# Optimal Power Flow Using Graph Neural Networks

@article{Owerko2020OptimalPF, 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={2020}, 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…

## 55 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.

### Reduced Optimal Power Flow Using Graph Neural Network

- Engineering, Computer Science
- 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.

### 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.

### 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.

### Leveraging power grid topology in machine learning assisted optimal power flow

- Computer ScienceArXiv
- 2021

This work introduces a concise framework for generalizing methods for machine learning assisted OPF and assess the performance of a variety of FCNN, CNN and GNN models for two fundamental approaches in this domain: regres- sion (predicting optimal generator set-points) and classiﬁcation (p Predicting the active set of constraints).

### 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.

### Topology-aware Graph Neural Networks for Learning Feasible and Adaptive ac-OPF Solutions

- EngineeringArXiv
- 2022

—Solving the optimal power ﬂow (OPF) problem is a fundamental task to ensure the system efﬁciency and reliability in real-time electricity grid operations. We develop a new topology-informed graph…

### Fast DC Optimal Power Flow Based on Deep Convolutional Neural Network

- Engineering2022 IEEE 5th International Electrical and Energy Conference (CIEEC)
- 2022

The optimal power flow is the cornerstone of the operation and management of electric power systems. However, the stochastic and intermittent uncertainty due to the proliferation of renewable energy…

### Graph Neural Networks for Learning Real-Time Prices in Electricity Market

- EngineeringArXiv
- 2021

A new graph neural network (GNN) framework for predicting the electricity market prices from solving OPFs innovatively exploits the locality property of prices and introduces physics-aware regularization, while attaining reduced model complexity and fast adaptivity to varying grid topology.

### Learning to Solve the AC-OPF Using Sensitivity-Informed Deep Neural Networks

- Computer ScienceIEEE Transactions on Power Systems
- 2022

Numerical tests on three benchmark power systems corroborate the advanced generalization and constraint satisfaction capabilities for the OPF solutions predicted by an SI-DNN over a conventionally trained DNN, especially in low-data setups.

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