Machine learning of power grid frequency dynamics and control: prediction, explanation and stochastic modelling

Author:
Description:
A reliable supply of electric power is not a matter of course. Power grids enable the transport of power from generators to consumers, but their stable operation constantly requires corrective measures and a careful supervision. In particular, power generation and demand have to be balanced at all times. A large power imbalance threatens the reliability of the power supply and can, in extreme cases, lead to a large-scale blackout. Therefore, the power imbalance is constantly corrected through distinct control schemes. The power grid frequency measures the balance of power generation and demand. To guarantee frequency stability, and thereby a balance of generation and demand, load-frequency control constantly counteracts large frequency deviations. However, the transition of the energy system to renewable energy sources challenges frequency stability and control. Wind and solar power do not provide intrinsic inertia, which leads to increasingly fast frequency dynamics. Different economic sectors become strongly coupled to the power system, as, for example, the adoption of electric vehicles will interconnect the transport sector and the power system. Finally, wind and solar power are weather-dependent, which increases the variability of power generation. All in all, this gives rise to diverse, interdependent and stochastic impact factors, that drive the balance of power demand and generation, and thus the grid frequency. How can we predict, explain and model frequency dynamics given its strong non-autonomous and stochastic character? In this thesis, I use machine learning to disentangle the effects of external drivers on grid frequency dynamics and control. First, I propose a prediction model that only uses historic frequency data, but fails in representing external impacts. Therefore, I include time series of techno-economic drivers and model their impact on grid frequency data using explainable machine learning methods. These methods reveal the dependencies between external drivers and frequency deviations, such ...
Year of Publication:
2023
Document Type:
doc-type:doctoralThesis ; Text ; [Doctoral and postdoctoral thesis]
Language:
de ; eng
Subjects:
ddc:004 ; ddc:530
DDC:
004 Data processing & computer science ; 530 Physics ; 600 Technology (computed)
Relations:
https://kups.ub.uni-koeln.de/65570/1/dissertation_kruse.pdf ; Kruse, Johannes orcid:0000-0002-3478-3379 (2023). Machine learning of power grid frequency dynamics and control: prediction, explanation and stochastic modelling. PhD thesis, Universität zu Köln.
URL:
https://kups.ub.uni-koeln.de/65570/
https://kups.ub.uni-koeln.de/65570/1/dissertation_kruse.pdf
Content Provider:
Universität zu Köln: KUPS (Kölner Universitäts- und Publikationsserver)
Further nameCologne University: KUPS  Flag of Germany