# Graph Neural Network-Accelerated Network-Reconfigured Optimal Power Flow

arXiv: https://arxiv.org/abs/2410.17460 · HTML: https://mlsanity.com/paper/2410.17460/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 30 references that are in the map.
- First posted on arXiv 22 Oct 2024.

## Builds on (most cited first, 13 of 13)

- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Modeling polypharmacy side effects with graph convolutional networks](https://mlsanity.com/paper/1802.00543/) (2018)
- [A Review of Graph Neural Networks and Their Applications in Power Systems](https://mlsanity.com/paper/2101.10025/) (2021)
- [Graph Neural Networks in TensorFlow and Keras with Spektral](https://mlsanity.com/paper/2006.12138/) (2020)
- [Reduced Optimal Power Flow Using Graph Neural Network](https://mlsanity.com/paper/2206.13591/) (2022)
- [Neural Network-based Power Flow Model](https://mlsanity.com/paper/2112.08418/) (2021)
- [Long-Term Recurrent Convolutional Network-based Inertia Estimation using Ambient Measurements](https://mlsanity.com/paper/2112.00926/) (2021)
- [Wholesale Electricity Price Forecasting using Integrated Long-term Recurrent Convolutional Network Model](https://mlsanity.com/paper/2112.13681/) (2021)
- [Security Constrained Unit Commitment with Corrective Transmission Switching](https://mlsanity.com/paper/2001.00597/) (2020)
- [Graph Neural Network-based Power Flow Model](https://mlsanity.com/paper/2307.02049/) (2023)
- [N-1 Reduced Optimal Power Flow Using Augmented Hierarchical Graph Neural Network](https://mlsanity.com/paper/2402.06226/) (2024)
- [Optimal Dynamic Reconfiguration of Distribution Networks](https://mlsanity.com/paper/2308.01984/) (2023)
- [Evaluation of Battery Energy Storage System to Provide Virtual Transmission Service](https://mlsanity.com/paper/2309.07237/) (2023)

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

- [Constraints and Variables Reduction for Optimal Power Flow Using Hierarchical Graph Neural Networks with Virtual Node-Splitting](https://mlsanity.com/paper/2411.06268/) (2024)

## Abstract (arXiv)

Optimal power flow (OPF) has been used for real-time grid operations. Prior efforts demonstrated that utilizing flexibility from dynamic topologies will improve grid efficiency. However, this will convert the linear OPF into a mixed-integer linear programming network-reconfigured OPF (NR-OPF) problem, substantially increasing the computing time. Thus, a machine learning (ML)-based approach, particularly utilizing graph neural network (GNN), is proposed to accelerate the solution process. The GNN model is trained offline to predict the best topology before entering the optimization stage. In addition, this paper proposes an offline pre-ML filter layer to reduce GNN model size and training time while improving its accuracy. A fast online post-ML selection layer is also proposed to analyze GNN predictions and then select a subset of predicted NR solutions with high confidence. Case studies have demonstrated superior performance of the proposed GNN-accelerated NR-OPF method augmented with the proposed pre-ML and post-ML layers.