# Power grid operational risk assessment using graph neural network surrogates

arXiv: https://arxiv.org/abs/2311.12309 · HTML: https://mlsanity.com/paper/2311.12309/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 13 references that are in the map.
- First posted on arXiv 21 Nov 2023.

## Builds on (most cited first, 4 of 4)

- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [A Review of Graph Neural Networks and Their Applications in Power Systems](https://mlsanity.com/paper/2101.10025/) (2021)
- [An Accelerated-Decomposition Approach for Security-Constrained Unit Commitment with Corrective Network Reconfiguration](https://mlsanity.com/paper/1912.01764/) (2019)
- [Stochastic Unit Commitment in Low-Inertia Grids](https://mlsanity.com/paper/1904.03030/) (2019)

## Built on by (most cited first, 1 of 1)

- [Graph neural networks for power grid operational risk assessment under evolving grid topology](https://mlsanity.com/paper/2405.07343/) (2024)

## Abstract (arXiv)

We investigate the utility of graph neural networks (GNNs) as proxies of power grid operational decision-making algorithms (optimal power flow (OPF) and security-constrained unit commitment (SCUC)) to enable rigorous quantification of the operational risk. To conduct principled risk analysis, numerous Monte Carlo (MC) samples are drawn from the (foretasted) probability distributions of spatio-temporally correlated stochastic grid variables. The corresponding OPF and SCUC solutions, which are needed to quantify the risk, are generated using traditional OPF and SCUC solvers to generate data for training GNN model(s). The GNN model performance is evaluated in terms of the accuracy of predicting quantities of interests (QoIs) derived from the decision variables in OPF and SCUC. Specifically, we focus on thermal power generation and load shedding at system and individual zone level. We also perform reliability and risk quantification based on GNN predictions and compare with that obtained from OPF/SCUC solutions. Our results demonstrate that GNNs are capable of providing fast and accurate prediction of QoIs and thus can be good surrogate models for OPF and SCUC. 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.