Power grid models are important in relation to several topics and applications, especially the modelling, optimisation and extension of electrical grids. The significance of grid models is heightened by the increase in renewable energy generation and the challenges associated with its integration into the power grid. However, despite their crucial importance, grid models have generally not been made publicly available for scientific studies or technical analyses. Little information has been published about either the details and methods used in the derivation of these models, or their input and output data. Recently, several projects were initiated in an effort to address this by developing open source grid models and associated data. These projects used different approaches and methods, but most are based on the OpenStreetMap database. The goal of this paper is to compare the different available grid models on the basis of the structure and derivation methods used. Therefore, a novel combination of a graph-theoretical, Geographic Information System (GIS)-based and power-related comparison level is introduced using the open source tool AutoGridComp, which was developed by the authors. The grid models considered in this study are the Scientific Grid Model (SciGRID), GridKit and open street map Transmission Grid Model (osmTGmod) models for Germany.

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.

In this work, we investigate the economic viability of demand response (DR) as a balancing option for variable renewable energies, such as wind and solar. Our assessment is based on a highly resolved national energy system model for Germany coupled with a regional DR optimisation model. First, this allows us to determine the spatially resolved flexibility demand, e.g., for avoiding transmission grid congestion. Second, a high number of DR technologies from the residential, commercial and industrial sector, as well as sector coupling, can be considered to cover the regional flexibility demand. Our analysis is based on a scenario for 2035 with a $66\%$ share of renewable energy sources in the power generation. The results show that the largest DR capacity is being installed in the west of Germany, an area with a high density of population and industry. All DR units have an aggregated capacity below 100 MW per transmission grid node. For the economic assessment, we further differentiate between two cases. In the first case with full DR cost consideration, the optimisation selects only large-scale technologies with low specific investment costs. The second case assumes that the required communication components are already installed. Here, we consider only variable costs and disregard the investment costs. As a result, several small-scale DR technologies are used, such as e-mobility. We publish the developed methodology as an open-source model, which allows reuse for other scientific purposes.