  The future energy system will largely depend on volatile renewable energy
sources and temperature-dependent loads, which makes the weather a central
influencing factor. This article presents a novel approach for simulating
weather scenarios for robust large-scale power system analysis. By applying
different signal analysis methods, historical weather data is decomposed into
its spectral components, processed appropriately, and then used to generate
random, self-consistent weather data. In this process, any weather parameters
of different locations can be considered, while their respective dependencies
are mapped. The added value is demonstrated by coupling with a state-of-the-art
large-scale energy system model for Europe. It is shown that the integrated
consideration of different weather influences allows a quantification of the
range of fluctuation of various parameters - such as the feed-in of wind and
solar power - and thus provides the basis for future resilient grid planning
approaches.
