Matthias Lange

Short-term prediction of the aggregated power output of wind farms—a statistical analysis of the reduction of the prediction error by spatial smoothing effects

Abstract We discuss the accuracy of the prediction of the aggregated power output of wind farms distributed over given regions. Our forecasting procedure provides the expected power output for a time horizon up to 48xa0h ahead. It is based on the large-scale wind field prediction which is generated operationally by the German weather service. Our investigation focuses on the statistical analysis of the power prediction error of an ensemble of wind farms compared to single sites. Due to spatial smoothing effects the relative prediction error decreases considerably. Using measurements of the power output of 30 wind farms in Germany we find that this reduction depends on the size of the region. To generalize these findings an analytical model based on the spatial correlation function of the prediction error is derived to describe the statistical characteristics of arbitrary configurations of wind farms. This analysis shows that the magnitude of the error reduction depends only weakly on the number of sites and is mainly determined by the size of the region, e.g for the size of a typical large utility (∼370xa0km in diameter)

New developments in wind energy forecasting

An overview of new and current developments in wind power forecasting is given where the focus lies upon practical implementations and experiences concerning the operational systems in Europe. In general, modern short-term wind power prediction systems use either statistical or physical approaches to determine the anticipated wind power based on numerical weather forecasts. As an example the physical system Previento is described in detail. The typical accuracy of the forecasts for single wind farms as well as the aggregated production is shown. One focus of this paper is the intelligent use of multiple input from numerical weather models to improve the accuracy of the power forecast. The two main approaches that are applied operationally are ensemble predictions from one weather model and the combination of different numerical weather models. A weather-dependent combination tool that exploits the capabilities of numerical models of different weather services is described in detail. In the future, wind power predictions will be embedded deeper in the processes of grid operators and traders. An example shown here are highly localized predictions for specific grid points which can directly be used as input for power flow calculations, grid management or day-ahead congestion forecasts (DACF). In addition, the market integration of wind energy is pushed in a number of countries such that trading platforms which convert fluctuating wind power production into electricity products become more important.

The Power of Small: The Effects of Distributed Energy Resources on System Reliability

Its a sunny day in Honolulu, Hawaii , and rooftop photovoltaic (PV) systems across the island are serving a significant penetration of the load. A large generator trips offline, and frequency drops quickly-to the point where a block of load is shed to restore the balance between generation and load. However, disengaging those feeders disconnects not only the load but also the PV generation on those feeders; so balance is not restored and frequency continues to drop.