Multi-fidelity graph neural networks for efficient power flow analysis under high-dimensional demand and renewable generation uncertainty
The modernization of power systems faces uncertainties due to fluctuating renewable
energy sources, electric vehicle expansion, and demand response initiatives. These …
energy sources, electric vehicle expansion, and demand response initiatives. These …
Artificial intelligence and mathematical models of power grids driven by renewable energy sources: A survey
To face the impact of climate change in all dimensions of our society in the near future, the
European Union (EU) has established an ambitious target. Until 2050, the share of …
European Union (EU) has established an ambitious target. Until 2050, the share of …
Machine learning accelerated carbon neutrality research using big data—from predictive models to interatomic potentials
Carbon neutrality has been proposed as a solution for the current severe energy and climate
crisis caused by the overuse of fossil fuels, and machine learning (ML) has exhibited …
crisis caused by the overuse of fossil fuels, and machine learning (ML) has exhibited …
Towards adoption of GNNs for power flow applications in distribution systems
An essential component of smart grid applications is the ability to solve the power flow (PF)
problem in real-time. As numerical methods are too slow, the use of neural networks (NNs) …
problem in real-time. As numerical methods are too slow, the use of neural networks (NNs) …
[HTML][HTML] PowerDynamics. jl—An experimentally validated open-source package for the dynamical analysis of power grids
A Plietzsch, R Kogler, S Auer, J Merino, A Gil-de-Muro… - SoftwareX, 2022 - Elsevier
PowerDynamics. jl is a Julia package for time-domain modeling of power grids that is
specifically designed for the stability analysis of systems with high shares of renewable …
specifically designed for the stability analysis of systems with high shares of renewable …
Asymmetry induces critical desynchronization of power grids
Dynamical stability of the synchronous regime remains a challenging problem for secure
functioning of power grids. Based on the symmetric circular model [Hellmann et al., Nat …
functioning of power grids. Based on the symmetric circular model [Hellmann et al., Nat …
Toward dynamic stability assessment of power grid topologies using graph neural networks
To mitigate climate change, the share of renewable energies in power production needs to
be increased. Renewables introduce new challenges to power grids regarding the dynamic …
be increased. Renewables introduce new challenges to power grids regarding the dynamic …
A framework for synthetic power system dynamics
We present a modular framework for generating synthetic power grids that consider the
heterogeneity of real power grid dynamics but remain simple and tractable. This enables the …
heterogeneity of real power grid dynamics but remain simple and tractable. This enables the …
Predicting Instability in Complex Oscillator Networks: Limitations and Potentials of Network Measures and Machine Learning
A central question of network science is how functional properties of systems arise from their
structure. For networked dynamical systems, structure is typically quantified with network …
structure. For networked dynamical systems, structure is typically quantified with network …
Master Stability Functions in Complex Networks
Synchronization is an emergent phenomenon in coupled dynamical networks. The Master
Stability Function (MSF) is a highly elegant and powerful tool for characterizing the stability …
Stability Function (MSF) is a highly elegant and powerful tool for characterizing the stability …