ProPower: A new tool to assess the value of probabilistic forecasts in power systems management

Kurzfassung

Objective and Background Ensemble 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. Method Usual approaches to derive the cost-optimal power dispatch within a market region considering power constraints (e.g. grid capacities, ramp rates) do not account for potential balancing costs arising from errors in wind and solar forecasts. Dispatch decisions based on pure deterministic forecasts lead to sub-optimal market clearing as shown by Morales et al. [2014] for a simplistic network. To overcome this issue they proposed a stochastic market clearing model. In this model, average balancing costs are estimated from a set of scenarios of renewables feed-in that are equivalent to ensemble members from an ensemble prediction system. 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 [Leutbecher and Palmer, 2007] for the day-ahead market clearing and the intraday market clearing. However, in the research project WindRamp the benefit of shortest-term Lidar forecasts [Theuer et al., 2022] 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 Findings We 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. However, it is hard to demonstrate that the use of Lidar forecasts is more beneficial than persistence forecasts for the offshore wind park. Most often in conditions with unstable atmospheric stratification Lidar forecasts are superior. 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.