Herzog, Alina KatharinaAlina KatharinaHerzogJost, DominikDominikJostMeinecke, SteffenSteffenMeineckePauscher, LukasLukasPauscher2025-08-122025-08-122025-06-26https://publica.fraunhofer.de/handle/publica/490467Renewable energy injection forecasts are indispensable for a stable energy supply. Due to their strong dependence on the weather forecast, they are subject to uncertainties. This conference contribution analyzes uncertainties in wind energy forecast from the perspective of the electrical grid in Germany and shows ways of dealing with these uncertainties in grid operation. The successful expansion of renewable energy in recent years has greatly increased the impact of uncertainty in renewable energy forecasts and is likely to continue with the expected increase in the installed capacity of wind and solar energy. This requires new optimized methods for dealing with uncertainties in the operational grid planning processes. To better understand the uncertainty characteristics, this contribution performs an analysis of the aggregation level of transmission grid buses over Germany, focused on day-ahead wind energy forecast uncertainty. The contribution’s focus is the regional, seasonal, and power production dependency of the uncertainty. The chosen aggregation level plays a particularly important role for the transmission system operators (TSOs). Forecast errors and uncertainties on this level influence the entire grid calculation and operational planning process. Furthermore, the contribution evaluates the use of ensemble weather forecasts to quantify the uncertainties in operational grid planning. Probabilistic renewable power forecasts based on weather ensembles offer the possibility of dynamically adapting the safety margins to the uncertainties of the given weather situation. This requires knowledge of the quality and reliability of the probabilistic forecast. For this purpose, this contribution analyzes the representation of the full range uncertainty in the probabilistic wind energy forecast spread for several weather ensembles and calibrations.enForecastuncertaintyensemblepresentation