Application of Auto-Regressive Models to U.K. Wind Speed Data for Power System Impact Studies

作者：Hill David C^*; McMillan David; Bell Keith R W; Infield David

来源：IEEE Transactions on Sustainable Energy, 2012, 3(1): 134-141.

DOI：10.1109/TSTE.2011.2163324

• Autoregression moving average (ARMA) models
• power system impact
• regression
• wind energy
• wind forecasting

Scientific research to characterize the long-term wind energy resource is plentiful. However, if the impact of wind power on the electric power system is the goal of modeling, consideration must be given to diurnal and seasonal effects, as well as the correlation of wind speed between geographical areas. This paper provides such detail bymodeling these effects explicitly, enabling accurate evaluations of wind power impact on future power systems to be carried out. This is increasingly important in the context of ambitious wind energy targets driven in the U.K., for example, by the requirement for 20% of Europe%26apos;s energy to bemet from renewable energy sources by 2020. Both univariate and multivariate auto-regressive models are presented here and it is shown how they can be applied to geographically dispersed wind speed data. These models are applied to suitably de-trended data. The accuracy of the models is assessed both by inspection of the residuals and by assessment of the forecasting accuracy of the models. Finally, it is shown how the models can be used to synthesize wind speed and thus wind power time series with the correct seasonal, diurnal, and spatial diversity characteristics.

• 出版日期2012-1