The progressing integration of renewables (RE) into distribution grids leads to bidirectional power flows. Due to the fact that the power infeed by RE exceeds the grid capacities, bottlenecks occur increasingly and thereby renewable power generation has to be curtailed. For the decision if countermeasures like flexibility options could be applied, the knowledge of the amount of curtailed power as well as its local and temporal availability is crucial. This study proposes an approach to determine the curtailed power characteristics, which creates time series simulating the potential vertical power flow at medium voltage/ high voltage (MV/HV) transformers without curtailment. The approach is thereby modular and adjustable to the investigated (or any other) grid, so it achieves a high precision. The power flow at these transformers is modelled by load profiles and power curves of connected RE, which are adjusted individually for each transformer on the basis of the corresponding historical power flow. For validation, the transformers are clustered corresponding to their power flow characteristics and the uncertainty is determined for each cluster. Modelled wind power shows a mean deviation below 2% of total installed wind power. Thereby, the presented modelling approach is able to determine the curtailed power in regions with wind power-related curtailments.

Renewable energies curtailment induced by grid congestions increase due to grown renewable energies integration and the resulting mismatch of grid expansion. Short-term predictions for curtailment can help to increase the efficiency of its management. This paper proposes a novel, holistic approach of a short-term curtailment prediction for distribution grids. The load flow calculations for congestion detection are realized by taking different operational security criteria into account, whereas the models for the node-injections are adjusted to the characteristic of each grid node specifically. The determination of required curtailment based on the resulting congestions considers uncertainties of component loading and its corresponding probability. The forecast model is validated using an actual 110 kV distribution grid located in Germany. In order to meet the requirements of a forecast model designed for operational business, prediction accuracy, and its greatest source of error are analyzed. Furthermore, a suitable length of training data is investigated. Results indicate that a six month time period for maintenance gains the highest accuracy. Curtailment prediction accuracy is better for transmission system operator components than for distribution system operator components, but the Sørensen Dice factor for the aggregated grid shows a high match of historic and predicted curtailment with a value of 0.84 and a low error for curtailed energy, which makes 2.23% of the historic curtailed energy. The model is a promising approach, which can contribute to improvement of curtailment strategies and enable valuable insight into distribution grids.

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Large shares of renewable energy production in the electricity grid make grid expansion and new technologies necessary. The unavailability of grid models to address upcoming research questions led to the development of open source grid models. Our work contributes to establish the open_eGo model for grid simulations by validating its assumptions and results for a rural region with high share of wind energy. In particular, assumptions on electrical parameters and the graph structure of the model are compared to the grid owner's model along with a validation of AC load flow results at the model boundaries. It was found that the graph structure deviates in the degree of nodes and connection characteristic. These deviations are less exterior nodes and a lower maximum degree of nodes as well as a higher number of parallel lines in the open_eGo model. The AC load flow results differ slightly in active power and significantly in reactive power, but are more reliable than an aggregation of loads and generation to the extra high voltage (EHV) nodes. Concluding, the open_eGo model has a limited usability for simulating, understanding and optimising DSO grid operation but can enhance EHV-only analysis in large area contexts.

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