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Unlocking Hydrogen Load Flexibility via Data-Driven Modeling for Enhanced Integrated Energy System Operation.

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Title: Unlocking Hydrogen Load Flexibility via Data-Driven Modeling for Enhanced Integrated Energy System Operation.
Authors: He, Rongwei1 (AUTHOR), Jin, Hongyang1 (AUTHOR) jinhy@sie.edu.cn, Zhang, Dong1 (AUTHOR)
Source: Energies (19961073). May2026, Vol. 19 Issue 10, p2406. 33p.
Subject Terms: *Hydrogen as fuel, *Data modeling, *Clean energy, *Energy consumption
Abstract: Hydrogen energy, owing to its advantages of low-carbon cleanliness, long-term storage capacity, and multi-energy coupling potential, has emerged as a crucial medium for enhancing renewable energy accommodation within integrated energy systems. However, the pronounced heterogeneity in hydrogen load behaviors, temporal characteristics, and regulation capabilities poses significant challenges for unified modeling approaches, which struggle to accurately capture the multi-modal regulation potential of hydrogen demand, thereby limiting the precision of system operation optimization. To address this issue, this paper proposes a data-driven hydrogen load flexibility modeling method for integrated energy system (IES) operation optimization. A hybrid LSTM-ISODATA framework is designed to extract deep temporal dependencies and identify six representative hydrogen consumption patterns from typical load sequences. Each hydrogen load category is decomposed into shiftable, transferable, and reducible flexible forms, and a category-specific time-varying flexibility constraint matrix is established to characterize differentiated regulation capabilities. An electricity–heat–hydrogen integrated energy system operation optimization model embedded with classified flexible hydrogen loads is developed and solved via mathematical programming. Simulation results show that the proposed method reduces system operating costs by 10.3% compared with conventional unified modeling, while significantly promoting renewable energy utilization and system operational flexibility. The effectiveness and engineering applicability of the proposed model in IES optimal scheduling are fully validated. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 194141521
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Label: Title
  Group: Ti
  Data: Unlocking Hydrogen Load Flexibility via Data-Driven Modeling for Enhanced Integrated Energy System Operation.
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  Data: <searchLink fieldCode="AR" term="%22He%2C+Rongwei%22">He, Rongwei</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jin%2C+Hongyang%22">Jin, Hongyang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> jinhy@sie.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Dong%22">Zhang, Dong</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. May2026, Vol. 19 Issue 10, p2406. 33p.
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  Data: *<searchLink fieldCode="DE" term="%22Hydrogen+as+fuel%22">Hydrogen as fuel</searchLink><br />*<searchLink fieldCode="DE" term="%22Data+modeling%22">Data modeling</searchLink><br />*<searchLink fieldCode="DE" term="%22Clean+energy%22">Clean energy</searchLink><br />*<searchLink fieldCode="DE" term="%22Energy+consumption%22">Energy consumption</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Hydrogen energy, owing to its advantages of low-carbon cleanliness, long-term storage capacity, and multi-energy coupling potential, has emerged as a crucial medium for enhancing renewable energy accommodation within integrated energy systems. However, the pronounced heterogeneity in hydrogen load behaviors, temporal characteristics, and regulation capabilities poses significant challenges for unified modeling approaches, which struggle to accurately capture the multi-modal regulation potential of hydrogen demand, thereby limiting the precision of system operation optimization. To address this issue, this paper proposes a data-driven hydrogen load flexibility modeling method for integrated energy system (IES) operation optimization. A hybrid LSTM-ISODATA framework is designed to extract deep temporal dependencies and identify six representative hydrogen consumption patterns from typical load sequences. Each hydrogen load category is decomposed into shiftable, transferable, and reducible flexible forms, and a category-specific time-varying flexibility constraint matrix is established to characterize differentiated regulation capabilities. An electricity–heat–hydrogen integrated energy system operation optimization model embedded with classified flexible hydrogen loads is developed and solved via mathematical programming. Simulation results show that the proposed method reduces system operating costs by 10.3% compared with conventional unified modeling, while significantly promoting renewable energy utilization and system operational flexibility. The effectiveness and engineering applicability of the proposed model in IES optimal scheduling are fully validated. [ABSTRACT FROM AUTHOR]
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        Value: 10.3390/en19102406
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        Text: English
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        PageCount: 33
        StartPage: 2406
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      – SubjectFull: Hydrogen as fuel
        Type: general
      – SubjectFull: Data modeling
        Type: general
      – SubjectFull: Clean energy
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      – SubjectFull: Energy consumption
        Type: general
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      – TitleFull: Unlocking Hydrogen Load Flexibility via Data-Driven Modeling for Enhanced Integrated Energy System Operation.
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            NameFull: He, Rongwei
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            NameFull: Jin, Hongyang
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            – D: 15
              M: 05
              Text: May2026
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              Y: 2026
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              Value: 19
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            – TitleFull: Energies (19961073)
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