Analysing wind power ramp events and improving very short-term wind power predictions by including wind speed observations

Though wind power predictions have been consistently improved in the last decade, persistent reasons for remaining uncertainties are sudden large changes in wind speed, so-called ramps. Here, we analyse the occurrence of ramp events in a wind farm in Eastern Germany and the performance of a wind power prediction tool in forecasting these events for forecasting horizons of 15 and 30 min. Results on the seasonality of ramp events and their diurnal cycle are presented for multiple ramp definition thresholds. Ramps were found to be most frequent in March and April and least frequent in November and December. For the analysis, the wind power prediction tool is fed by different wind velocity forecast products, for example, numerical weather prediction (NWP) model and measurement data. It is shown that including observational wind speed data for very short-term wind power forecasts improves the performance of the power prediction tool compared to the NWP reference, both in terms of ramp detection and in decreasing the mean absolute error between predicted and generated wind power. This improvement is enhanced during ramp events, highlighting the importance of wind observations for very short-term wind power prediction.

Automated summary

Although wind power predictions have shown improvement in the past decade, uncertainties still remain due to sudden large changes in wind speed. Analysis of a wind farm in Eastern Germany found that ramp events were most frequent in March and April, and least frequent in November and December. Furthermore, incorporating observational wind speed data significantly improved the performance of the wind power prediction tool, especially during ramp events.