Simulation of Short-term Wind Speed Forecast Errors using a Multi-variate ARMA(1,1) Time-series Model

Abstract: The short-term (1 to 48 hours) predictability of wind power production from wind power plants in a power system is critical to the value of wind power. Advanced wind power prediction tools, based on numerical weather prediction models and designed for power system operators, are being developed and continuously improved. One objective of the EU-supported WILMAR (Wind power Integration in Liberalised electricity MARkets) project is to simulate the stochastic optimization of the operation of the Nordic and German power systems, in order to estimate the value of potential improvements of wind power prediction tools. For power system simulations including wind power, a model must be developed to simulate realistic wind speed predictions with adjustable accuracy, in which the correlations between wind speed prediction error at the spatially distributed wind power plants is accurate. The simulated wind speed predictions are then converted to aggregate wind power predictions for regions within the Nordic and German power systems. A Wind Speed Forecast Error Simulation Model, based on a multi-variate ARMA(1,1) time-series model, has been developed in Matlab. The accuracy of the model in representing real wind speed predictions in Denmark has been assessed, and various errors resulting from practical limitations of input data have been quantified.