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Abstract

The aim of the project is in the extension of results from Phase 1 on characterizing nomination validity in computationally feasible fashion for strongly meshed gas networks under stochastic uncertainty. To this end, recent results from symbolic computation (comprehensive Gröbner systems) are employed. Moreover, approaches to risk averse optimization in stochastic gas networks with network constraints and different market models, for instance, with or without nodal pricing will be developed.