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Energy system integration enables raising operational synergies by coupling the energy infrastructures for electricity, methane, and hydrogen. However, this coupling reinforces the infrastructure interdependencies, increasing the need for integrated modeling of these infrastructures. To analyze the cost-efficient, sustainable, and secure dispatch of applied, large-scale energy infrastructures, an extensive and non-linear optimization problem needs to be solved. This paper introduces a nested decomposition approach with three stages. The method enables an integrated and full-year consideration of large-scale multi-energy systems in hourly resolution, taking into account physical laws of power flows in electricity and gas transmission systems as boundary conditions. For this purpose, a zooming technique successively reduces the temporal scope while first increasing the spatial and last the technical resolution. A use case proves the applicability of the presented approach to large-scale energy systems. To this end, the model is applied to an integrated European energy system model with a detailed focus on Germany in a challenging transport situation. The use case demonstrates the temporal, regional, and cross-sectoral interdependencies in the dispatch of integrated energy infrastructures and thus the benefits of the introduced approach. [ABSTRACT FROM AUTHOR]

Dynamic thermal rating of overhead lines is a promising approach to increase transmission capacity by calculating weather-dependent thermal ratings (ampacities) of overhead lines in real time instead of using constant ratings. However, knowledge about ampacity is not only needed in real time but also on a day-ahead basis within network operational planning in order to assess network security. As life of humans may be endangered by inadmissible sag of overhead lines when current limits are violated there are high safety requirements concerning thermal ratings. Therefore, ampacity forecasts have to be complemented by a description of forecast uncertainty. So far, there is no method to forecast ampacities on a day-ahead basis considering uncertainty. As a comprehensive description of uncertainty is given by probability densities, this paper presents a novel method to calculate probability density functions of future ampacities based on probabilistic weather forecasts. The method's functionality is proved by application to exemplary data. [ABSTRACT FROM AUTHOR]