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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2021, Vol. 55 Issue (11): 2125-2133    DOI: 10.3785/j.issn.1008-973X.2021.11.013
    
Study of heat transportation flexibility in urban heating network based on mapping model
Jia-ying CHEN1(),Zhong-bo LI1,2,Xiao-jie LIN1,3,*(),Rong LIU2,Wei ZHONG1,Zi-tao YU1
1. Institute of Thermal Science and Power Systems, Zhejiang University, Hangzhou 310027, China
2. Beijing District Heating Group, Beijing 100028, China
3. Changzhou Industrial Technology Research Institute of Zhejiang University, Changzhou 213022, China
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Abstract  

The theory of heat transportation flexibility in the urban heating network was developed based on the mapping model to analyze heat transportation flexibility with the uncertainty of source and load. Firstly, the high-precision mapping modeling technology combining mechanism model and data identification was adopted to establish the mapping model reflecting the operation characteristics of the actual heating system. The accuracy of simulation calculation was improved through model feature parameter estimation. Secondly, a quantitative method for the flexibility analysis of heat transportation was proposed on the above basis. Finally, flexibility evaluation in a heating network in a northern city under conditions of diverse supply and demand combinations was carried out. Moreover, the flexibility results were compared with that of the existing reliability theory of the pipe network. The flexibility and the reliability of 23 heating substations were analyzed, and results show that flexibility analysis focuses more on the overall flexible transport capacity of the network, which can be used to the analysis of the transport bottleneck. Reliability focuses more on analyzing the properties of the components in the heating network, which can be used to analyze the capacity and the reliability of the pipeline. Flexibility evaluation lays a preliminary theoretical foundation for improving the dispatch flexibility of heating network.

Key words： urban heating network      centralized heating system      mapping model      transportation flexibility      supply and demand balance     
Received: 24 October 2020      Published: 05 November 2021
CLC:  TK 11  
Fund:  国家重点研发计划资助项目(2019YFE0126000)；国家自然科学基金资助项目(51806190)
Corresponding Authors: Xiao-jie LIN     E-mail: jiayingchen@zju.edu.cn;xiaojie.lin@zju.edu.cn
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Jia-ying CHEN
Zhong-bo LI
Xiao-jie LIN
Rong LIU
Wei ZHONG
Zi-tao YU
Cite this article:

Jia-ying CHEN,Zhong-bo LI,Xiao-jie LIN,Rong LIU,Wei ZHONG,Zi-tao YU. Study of heat transportation flexibility in urban heating network based on mapping model. Journal of ZheJiang University (Engineering Science), 2021, 55(11): 2125-2133.

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https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2021.11.013     OR     https://www.zjujournals.com/eng/Y2021/V55/I11/2125


基于映射模型的城市热网热能输运灵活性研究

为了分析源荷不确定下的热网热能灵活输运问题，开展基于映射模型的城市热网热能输运灵活性研究. 采用机理建模与运行数据辨识结合的热网高精度映射建模方法，建立与物理热网行为特性一致的映射模型，通过模型特征参数估计，提高仿真计算精度. 同时，在此基础上提出量化分析热能输配灵活性的方法. 结合某北方城市热网开展多样化供需组合条件下的灵活性定量评估，并将该分析方法与现有管网可靠度理论进行对比. 对23个热力站开展灵活性与管网可靠度分析，对比结果表明，灵活性更侧重分析管网整体的灵活输运能力，可以用于管网输运瓶颈的分析，可靠度更侧重分析热网中元件本身的属性，可以用于管道容量及可靠性的分析. 该灵活性分析方法可以为提升热网调控灵活性奠定理论基础.


关键词： 城市热网,  集中供热系统,  映射模型,  输运灵活性,  供需平衡 
Fig.1 Flowchart of heat transportation flexibility assessment
Fig.2 Schematic diagram of heat energy transmission process of centralized heating network
Fig.3 Identification of heat network characteristic parameters based on finite observation points
Fig.4 Modeling method of heat network mapping model
Fig.5 Topology of target heating network
Fig.6 Comparison of measured and calculated data of water supply pressure
Fig.7 Comparison of measured and calculated data of water return pressure
Fig.8 Error statistical results of comparison between measured and calculated data of water supply and return pressure
Fig.9 Flexibility distribution of target heating network
Fig.10 Pipeline reliability of target heating network
Fig.11 Reliability distribution of target heating network
Fig.12 Number distribution diagram of local pipes in heating network
IDp2 Rp IDp11 Rp
93 0.998 238 0.998
92 0.999 239 0.995
91 0.999 240 0.997
90 0.993 241 0.998
89 0.998 51 0.990
88 0.954 50 0.979
87 0.988 49 0.999
86 0.978 48 0.973
85 0.996 47 0.995
84 0.998 46 0.997
Tab.1 Reliability of upstream pipelines of No. 2 and No. 11 heating substations
[1]   中华人民共和国住房和城乡建设部. 中国城市建设统计年鉴[M]. 北京: 中国计划出版社, 2017.
[2]   江亿. 我国供热行业发展状况[R]. [s.l.]: 2020年全国供热学术年会报告, 2020.
[3]   WANG D, ZHI Y, JIA H, et al Optimal scheduling strategy of district integrated heat and power system with wind power and multiple energy stations considering thermal inertia of buildings under different heating regulation modes[J]. Applied Energy, 2019, 240: 341- 358
doi: 10.1016/j.apenergy.2019.01.199
[4]   清华大学建筑节能研究中心. 中国建筑节能年度发展研究报告2015[M]. 北京: 中国建筑工业出版社, 2015.
[5]   李琦, 李梅 PSO优化在供热网络控制中的应用研究[J]. 计算机仿真, 2013, 30 (12): 294- 297
LI Qi, LI Mei Study and application of PSO in heating network control[J]. Computer Simulation, 2013, 30 (12): 294- 297
doi: 10.3969/j.issn.1006-9348.2013.12.069
[6]   郭洪武, 蒲雷, 张予燮, 等 基于粗糙集理论的风光蓄互补系统优化模型[J]. 浙江大学学报:工学版, 2019, 53 (4): 801- 810
GUO Hong-wu, PU Lei, ZHANG Yu-xie, et al Optimization model for integrated complementary system of wind-PV-pump storage based on rough set theory[J]. Journal of Zhejiang University: Engineering Science, 2019, 53 (4): 801- 810
[7]   LUND H, WERNER S, WILTSHIRE R, et al 4th generation district heating (4GDH): integrating smart thermal grids into future sustainable energy systems[J]. Energy, 2014, 68 (4): 1- 11
[8]   王芃, 邹平华 供热管网连通可靠度研究[J]. 哈尔滨工业大学学报, 2011, 43 (8): 94- 97
WANG Peng, ZOU Ping-hua Connectedness reliability of district heating network[J]. Journal of Harbin Institute of Technology, 2011, 43 (8): 94- 97
doi: 10.11918/j.issn.0367-6234.2011.08.018
[9]   MARLIM M S, JEONG G, KANG D Identification of critical pipes using a criticality index in water distribution networks[J]. Applied Sciences, 2019, 9 (19): 4052
doi: 10.3390/app9194052
[10]   王芃. 基于图论的供热系统可靠性研究[D]. 哈尔滨: 哈尔滨工业大学, 2010.
WANG Peng. Reliability of district heating system based on graph theory [D]. Harbin: Harbin Institute of Technology, 2020.
[11]   王晓霞. 多热源环状热水管网故障工况及可靠性研究[D]. 哈尔滨: 哈尔滨工业大学, 2004.
WANG Xiao-xia. Reliability research of multi-heat source ring-shaped heat-supply network under fault conditions[D]. Harbin: Harbin Institute of Technology, 2004.
[12]   North American Electric Reliability Corporation. Special report: potential reliability impacts of emerging flexible resources[R]. [s.l.]: North American Electric Reliability Corporation (NERC), 2010.
[13]   International Energy Agency. Empowering variable renewables-options for flexible electricity systems[M]. [s.l.]: OECD Publishing, 2009: l1-13.
[14]   肖定垚, 王承民, 曾平良, 等 电力系统灵活性及其评价综述[J]. 电网技术, 2014, 38 (6): 1569- 1576
XIAO Ding-yao, WANG Cheng-min, ZENG Ping-liang, et al Power system flexibility renewable energy load management and load response energy storage microgrid[J]. Power System Technology, 2014, 38 (6): 1569- 1576
[15]   鲁宗相, 李海波, 乔颖 含高比例可再生能源电力系统灵活性规划及挑战[J]. 电力系统自动化, 2016, 40 (13): 147- 158
LU Zong-xiang, LI Hai-bo, QIAO Ying High proportion of renewable energy flexibility evaluation multiple temporal and spatial scales power system planning[J]. Automation of Electric Power Systems, 2016, 40 (13): 147- 158
doi: 10.7500/AEPS20151215008
[16]   王鹏, 李宏仲, 吕风磊, 等 电力系统多时间尺度灵活性指标研究[J]. 电力建设, 2016, 37 (2): 57- 62
WANG Peng, LI Hong-zhong, LYU Feng-lei, et al Power system flexibility wind power uncertainty multi-time scale evaluation index[J]. Electric Power Construction, 2016, 37 (2): 57- 62
doi: 10.3969/j.issn.1000-7229.2016.02.008
[17]   ABDIN I F, ZIO E An integrated framework for operational flexibility assessment in multi-period power system planning with renewable energy production[J]. Applied Energy, 2018, 222: 898- 914
doi: 10.1016/j.apenergy.2018.04.009
[18]   NIU J, TIAN Z, ZHU J, et al Implementation of a price-driven demand response in a distributed energy system with multi-energy flexibility measures[J]. Energy Conversion and Management, 2020, 208: 112575
doi: 10.1016/j.enconman.2020.112575
[19]   陈欢, 王红梅, 俞自涛, 等 自然循环槽式太阳能中高温集热系统实验研究[J]. 浙江大学学报:工学版, 2012, 46 (9): 1666- 1670
CHEN Huan, WANG Hong-mei, YU Zi-tao, et al PTC steam generation system natural circulation solar thermal utilization[J]. Journal of Zhejiang University: Engineering Science, 2012, 46 (9): 1666- 1670
doi: 10.3785/j.issn.1008-973X.2012.09.018
[20]   YILMAZ H Ü, KELES D, CHIODI A, et al Analysis of the power-to-heat potential in the European energy system[J]. Energy Strategy Reviews, 2018, 20: 6- 19
doi: 10.1016/j.esr.2017.12.009
[21]   HANMIN C, CHARALAMPOS Z, SHI Y, et al Demand side management in urban district heating networks[J]. Applied Energy, 2018, 230: 506- 518
doi: 10.1016/j.apenergy.2018.08.105
[22]   唐雅洁, 江全元, 程中林, 等 可扩展型微电网SCADA系统关键技术[J]. 浙江大学学报:工学版, 2018, 52 (8): 1558- 1565
TANG Ya-jie, JIANG Quan-yuan, CHENG Zhong-lin, et al Microgrids SCADA service encapsulation integrated modeling scalable design[J]. Journal of Zhejiang University: Engineering Science, 2018, 52 (8): 1558- 1565
[23]   ZHONG W, CHEN J, ZHOU Y, et al Network flexibility study of urban centralized heating system: concept, modeling and evaluation[J]. Energy, 2019, 177: 334- 346
doi: 10.1016/j.energy.2019.04.081
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