Project’s aim, its history, funding, and people involved.

Scientific Aim

The aim of this project is to collect the growing data in power-grid systems, in particular, in power-grid frequency recordings. These recordings are of immense value for the scientific community, as they carry relevant information of a power-grid stability and dynamics. In this database we collect open-data power-grid frequency recordings from all around the world. We preprocess the data and make it available, formatted for scientific purposes.

Collaboration

Data should be open. Science should be transparent. We accept contributions from all parties, private or personal, if those naturally have the same interest at heart: sharing the data openly. We review the data ourselves and, if needed, process it using our scripts, to ensure a uniform structure oriented for research usage. This means the data are saved in .csv files, which can be read in any platform, and is compressed in .zip format to considerably reduce overhead and making easy to download. If you wish to contribute, open an issue on the GitHub repository Power-Grid-Frequency and we will add the data, convert it into the desired format, report on the relevant information, and upload it to the repository.

The project

History

This project started in 2018 as a collaboration between Leonardo Rydin Gorjão and Benjamin Schäfer to extent the accessibility of data in research in power-grid systems. Richard Jumar and Heiko Maaß, from the Karlsruhe Institute for Technology ─ Institute for Automation and Applied Informatics, have developed a recording apparatus, the EDR, Electrical Data Recorder, similar in nature to a PMU, and in a collaborative manner we set out to record power-grid frequency across twelve independent regions. This project, and our general pursuit, is to further collaborations in the research community─between all field, engineering, physics, mathematics, etc.─by advocating a more transparent and open-access policy of data. Since the start of the project, an increasing number of people contributed data, hosted devices or advanced the website.

People

This project as seen input over the years from many people. We are grateful to everyone who as collaborated with us, in one way of another: Mehrnaz Anvari, Philipp Böttcher, Christian Beck, Marc Timme, Dirk Witthaut, Johannes Kruse, Raphael Biertz, Holger Kantz, Veit Hagenmeyer, Damià Gomila, Malte Schröder, Jan Wohland, Cigdem Yalcin, Filipe Pereira, André Frazão, Kaur Tuttelberg, Jako Kilter, Hauke Hähne, and Bálint Hartmann.

Website and repositories

Associated with this project are two main online sources: the data is stored on the Open Science Framework repository Open Access Power-Grid Frequency Database and the website is stored and rendered in the GitHub repository Power-Grid-Frequency. This allows the users to access the OSF repository data without also having to get all the data for the website. Data that already existent in open-data frames is linked in the Open Access Power-Grid Frequency Database repository. This website was build by Leonardo Rydin Gorjão and Galib Hassan. The lastest version is maintained by the DRACOS group at KIT, including Hallah Butt, Xinyi Wen, Ulrich Oberhofer, Sebastian Pütz and Benjamin Schäfer.

Funding

We have received funding from numerous sources while creating, maintaining and updating this website. This includes:

  • Federal Ministry of Education and Research (BMBF grants No 03SF0472 and 03EK3055)
  • Helmholtz Association (via the joint initiative Energy System 2050 - A Contribution of the Research Field Energy and the associative Uncertainty Quantification – From Data to Reliable Knowledge (UQ) with grant No ZT-I-0029)
  • Helmholtz Association’s Initiative and Networking Fund through Helmholtz AI and grant No VH-NG-1025
  • Helmholtz School for Data Science in Life, Earth and Energy (HDS-LEE)
  • German Science Foundation (DFG) by a grant toward the Cluster of Excellence Center for Advancing Electronics Dresden (cfaed)
  • Scientific Research Projects Coordination Unit of Istanbul University, Project No 32990
  • European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 840825
  • E.ON Stipendienfonds
  • STORM - Stochastics for Time-Space Risk Models project of the Research Council of Norway (RCN) No. 274410.