# Optimal Power Flow Using Graph Neural Networks

@article{Owerko2019OptimalPF, title={Optimal Power Flow Using Graph Neural Networks}, author={Damian Owerko and Fernando Gama and Alejandro Ribeiro}, journal={ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, year={2019}, pages={5930-5934} }

Optimal power flow (OPF) is one of the most important optimization problems in the energy industry. In its simplest form, OPF attempts to find the optimal power that the generators within the grid have to produce to satisfy a given demand. Optimality is measured with respect to the cost that each generator incurs in producing this power. The OPF problem is non-convex due to the sinusoidal nature of electrical generation and thus is difficult to solve. Using small angle approximations leads to a…

## 68 Citations

### Unsupervised Optimal Power Flow Using Graph Neural Networks

- Computer ScienceArXiv
- 2022

A novel barrier method is proposed that is differentiable and works on initially infeasible points, and it is shown that the use of GNNs in this unsupervised learning context leads to solutions comparable to standard solvers while being computationally efﬁcient and avoiding constraint violations most of the time.

### Proximal Policy Optimization with Graph Neural Networks for Optimal Power Flow

- Computer ScienceArXiv
- 2022

A novel architecture based on the Proximal Policy Optimization algorithm with Graph Neural Networks to solve the Optimal Power Flow is proposed, which is to design an architecture that learns how to solves the optimization problem and that is at the same time able to generalize to unseen scenarios.

### Power Flow Optimization with Graph Neural Networks

- Computer Science

Several supervised GNN models are proposed to solve the power flow problem, using established GNN blocks from the literature, and the experimental results show that the GNNs are comparatively successful at generalizing to widely different topologies seen during training, but do not manage to generalize to unseen topologies and are not able to outperform an MLP on slight perturbations of the same energy system.

### Solving AC Power Flow with Graph Neural Networks under Realistic Constraints

- Computer ScienceArXiv
- 2022

A model architecture on which unsupervised training is performed to learn a general solution of the AC power power formulation that is independent of the speciﬁc topologies and supply tasks used for training is presented.

### Reduced Optimal Power Flow Using Graph Neural Network

- Engineering, Computer Science2022 North American Power Symposium (NAPS)
- 2022

A new method to reduce the number of constraints in the original OPF problem using a graph neural network (GNN) is presented, an innovative machine learning model that utilizes features from nodes, edges, and network topology to maximize its performance.

### Learning to Solve AC Optimal Power Flow by Differentiating through Holomorphic Embeddings

- EngineeringArXiv
- 2020

This approach constitutes the first learning-based approach that successfully respects the full non-linear AC-OPF equations and reports a 12x increase in speed and a 40% increase in robustness compared to a traditional solver.

### Learning to Optimize Power Distribution Grids using Sensitivity-Informed Deep Neural Networks

- Computer Science2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)
- 2020

Numerical tests showcase that sensitivity-informed deep learning can enhance prediction accuracy in terms of mean square error (MSE) by 2-3 orders of magnitude at minimal computational overhead.

### Graph Neural Networks for Voltage Stability Margins With Topology Flexibilities

- EngineeringIEEE Open Access Journal of Power and Energy
- 2023

High penetration of distributed energy resources (DERs) changes the flows in power grids causing thermal congestions which are managed by real-time corrective topology switching. It is crucial to…

### Learning to Solve the AC Optimal Power Flow via a Lagrangian Approach

- Computer Science2022 North American Power Symposium (NAPS)
- 2022

This work uses deep neural networks to learn the dual variables of the ACOPF problem and proposes a Lagrangian-based approach that can reach more globally optimal solutions with significant computational speedup even when the training data consists of mostly suboptimal solutions.

### Unsupervised Deep Learning for AC Optimal Power Flow via Lagrangian Duality

- Computer ScienceGLOBECOM 2022 - 2022 IEEE Global Communications Conference
- 2022

A deep neural network is developed to output a partial set of decision variables while the remaining variables are recovered by solving AC power flow equations and the fast decoupled power flow solver is adopted to further reduce the computational time.

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