Systemic approach for modeling a generic smart grid
- URL: http://arxiv.org/abs/2511.19460v1
- Date: Fri, 21 Nov 2025 14:51:23 GMT
- Title: Systemic approach for modeling a generic smart grid
- Authors: Sofiane Ben Amor, Guillaume Guerard, Loup-NoƩ Levy,
- Abstract summary: 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.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Smart grid technological advances present a recent class of complex interdisciplinary modeling and increasingly difficult simulation problems to solve using traditional computational methods. To simulate a smart grid requires a systemic approach to integrated modeling of power systems, energy markets, demand-side management, and much other resources and assets that are becoming part of the current paradigm of the power grid. This paper presents a backbone model of a smart grid to test alternative scenarios for the grid. This tool simulates disparate systems to validate assumptions before the human scale model. Thanks to a distributed optimization of subsystems, the production and consumption scheduling is achieved while maintaining flexibility and scalability.
Related papers
- A Context-Free Smart Grid Model Using Complex System Approach [0.0]
Smart grid may evolve into different systems by means of size, elements and strategies, but its fundamental requirements and objectives will not change.<n>We propose a complex system based approach to the smart grid modeling, accentuating on the optimization by combining game theoretical and classical methods in different levels.
arXiv Detail & Related papers (2025-12-05T19:53:30Z) - Optimizing Energy Management of Smart Grid using Reinforcement Learning aided by Surrogate models built using Physics-informed Neural Networks [29.49941497527361]
Reinforcement Learning (RL) is gaining prominence as a solution for addressing the challenges of Optimal Power Flow in smart grids.<n>We address this problem by substituting costly smart grid simulators with surrogate models built using Phisics-informed Neural Networks (PINNs)
arXiv Detail & Related papers (2025-10-20T10:17:42Z) - 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) - Grid-Agent: An LLM-Powered Multi-Agent System for Power Grid Control [4.3210078529580045]
This paper introduces Grid-Agent, an autonomous AI-driven framework to detect and remediate grid violations.<n>Grid-Agent integrates semantic reasoning with numerical precision through modular agents.<n>Experiments on IEEE and CIGRE benchmark networks demonstrate superior mitigation performance.
arXiv Detail & Related papers (2025-08-07T01:10:28Z) - Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey [58.50944604905037]
Edge-cloud collaborative computing (ECCC) has emerged as a pivotal paradigm for addressing the computational demands of modern intelligent applications.<n>Recent advancements in AI, particularly deep learning and large language models (LLMs), have dramatically enhanced the capabilities of these distributed systems.<n>This survey provides a structured tutorial on fundamental architectures, enabling technologies, and emerging applications.
arXiv Detail & Related papers (2025-05-03T13:55:38Z) - Optimizing Load Scheduling in Power Grids Using Reinforcement Learning and Markov Decision Processes [0.0]
This paper proposes a reinforcement learning (RL) approach to address the challenges of dynamic load scheduling.
Our results show that the RL-based method provides a robust and scalable solution for real-time load scheduling.
arXiv Detail & Related papers (2024-10-23T09:16:22Z) - 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) - Grounding and Enhancing Grid-based Models for Neural Fields [52.608051828300106]
This paper introduces a theoretical framework for grid-based models.
The framework points out that these models' approximation and generalization behaviors are determined by grid tangent kernels (GTK)
The introduced framework motivates the development of a novel grid-based model named the Multiplicative Fourier Adaptive Grid (MulFAGrid)
arXiv Detail & Related papers (2024-03-29T06:33:13Z) - Machine Learning Insides OptVerse AI Solver: Design Principles and
Applications [74.67495900436728]
We present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI solver.
We showcase our methods for generating complex SAT and MILP instances utilizing generative models that mirror multifaceted structures of real-world problem.
We detail the incorporation of state-of-the-art parameter tuning algorithms which markedly elevate solver performance.
arXiv Detail & Related papers (2024-01-11T15:02:15Z) - 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) - Constructing Neural Network-Based Models for Simulating Dynamical
Systems [59.0861954179401]
Data-driven modeling is an alternative paradigm that seeks to learn an approximation of the dynamics of a system using observations of the true system.
This paper provides a survey of the different ways to construct models of dynamical systems using neural networks.
In addition to the basic overview, we review the related literature and outline the most significant challenges from numerical simulations that this modeling paradigm must overcome.
arXiv Detail & Related papers (2021-11-02T10:51:42Z)
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.