mlsanityData to 7 Oct 2026

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

arXiv 2106.10529 ↗First posted 19 Jun 2021cs.LG cs.SY eess.SYcited by 5

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Abstract (arXiv)

Solving the optimal power flow (OPF) problem in real-time electricity market improves the efficiency and reliability in the integration of low-carbon energy resources into the power grids. To address the scalability and adaptivity issues of existing end-to-end OPF learning solutions, we propose a new graph neural network (GNN) framework for predicting the electricity market prices from solving OPFs. The proposed GNN-for-OPF framework innovatively exploits the locality property of prices and introduces physics-aware regularization, while attaining reduced model complexity and fast adaptivity to varying grid topology. Numerical tests have validated the learning efficiency and adaptivity improvements of our proposed method over existing approaches.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 17 of its 25 references that are in the map. First posted on arXiv 19 Jun 2021.

Builds on

17 of 25 references.

References · 17

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2021Convex optimizationcited by 2,479
  3. 2020Generalization and Representational Limits of Graph Neural Networkscited by 217
  4. 2019Learning Optimal Solutions for Extremely Fast AC Optimal Power Flowcited by 76
  5. 2020Graphs, Convolutions, and Neural Networks: From Graph Filters to Graph Neural Networkscited by 73
  6. 2019The Power Grid Library for Benchmarking AC Optimal Power Flow Algorithmscited by 63
  7. 2019Optimal Power Flow Using Graph Neural Networkscited by 60
  8. 2018Learning for Constrained Optimization: Identifying Optimal Active Constraint Setscited by 36
  9. 2020EdgeNets:Edge Varying Graph Neural Networkscited by 36
  10. 2019DeepOPF: Deep Neural Network for DC Optimal Power Flowcited by 32
  11. 2019Learning Warm-Start Points for AC Optimal Power Flowcited by 32
  12. 2019Learning for DC-OPF: Classifying active sets using neural netscited by 30
5 more
  1. 2019Machine Learning for AC Optimal Power Flowcited by 24
  2. 2021Grid-Graph Signal Processing (Grid-GSP): A Graph Signal Processing Framework for the Power Gridcited by 12
  3. 2019Physics-Aware Neural Networks for Distribution System State Estimationcited by 11
  4. 2020Learning to Solve Network Flow Problems via Neural Decodingcited by 9
  5. 2016Learning the LMP-Load Coupling From Data: A Support Vector Machine Based Approachcited by 3

Built on by

Cited by year

Citing papers per year: 2021: 2; 2022: 2; 2023: 1.

5 papers.

  1. 2022Topology-aware Graph Neural Networks for Learning Feasible and Adaptive ac-OPF Solutionscited by 28
  2. 2021Leveraging power grid topology in machine learning assisted optimal power flowcited by 21
  3. 2021Risk-Aware Learning for Scalable Voltage Optimization in Distribution Gridscited by 5
  4. 2023A Physics-Informed Machine Learning for Electricity Markets: A NYISO Case Studycited by 4
  5. 2022Wind Power Scenario Generation Using Graph Convolutional Generative Adversarial Networkcited by 1