电网与AI数据中心的隐私保护协同运行：一种检查点感知的三阶段方案

Electrical Engineering and Systems Science > Systems and Control arXiv:2609.26365 (eess) [Submitted on 22 Sep 2026] Title:Privacy-Preserving Coordinated Operation of Power Grids and AI Data Centers: A Checkpoint-Aware Three-Phase Scheme Authors:Ziang Liu, Ruizhang Yang, Xin Cui, Francis Yunhe Hou View a PDF of the paper titled Privacy-Preserving Coordinated Operation of Power Grids and AI Data Centers: A Checkpoint-Aware Three-Phase Scheme, by Ziang Liu and 3 other authors View PDF HTML (experimental) Abstract:The rapid growth of large language model training and serving is driving AI data centers (AIDCs) toward gigawatt scale. Unlike conventional commercial loads, AIDCs possess significant operational flexibility through dynamic voltage and frequency scaling (DVFS) of training and inference workloads, while periodic model checkpointing can induce abrupt power drops and rebounds that erode operating reserves and increase transmission congestion risks. Coordinating AIDC operation with grid scheduling under these unique operational characteristics is challenging because grid and AIDC operators are generally unwilling to share proprietary data and decision-making authority. This paper proposes a hierarchical privacy-preserving coordinated operation scheme between the power grid and AIDCs to address this gap. The proposed scheme contains three phases. In Phase I, the grid operator computes a certified inner approximation of the AIDCs security region for subsequent coordination. In Phase II, the AIDC operator coordinates training and inference AIDCs to optimize workload allocation within the certified security region and generate power schedules and checkpoint alerts. In Phase III, the grid operator solves a checkpoint-aware two-stage robust optimal power flow (OPF) considering renewable generation and checkpoint uncertainties. By exchanging only compact interface information, the framework preserves the privacy of both grid and AIDCs, avoids frequent iterative communication, and enables secure coordination with guaranteed feasibility. Numerical studies on a modified IEEE 14-bus system and a modified NYISO system demonstrate the effectiveness, robustness, and security of the proposed framework. Comments: Subjects: Systems and Control (eess.SY) Cite as: arXiv:2609.26365 [eess.SY] (or arXiv:2609.26365v1 [eess.SY] for this version) https://doi.org/10.48550/arXiv.2609.26365 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Ziang Liu [view email] [v1] Tue, 22 Sep 2026 13:07:37 UTC (2,652 KB) Full-text links: Access Paper: View a PDF of the paper titled Privacy-Preserving Coordinated Operation of Power Grids and AI Data Centers: A Checkpoint-Aware Three-Phase Scheme, by Ziang Liu and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: eess.SY new | recent | 2026-09 Change to browse by: cs cs.SY eess References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)