Open AccessTime series modelling of power output for large‐scale wind fleets
Author(s) -
Sturt Alexander,
Strbac Goran
Publication year - 2011
Publication title -
wind energy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.743
H-Index - 92
eISSN - 1099-1824
pISSN - 1095-4244
DOI - 10.1002/we.459
Subject(s) - wind power , volatility (finance) , econometrics , univariate , wind speed , autoregressive integrated moving average , autoregressive model , time series , dispatchable generation , meteorology , wind power forecasting , electric power system , power (physics) , environmental science , computer science , statistics , mathematics , engineering , geography , renewable energy , distributed generation , electrical engineering , physics , multivariate statistics , quantum mechanics
Simulations of power systems with high wind penetration need to represent the stochastic output of the wind farms. Many studies use historic wind data directly in the simulation. However, even if historic data are used to drive the realized wind output in scheduling simulations, a model of the wind's statistical properties may be needed to inform the commitment decisions for the dispatchable units. There are very few published studies that fit models to the power output of nation‐sized wind fleets rather than the output at a single location. We fitted a time series model to hourly, time‐averaged, aggregated wind power data from New Zealand, Denmark and Germany, based on univariate, second‐order autoregressive drivers. Our model is designed to reproduce the asymptotic distribution of power output, the diurnal variation and the volatility of power output over timescales up to several hours. For the cases examined here, it was also found to provide a generally good representation of the overall distribution of power output changes and the variation of volatility with power output level, as well as an acceptable representation of the distribution of calm periods. Copyright © 2011 John Wiley & Sons, Ltd.