Operational risk quantification of power grids using graph neural network surrogates of the DC OPF

arXiv:2311.03661v2 Announce Type: replace-cross
Abstract: A DC OPF surrogate modeling framework is developed for Monte Carlo (MC) sampling-based risk quantification in power grid operation. 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. Computationally inexpensive surrogates of OPF provide an attractive alternative for expedited MC simulation. Graph neural network (GNN) surrogates of DC OPF, which are especially suitable to graph-structured data, …

abstract arxiv cs.lg cs.sy demand eess.sy framework graph graph neural network grid modeling network neural network power quantification risk samples sampling simulation stochastic type variables