6627-1
  • Type

    conf

  • DOI
    10.1109/IJCNN.2014.6889548
  • Filename
    6889548