Application of the secondary network intelligent balance system based on big data analysis

doi:10.3969/j.issn.2097-0706.2022.03.007

Abstract

Abstract:

A secondary network intelligent balance system for a residential district in Tianjin was built. Based on big data analysis,heat can be accurately supplied to each dwelling house,and a "station-load linkage" intelligent regulation strategy aiming at providing comfortable room temperature for thermal power users was established to guide the energy-saving and optimal operation of the thermal power station.On the premise of ensuring the heating quality,the strategy realizes the heating on demand,energy-saving and consumption reduction.By comparing the heating operation data before and after the optimization,it can be seen that the temperature difference between the supply and return water of the secondary network,the dispersion of the household return water temperature,the compliance rate of indoor temperature,the operation frequency of the circulating water pump,the power consumption of the circulating pump and the heat consumption per unit area changed significantly.The research results showed that after taking the secondary network intelligent balance system,the heat consumption per unit area and power consumption per unit area in this residential area were reduced by 14% and 23% respectively,and the compliance rate of indoor temperature reached 99%.Moreover,the heating quality was improved significantly while the energy-saving effect was remarkable.The study on this project can provide reference for the transformation and design of similar projects.

Key words: carbon neutrality, carbon peaking, secondary network intelligent balance, big data analysis, linkage between thermal station and heat load, intelligent regulation, accurate heating

CLC Number:

TK01

ZUO Wendong, LI Biao, GUO Baogang, GAO Xiaoyu, GU Jihao, WANG Weihe, DONG Zhipeng. Application of the secondary network intelligent balance system based on big data analysis[J]. Integrated Intelligent Energy, 2022, 44(3): 44-49.

Figures/Tables 13

Fig.1

Table 1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

Table 2

Fig.9

Fig.10

Table 3

References 20

[1]	何建坤. 碳达峰碳中和目标导向下能源和经济的低碳转型[J]. 环境经济研究, 2021, 6(1):1-9.
	HE Jiankun. Low carbon transformation of energy and economy aiming for the peaking of carbon emission and carbon neutrality[J]. Journal of Environmental Economics, 2021, 6(1):1-9.
[2]	江亿, 胡姗. 中国建筑部门实现碳中和的路径[J]. 暖通空调, 2021, 51(5):1-13.
	JIANG Yi, HU Shan. Paths to carbon neutrality in China's building sector[J]. Heating Ventilating & Air Conditioning, 2021, 51(5):1-13.
[3]	穆连波, 王随林, 朱峰, 等. 燃气锅炉烟气冷凝余热深度回收系统应用与节能分析[J]. 暖通空调, 2020, 50(12):65-69.
	MU Lianbo, WANG Suilin, ZHU Feng, et al. Application and energy saving analysis of flue gas condensation waste heat deep recovery systems for gas-fired boilers[J]. Heating Ventilating & Air Conditioning, 2020, 50(12):65-69.
[4]	李琦, 韩冰城. 基于深度确定性策略梯度的热力站一次侧优化控制[J]. 科学技术与工程, 2019, 19(29):193-200.
	LI Qi, HAN Bingcheng. Primary side optimization control of heating substations based on deep deterministic policy gradient[J]. Science Technology and Engineering, 2019, 19(29):193-200.
[5]	VESTERLUND M, DAHL J. A method for the simulation and optimization of district heating systems with meshed networks[J]. Energy Conversion and Management, 2015, 89:555-567. doi: 10.1016/j.enconman.2014.10.002
[6]	YOKOYAMA R, KITANO H, WAKUI T. Optimal operation of heat supply systems with piping network[J]. Energy, 2017, 137:888-897. doi: 10.1016/j.energy.2017.03.146
[7]	曹进喜. 热量计量数据在二次热网智能调节中的应用[J]. 中国计量, 2019(9):46-51.
[8]	郑刚, 李金刚, 刘依婷. 基于建筑综合物性系数的换热站运行调节策略分析[J]. 华电技术, 2021, 43(12):72-78.
	ZHENG Gang, LI Jingang, LIU Yiting. Analysis on operation regulation strategy for heat exchange stations based on building comprehensive physical property coefficients[J]. Huadian Technology, 2021, 43(12):72-78.
[9]	胡雪, 杨俊红, 刘德朝, 等. 基于人工智能与热力系统融合的综合节能[J]. 华电技术, 2020, 42(11):21-33.
	HU Xue, YANG Junhong, LIU Dechao, et al. Research on comprehensive energy-saving technology based on integration of artificial intelligence into thermal systems[J]. Huadian Technology, 2020, 42(11):21-33.
[10]	李甲年, 曲辰. 浅谈供热运行管理工作中二次网流量平衡调节的有效方法[J]. 区域供热, 2016(5):99-102.
[11]	史凯, 花博, 侯家涛, 等. 基于动态压差平衡阀和静态平衡阀的供热二次管网水力平衡案例分析[J]. 区域供热, 2018(6):81-88.
[12]	李更生, 王磊, 等. 基于回水温度平衡法智能二网平衡系统的应用分析[J]. 区域供热, 2019(1):66-70.
[13]	何乐. 智能楼栋平衡系统在供热二次网中的应用分析[J]. 区域供热, 2018(2):27-43.
[14]	刘剑, 付国栋, 付强, 等. 以用户室温作为调控目标的二次网智能平衡技术的应用[J]. 区域供热, 2020(4):129-133,145.
[15]	王建浮, 王炎, 吴钧, 等. 应用“通断法”热计量设备实现供热二网水利平衡自动调节方法[J]. 区域供热, 2020(1):14-22.
[16]	史登峰, 何乐, 王珂. 二次网热力平衡度概念及其分析应用[J]. 区域供热, 2020(2):106-109.
[17]	方修睦, 杨大易, 周志刚, 等. 供热自动化、信息化及智慧化的差异探讨[J]. 华电技术, 2020, 42(11):34-38.
	FANG Xiumu, YANG Dayi, ZHOU Zhigang, et al. Discussion on automation,informatization and intellectualization of heating[J]. Huadian Technology, 2020, 42(11):34-38.
[18]	张杰, 赵睿, 朱雷, 等. 岳康园高区户间热平衡系统的初步应用与研究[J]. 区域供热, 2021(1):116-124.
[19]	杨瑞. 智能化、数字化、网络化供热系统的构建与实施[J]. 华电技术, 2020, 42(6):83-86.
	YANG Rui. Construction and implementation of intelligent,digital and networked heat-supply system[J]. Huadian Technology, 2020, 42(6):83-86.
[20]	朱翼虎, 王芃. 水力平衡技术的现状分析与物联网水力平衡阀的应用[J]. 区域供热, 2017(5):80-83,117.

时段	起止时间	时长/d	室内均温/℃	室外均温/℃
1	11-26—12-10	15	23.00	0.42
2	12-11—12-25	15	22.80	-2.20

时间段	标准差S	极差ζ
调控前	3.50	9.5
调控后	1.32	3.5