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Short-term wind power predidion based on spatial model
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DOI：10.1016/j.renene.2016.09.069.png)
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Large-scale integration of wind energy into power systems may cause operational problems due to the stochastic nature of wind. A short-term wind power prediction model based on physical approach and spatial correlation is proposed to characterize the uncertainty and dependence structure of wind turbines' outputs in the wind farm. Firstly, continuous partial differential equation of each wind turbine has been developed according to its specific spatial location and the layout of its neighboring correlated wind turbines. Then, spatial correlation matrix of wind speed is derived by discretizing differential equation at each wind turbine using a finite volume method (FVM). Wind speed at each turbine is acquired by solving the relevant differential equation under given boundary conditions. Finally, the wind speed is converted to wind power production via a practical power curve model. Prediction results showed that the spatial correlation model can accurately characterize the correlations among outputs of wind turbines and reduce the error of short-term wind power prediction. (C) 2016 Elsevier Ltd. All rights reserved.
Keyword:
Wind farm
Power prediction
Spatial correlation model
Wake effect
Finite volume method (FVM)
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期刊
IF:
9.1
论文数:
2.6W
被引数:
12.1W
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