mlsanityData to 7 Oct 2026

Reduced Optimal Power Flow Using Graph Neural Network

arXiv 2206.13591 ↗First posted 27 Jun 2022eess.SY cs.LG cs.SYcited by 11

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Abstract (arXiv)

OPF problems are formulated and solved for power system operations, especially for determining generation dispatch points in real-time. For large and complex power system networks with large numbers of variables and constraints, finding the optimal solution for real-time OPF in a timely manner requires a massive amount of computing power. This paper presents a new method to reduce the number of constraints in the original OPF problem using a graph neural network (GNN). GNN is an innovative machine learning model that utilizes features from nodes, edges, and network topology to maximize its performance. In this paper, we proposed a GNN model to predict which lines would be heavily loaded or congested with given load profiles and generation capacities. Only these critical lines will be monitored in an OPF problem, creating a reduced OPF (ROPF) problem. Significant saving in computing time is expected from the proposed ROPF model. A comprehensive analysis of predictions from the GNN model was also made. It is concluded that the application of GNN for ROPF is able to reduce computing time while retaining solution quality.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 9 of its 18 references that are in the map. First posted on arXiv 27 Jun 2022.

Builds on

9 of 18 references.

References · 9

  1. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,571
  2. 2021A Review of Graph Neural Networks and Their Applications in Power Systemscited by 62
  3. 2019Optimal Power Flow Using Graph Neural Networkscited by 60
  4. 2020Graph Neural Networks in TensorFlow and Keras with Spektralcited by 59
  5. 2019DeepOPF: A Deep Neural Network Approach for Security-Constrained DC Optimal Power Flowcited by 50
  6. 2021Neural Network-based Power Flow Modelcited by 10
  7. 2021Long-Term Recurrent Convolutional Network-based Inertia Estimation using Ambient Measurementscited by 8
  8. 2021Wholesale Electricity Price Forecasting using Integrated Long-term Recurrent Convolutional Network Modelcited by 8
  9. 2021Machine Learning Assisted Approach for Security-Constrained Unit Commitmentcited by 5

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Cited by year

Citing papers per year: 2022: 3; 2023: 4; 2024: 3; 2025: 1.

11 papers.

  1. 2023End-to-End Feasible Optimization Proxies for Large-Scale Economic Dispatchcited by 35
  2. 2022Unsupervised Optimal Power Flow Using Graph Neural Networkscited by 13
  3. 2022Feasibility Layer Aided Machine Learning Approach for Day-Ahead Operationscited by 6
  4. 2023Spatio-Temporal Deep Learning-Assisted Reduced Security-Constrained Unit Commitmentcited by 6
  5. 2022Proximal Policy Optimization with Graph Neural Networks for Optimal Power Flowcited by 4
  6. 2024N-1 Reduced Optimal Power Flow Using Augmented Hierarchical Graph Neural Networkcited by 3
  7. 2023Analysis of Weather and Time Features in Machine Learning-aided ERCOT Load Forecastingcited by 2
  8. 2023Linearization of ReLU Activation Function for Neural Network-Embedded Optimization: Optimal Day-Ahead Energy Schedulingcited by 1
  9. 2024Graph Neural Network-Accelerated Network-Reconfigured Optimal Power Flowcited by 1
  10. 2024Constraints and Variables Reduction for Optimal Power Flow Using Hierarchical Graph Neural Networks with Virtual Node-Splittingcited by 1
  11. 2025Revisiting Deep AC-OPFcited by 0