Optimization of secondary network operation of heating system based on user load quantification

doi:10.3785/j.issn.1008-973X.2025.08.009

Journal of ZheJiang University (Engineering Science)

2025, Vol. 59

Issue (8): 1624-1633 DOI: 10.3785/j.issn.1008-973X.2025.08.009

Optimization of secondary network operation of heating system based on user load quantification

Xu ZHOU1,2(

),Ziyu YANG1,Junwei ZHANG1,Yanling WU3,*(

),Xiaojie LIN3,Wei ZHONG1,3,Baoqin LIU2

1. Polytechnic Institute, Zhejiang University, Hangzhou 310015, China
2. Jinan Heating Group Limited Company, Jinan 250000, China
3. College of Energy Engineering, Zhejiang University, Hangzhou 310007, China

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Abstract

A mechanism-based quantitative model of heat supply load was established aiming at the problem of unbalanced heat supply in the heat supply system caused by inaccurate assessment of user heat load. An operation optimization method for the secondary heat supply network was proposed based on the accurate quantification of user heat load. The actual thermal demand of different users was analyzed according to the building characteristics and climatic conditions. The particle swarm optimization algorithm was used to optimize the valve opening of users and the secondary side combined with the hydraulic and thermal modeling analysis of the pipe network. Then the optimal valve opening adjustment strategy matching the user load was obtained. The result of the case analysis showed that the overall variance of backwater temperature of hot users after optimization was reduced by 12.16% compared with that before optimization, and the power consumption of the circulating pump in a secondary network was reduced by 16.46%. The personalized heating needs of users can be met based on ensuring the hydraulic balance of the heating system. Then the comfort of thermal users is effectively improved, and energy consumption is reduced.

Key words： heating system load quantification mechanism modeling operational optimization secondary network

Received: 10 August 2024 Published: 28 July 2025

CLC:

TP 393

Fund: 国家自然科学基金资助项目（51806190）；浙江省“尖兵”“领雁”研发攻关计划资助项目（2024C03117）; 国家重点研发计划资助项目（2023YFE0108600）.

Corresponding Authors: Yanling WU E-mail: zhouxu@jinanenergy.cn;shelleywu@zju.edu.cn

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	Xu ZHOU
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	Wei ZHONG
	Baoqin LIU

Cite this article:

Xu ZHOU,Ziyu YANG,Junwei ZHANG,Yanling WU,Xiaojie LIN,Wei ZHONG,Baoqin LIU. Optimization of secondary network operation of heating system based on user load quantification. Journal of ZheJiang University (Engineering Science), 2025, 59(8): 1624-1633.

URL:

https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2025.08.009 OR https://www.zjujournals.com/eng/Y2025/V59/I8/1624

基于用户负荷量化的供热系统二级网运行优化

针对用户热负荷评估不精确导致的供热系统供热不均衡问题，建立基于机理的供热负荷量化模型，基于用户热负荷的精确量化，提出供热二级网的运行优化方法. 根据建筑特性和气候条件分析不同用户的实际用热需求，结合管网水力和热力建模分析，采用粒子群优化算法，对用户和二次侧阀门开度进行寻优，得到匹配用户负荷的最佳阀门开度调节策略. 案例分析结果显示，优化后热用户的回水温度整体方差较优化前降低了12.16%，二级网循环水泵电耗减少了16.46%. 在保证供热系统水力平衡的基础上，满足了用户的个性化供热需求，有效提升了热用户舒适度，减少了能源消耗.

关键词： 供热系统, 负荷量化, 机理建模, 运行优化, 二级网

Fig.1 Typical application scenario for quantitative model of heating load

Fig.2 Flowchart of secondary network operation optimization method based on user load quantification

Fig.3 Location distribution of thermal user in case secondary network

Fig.4 Illustration of node and branch configuration in case secondary network topology

Fig.5 Distribution of outdoor temperature on case study day

Fig.6 Distribution of hourly heat demand for each thermal user

Fig.7 Secondary side supply temperature and flow distribution at heating station

Tab.1 Comparison of target flow and actual flow at certain time point

Tab.2 Comparison of hourly electricity consumption of circulating pumps after optimization

Fig.8 Comparison of return water temperature variance distribution between optimized and historical condition for thermal user

Fig.9 Comparison of secondary side valve opening strategy at heating station after optimization

Fig.10 Comparison of valve opening strategies for thermal user 1-4 after optimization

Fig.11 Comparison of valve opening strategies for thermal user 5-16 after optimization


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