Data and models

The SimBench dataset is a development for solutions in the field of network analysis, network planning and network operation management. It is intended to enable the development of new methods and solutions independently of individual grid data sets that are not publicly available, thus ensuring reproducibility, comparability and transparency of various developments in this field.

The data set contains electrical parameters for the static modeling of electricity grids and covers voltage levels from low to extra-high voltage. All 13 basic grids can be interconnected and are available in three variants (development scenarios). Annual time series in 15-minute resolution for one year and planning-relevant calculation cases are also integrated.

The data set was developed as part of the SimBench project. The project was funded by the Federal Ministry for Economic Affairs and Energy and the Jülich project management organization (FKZ: 0325917).

Two different models were developed, implemented and validated for PowerFactory:

WECC Generic Photovoltaic System Model
Paper: Implementation and validation of WECC generic photovoltaic system models in DIgSILENT PowerFactory
Download: WECC Generic Photovoltaic System Model [PFD]

Nordic Test System
Paper: Implementation and validation of the Nordic test system in DIgSILENT PowerFactory
Download: Nordic Test System [PFD]

Cold Load Pickup Model Adequacy for Power System Restoration Studies
Paper: Cold Load Pickup Model Adequacy for Power System Restoration Studies
Download: Cold Load Pickup Data

The OpenSource QGis Plugin Deepness: Deep Neural Remote Sensing QGIS Plugin, developed mainly by the PUT Vision Lab (Computer Vision of the Poznan University of Technology), offers an intuitive user interface for the application of image recognition, object regression and semantic segmentation in the domain of remote sensing. The tool manual contains installation instructions and a selection of freely available models.

As part of the project "Development and Demonstration of a Sustainable Open Access AU-EU Ecosystem for Energy System Modeling" (OASES), another model was added to the plugin. A deep learning-based model for the segmentation of PV systems in various resolved aerial and satellite images, developed jointly by the Sustainable Electrical Energy Systems department, Fraunhofer IEE and the Council for Scientific and Industrial Research (CSIR) from South Africa, has been integrated so that users can apply it directly to any location on the globe without any further programming knowledge. A detailed description of the method is summarized in the scientific publication.

The project is part of the LEAP-RE program. LEAP-RE has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement 963530 and the BMBF.

The project is part of the LEAP-RE program. LEAP-RE has received funding from the European Union's Horizon 2020 research and innovation program under Grant Agreement 963530 and the BMBF.