Knowledge- and Data-driven Services for Energy Systems using Graph Neural Networks

• URL: http://arxiv.org/abs/2103.07248v1
• Date: Fri, 12 Mar 2021 13:00:01 GMT
• Title: Knowledge- and Data-driven Services for Energy Systems using Graph Neural Networks
• Authors: Francesco Fusco, Bradley Eck, Robert Gormally, Mark Purcell, Seshu Tirupathi
• Abstract summary: We propose a data- and knowledge-driven probabilistic graphical model for energy systems based on the framework of graph neural networks (GNNs) The model can explicitly factor in domain knowledge, in the form of grid topology or physics constraints, thus resulting in sparser architectures and much smaller parameters dimensionality. Results obtained from a real-world smart-grid demonstration project show how the GNN was used to inform grid congestion predictions and market bidding services.
• Score: 0.9809636731336702
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The transition away from carbon-based energy sources poses several challenges for the operation of electricity distribution systems. Increasing shares of distributed energy resources (e.g. renewable energy generators, electric vehicles) and internet-connected sensing and control devices (e.g. smart heating and cooling) require new tools to support accurate, datadriven decision making. Modelling the effect of such growing complexity in the electrical grid is possible in principle using state-of-the-art power-power flow models. In practice, the detailed information needed for these physical simulations may be unknown or prohibitively expensive to obtain. Hence, datadriven approaches to power systems modelling, including feedforward neural networks and auto-encoders, have been studied to leverage the increasing availability of sensor data, but have seen limited practical adoption due to lack of transparency and inefficiencies on large-scale problems. Our work addresses this gap by proposing a data- and knowledge-driven probabilistic graphical model for energy systems based on the framework of graph neural networks (GNNs). The model can explicitly factor in domain knowledge, in the form of grid topology or physics constraints, thus resulting in sparser architectures and much smaller parameters dimensionality when compared with traditional machine-learning models with similar accuracy. Results obtained from a real-world smart-grid demonstration project show how the GNN was used to inform grid congestion predictions and market bidding services for a distribution system operator participating in an energy flexibility market.

Related papers

• Optimal Power Grid Operations with Foundation Models [0.0]
  We propose the use of AI Foundation Models (FMs) and advances in Graph Neural Networks to efficiently exploit poorly available grid data for different downstream tasks. For capturing the grid's underlying physics, we believe that building a self-supervised model learning the power flow dynamics is a critical first step towards developing an FM for the power grid.
  arXiv  Detail & Related papers  (2024-09-03T09:06:13Z)
• SafePowerGraph: Safety-aware Evaluation of Graph Neural Networks for Transmission Power Grids [55.35059657148395]
  We present SafePowerGraph, the first simulator-agnostic, safety-oriented framework and benchmark for Graph Neural Networks (GNNs) in power systems (PS) operations. SafePowerGraph integrates multiple PF and OPF simulators and assesses GNN performance under diverse scenarios, including energy price variations and power line outages.
  arXiv  Detail & Related papers  (2024-07-17T09:01:38Z)
• A Perspective on Foundation Models for the Electric Power Grid [53.02072064670517]
  Foundation models (FMs) currently dominate news headlines. We argue that an FM learning from diverse grid data and topologies could unlock transformative capabilities. We discuss a power grid FM concept, namely GridFM, based on graph neural networks and show how different downstream tasks benefit.
  arXiv  Detail & Related papers  (2024-07-12T17:09:47Z)
• Mechanistic Neural Networks for Scientific Machine Learning [58.99592521721158]
  We present Mechanistic Neural Networks, a neural network design for machine learning applications in the sciences. It incorporates a new Mechanistic Block in standard architectures to explicitly learn governing differential equations as representations. Central to our approach is a novel Relaxed Linear Programming solver (NeuRLP) inspired by a technique that reduces solving linear ODEs to solving linear programs.
  arXiv  Detail & Related papers  (2024-02-20T15:23:24Z)
• Data-driven Energy Efficiency Modelling in Large-scale Networks: An Expert Knowledge and ML-based Approach [8.326834499339107]
  This paper introduces the simulated reality of communication networks (SRCON) framework. It harnesses live network data and employs a blend of machine learning (ML)- and expert-based models. Results show significant gains over a state-of-the art method used by a operator for network energy efficiency modeling.
  arXiv  Detail & Related papers  (2023-12-31T10:03:08Z)
• Non-Intrusive Electric Load Monitoring Approach Based on Current Feature Visualization for Smart Energy Management [51.89904044860731]
  We employ computer vision techniques of AI to design a non-invasive load monitoring method for smart electric energy management. We propose to recognize all electric loads from color feature images using a U-shape deep neural network with multi-scale feature extraction and attention mechanism.
  arXiv  Detail & Related papers  (2023-08-08T04:52:19Z)
• Joint Feature and Differentiable $ k $-NN Graph Learning using Dirichlet Energy [103.74640329539389]
  We propose a deep FS method that simultaneously conducts feature selection and differentiable $ k $-NN graph learning. We employ Optimal Transport theory to address the non-differentiability issue of learning $ k $-NN graphs in neural networks. We validate the effectiveness of our model with extensive experiments on both synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2023-05-21T08:15:55Z)
• Evaluating Distribution System Reliability with Hyperstructures Graph Convolutional Nets [74.51865676466056]
  We show how graph convolutional networks and hyperstructures representation learning framework can be employed for accurate, reliable, and computationally efficient distribution grid planning. Our numerical experiments show that the proposed Hyper-GCNNs approach yields substantial gains in computational efficiency.
  arXiv  Detail & Related papers  (2022-11-14T01:29:09Z)
• Toward Dynamic Stability Assessment of Power Grid Topologies using Graph Neural Networks [0.0]
  Renewables introduce new challenges to power grids regarding the dynamic stability due to decentralization, reduced inertia, and volatility in production. graph neural networks (GNNs) are a promising method to reduce the computational effort of analyzing the dynamic stability of power grids. GNNs are surprisingly effective at predicting the highly non-linear targets from topological information only.
  arXiv  Detail & Related papers  (2022-06-10T07:23:22Z)
• Solving AC Power Flow with Graph Neural Networks under Realistic Constraints [3.114162328765758]
  We propose a graph neural network architecture to solve the AC power flow problem under realistic constraints. In our approach, we demonstrate the development of a framework that uses graph neural networks to learn the physical constraints of the power flow.
  arXiv  Detail & Related papers  (2022-04-14T14:49:34Z)
• Physics-Informed Graphical Neural Network for Parameter & State Estimations in Power Systems [4.416484585765027]
  We present a hybrid scheme which embeds physics modeling of power systems into Graphical Neural Networks (GNN) We build a physics-informed method, named Power-GNN, which reconstructs physical, thus interpretable, parameters within Effective Power Flow (EPF) models. In our experiments, we test the Power-GNN on different realistic power networks, including these with thousands of loads and hundreds of generators.
  arXiv  Detail & Related papers  (2021-02-12T04:32:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.