• DocumentCode
    30639
  • Title

    A Two-Echelon Wind Farm Layout Planning Model

  • Author

    Huan Long ; Zijun Zhang

  • Author_Institution
    Dept. of Syst. Eng. & Eng. Manage., City Univ. of Hong Kong, Kowloon, China
  • Volume
    6
  • Issue
    3
  • fYear
    2015
  • fDate
    Jul-15
  • Firstpage
    863
  • Lastpage
    871
  • Abstract
    In this paper, a two-echelon layout planning model is proposed to determine the optimal wind farm layout to maximize its expected power output. In the first echelon, a grid composed of cells with equal size is utilized to model the wind farm, whereas the center of each cell is the potential slot for locating a wind turbine. Optimization models are developed to determine the optimal size of grid cells and the optimal cells for locating wind turbines. In the second echelon, the selected grid cells are then translated to sets of Cartesian coordinates. The model for determining the optimal coordinate rather than the center in a grid cell for locating each wind turbine is formulated. Due to the model complexity in both echelons, the random key genetic algorithm (RKGA) and particle swarm optimization (PSO) algorithm are applied to obtain the optimal solutions in the first and second echelon separately. The comparative analysis between the proposed two-echelon planning model and the traditional grid/coordinate-based planning models is conducted.
  • Keywords
    genetic algorithms; particle swarm optimisation; power generation planning; power grids; power system simulation; wind power plants; Cartesian coordinates; PSO algorithm; RKGA; coordinate-based planning models; grid cells; grid-based planning models; optimal cells; optimal wind farm layout; optimization models; particle swarm optimization algorithm; random key genetic algorithm; two-echelon wind farm layout planning model; wind turbine; Computational modeling; Layout; Planning; Wind farms; Wind speed; Wind turbines; Genetic algorithm; layout planning; optimization; particle swarm optimization (PSO); wind farm;
  • fLanguage
    English
  • Journal_Title
    Sustainable Energy, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1949-3029
  • Type

    jour

  • DOI
    10.1109/TSTE.2015.2415037
  • Filename
    7087404