Abstract in Englisch:

Increasing infeed from renewable energy sources poses considerable challenges to system operators (SOs) who are in charge of power system reliability. Accordingly, the frequency of network congestion and the corresponding congestion management costs have increased dramatically over the last years and give reason to extensively discuss alternative approaches. Among these, flexibility markets bear the potential to complement existing congestion management practices by incentivising decentralised resources with large potentials of flexibility to participate in relieving congestion. For this reason, multiple demonstration projects across Europe are currently testing different flexibility market designs. We contribute to this on-going discussion by investigating the auction design of such a flexibility market. We analytically derive the optimal procurement strategy of a SO within a flexibility market platform, recurring to the well-established methodology of the classical Newsvendor problem and extending it in a stochastic programming framework with two stages. We apply our model to a case study of a transformer that is frequently congested due to high infeed from wind farms. Based on an analysis of relevant sources of flexibility, differentiated concerning lead time and cost structure, we explore the effects of demand uncertainty and information updates between auctions. The results of the case study, including a comprehensive sensitivity analysis, reveal insights that are used to provide policy advice on how to design flexibility procurement markets under specific conditions.