Response-Based Frequency Stability Assessment under Multi-Scale Disturbances in High-Renewable Power Systems

[2511.21269] Response-Based Frequency Stability Assessment under Multi-Scale Disturbances in High-Renewable Power Systems Skip to main content Search Submit Donate Log in Electrical Engineering and Systems Science > Systems and Control arXiv:2511.21269 (eess) [Submitted on 26 Nov 2025 ( v1 ), last revised 24 Aug 2026 (this version, v3)] Title: Response-Based Frequency Stability Assessment under Multi-Scale Disturbances in High-Renewable Power Systems Authors: Jinhui Chen , Huadong Sun , Ping Wu , Baocai Wang , Bing Zhao View PDF Abstract: In high-renewable power systems, active-power disturbances are becoming larger and exhibit increasingly diverse time scales, which complicates frequency stability assessment under unanticipated events. This paper presents a response-based frequency stability assessment method that uses disturbance power, inferred from generator electrical responses, to provide a unified treatment of multi-scale disturbances. Unanticipated disturbances are first classified into short-term and permanent events; permanent disturbances are further divided into step, second-level slope and minute-level slope disturbances. Based on the measured power responses of generator groups, a unified disturbance-power model is constructed to identify the disturbance type online and to quantify disturbance intensity through the disturbance power and its rate of change. Analytical frequency-response models are then derived for each disturbance class. For step disturbances, the maximum tolerable disturbance power is obtained under steady-state and transient frequency deviation constraints, and a safety-margin index is defined. For slope-type disturbances, an improved system frequency response (SFR) model and the rotor motion equation after exhaustion of primary frequency regulation are used to compute the over-limit time of frequency deviation. The proposed response-based assessment method is validated on the CSEE-FS frequency-stability benchmark system, demonstrating its effectiveness and accuracy for quantitative frequency stability assessment in high-renewable power systems. Comments: 14 pages, 20 figures. Accepted by CSEE Journal of Power and Energy Systems in July 2026 Subjects: Systems and Control (eess.SY) Cite as: arXiv:2511.21269 [eess.SY] (or arXiv:2511.21269v3 [eess.SY] for this version) https://doi.org/10.48550/arXiv.2511.21269 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Jinhui Chen [ view email ] [v1] Wed, 26 Nov 2025 10:48:35 UTC (509 KB) [v2] Thu, 13 Aug 2026 09:08:45 UTC (920 KB) [v3] Mon, 24 Aug 2026 18:08:34 UTC (920 KB) Full-text links: Access Paper: View PDF view license Current browse context: eess.SY < prev | next > new | recent | 2025-11 Change to browse by: cs 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 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? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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