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. |
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| 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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| 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] |
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| ISSN: | 20500505 |
| DOI: | 10.1002/ese3.70501 |
