Objective and BackgroundEnsemble weather forecasts have been promoted by meteorologists for use due to their inherent capability of quantifying forecast uncertainty. Despite this advantage over deterministic forecasts, their application is still limited since many processes to manage power systems are not ready to deal with uncertain information. The probabilistic power forecast evaluation tool ProPower has been developed at DLR to demonstrate possible applications of probabilistic forecasts in power systems. Furthermore, ProPower is used to assess the value of probabilistic forecasts for PV and wind power systems compared to the usage of deterministic forecasts, but also to compare the value of different probabilistic forecasts. This includes post-processing of ensemble forecast, e.g. calibration.MethodUsual approaches to derive the cost-optimal power dispatch within a market zone considering power constraints (e.g. grid capacities, ramp rates) do not account for potential balancing costs arising from errors in wind and solar forecasts. Following [1] DLR has designed a stochastic market clearing model. In this model, expected balancing costs are estimated from a set of scenarios of renewables feed-in that are equivalent to ensemble members. Lately, a second market clearing based on updated forecasts of higher skills has been implemented in ProPower and is thoroughly tested. Currently, we use ECMWF ensemble forecasts [2] for the day-ahead market clearing and the intraday market clearing. However, in the research project WindRamp the benefit of shortest-term Lidar forecasts [3] of an offshore wind farm is tested in a sample power system. In this context the Lidar forecasts got calibrated with the EMOS method suggested by Thorarinsdottir, T., and T. Gneiting [2010].Principal FindingsWe found a positive impact of stochastic market clearing to reduce total power system compared to the deterministic market clearing. The use of Lidar forecasts as forecast updates in an intraday market is beneficial compared with NWP forecasts. Persistence forecasts (+15 min) can be outperformed in unstable atmospheric conditions.Conclusion The ProPower tool is capable to translate probabilistic forecast skill into benefits for sample power systems. ProPower has the potential to analyze which forecasts errors are most expensive to balance and how valuable skillful uncertainty information from different sources (e.g. Lidar shortest-term forecast) is.References[1] Morales, J.M., Zugno, M., Pineda, S., and Pinson, P. (2014): Electricity Market Clearing with Improved Scheduling of Stochastic Production, European Journal of Operational Research[2] Leutbecher, M., and Palmer, T.N. (2007): Ensemble forecasting[3] Theuer, F., Rott, A., Schneemann, J., von Bremen, L., and Kühn, M.: Observer-based power forecast of individual and aggregated offshore wind turbines, Wind Energy Science[4] Thorarinsdottir, T., and T. Gneiting, 2010: Probabilistic forecasts of wind speed: Ensemble model output statistics by using heteroscedastic censored regression. J. Roy. Stat. Soc.