The integration of increasing amounts of volatile renewable energy sources leads to a growing amount of uncertainty. In addition, the number of small-scale renewable units grows rapidly, and the resulting decentralization of supply and demand patterns affects especially the underlying electricity networks. Consequently, reliable and performative forecasts are required, especially for network operators, to cope with the stochastic nature of highly decentralized electricity systems. Therefore, this paper addresses the challenge of selecting among a set of competing forecasting models to predict congestions in power systems with high shares of distributed and uncertain generation. Here, an event-based evaluation framework is applied to different, inter alia, copula-based forecasting models, and compared to other probabilistic metrics, with regard to the resulting congestions. To demonstrate the applicability of this event-based approach, empirical data for a high-voltage distribution network in Germany is used together with intra-day weather forecasts. Furthermore, the different forecasting models are benchmarked to each other with regard to their computational performance.

The energy sector is shifting towards decentralization and digitalization in view of climate change mitigation and decarbonization. In this context, the Web of Cells (WoC) approach is emerging. It aims at providing seamless coordination of flexibilities across network operators with the potential to incentivise flexibility usage more approriately, aligning it with system needs. Within this context, this paper makes use of a large-scale multi-energy system model to delve into the role of decentralized, small-scale flexibilities, with an emphasis on heat pump systems in residential buildings. Using a computationally tractable approximation of demand-side flexibilities, we explore the economic ramifications of integrating these flexibilities into the future energy system. A case study, envisioning decarbonization in Germany by 2050, sheds light on the implications for a decarbonized and decentralized energy sector. Key findings reveal that while these demand-side flexibilities can be economically advantageous for consumers, they also exert influence on system prices and the profitability of storage, as well as cross-sectoral units. The potential surge in market value for renewables and other technologies within a system following the WoC approach is highlighted. To foster collaborative exploration, the model's open-source code and the data set, vital for a 2050 scenario, are made available, supporting a deeper, collective assessment of the future's energy system.

This paper proposes a novel approach to forecast congestions in high-voltage grids with high shares of distributed photovoltaic (PV) infeed. The approach is based on a physical PV model using intra-day numerical weather prediction (NWP) input data. Subsequently, probabilistic forecasts are generated based on Kernel density estimators (KDE) and Copula, describing the multivariate spatial dependencies for the marginal distributions of forecasting and approximation errors. Finally, a probabilistic power flow (PPF) using a linearized AC version is proposed, combining the benefits of high accuracy with high computational performance. To assess and quantify the overall advantages of this approach, a case study is carried out for an existing power system.