The development of realistic dynamic models for active distribution networks (ADNs) is a challenging problem faced by both the transmission system operators and academia due to the random and complex nature of loads and distributed generation. Real field measurements are often not available and when they are, they usually contain various dynamics, irrelevant data, and outliers. This article proposes a three-stage methodology to effectively build a set of dynamic models for an ADN based on field measurements. In the first stage, an unsupervised learning method identifies and then discards all the irrelevant data for the model parameter estimation. In the next stage, the remaining data are clustered into groups with similar dynamics while in the third stage, a nonlinear dynamic model is developed for each of the derived groups. This papers concludes that, in spite of the large number of measurements representing a wide range of grid configurations, the general dynamic characteristics of an ADN can be accurately captured and modeled using a limited number of clusters. The proposed method is validated using real data from six substations in Southern Germany.

The evolution of load-dominated to active distribution networks with significant dispersed generation calls for accurate models of these networks in the context of system stability analysis. Embedding detailed distribution network models in a dynamic transmission system model is impracticable due to model complexity. Complexity-reduced equivalent dynamic models solve this obstacle. However, equivalent models that consider grid forming converters emulating inertia have not been addressed in research yet. To fill this gap, this work proposes a novel gray-box method for aggregating active distribution networks containing grid forming converters. The approach utilizes voltage sensitivities to represent the grid’s strength at the grid forming converter’s connection point. We compare the proposed method with an existing gray-box approach capable of creating equivalent models for networks dominated by conventional grid following converters. Both aggregation approaches are applied on an active distribution network model. Simulation results for different events of the detailed and both equivalent models are compared. The equivalent model aggregated with the proposed approach reproduces the detailed network’s dynamic behavior adequately, while the existing approach fails to meet validation criteria. Hence, the proposed method provides an equivalent model that is capable of substituting detailed distribution network models in stability studies of renewable power systems.

The comprehensive evaluation of strategies for decarbonizing large‐scale energy systems requires insights from many different perspectives. In energy systems analysis, optimization models are widely used for this purpose. However, they are limited in incorporating all crucial aspects of such a complex system to be sustainably transformed. Hence, they differ in terms of their spatial, temporal, technological, and economic perspective and either have a narrow focus with high resolution or a broad scope with little detail. Against this background, we introduce the so‐called granularity gaps and discuss two possibilities to address them: increasing the resolutions of the established optimization models, and the different kinds of model coupling. After laying out open challenges, we propose a novel framework to design power systems in particular. Our exemplary concept exploits the capabilities of power system optimization, transmission network simulation, distribution grid planning, and agent‐based simulation. This integrated framework can serve to study the energy transition with greater comprehensibility and may be a blueprint for similar multimodel analyses.

This letter introduces an enhancement of the exponential recovery model (ERM) in order to widen its applicability and generalization capability from purely load-composed distribution networks to active distribution networks (ADNs), while maintaining its nonlinear characteristics. Although recent approaches proposed modifications of the ERM to tackle its limitation to capture bidirectional power flows, these may fail to generalize to new test scenarios with significant distributed generation (DG) due to their direct dependence on the measured power at the point of common coupling. In ADNs, this value does not represent the actual power demand of the loads. To address this issue, a new methodology based on nonlinear functions and the actual change of the signals is presented. The efficacy of the proposed model to capture the general ADN dynamics is validated using field measurements acquired over one year.

In this study, the integration of grid‐forming (GFM) converters in power systems is discussed in terms of both the fundamental aspects of system stability and the technical possibilities of converter‐based resources. The paper provides a survey and comparison of various GFM control concepts with respect to their transient and stationary behavior. A method for black box characterization and conformity testing of GFM converters is presented and relevant results of lab and field tests with GFM converters are discussed. Finally, important system aspects are addressed. This includes an approach to consider the behavior of GFM converters in distribution grid models and investigations on the minimum share of GFM units required to ensure system stability. The findings of this paper provide valuable insights for the design and implementation of GFM converters in modern converter‐based power systems.