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Technical Report on Resilient and Secure Large-Scale Energy Internet Systems

arXiv Security Archived Aug 14, 2026 ✓ Full text saved

arXiv:2608.12916v1 Announce Type: cross Abstract: This IEEE PES Task Force report examines the security and resilience of large-scale Energy Internet (EI) systems, in which electricity, information, and market layers are tightly coupled through pervasive digitalization. The report characterizes the EI cyber-physical threat landscape and surveys detection, assurance, and mitigation techniques, presents modeling, control, and decision-making frameworks that capture cyber-physical interdependencies

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    Electrical Engineering and Systems Science > Systems and Control [Submitted on 13 Aug 2026] Technical Report on Resilient and Secure Large-Scale Energy Internet Systems Ioannis Zografopoulos, Karen Largman, Isaac Ortega Romero, S M Zia Ur Rashid, Yexiang Chen, George Fragkos, Charalambos Konstantinou, Subhash Lakshminarayana, Juan Ospina, Airin Rahman, Suman Rath, Vivek Kumar Singh, Mucun Sun, Wei Sun This IEEE PES Task Force report examines the security and resilience of large-scale Energy Internet (EI) systems, in which electricity, information, and market layers are tightly coupled through pervasive digitalization. The report characterizes the EI cyber-physical threat landscape and surveys detection, assurance, and mitigation techniques, presents modeling, control, and decision-making frameworks that capture cyber-physical interdependencies, including storage integration, multi-dimensional resilience, and electricity price forecasting, examines adversarial risks and trustworthy deployment of artificial intelligence, and introduces graph-based, attack-resilient information routing. The report closes with recommendations for research, standardization, and regulatory efforts needed to realize a resilient and secure large-scale EI. Comments: Task Force on Resilient and Secure Large-Scale Energy Internet Systems, August 2026 Subjects: Systems and Control (eess.SY); Cryptography and Security (cs.CR) Cite as: arXiv:2608.12916 [eess.SY]   (or arXiv:2608.12916v1 [eess.SY] for this version)   https://doi.org/10.48550/arXiv.2608.12916 Focus to learn more Submission history From: Ioannis Zografopoulos [view email] [v1] Thu, 13 Aug 2026 07:58:03 UTC (3,547 KB) Access Paper: HTML (experimental) view license Current browse context: eess.SY < prev   |   next > new | recent | 2026-08 Change to browse by: cs cs.CR cs.SY eess References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation 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 Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv Security
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    ◬ AI & Machine Learning
    Published
    Aug 14, 2026
    Archived
    Aug 14, 2026
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