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Intelligent Control Framework of District Heating Systems Considering Waste Heat Utilization in Data Centers.

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Title: Intelligent Control Framework of District Heating Systems Considering Waste Heat Utilization in Data Centers.
Authors: Shen, Rendong1,2,3 (AUTHOR) shenrendong@163.com, Zheng, Ruifan3 (AUTHOR), Zhong, Shengyuan4 (AUTHOR), Gu, Lei3 (AUTHOR), Yang, Dongfang5 (AUTHOR), Zhao, Jun3 (AUTHOR)
Source: Energy Science & Engineering. Jun2026, Vol. 14 Issue 6, p2704-2720. 17p.
Subject Terms: *Heat recovery, *Data centers, *Reinforcement learning, *Energy consumption, *Heat pumps, *Energy storage, *Heating from central stations
Abstract: A significant proportion of power consumption in data centers is ultimately converted into low‐grade waste heat (WH), which is typically discharged into the atmosphere, resulting in substantial energy loss and environmental degradation. Utilizing heat pump (HP) systems to recover this WH for district heating presents a promising approach to improving energy efficiency and reducing environmental impact. While previous research primarily focused on the feasibility and technical implementation of such recovery systems, limited attention has been given to the co‐optimization of WH utilization, particularly in systems that integrate HPs with energy storage. To address this gap, this study proposes an intelligent control framework that integrates user‐side demand response with deep reinforcement learning to optimize system performance. Specifically, the twin delayed deep deterministic policy gradient algorithm is employed to generate real‐time, adaptive control strategies. Additionally, a feasible action screening mechanism is introduced to ensure that control actions conform to the physical constraints of the system, thereby enhancing training stability and learning efficiency. Simulation results demonstrate that, compared with a benchmark model, the proposed approach improves system profit by 61.8% and increases renewable energy surplus by 25.7%. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 194418740
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Label: Title
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  Data: Intelligent Control Framework of District Heating Systems Considering Waste Heat Utilization in Data Centers.
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  Data: <searchLink fieldCode="AR" term="%22Shen%2C+Rendong%22">Shen, Rendong</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<i> shenrendong@163.com</i><br /><searchLink fieldCode="AR" term="%22Zheng%2C+Ruifan%22">Zheng, Ruifan</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhong%2C+Shengyuan%22">Zhong, Shengyuan</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Gu%2C+Lei%22">Gu, Lei</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yang%2C+Dongfang%22">Yang, Dongfang</searchLink><relatesTo>5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhao%2C+Jun%22">Zhao, Jun</searchLink><relatesTo>3</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Energy+Science+%26+Engineering%22">Energy Science & Engineering</searchLink>. Jun2026, Vol. 14 Issue 6, p2704-2720. 17p.
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  Data: *<searchLink fieldCode="DE" term="%22Heat+recovery%22">Heat recovery</searchLink><br />*<searchLink fieldCode="DE" term="%22Data+centers%22">Data centers</searchLink><br />*<searchLink fieldCode="DE" term="%22Reinforcement+learning%22">Reinforcement learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Energy+consumption%22">Energy consumption</searchLink><br />*<searchLink fieldCode="DE" term="%22Heat+pumps%22">Heat pumps</searchLink><br />*<searchLink fieldCode="DE" term="%22Energy+storage%22">Energy storage</searchLink><br />*<searchLink fieldCode="DE" term="%22Heating+from+central+stations%22">Heating from central stations</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: A significant proportion of power consumption in data centers is ultimately converted into low‐grade waste heat (WH), which is typically discharged into the atmosphere, resulting in substantial energy loss and environmental degradation. Utilizing heat pump (HP) systems to recover this WH for district heating presents a promising approach to improving energy efficiency and reducing environmental impact. While previous research primarily focused on the feasibility and technical implementation of such recovery systems, limited attention has been given to the co‐optimization of WH utilization, particularly in systems that integrate HPs with energy storage. To address this gap, this study proposes an intelligent control framework that integrates user‐side demand response with deep reinforcement learning to optimize system performance. Specifically, the twin delayed deep deterministic policy gradient algorithm is employed to generate real‐time, adaptive control strategies. Additionally, a feasible action screening mechanism is introduced to ensure that control actions conform to the physical constraints of the system, thereby enhancing training stability and learning efficiency. Simulation results demonstrate that, compared with a benchmark model, the proposed approach improves system profit by 61.8% and increases renewable energy surplus by 25.7%. [ABSTRACT FROM AUTHOR]
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1002/ese3.70501
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 17
        StartPage: 2704
    Subjects:
      – SubjectFull: Heat recovery
        Type: general
      – SubjectFull: Data centers
        Type: general
      – SubjectFull: Reinforcement learning
        Type: general
      – SubjectFull: Energy consumption
        Type: general
      – SubjectFull: Heat pumps
        Type: general
      – SubjectFull: Energy storage
        Type: general
      – SubjectFull: Heating from central stations
        Type: general
    Titles:
      – TitleFull: Intelligent Control Framework of District Heating Systems Considering Waste Heat Utilization in Data Centers.
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          Name:
            NameFull: Shen, Rendong
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          Name:
            NameFull: Zheng, Ruifan
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            NameFull: Zhong, Shengyuan
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            NameFull: Gu, Lei
      – PersonEntity:
          Name:
            NameFull: Yang, Dongfang
      – PersonEntity:
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            NameFull: Zhao, Jun
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          Dates:
            – D: 01
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
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              Value: 20500505
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              Value: 14
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              Value: 6
          Titles:
            – TitleFull: Energy Science & Engineering
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