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Integrated Two-Stage Scheduling Framework for Compressor Units via a Hybrid Algorithm and Dynamic Programming.

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Title: Integrated Two-Stage Scheduling Framework for Compressor Units via a Hybrid Algorithm and Dynamic Programming.
Authors: Chen, Cheng1 (AUTHOR), Zhao, Chun2 (AUTHOR), Zhang, Yunpeng2,3 (AUTHOR), Gao, Xi1,2 (AUTHOR), Chen, Linying2 (AUTHOR), Wei, Qi3 (AUTHOR), Xing, Likai3 (AUTHOR), Song, Feng3 (AUTHOR), Chen, Xiaoming1 (AUTHOR) chen_xm@dlut.edu.cn
Source: Energies (19961073). Jun2026, Vol. 19 Issue 11, p2566. 31p.
Subject Terms: *Natural gas storage, *Dynamic programming, *Compressor performance, *Time-based pricing, *Swarm intelligence, *Resource allocation, *Metaheuristic algorithms, *Electric power management
Abstract: Electrically driven compressors are a primary energy consumer in natural gas storage facilities. Formulating an optimal gas injection allocation strategy considering their nonlinear characteristics and time-of-use (TOU) electricity prices is crucial. However, single-model optimizations struggle with this due to high dimensionality and strongly coupled variables. To overcome these challenges, we propose a two-stage "instantaneous load allocation—day-ahead scheduling" framework. Stage I employs a hybrid algorithm (ICSA-WOA) to optimize load allocations across various flow rates, generating a lookup table that effectively decouples the underlying physical model. Stage II utilizes this table alongside TOU prices to perform rapid day-ahead scheduling via dynamic programming (DP). Results demonstrate that ICSA-WOA achieves superior comprehensive performance compared to seven classical swarm intelligence algorithms. Furthermore, joint optimization of the pressure ratio and load via ICSA-WOA reduces the total power consumption by 9.7–10.9% relative to traditional fixed-ratio modes. Most significantly, while rigorously ensuring daily injection targets and safety, the proposed method reduces daily electricity costs by 3.3–14.2% compared to single-model approaches, providing a reasonable strategy for economic gas storage operations. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 194587954
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Integrated Two-Stage Scheduling Framework for Compressor Units via a Hybrid Algorithm and Dynamic Programming.
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  Data: <searchLink fieldCode="AR" term="%22Chen%2C+Cheng%22">Chen, Cheng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhao%2C+Chun%22">Zhao, Chun</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Yunpeng%22">Zhang, Yunpeng</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Gao%2C+Xi%22">Gao, Xi</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Linying%22">Chen, Linying</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wei%2C+Qi%22">Wei, Qi</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xing%2C+Likai%22">Xing, Likai</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Song%2C+Feng%22">Song, Feng</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Xiaoming%22">Chen, Xiaoming</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> chen_xm@dlut.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Jun2026, Vol. 19 Issue 11, p2566. 31p.
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  Data: *<searchLink fieldCode="DE" term="%22Natural+gas+storage%22">Natural gas storage</searchLink><br />*<searchLink fieldCode="DE" term="%22Dynamic+programming%22">Dynamic programming</searchLink><br />*<searchLink fieldCode="DE" term="%22Compressor+performance%22">Compressor performance</searchLink><br />*<searchLink fieldCode="DE" term="%22Time-based+pricing%22">Time-based pricing</searchLink><br />*<searchLink fieldCode="DE" term="%22Swarm+intelligence%22">Swarm intelligence</searchLink><br />*<searchLink fieldCode="DE" term="%22Resource+allocation%22">Resource allocation</searchLink><br />*<searchLink fieldCode="DE" term="%22Metaheuristic+algorithms%22">Metaheuristic algorithms</searchLink><br />*<searchLink fieldCode="DE" term="%22Electric+power+management%22">Electric power management</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Electrically driven compressors are a primary energy consumer in natural gas storage facilities. Formulating an optimal gas injection allocation strategy considering their nonlinear characteristics and time-of-use (TOU) electricity prices is crucial. However, single-model optimizations struggle with this due to high dimensionality and strongly coupled variables. To overcome these challenges, we propose a two-stage "instantaneous load allocation—day-ahead scheduling" framework. Stage I employs a hybrid algorithm (ICSA-WOA) to optimize load allocations across various flow rates, generating a lookup table that effectively decouples the underlying physical model. Stage II utilizes this table alongside TOU prices to perform rapid day-ahead scheduling via dynamic programming (DP). Results demonstrate that ICSA-WOA achieves superior comprehensive performance compared to seven classical swarm intelligence algorithms. Furthermore, joint optimization of the pressure ratio and load via ICSA-WOA reduces the total power consumption by 9.7–10.9% relative to traditional fixed-ratio modes. Most significantly, while rigorously ensuring daily injection targets and safety, the proposed method reduces daily electricity costs by 3.3–14.2% compared to single-model approaches, providing a reasonable strategy for economic gas storage operations. [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=194587954
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        Value: 10.3390/en19112566
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 31
        StartPage: 2566
    Subjects:
      – SubjectFull: Natural gas storage
        Type: general
      – SubjectFull: Dynamic programming
        Type: general
      – SubjectFull: Compressor performance
        Type: general
      – SubjectFull: Time-based pricing
        Type: general
      – SubjectFull: Swarm intelligence
        Type: general
      – SubjectFull: Resource allocation
        Type: general
      – SubjectFull: Metaheuristic algorithms
        Type: general
      – SubjectFull: Electric power management
        Type: general
    Titles:
      – TitleFull: Integrated Two-Stage Scheduling Framework for Compressor Units via a Hybrid Algorithm and Dynamic Programming.
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            NameFull: Chen, Cheng
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            NameFull: Chen, Linying
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            – D: 01
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 19961073
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              Value: 19
            – Type: issue
              Value: 11
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            – TitleFull: Energies (19961073)
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