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Response-Driven Optimal Emergency Control of Power Systems via Deep Learning-Based Sensitivity Embedded Optimization.

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Title: Response-Driven Optimal Emergency Control of Power Systems via Deep Learning-Based Sensitivity Embedded Optimization.
Authors: Cheng, Lin1,2 (AUTHOR), Wang, Han1,2 (AUTHOR) wh184797255@stu.xjtu.edu.cn, Su, Yiwei1 (AUTHOR), Li, Gengfeng1,2 (AUTHOR)
Source: Energies (19961073). Mar2026, Vol. 19 Issue 5, p1284. 27p.
Subject Terms: *Electric power systems, *Real-time control, *Mathematical optimization, *Linear programming, *Dynamic stability, *Emergency management, *Deep learning, *Electric power system control
Abstract: The transition towards high-renewable power systems introduces high-dimensional nonlinearity and uncertainty, rendering traditional offline look-up table schemes prone to control mismatch against "unseen" contingencies. Meanwhile, existing response-driven approaches face a dilemma between the computational latency of physics-based optimization and the safety risks of end-to-end AI. To bridge this gap, this paper proposes a Response-Driven Optimal Emergency Control Framework that ensures both millisecond-level speed and rigorous physical constraints. First, a deep learning-based predictor is employed to extract spatiotemporal features from real-time PMU data, enabling high-fidelity prediction of stability margins. Crucially, instead of direct black-box control, the data-driven model is utilized to derive linear control sensitivities via a batch-processing perturbation mechanism. This transforms the intractable Transient Stability Constrained Optimal Power Flow (TSC-OPF) problem into a real-time solvable Linear Programming model. Case studies on a regional AC/DC hybrid grid demonstrate that the proposed framework achieves high prediction accuracy and effectively restores stability in mismatch scenarios where traditional schemes fail. Furthermore, the decision speed of the proposed method is significantly improved compared to traditional time-domain simulations, thus strictly satisfying the real-time requirements of the second line of defense. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 192641009
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Label: Title
  Group: Ti
  Data: Response-Driven Optimal Emergency Control of Power Systems via Deep Learning-Based Sensitivity Embedded Optimization.
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  Data: <searchLink fieldCode="AR" term="%22Cheng%2C+Lin%22">Cheng, Lin</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Han%22">Wang, Han</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> wh184797255@stu.xjtu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Su%2C+Yiwei%22">Su, Yiwei</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Gengfeng%22">Li, Gengfeng</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Mar2026, Vol. 19 Issue 5, p1284. 27p.
– Name: Subject
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  Data: *<searchLink fieldCode="DE" term="%22Electric+power+systems%22">Electric power systems</searchLink><br />*<searchLink fieldCode="DE" term="%22Real-time+control%22">Real-time control</searchLink><br />*<searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br />*<searchLink fieldCode="DE" term="%22Linear+programming%22">Linear programming</searchLink><br />*<searchLink fieldCode="DE" term="%22Dynamic+stability%22">Dynamic stability</searchLink><br />*<searchLink fieldCode="DE" term="%22Emergency+management%22">Emergency management</searchLink><br />*<searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Electric+power+system+control%22">Electric power system control</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The transition towards high-renewable power systems introduces high-dimensional nonlinearity and uncertainty, rendering traditional offline look-up table schemes prone to control mismatch against "unseen" contingencies. Meanwhile, existing response-driven approaches face a dilemma between the computational latency of physics-based optimization and the safety risks of end-to-end AI. To bridge this gap, this paper proposes a Response-Driven Optimal Emergency Control Framework that ensures both millisecond-level speed and rigorous physical constraints. First, a deep learning-based predictor is employed to extract spatiotemporal features from real-time PMU data, enabling high-fidelity prediction of stability margins. Crucially, instead of direct black-box control, the data-driven model is utilized to derive linear control sensitivities via a batch-processing perturbation mechanism. This transforms the intractable Transient Stability Constrained Optimal Power Flow (TSC-OPF) problem into a real-time solvable Linear Programming model. Case studies on a regional AC/DC hybrid grid demonstrate that the proposed framework achieves high prediction accuracy and effectively restores stability in mismatch scenarios where traditional schemes fail. Furthermore, the decision speed of the proposed method is significantly improved compared to traditional time-domain simulations, thus strictly satisfying the real-time requirements of the second line of defense. [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=192641009
RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.3390/en19051284
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 27
        StartPage: 1284
    Subjects:
      – SubjectFull: Electric power systems
        Type: general
      – SubjectFull: Real-time control
        Type: general
      – SubjectFull: Mathematical optimization
        Type: general
      – SubjectFull: Linear programming
        Type: general
      – SubjectFull: Dynamic stability
        Type: general
      – SubjectFull: Emergency management
        Type: general
      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Electric power system control
        Type: general
    Titles:
      – TitleFull: Response-Driven Optimal Emergency Control of Power Systems via Deep Learning-Based Sensitivity Embedded Optimization.
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            NameFull: Cheng, Lin
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            NameFull: Wang, Han
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            NameFull: Su, Yiwei
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            NameFull: Li, Gengfeng
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          Dates:
            – D: 01
              M: 03
              Text: Mar2026
              Type: published
              Y: 2026
          Identifiers:
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              Value: 19961073
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              Value: 19
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              Value: 5
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            – TitleFull: Energies (19961073)
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