低炭素エネルギーサービスのためのLLM対応クラウドエッジPIoT：仮想発電所、デジタルツイン、デマンドレスポンスのレビュー
LLM-Enabled Cloud-Edge PIoT for Low-Carbon Energy Services: A Review of Virtual Power Plants, Digital Twins, and Demand Response (原題)
Chao He, Yunjie Su, Sirui Zhang, Xin Xie, Cheng Yang
🤖 gxceed AI 要約
日本語
本レビューは、低炭素スマートエネルギーシステムにおけるLLMの活用を、電力IoTのクラウドエッジ環境に焦点を当てて体系的に整理。タスクオフローディング、動的エッジリソース割り当て、低遅延通信、セキュリティ、グリーンコンピューティングの5テーマを分析し、LLMは直接制御ではなく、運用エビデンスと検証済みツールの間で判断支援に用いる実用的パターンを提示。低炭素効果は性能・信頼性・セキュリティ・エネルギー消費・炭素排出の総合評価に基づくべきと主張。
English
This review systematically examines the use of large language models (LLMs) in low-carbon smart energy systems, focusing on cloud-edge power IoT. It analyzes five technical themes: task offloading, dynamic edge resource allocation, low-latency communication, security/privacy, and green computing. The authors propose a practical deployment pattern where LLMs assist operator judgment between operational evidence and verified tools, rather than direct control. They emphasize that low-carbon benefits must be assessed jointly across performance, reliability, security, energy consumption, and carbon emissions.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本では、再生可能エネルギーの導入拡大に伴い、VPPやデマンドレスポンスの重要性が高まっており、本レビューの知見は、電力システムのデジタル化と脱炭素化を進める上で参考になる。特に、LLMを活用した運用支援は、日本の電力会社やアグリゲーターが直面する需給調整や系統安定化の課題に応用可能。
In the global GX context
Globally, the integration of LLMs into energy systems is an emerging area with implications for grid modernization and decarbonization. This review provides a structured framework for evaluating LLM applications in virtual power plants and demand response, which is relevant for countries pursuing smart grid and renewable integration. The emphasis on joint assessment of performance and carbon emissions aligns with international efforts to ensure that digitalization supports climate goals.
👥 読者別の含意
🔬研究者:Provides a taxonomy and evidence-maturity hierarchy for LLM applications in low-carbon energy services, useful for identifying research gaps.
🏢実務担当者:Offers guidance on deploying LLMs in a safe and effective manner for VPP and demand response, emphasizing the importance of deterministic control and joint performance-carbon assessment.
🏛政策担当者:Highlights the need for regulatory frameworks that ensure LLM-based systems maintain reliability and security while contributing to low-carbon objectives.
📄 Abstract（原文）
Abstract Low-carbon smart energy systems increasingly rely on dense sensing, distributed energy resources, virtual power plants, digital twins and demand response. These services require cloud-edge intelligence, but practical deployment is constrained by latency, reliability, privacy, cybersecurity and the energy and carbon cost of computation. This review examines how large language models can be introduced into the power internet of things without shifting them into the role of direct grid control agents. The literature is organised around five technical themes: task offloading, dynamic edge resource allocation, low-latency communication and collaborative computing, security and privacy protection, and green computing. The review then evaluates intelligent inspection, digital-twin assistance, virtual power plants, demand response, and load forecasting through an explicit evidence-maturity hierarchy. Across the reviewed studies, the most practical deployment pattern places large language models between heterogeneous operational evidence and verified engineering tools. Language models can organise evidence, invoke approved tools, and assist operator judgement; authority over physical control and market execution remains with deterministic models. Claims of low-carbon benefit should be based on the joint assessment of service performance, reliability, security, energy consumption, and carbon emissions.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1093/ce/zkag058first seen 2026-08-31 04:59:57
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