Large Language Models integration in Smart Grids

• URL: http://arxiv.org/abs/2504.09059v1
• Date: Sat, 12 Apr 2025 03:29:30 GMT
• Title: Large Language Models integration in Smart Grids
• Authors: Seyyedreza Madani, Ahmadreza Tavasoli, Zahra Khoshtarash Astaneh, Pierre-Olivier Pineau,
• Abstract summary: Large Language Models (LLMs) are changing the way we operate our society and will undoubtedly impact power systems as well.<n>This paper provides a comprehensive analysis of 30 real-world applications across eight key categories.<n>Critical technical hurdles, such as data privacy and model reliability, are examined, along with possible solutions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) are changing the way we operate our society and will undoubtedly impact power systems as well - but how exactly? By integrating various data streams - including real-time grid data, market dynamics, and consumer behaviors - LLMs have the potential to make power system operations more adaptive, enhance proactive security measures, and deliver personalized energy services. This paper provides a comprehensive analysis of 30 real-world applications across eight key categories: Grid Operations and Management, Energy Markets and Trading, Personalized Energy Management and Customer Engagement, Grid Planning and Education, Grid Security and Compliance, Advanced Data Analysis and Knowledge Discovery, Emerging Applications and Societal Impact, and LLM-Enhanced Reinforcement Learning. Critical technical hurdles, such as data privacy and model reliability, are examined, along with possible solutions. Ultimately, this review illustrates how LLMs can significantly contribute to building more resilient, efficient, and sustainable energy infrastructures, underscoring the necessity of their responsible and equitable deployment.

Related papers

• Exploring the Roles of Large Language Models in Reshaping Transportation Systems: A Survey, Framework, and Roadmap [51.198001060683296]
  Large Language Models (LLMs) offer transformative potential to address transportation challenges.<n>This survey first presents LLM4TR, a novel conceptual framework that systematically categorizes the roles of LLMs in transportation.<n>For each role, our review spans diverse applications, from traffic prediction and autonomous driving to safety analytics and urban mobility optimization.
  arXiv  Detail & Related papers  (2025-03-27T11:56:27Z)
• Exploration of Multi-Element Collaborative Research and Application for Modern Power System Based on Generative Large Models [4.854786697610143]
  Generative Large Models (GLMs) provide a data-driven approach to enhancing forecasting, scheduling, and market operations.<n>By leveragingtemporal modeling and reinforcement learning, GLMs enable dynamic energy scheduling, improve grid stability, enhance carbon trading strategies, and strengthen resilience against extreme weather events.
  arXiv  Detail & Related papers  (2025-03-26T17:58:49Z)
• Advancing Generative Artificial Intelligence and Large Language Models for Demand Side Management with Internet of Electric Vehicles [52.43886862287498]
  This paper explores the integration of large language models (LLMs) into energy management.<n>We propose an innovative solution that enhances LLMs with retrieval-augmented generation for automatic problem formulation, code generation, and customizing optimization.<n>We present a case study to demonstrate the effectiveness of our proposed solution in charging scheduling and optimization for electric vehicles.
  arXiv  Detail & Related papers  (2025-01-26T14:31:03Z)
• Integrating LLMs with ITS: Recent Advances, Potentials, Challenges, and Future Directions [1.6121249557846946]
  This paper comprehensively reviews the transformative potential of Large Language Models (LLMs) in optimizing ITS.<n>Our analysis reveals how these advanced models can significantly enhance traffic management and safety.<n>This paper aims to guide researchers and practitioners through the complexities and opportunities of integrating LLMs in ITS.
  arXiv  Detail & Related papers  (2025-01-08T11:37:35Z)
• Large Language Model as a Catalyst: A Paradigm Shift in Base Station Siting Optimization [62.16747639440893]
  Large language models (LLMs) and their associated technologies advance, particularly in the realms of prompt engineering and agent engineering. Our proposed framework incorporates retrieval-augmented generation (RAG) to enhance the system's ability to acquire domain-specific knowledge and generate solutions.
  arXiv  Detail & Related papers  (2024-08-07T08:43:32Z)
• 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)
• Efficient Prompting for LLM-based Generative Internet of Things [88.84327500311464]
  Large language models (LLMs) have demonstrated remarkable capacities on various tasks, and integrating the capacities of LLMs into the Internet of Things (IoT) applications has drawn much research attention recently. Due to security concerns, many institutions avoid accessing state-of-the-art commercial LLM services, requiring the deployment and utilization of open-source LLMs in a local network setting. We propose a LLM-based Generative IoT (GIoT) system deployed in the local network setting in this study.
  arXiv  Detail & Related papers  (2024-06-14T19:24:00Z)
• Cooperate or Collapse: Emergence of Sustainable Cooperation in a Society of LLM Agents [101.17919953243107]
  GovSim is a generative simulation platform designed to study strategic interactions and cooperative decision-making in large language models (LLMs)<n>We find that all but the most powerful LLM agents fail to achieve a sustainable equilibrium in GovSim, with the highest survival rate below 54%.<n>We show that agents that leverage "Universalization"-based reasoning, a theory of moral thinking, are able to achieve significantly better sustainability.
  arXiv  Detail & Related papers  (2024-04-25T15:59:16Z)
• Rethinking Machine Unlearning for Large Language Models [85.92660644100582]
  We explore machine unlearning in the domain of large language models (LLMs) This initiative aims to eliminate undesirable data influence (e.g., sensitive or illegal information) and the associated model capabilities.
  arXiv  Detail & Related papers  (2024-02-13T20:51:58Z)
• Transforming Agriculture with Intelligent Data Management and Insights [3.027257459810039]
  Modern agriculture faces grand challenges to meet increased demands for food, fuel, feed, and fiber under the constraints of climate change and dwindling natural resources. Data innovation is urgently required to secure and improve the productivity, sustainability, and resilience of our agroecosystems.
  arXiv  Detail & Related papers  (2023-11-07T22:02:54Z)
• Empowering Distributed Solutions in Renewable Energy Systems and Grid Optimization [3.8979646385036175]
  Machine learning (ML) advancements play a crucial role in empowering renewable energy sources and improving grid management. The incorporation of big data and ML into smart grids offers several advantages, including heightened energy efficiency. However, challenges like handling large data volumes, ensuring cybersecurity, and obtaining specialized expertise must be addressed.
  arXiv  Detail & Related papers  (2023-10-24T02:45:16Z)

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.