“21st PSCC 2020 papers submission and review platform

Full Program »

Application of microscale wind and detailed wind power plant data in large-scale wind generation simulations

View File
PDF
0.6MB

With increasing wind installations, there is a need to analyze wind generation variability in detail. This paper applies the reanalysis approach for modelling the variability; however, with two important additions. Firstly, high-resolution microscale data is combined with mesoscale reanalysis time series to model local variability in wind. Secondly, as there are often missing technical parameters in large-scale wind power plant datasets, machine learning is used to estimate the missing values. It is shown that such detailed modelling leads to more accurate simulations than a baseline model when compared to historical data from multiple European countries. In addition, applicability of the methodology for analyzing future scenarios with changing wind installations is demonstrated.

Author(s):

Matti Koivisto    
Technical University of Denmark, Department of Wind Energy
Denmark

Konstantinos Plakas    
Technical University of Denmark, Department of Wind Energy
Denmark

Ernesto Rodrigo Hurtado Ellmann    
Technical University of Denmark, Department of Wind Energy
Denmark

Neil Davis    
Technical University of Denmark, Department of Wind Energy
Denmark

Poul Sørensen    
Technical University of Denmark, Department of Wind Energy
Denmark

 

Powered by OpenConf®
Copyright ©2002-2014 Zakon Group LLC