pandapower - an Open Source Python Tool for Convenient Modeling, Analysis and Optimization of Electric Power Systems

pandapower is a Python based, BSD-licensed power system analysis tool aimed at automation of static and quasi-static analysis and optimization of balanced power systems. It provides power flow, optimal power flow, state estimation, topological graph searches and short circuit calculations according to IEC 60909. pandapower includes a Newton-Raphson power flow solver formerly based on PYPOWER, which has been accelerated with just-in-time compilation. Additional enhancements to the solver include the capability to model constant current loads, grids with multiple reference nodes and a connectivity check. The pandapower network model is based on electric elements, such as lines, two and three-winding transformers or ideal switches. All elements can be defined with nameplate parameters and are internally processed with equivalent circuit models, which have been validated against industry standard software tools. The tabular data structure used to define networks is based on the Python library pandas, which allows comfortable handling of input and output parameters. The implementation in Python makes pandapower easy to use and allows comfortable extension with third-party libraries. pandapower has been successfully applied in several grid studies as well as for educational purposes. A comprehensive, publicly available case-study demonstrates a possible application of pandapower in an automated time series calculation

Performance monitoring for Galileo and other GNSS at the Galileo Competence Center

Satellite navigation has become a vital part of our daily lives by ensuring navigation on land, in air and at sea, and by providing precise timing information for the energy, communications and finance sector. It is therefore essential to monitor the performance of the four main global navigation satellite systems (GNSS) Galileo, GPS, GLONASS and BeiDou. The Galileo Competence Center (GK), part of the German Aerospace Center (DLR), is dedicated to the further development of the European GNSS consisting of Galileo and EGNOS. Within the SigPerMon project, the GK monitors the reliability and quality of navigation signals with comparable metrics for all four GNSS, and detects deviations from the nominal state of navigation systems. Necessary data are sourced from a global network of GNSS receiver stations. These data are used to compute performance indicators to monitor and analyze the availability and health status of navigation signals, and the precision of positioning and timing solutions. In the future, machine learning models will be used to detect anomalies in the satellite signals. A summary of the results will be presented on a dedicated webpage, which provides both detailed analyses for authorized researchers and personnel, and interactive data visualizations for the general public

Review of Steady-State Electric Power Distribution System Datasets

Gefördert durch den Publikationsfonds der Universität Kasse

New Distributed Optimization Method for TSO-DSO Coordinated Grid Operation Preserving Power System Operator Sovereignity

Gefördert durch den Publikationsfonds der Universität Kasse

New Distributed Optimization Method for TSO–DSO Coordinated Grid Operation Preserving Power System Operator Sovereignty

Electrical power system operators (SOs) are free to realize grid operations according to their own strategies. However, because resulting power flows also depend on the actions of neighboring SOs, appropriate coordination is needed to improve the resulting system states from an overall perspective and from an individual SO perspective. In this paper, a new method is presented that preserves the data integrity of the SOs and their independent operation of their grids. This method is compared with a non-coordinated local control and another sequential method that has been identified as the most promising distributed optimization method in previous research. The time series simulations use transformer tap positioning as well as generation unit voltage setpoints and reactive power injections as flexibilities. The methods are tested on a multi-voltage, multi-SO, realistic benchmark grid with different objective combinations of the SOs. In conclusion, the results of the new method are much closer to the theoretical optimum represented by central optimization than those of the other two methods. Furthermore, the introduced method integrates a sophisticated procedure to provide fairness between SOs that is missing in other methods

SimBench—A Benchmark Dataset of Electric Power Systems to Compare Innovative Solutions Based on Power Flow Analysis

Gefördert durch den Publikationsfonds der Universität Kasse