LLM-Enabled Cloud-Edge PIoT for Low-Carbon Energy Services: A Review of Virtual Power Plants, Digital Twins, and Demand Response

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ID: 327389
2026
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Abstract
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.
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openalex_W7204670139 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Chao He, Yunjie Su, Sirui Zhang, Xin Xie, Cheng Yang
Journal journal of modern power systems and clean energy
Year 2026
DOI
10.1093/ce/zkag058
URL
https://academic.oup.com/ce/advance-article-pdf/doi/10.1093/ce/zkag058/70834118/zkag058.pdf
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