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To support evidence-based decision-making, energy system modelers in research, administration and industry rely on the best-available meteorological and/or climatological information on a variety of scales, from the local distribution grid level to the European transmission system, and from short-term forecasts for the operation to climate scenarios for making investment decisions. However, there is a lack of knowledge among energy system modellers about available meteorological data sets, their characteristics and the implications of using different data sets for grid planning and adequacy assessment activities. Standardised tools and methods to add the analysis of climate change and/or climate uncertainty to user workflows or to conduct tailored bias correction, validation or downscaling tasks rarely exist. The energy systems modelling and climate modelling communities can be considered at least partially disconnected. This observed disconnect hinders energy system modellers in fully making use of the available meteorological information, prevents them from tapping the full potential of the data and, consequently, potentially leads to sub-optimal or inefficient decisions.In this presentation, we introduce approaches for deriving relevant information for grid planning and adequacy assessment from climate simulations to support overcoming the current disconnect between the two disciplines. In particular, we show how results obtained from energy system simulations can be linked to certain characteristics of the meteorological input, e.g. the occurrence of extreme events or prevailing weather regimes, through running and evaluating a semi-operational energy system modeling workflow for a great number of weather scenarios. Furthermore, using insights gained from this analysis we derive a criticality measure for certain weather situations and show how criticality can be used to group weather years limiting thereby the weather scenarios required for adequacy assessment and/or grid planning purposes. For this study, we use meteorological data from the new Pan-European Climatic Data Base (PECD version 4.1) and, as one of the first studies, climate projections and historical simulations from the Destination Earth Digital Twin for Climate Adaptation.
Schyska et al. (Fri,) studied this question.