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Research on data-driven operation control of secondary loop of district heating system

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  • Zhong, Wei
  • Feng, Encheng
  • Lin, Xiaojie
  • Xie, Jinfang

Abstract

District heating system (DHS) is a major part of urban energy system. DHS consists of primary and secondary loop. Compared to the primary loop, the secondary loop is closer to the heating user side. The operation control of heating substation and its corresponding secondary loop is a challenging topic. At present, the existing control of secondary loop is a manual and intermittent regulation process of its water supply temperature regardless of the weather condition, leading to either overheating or insufficient heating on the user side. Intelligent operation control of secondary loop has become a major focus of DHS digitalization. This paper proposes a new method of secondary loop operation control via a data-driven approach by dividing the process into two parts: primary loop valve opening (PLVO) prediction and corresponding secondary loop supply temperature (SLST) prediction. The goal is to provide both a time sequence of enhanced valve opening and its expected supply water temperature. Both models are trained with multilayer perceptron (MLP) and extreme gradient boosting (XGBoost) methods with consideration of the impact of input and prediction length. A demo heating substation and its secondary loop in Zhengzhou city are selected to illustrate the effectiveness of this approach. The application results show that the enhanced PLVO strategy proposed in this paper could reduce the supply water temperature deviation from the expected SLST and adapt to the change of ambient conditions. Compared with the existing operation strategy, the proposed approach reduces the supply water temperature deviation from 12.68% to 3.18% in an overheating scenario and from 6.18% to 3.87% in an insufficient heating scenario. It indicates that the proposed PLVO strategy could overcome the issue of overheating and insufficient heating in existing DHS. The results from this study could provide a basis for flexible and energy-saving operation of decentralized heating systems demanding a delicate and dynamic control of its secondary loop.

Suggested Citation

  • Zhong, Wei & Feng, Encheng & Lin, Xiaojie & Xie, Jinfang, 2022. "Research on data-driven operation control of secondary loop of district heating system," Energy, Elsevier, vol. 239(PB).
    • Handle: RePEc:eee:energy:v:239:y:2022:i:pb:s0360544221023094
    DOI: 10.1016/j.energy.2021.122061
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    References listed on IDEAS

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    Cited by:

    1. Hong, Yejin & Yoon, Sungmin, 2022. "Holistic Operational Signatures for an energy-efficient district heating substation in buildings," Energy, Elsevier, vol. 250(C).
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    3. Ling, Jihong & Zhang, Bingyang & Dai, Na & Xing, Jincheng, 2023. "Coupling input feature construction methods and machine learning algorithms for hourly secondary supply temperature prediction," Energy, Elsevier, vol. 278(C).

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