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Graph neural networks for power grid operational risk assessment under evolving grid topology
The excellent accuracy of GNN-based reliability and risk assessment suggests that GNN models can substantially improve situational awareness by quickly providing rigorous reliability and risk estimates.
Power grid operational risk assessment using graph neural network surrogates
The excellent accuracy of GNN-based reliability and risk assessment further suggests that GNN surrogate has the potential to be applied in real-time and hours-ahead risk quantification.
Operational risk quantification of power grids using graph neural network surrogates of the DC OPF
MC simulation necessitates solving a large number of DC OPF problems corresponding to the samples of stochastic grid variables (power demand and renewable generation), which is computationally prohibitive.
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