Optimal Power Grid Operations with Foundation Models

• URL: http://arxiv.org/abs/2409.02148v1
• Date: Tue, 3 Sep 2024 09:06:13 GMT
• Title: Optimal Power Grid Operations with Foundation Models
• Authors: Alban Puech, Jonas Weiss, Thomas Brunschwiler, Hendrik F. Hamann,
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The energy transition, crucial for tackling the climate crisis, demands integrating numerous distributed, renewable energy sources into existing grids. Along with climate change and consumer behavioral changes, this leads to changes and variability in generation and load patterns, introducing significant complexity and uncertainty into grid planning and operations. While the industry has already started to exploit AI to overcome computational challenges of established grid simulation tools, 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, enhancing grid operations. 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. We show how this approach may close the gap between the industry needs and current grid analysis capabilities, to bring the industry closer to optimal grid operation and planning.

Related papers

• Systemic approach for modeling a generic smart grid [0.0]
  This paper presents a backbone model of a smart grid to test alternative scenarios for the grid.<n>Thanks to a distributed optimization of subsystems, the production and consumption scheduling is achieved while maintaining flexibility and scalability.
  arXiv  Detail & Related papers  (2025-11-21T14:51:23Z)
• Power Grid Control with Graph-Based Distributed Reinforcement Learning [60.49805771047161]
  This work advances a graph-based distributed reinforcement learning framework for real-time, scalable grid management.<n>A Graph Neural Network (GNN) is employed to encode the network's topological information within the single low-level agent's observation.<n>Experiments on the Grid2Op simulation environment show the effectiveness of the approach.
  arXiv  Detail & Related papers  (2025-09-02T22:17:25Z)
• PowerGrow: Feasible Co-Growth of Structures and Dynamics for Power Grid Synthesis [75.14189839277928]
  We present PowerGrow, a co-generative framework that significantly reduces computational overhead while maintaining operational validity.<n> Experiments across benchmark settings show that PowerGrow outperforms prior diffusion models in fidelity and diversity.<n>This demonstrates its ability to generate operationally valid and realistic power grid scenarios.
  arXiv  Detail & Related papers  (2025-08-29T01:47:27Z)
• Predicting Large-scale Urban Network Dynamics with Energy-informed Graph Neural Diffusion [51.198001060683296]
  Networked urban systems facilitate the flow of people, resources, and services.<n>Current models such as graph neural networks have shown promise but face a trade-off between efficacy and efficiency.<n>This paper addresses this trade-off by drawing inspiration from physical laws to inform essential model designs.
  arXiv  Detail & Related papers  (2025-07-31T01:24:01Z)
• From Transformers to Large Language Models: A systematic review of AI applications in the energy sector towards Agentic Digital Twins [0.0]
  We review the rapid expanding field of AI applications in the energy domain focusing on Transformers and Large Language Models.<n>We highlight practical implementations, innovations, and areas where the research frontier is rapidly expanding.<n>We introduce the concept of the Agentic Digital Twin, a next-generation model that integrates LLMs to bring autonomy, proactivity, and social interaction into digital twin-based energy management systems.
  arXiv  Detail & Related papers  (2025-06-03T10:02:07Z)
• Accelerating Quasi-Static Time Series Simulations with Foundation Models [36.7183558293052]
  We envision how recently introduced grid foundation models could improve the economic viability of neural power flow solvers. We call for collaboration between the AI and power grid communities to develop and open-source these models.
  arXiv  Detail & Related papers  (2024-11-13T14:42:32Z)
• 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)
• 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)
• 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)
• GP CC-OPF: Gaussian Process based optimization tool for Chance-Constrained Optimal Power Flow [54.94701604030199]
  The Gaussian Process (GP) based Chance-Constrained Optimal Flow (CC-OPF) is an open-source Python code for economic dispatch (ED) problem in power grids. The developed tool presents a novel data-driven approach based on the CC-OP model for solving the large regression problem with a trade-off between complexity and accuracy.
  arXiv  Detail & Related papers  (2023-02-16T17:59:06Z)
• 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)
• Unsupervised Optimal Power Flow Using Graph Neural Networks [172.33624307594158]
  We use a graph neural network to learn a nonlinear parametrization between the power demanded and the corresponding allocation. We show through simulations that the use of GNNs in this unsupervised learning context leads to solutions comparable to standard solvers.
  arXiv  Detail & Related papers  (2022-10-17T17:30:09Z)
• 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)
• Knowledge- and Data-driven Services for Energy Systems using Graph Neural Networks [0.9809636731336702]
  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.
  arXiv  Detail & Related papers  (2021-03-12T13:00:01Z)
• NeurOpt: Neural network based optimization for building energy management and climate control [58.06411999767069]
  We propose a data-driven control algorithm based on neural networks to reduce this cost of model identification. We validate our learning and control algorithms on a two-story building with ten independently controlled zones, located in Italy.
  arXiv  Detail & Related papers  (2020-01-22T00:51:03Z)

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.