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Task-Decoupled and Multi-Task Synergistic LLM-MoE Method for Power System Operation Simulation.

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Title: Task-Decoupled and Multi-Task Synergistic LLM-MoE Method for Power System Operation Simulation.
Authors: Guo, Qian1 (AUTHOR), Jiang, Lizhou1,2 (AUTHOR), Shen, Zhijun1 (AUTHOR), Cai, Xinlei1,2 (AUTHOR), Meng, Zijie1 (AUTHOR), Chen, Zongyuan2 (AUTHOR) scutczy@gmail.com, Yu, Tao2 (AUTHOR)
Source: Energies (19961073). Jun2026, Vol. 19 Issue 11, p2506. 25p.
Subject Terms: *Electric power system management, *Electric generators, *Language models, *Clean energy, *Computer simulation, *Deep learning, *Ensemble learning
Abstract: With the increasing integration of high-penetration renewable energy and emerging loads, power system operation simulation faces two major challenges, namely strong uncertainty and significant heterogeneity in the output characteristics of multiple generator types. Traditional mathematical programming methods struggle to effectively handle uncertainty while meeting real-time computational requirements. Existing deep learning approaches fail to decouple the heterogeneous output characteristics of different generator types, which limits their ability to achieve coordinated operation. To address these issues, this paper proposes a task-decoupled and multi-task synergistic LLM-MoE method for power system operation simulation. First, a feature encoder based on Residual-Gated Linear Units is constructed to perform deep filtering and efficient representation of multi-source heterogeneous data. Second, a pre-trained large language model is employed as a temporal feature extractor to enhance temporal modeling capability and cross-scenario generalization. Finally, a customized gating-controlled mixture-of-experts decoder is developed. It dynamically coordinates task-specific and shared experts, which enables unified modeling of task decoupling, cross-task information sharing, and system physical constraints. Simulation results based on a provincial-level power grid in China demonstrate that the proposed method achieves high-accuracy and high-efficiency operation simulation while ensuring physical consistency. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 194587894
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Label: Title
  Group: Ti
  Data: Task-Decoupled and Multi-Task Synergistic LLM-MoE Method for Power System Operation Simulation.
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  Data: <searchLink fieldCode="AR" term="%22Guo%2C+Qian%22">Guo, Qian</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jiang%2C+Lizhou%22">Jiang, Lizhou</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shen%2C+Zhijun%22">Shen, Zhijun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cai%2C+Xinlei%22">Cai, Xinlei</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Meng%2C+Zijie%22">Meng, Zijie</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Zongyuan%22">Chen, Zongyuan</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> scutczy@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Yu%2C+Tao%22">Yu, Tao</searchLink><relatesTo>2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Jun2026, Vol. 19 Issue 11, p2506. 25p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Electric+power+system+management%22">Electric power system management</searchLink><br />*<searchLink fieldCode="DE" term="%22Electric+generators%22">Electric generators</searchLink><br />*<searchLink fieldCode="DE" term="%22Language+models%22">Language models</searchLink><br />*<searchLink fieldCode="DE" term="%22Clean+energy%22">Clean energy</searchLink><br />*<searchLink fieldCode="DE" term="%22Computer+simulation%22">Computer simulation</searchLink><br />*<searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Ensemble+learning%22">Ensemble learning</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: With the increasing integration of high-penetration renewable energy and emerging loads, power system operation simulation faces two major challenges, namely strong uncertainty and significant heterogeneity in the output characteristics of multiple generator types. Traditional mathematical programming methods struggle to effectively handle uncertainty while meeting real-time computational requirements. Existing deep learning approaches fail to decouple the heterogeneous output characteristics of different generator types, which limits their ability to achieve coordinated operation. To address these issues, this paper proposes a task-decoupled and multi-task synergistic LLM-MoE method for power system operation simulation. First, a feature encoder based on Residual-Gated Linear Units is constructed to perform deep filtering and efficient representation of multi-source heterogeneous data. Second, a pre-trained large language model is employed as a temporal feature extractor to enhance temporal modeling capability and cross-scenario generalization. Finally, a customized gating-controlled mixture-of-experts decoder is developed. It dynamically coordinates task-specific and shared experts, which enables unified modeling of task decoupling, cross-task information sharing, and system physical constraints. Simulation results based on a provincial-level power grid in China demonstrate that the proposed method achieves high-accuracy and high-efficiency operation simulation while ensuring physical consistency. [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=194587894
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
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        Value: 10.3390/en19112506
    Languages:
      – Code: eng
        Text: English
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      Pagination:
        PageCount: 25
        StartPage: 2506
    Subjects:
      – SubjectFull: Electric power system management
        Type: general
      – SubjectFull: Electric generators
        Type: general
      – SubjectFull: Language models
        Type: general
      – SubjectFull: Clean energy
        Type: general
      – SubjectFull: Computer simulation
        Type: general
      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Ensemble learning
        Type: general
    Titles:
      – TitleFull: Task-Decoupled and Multi-Task Synergistic LLM-MoE Method for Power System Operation Simulation.
        Type: main
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            NameFull: Guo, Qian
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            NameFull: Jiang, Lizhou
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            NameFull: Shen, Zhijun
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            NameFull: Cai, Xinlei
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            NameFull: Meng, Zijie
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            NameFull: Chen, Zongyuan
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            NameFull: Yu, Tao
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          Dates:
            – D: 01
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
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            – Type: issn-print
              Value: 19961073
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              Value: 19
            – Type: issue
              Value: 11
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            – TitleFull: Energies (19961073)
              Type: main
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