Post-processing for an on-demand extremes digital twin – a multi-model approach for wind and solar energy production

 

The amount of wind and solar energy fed into the European power grid increases rapidly and with the transition to a fossil fuel-free energy production, relying heavily on renewable energy sources, more accurate predictions for both high-resolution temporal and spatial scales are needed to ensure, most of all, grid stability. This is even more the case with extreme events, both extremes in weather across the nowcasting to weeks ahead time scale and combined and non-necessarily extreme weather, events such as Dunkelflaute or longer lasting solar/wind droughts. Accurate, frequently updated and especially on-demand available predictions of the expected power production are needed. Post-processing methods enable targeted forecasts of meteorological parameters both at site-location and regional level, which can server for a conversion to power production, particularly a direct conversion of NWP predictions and observations to power production.

For an on-demand extreme digital twin forecasting system, fast and transferable post-processing methods, able to account for the upper/lower bounds of the respective distributions are needed. Furthermore, they need to be able to either generate on-the-fly (semi-)synthetic power production data or a reduced set of both observation and NWP input data. The latter is essential when moving towards hyper-resolution NWP simulations with only a limited set of training data available.

Within the on-demand extreme digital twin initiative, several post-processing methods, statistical and (deep) machine learning, were implemented and applied to selected use cases for on/offshore wind and solar production extreme events. Here, we demonstrate (i) the capability of the Kalman filter, the analogs method, IrradPhyDNet, a sequence-2-sequence LSTM, Random Forest, and other machine learning/statistical methods in extreme event prediction, (ii) evaluate the methods skills by using heterogeneous (multiple NWP models, observations, climatologies, etc.) and varying length input data and real/(semi-)synthetic power data as target, and (iii) present a workflow for an on-demand prediction for wind and solar energy production including user-interaction.