Periodic Reporting for period 1 - DAMOSET (With data-driven modelling towards a successful energy transition)
Periodo di rendicontazione: 2019-06-01 al 2021-05-31
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 used 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 aimed to collect data and offered an open database of our measurements for the scientific community to analyse and to add to. In addition, we developed mathematical and computational tools to understand these measurements and eventually provide guidance to policy decisions.
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 used 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 aimed to collect data and offered an open database of our measurements for the scientific community to analyse and to add to. In addition, we developed mathematical and computational tools to understand these measurements and eventually provide guidance to policy decisions.
Within this project a total of 10 peer-reviewed journal publications have been published: Two times each in Scientific Reports, IEEE Access, Chaos and Physical Review Research and once in Nature Communications and New Journal of Physics. All but Chaos are gold-standard open access journals and for the two Chaos publications, we ensured green open access by depositing the published manuscript version both on arXiv and the institute's webservers. These cover the full range of anticipated results, from open data measurements over cascading failures to stochastic tools. Here, we discuss two highlights in more detail: An open database of power grid measurements published in Nature Communications 11, 6362 (December 2020) and a machine-learning approach published in IEEE Access 8, 2020.
In the Nature Communications article, we collected power grid data from 17 locations across three continents and covering 12 synchronous areas - regions containing different power plants and consumers that are connected and operate under the same frequency. This was a major collaborative effort involving scientists from six institutions (Forschungszentrum Jülich, Queen Mary University of London, Karlsruhe Institute of Technology (KIT), Technical University Dresden and Istanbul University). We used these experimental recordings to test theoretical predictions on how the size of a synchronous area influences its stability. In particular, we found that smaller areas tend to be much more volatile than larger areas in their fluctuations in frequency and we quantified this empirical observation with stochastic modelling.
Furthermore, by simultaneously measuring frequencies in several locations within a synchronous area, we also observed that whilst on longer time scales of minutes or more, the frequencies were identical everywhere, on a shorter time scale of seconds, substantial differences between locations were observed. We quantified the time needed for two locations to fully synchronize.
Meanwhile, in the IEEE Access article, we demonstrated how open power system data can be used to fuel modern machine learning applications. In particular, we investigated how a weighted-nearest-neighbour approach can be used to obtain precise forecasts of the power-grid frequency. We showcased how our new predictor consistently outperforms the daily profile (daily averages of the trajectory). The choice of weighted nearest neighbours as our forecasting method allowed us to identify patterns in the power grid frequency time series and advance our understanding of the underlying power system and the frequency dynamics.
In the Nature Communications article, we collected power grid data from 17 locations across three continents and covering 12 synchronous areas - regions containing different power plants and consumers that are connected and operate under the same frequency. This was a major collaborative effort involving scientists from six institutions (Forschungszentrum Jülich, Queen Mary University of London, Karlsruhe Institute of Technology (KIT), Technical University Dresden and Istanbul University). We used these experimental recordings to test theoretical predictions on how the size of a synchronous area influences its stability. In particular, we found that smaller areas tend to be much more volatile than larger areas in their fluctuations in frequency and we quantified this empirical observation with stochastic modelling.
Furthermore, by simultaneously measuring frequencies in several locations within a synchronous area, we also observed that whilst on longer time scales of minutes or more, the frequencies were identical everywhere, on a shorter time scale of seconds, substantial differences between locations were observed. We quantified the time needed for two locations to fully synchronize.
Meanwhile, in the IEEE Access article, we demonstrated how open power system data can be used to fuel modern machine learning applications. In particular, we investigated how a weighted-nearest-neighbour approach can be used to obtain precise forecasts of the power-grid frequency. We showcased how our new predictor consistently outperforms the daily profile (daily averages of the trajectory). The choice of weighted nearest neighbours as our forecasting method allowed us to identify patterns in the power grid frequency time series and advance our understanding of the underlying power system and the frequency dynamics.
The published mathematical and computation tools (e.g. in statistical physics applications, machine learning approaches etc.) have advanced the state of the art in the field. Even more critical, the project acted as a model for how to conduct open science and make scientific results transparent and accessible. To this end, all data collected during the project and even before the project start has been made openly available on a newly designed webpage: https://power-grid-frequency.org/
By publicly and visibly sharing our data, we encourage both academic as well as industrial actors to participate in this new form of open science. We have already been contacted by scientists wishing to host data using our platform. Furthermore, first studies have been conducted using the data shared on the webpage, using a new perspective and specific domain knowledge. Hence, this project is already acting as an important example of how transparent and open science should be conducted. Thereby, it is also facilitating international and interdisciplinary collaboration.
Finally, this projects contributes to the overall monumental societal effort to move towards a fully sustainable energy system. While one project alone cannot solve all challenges of the energy transition, our mathematical analysis supports researchers and industrial actors, such as Transmission System Operators (TSOs) to move our energy system towards an increasing share of renewables and distributed generation.
By publicly and visibly sharing our data, we encourage both academic as well as industrial actors to participate in this new form of open science. We have already been contacted by scientists wishing to host data using our platform. Furthermore, first studies have been conducted using the data shared on the webpage, using a new perspective and specific domain knowledge. Hence, this project is already acting as an important example of how transparent and open science should be conducted. Thereby, it is also facilitating international and interdisciplinary collaboration.
Finally, this projects contributes to the overall monumental societal effort to move towards a fully sustainable energy system. While one project alone cannot solve all challenges of the energy transition, our mathematical analysis supports researchers and industrial actors, such as Transmission System Operators (TSOs) to move our energy system towards an increasing share of renewables and distributed generation.