Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Short-term wind power prediction based on spatial model

Ye, Lin, Zhao, Yongning, Zeng, Cheng and Zhang, Cihang 2017. Short-term wind power prediction based on spatial model. Renewable Energy 101 , pp. 1067-1074. 10.1016/j.renene.2016.09.069

Full text not available from this repository.

Abstract

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.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Publisher: Elsevier
ISSN: 0960-1481
Date of Acceptance: 26 September 2016
Last Modified: 20 Oct 2021 01:21
URI: https://orca.cardiff.ac.uk/id/eprint/124805

Citation Data

Cited 67 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item