DAMOSET

DAMOSET (With data-driven modelling towards a successful energy transition) is funded within the Marie Skłodowska-Curie Actions under grant agreement No 840825 from June 2019 to May 2021.

Project description

The power grid is an integral part of the power system. It connects all electrical consumers with generators and powers everything from household appliances to large factory machinery. Without this grid, farmers would not be able to feed their animals, car factories would come to a halt, mobile phone systems would fail and many of us would not even be able to make a cup of tea. While the current power system is very reliable and offers a high quality of service, it remains unclear how this will develop in the future. The limited supply of fossil fuels as well as the necessary reduction of CO2 emissions to mitigate climate change will eventually lead to a power grid mainly supplied by renewable generators, such as wind and solar plants. These plants output smaller total power so that a large number is necessary which have to be geographically distributed for optimal weather conditions. The current power grid system slowly emerged within several decades of optimization processes. However, now we are discussing how to revolutionize the whole energy system within years.

Therefore, a fundamental understanding of the current power system is necessary to develop potential pathways to a future 100% sustainable system. In this project, we use data-driven approaches to work towards a quantitative understanding of fluctuations in the power grid, as they are for example introduced by the changing demand or volatile energy generation. We aim to collect data and offer an open database of our measurements for the scientific community to analyse and to add to. In addition, we develop mathematical and computational tools to understand these measurements and eventually provide guidance to policy decisions.

Project outreach

Data collected during the project’s lifetime has been published here: https://power-grid-frequency.org/

Project publications (particular highlights)

E. Mitsokapas, B. Schäfer, R. Harris, C. Beck Statistical Characterization of Airplane Delays, Scientific Reports 11, 7855 (2021) (joint first-authorship) article

L. R. Gorjão, R. Jumar, H. Maass, V. Hagenmeyer, G. C. Yalcin, J. Kruse, M. Timme, C. Beck, D. Witthaut, B. Schäfer, Open data base analysis of scaling and spatio-temporal properties of power grid frequencies Nature Communications, 11, 6362, 2020 article

J. Kruse, B. Schäfer, D. Witthaut, Predictability of Power Grid Frequency, IEEE Access 8, 2020 article

M. Smolla, B. Schäfer, H. Lesch, C. Beck, Universal properties of primary and secondary cosmic ray energy spectra, New Journal of Physics 22 093002, 2020 article

M. Anvari, L. R. Gorjão, B. Schäfer, D. Witthaut, M. Timme, H. Kantz, Stochastic properties of the frequency dynamics in real and synthetic power grids, Phys. Rev. Research 2, 013339, 2020 article

L. R. Gorjão, M. Anvari, H. Kantz, C. Beck, D. Witthaut, M. Timme, B. Schäfer, Data-driven model of the power-grid frequency dynamics, IEEE Access 8, 2020 article

I. Iacopini, B. Schäfer, E. Arcaute, C. Beck and V. Latora, Multi-layer modelling of adoption dynamics in energy demand management; Chaos 30, 013153, 2020 article pre-print

G. Williams, B. Schäfer and C. Beck, Superstatistical approach to air pollution statistics; Phys. Rev. Research 2, 013019, 2020 article

J. Weber, M. Reyers, C. Beck, M. Timme, J. G. Pinto, D. Witthaut and B. Schäfer, Wind Power Persistence is Governed by Superstatistics, Scientific Reports 9, 19971, 2019 article

B. Schäfer and C. Yalcin, Dynamical modelling of cascading failures in the Turkish power grid, Chaos 29 (093134), 2019 article pre-print

pre-prints

M. Anvari, E. Proedrou, B. Schäfer, C. Beck, H. Kantz and M Timme, Data-Driven Load Profiles and the Dynamics of Residential Electric Power Consumption, arXiv preprint arXiv:2009.09287 link

B. Schäfer, R. Verma, A. Giri, H. He, S. Nagendra, M. Khare, C. Beck, Covid-19 impact on air quality in megacities, arXiv preprint: 2007.00755 July 2020 link

R Jumar, H Maaß, B. Schäfer, LR Gorjão, V Hagenmeyer, Power grid frequency data base, arXiv preprint:2006.01771 June 2020 link