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
Link To Document :
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