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基于能量流网络仿真的钢铁工业多能源介质优化调配

孙彦广 梁青艳 李文兵 贾天云

孙彦广, 梁青艳, 李文兵, 贾天云. 基于能量流网络仿真的钢铁工业多能源介质优化调配. 自动化学报, 2017, 43(6): 1065-1079. doi: 10.16383/j.aas.2017.c170184
引用本文: 孙彦广, 梁青艳, 李文兵, 贾天云. 基于能量流网络仿真的钢铁工业多能源介质优化调配. 自动化学报, 2017, 43(6): 1065-1079. doi: 10.16383/j.aas.2017.c170184
SUN Yan-Guang, LIANG Qing-Yan, LI Wen-Bing, JIA Tian-Yun. Steel Industry Multi-type Energy Optimized Scheduling with Energy Flow Network Simulation. ACTA AUTOMATICA SINICA, 2017, 43(6): 1065-1079. doi: 10.16383/j.aas.2017.c170184
Citation: SUN Yan-Guang, LIANG Qing-Yan, LI Wen-Bing, JIA Tian-Yun. Steel Industry Multi-type Energy Optimized Scheduling with Energy Flow Network Simulation. ACTA AUTOMATICA SINICA, 2017, 43(6): 1065-1079. doi: 10.16383/j.aas.2017.c170184

基于能量流网络仿真的钢铁工业多能源介质优化调配

doi: 10.16383/j.aas.2017.c170184
基金项目: 

国家高技术研究发展计划(863计划) 2013BAE07B01

详细信息
    作者简介:

    梁青艳 钢铁研究总院博士研究生.2009年获得冶金自动化研究设计院控制理论与控制工程工学硕士学位.主要研究方向为钢铁行业能源管理与优化.E-mail:liang-qingyan@163.com

    李文兵 冶金自动化研究设计院教授级高工.2009年获得北京科技大学控制理论与控制工程工学博士学位.主要研究方向为工业信息化和工业流程仿真.E-mail:li-wenbing@126.com

    贾天云 冶金自动化研究设计院高级工程师.2009年获得冶金自动化研究设计院控制理论与控制工程工学硕士学位.主要研究方向为工业信息化和工业能源管理.E-mail:jiatianyun224@163.com

    通讯作者:

    孙彦广 冶金自动化研究设计院教授级高工.1990年获得中国科学院自动化所工学博士学位.主要研究方向为复杂工业过程建模和智能控制, 钢铁企业能源动态调控, 物质流能量流协同优化.E-mail:syanguang@263.net

Steel Industry Multi-type Energy Optimized Scheduling with Energy Flow Network Simulation

Funds: 

National Basic Research Program of China (863 Program) 2013BAE07B01

More Information
    Author Bio:

    Ph. D. candidate at the Central Iron & Steel Research Institute. She received her master degree in technical science from Automation Research and Design Institute of Metallurgical Industry in 2009. Her research interest covers energy management and optimization in iron and steel industry

    Professorate senior engineer at Automation Research and Design Institute of Metallurgical Industry. She received her Ph. D. degree in technical science degree from University of Science and Technology Beijing in 2009. Her research interest covers industrial informationization and industrial process simulation

    Senior engineer at Automation Research and Design Institute of Metallurgical Industry. He received his master degree in technical science degree from Automation Research and Design Institute of Metallurgical Industry in 2009. His research interest covers industrial informationization and industrial energy saving

    Corresponding author: SUN Yan-Guang Professorate senior engineer at Automation Research and Design Institute of Metallurgical Industry. He received his Ph. D. degree in technical science from Institute of Automation, Chinese Academy of Sciences in 1990. His research interest covers complex industrial process modeling and intelligent control, energy regulation and control of iron and steel enterprises, material flow and energy flow collaborative optimization. Corresponding author of this paper
  • 摘要: 钢铁工业发展面临能源的严重制约,能源优化调配是钢铁企业系统节能的关键技术之一.从钢铁制造流程物质流能量流耦合特点出发,首先,探讨了基于能量流网络仿真的钢铁工业多能源介质综合优化调配策略;然后,探讨了能量流网络化建模、生产流程与能源系统结合的能源仿真、多能源介质综合优化调配技术;最后,给出了在钢铁企业示范应用的效果.
    1)  本文责任编委 王伟
  • 图  1  能量流网络模型的信息流与控制

    Fig.  1  Information flow and control of energy flow network model

    图  2  钢铁企业能量流网络集成模型结构框图

    Fig.  2  Structural block diagram of energy flow network integration model for iron and steel enterprises

    图  3  高炉工序与能量流网络模型集成

    Fig.  3  Blast furnace process and energy flow network model integration

    图  4  电力能量流网络与其他介质能量流网络集成

    Fig.  4  Power energy flow network and other media energy flow network integration

    图  5  基于能量流网络模型的能源系统动态仿真

    Fig.  5  Dynamic simulation of energy system based on energy flow network model

    图  6  介质之间优先级依赖关系

    Fig.  6  Priority dependencies between media

    图  7  煤气的调配规则及各类负荷分配次序

    Fig.  7  Gas distribution rules and various types of load distribution order

    图  8  某钢铁企业一段时间内煤气和电力供需平衡情况

    Fig.  8  Gas and electricity supply and demand balance in a period of time in a steel enterprise

    图  9  基于能源仿真的多能源介质综合优化调配流程

    Fig.  9  Comprehensive optimization of multi-energy medium based on energy simulation

    图  10  某钢铁企业煤气-蒸汽-电力系统示意图

    Fig.  10  A diagram of a gas-steam-electric power system in a steel enterprise

    图  11  各周期系统运行费用优化前后对比

    Fig.  11  Comparison of system operating costs of each cycle before and after optimization

    表  1  各时段富余煤气供应及蒸汽和电力需求

    Table  1  Wealthy gas supply and steam and electricity demand for each period

    时段BFG (km3/h)COG (km3/h)电(MW)S1 (t/h)S2 (t/h)S3 (t/h)
    1535.5153.53706.2868.04232.0272.88
    2559.0454.92695.7265.46233.6386.54
    3596.7253.19687.0768.50244.1086.51
    4601.0854.33688.2168.00241.6587.06
    5603.8150.49683.9169.29232.4687.34
    6591.3441.40692.7066.46227.0975.43
    7519.2241.01687.9267.75212.8688.88
    8535.5739.67671.7168.88222.1092.51
    9551.9137.43645.1468.46231.1995.83
    10485.5829.66621.2867.25223.9895.74
    11450.5329.85606.1568.25213.3196.46
    12434.8341.19590.3269.25211.2395.31
    13526.8749.38598.7563.96210.0196.10
    14574.0951.22588.7264.46207.6895.13
    15575.1250.32580.6364.96211.2694.86
    16579.0650.39590.4066.46208.8394.80
    17585.3249.34614.2667.41204.7594.98
    18490.6549.33627.6365.41204.2095.43
    19465.1150.53630.0964.58204.9596.00
    20479.2945.10633.6868.68204.0795.14
    21555.1142.69639.9268.67199.9995.60
    22588.4642.71661.8768.17210.3995.31
    23583.3044.64665.5167.67207.1194.44
    24544.3247.94672.4966.00216.0890.16
    下载: 导出CSV

    表  2  正常工况优化前后各种费用比较

    Table  2  Comparison of various costs in normal working conditions before and after optimization

    费用优化前优化后
    外购煤费用(万元)233.6498234.1791
    外购天然气费用(万元)00
    给水费用(万元)7.51277.6305
    缓冲用户煤气使用费用(万元)122.0902123.1707
    外购电费用(万元)23.86389.18634
    外送电收益(万元)10.640118.7683
    设备运行维护费用(万元)98.9796101.4965
    总费用(万元)475.4562456.8951
    放散率00
    下载: 导出CSV

    表  3  电网电价

    Table  3  Grid price

    时段时间外购电价(元/kWh)外送电价(元/kWh)
    峰时段8:00~11:00和19:00~23:000.71880.3394
    平时段7:00~8:00和11:00~19:000.49170.3394
    谷时段0:00~7:00和23:00~24:000.27960.3394
    下载: 导出CSV

    表  4  铁钢系统减产10小时工况优化前后各种费用对比

    Table  4  The comparison of various costs for 10 hour reduction of the steel system before and after optimization

    费用优化前优化后
    外购煤费用(万元)253.68205.82
    外购天然气费用(万元)2.585.78
    缓冲用户煤气使用费用(万元)100.1597.5
    外购电费用(万元)36.3237.56
    外送电收益(万元)080.07
    设备运行维护费用(万元)82.2282.04
    总费用(万元)474.96348.64
    放散率00
    下载: 导出CSV

    表  5  2 250轧线停产12小时工况优化前后各种费用对比

    Table  5  The comparison of various costs for 12 hours cut ofi of 2 250 rolling line before and after the optimization

    费用优化前优化后
    外购煤费用(万元)213.17193.497
    外购天然气费用(万元)2.412.38
    缓冲用户煤气使用费用(万元)118.6396.119
    外购电费用(万元)29.1738.84
    外送电收益(万元)024.69
    设备运行维护费用(万元)81.7381.23
    总费用(万元)445.13387.388
    放散率00
    下载: 导出CSV
  • [1] 王维兴.钢铁工业能耗现状和节能潜力分析.中国钢铁业, 2011, (4): 19-22 http://www.cnki.com.cn/Article/CJFDTOTAL-GGTY201104009.htm

    Wang Wei-Xing. Steel energy consumption and energy saving margin analysis. China Steel, 2011, (4): 19-22 http://www.cnki.com.cn/Article/CJFDTOTAL-GGTY201104009.htm
    [2] 王岭, 江飞涛.中国钢铁工业节能减排效果分析与前景.产经评论, 2012, (5): 81-91 http://www.cnki.com.cn/Article/CJFDTOTAL-FGGL201711208.htm

    Wang Ling, Jiang Fei-Tao. The current situation and prospect of energy saving and emission reduction in China's steel industry. Industrial Economic Review, 2012, (5): 81-91 http://www.cnki.com.cn/Article/CJFDTOTAL-FGGL201711208.htm
    [3] 刘颖, 赵珺, 王伟, 吴毅平, 陈伟昌.基于数据的改进回声状态网络在高炉煤气发生量预测中的应用.自动化学报, 2009, 35(6): 731-738 http://www.aas.net.cn/CN/abstract/abstract13337.shtml

    Liu Ying, Zhao Jun, Wang Wei, Wu Yi-Ping, Chen Wei-Chang. Improved echo state network based on data-driven and its application to prediction of blast furnace gas output. Acta Automatica Sinica, 2009, 35(6): 731-738 http://www.aas.net.cn/CN/abstract/abstract13337.shtml
    [4] 张颜颜, 唐立新.改进的数据驱动子空间算法求解钢铁企业能源预测问题.控制理论与应用, 2012, 29(12): 1616-1622 http://www.cnki.com.cn/Article/CJFDTOTAL-KZLY201212013.htm

    Zhang Yan-Yan, Tang Li-Xin. Improved data-driven subspace algorithm for energy prediction in iron and steel industry. Control Theory & Applications, 2012, 29(12): 1616-1622 http://www.cnki.com.cn/Article/CJFDTOTAL-KZLY201212013.htm
    [5] 肖冬峰, 杨春节, 宋执环.基于改进BP网络的高炉煤气发生量预测模型.浙江大学学报(工学版), 2012, 46(11): 2013-2018 doi: 10.3785/j.issn.1008-973X.2012.11.012

    Xiao Dong-Feng, Yang Chun-Jie, Song Zhi-Huan. The forecasting model of blast furnace gas output based on improved BP network. Journal of Zhejiang University (Engineering Science), 2012, 46(11): 2013-2018 doi: 10.3785/j.issn.1008-973X.2012.11.012
    [6] Akimoto K, Sannomiya N, Nishikawa Y, Tsuda T. An optimal gas supply for a power plant using a mixed integer programming model. Automatica, 1991, 27(3): 513-518 doi: 10.1016/0005-1098(91)90108-E
    [7] 张琦, 蔡九菊, 庞兴露, 姜文豪.钢铁联合企业煤气系统优化分配模型.东北大学学报(自然科学版), 2011, 32(1): 98-101 http://www.cnki.com.cn/Article/CJFDTOTAL-DBDX201101023.htm

    Zhang Qi, Cai Jiu-Ju, Pang Xing-Lu, Jiang Wen-Hao. Optimal distribution of by-product gases in iron and steel complex. Journal of Northeastern University (Natural Science), 2011, 32(1): 98-101 http://www.cnki.com.cn/Article/CJFDTOTAL-DBDX201101023.htm
    [8] Porzio G F, Fornai B, Amato A, Matarese N, Vannucci M, Chiappelli L, Colla V. Reducing the energy consumption and CO2 emissions of energy intensive industries through decision support systems——an example of application to the steel industry. Applied Energy, 2013, 112: 818-833 doi: 10.1016/j.apenergy.2013.05.005
    [9] Porzio G F, Nastasi G, Colla V, Vannucci M, Branca T A. Comparison of multi-objective optimization techniques applied to off-gas management within an integrated steelwork. Applied Energy, 2014, 136: 1085-1097 doi: 10.1016/j.apenergy.2014.06.086
    [10] Yang J H, Cai J J, Sun W Q, Huang J. Optimal allocation of surplus gas and suitable capacity for buffer users in steel plant. Applied Thermal Engineering, 2017, 115: 586-596 doi: 10.1016/j.applthermaleng.2016.12.096
    [11] Han Z Y, Zhao J, Wang W. An optimized oxygen system scheduling with electricity cost consideration in steel industry. IEEE/CAA Journal of Automatica Sinica, 2017, 4(2): 216-222 doi: 10.1109/JAS.2017.7510439
    [12] 王小辉. 宝钢分公司电力负荷模拟与预测研究[硕士学位论文], 上海交通大学, 中国, 2008.

    Wang Xiao-Hui. Research on the Power Load Simulation and Forecasting of Baosteel Branch [Master dissertation], Shanghai Jiao Tong University, China, 2008.
    [13] Ashok S. Peak-load management in steel plants. Applied Energy, 2006, 83(5): 413-424 doi: 10.1016/j.apenergy.2005.05.002
    [14] 高云龙, 高峰, 潘金艳, 翟桥柱, 管晓宏.高耗能企业关口平衡优化调度及其输出功率控制方式.中国电机工程学报, 2010, 30(19): 76-83 http://www.cnki.com.cn/Article/CJFDTOTAL-ZGDC201019013.htm

    Gao Yun-Long, Gao Feng, Pan Jin-Yan, Zhai Qiao-Zhu, Guan Xiao-Hong. Self-scheduling for electrical energy balance and output power control of energy-intensive enterprises. Proceedings of the CSEE, 2010, 30(19): 76-83 http://www.cnki.com.cn/Article/CJFDTOTAL-ZGDC201019013.htm
    [15] 刘坤, 高峰, 翟桥柱, 吴江, 管晓宏, 王兆杰, 张海峰.考虑负荷及煤气量不确定性的企业微电网自发电调度模型.中国电机工程学报, 2014, 34(13): 2063-2070 http://www.cnki.com.cn/Article/CJFDTOTAL-ZGDC201413008.htm

    Liu Kun, Gao Feng, Zhai Qiao-Zhu, Wu Jiang, Guan Xiao-Hong, Wang Zhao-Jie, Zhang Hai-Feng. A self-power generation scheduling model under load demand and uncertainty of a by-product of gas production in enterprises microgrid. Proceedings of the CSEE, 2014, 34(13): 2063-2070 http://www.cnki.com.cn/Article/CJFDTOTAL-ZGDC201413008.htm
    [16] 张玉庆, 孙彦广.钢铁企业智能电力系统功能与运行架构研究.冶金自动化, 2011, 35(3): 8-13 http://www.cnki.com.cn/Article/CJFDTOTAL-YJZH201103003.htm

    Zhang Yu-Qing, Sun Yan-Guang. Function and operation frame research of intelligent power system for iron and steel enterprises. Metallurgical Industry Automation, 2011, 35(3): 8-13 http://www.cnki.com.cn/Article/CJFDTOTAL-YJZH201103003.htm
    [17] 李丹.基于价值最大化的能源系统综合调整.冶金能源, 2013, 32(1): 3-5, 58 http://www.cnki.com.cn/Article/CJFDTOTAL-YJLY201301003.htm

    Li Dan. Optimization adjustment of large energy systems base on maximizing the integrate value of energy. Energy for Metallurgical Industry, 2013, 32(1): 3-5, 58 http://www.cnki.com.cn/Article/CJFDTOTAL-YJLY201301003.htm
    [18] 孟华. 钢铁企业自备电厂机组配置优化及煤气优化调度研究[博士学位论文], 昆明理工大学, 中国, 2013.

    Meng Hua. Study on Optimization of Unit Configuration for Own Power Plant and Optimization of Gas Dispatching in Iron and Steel Enterprise [Ph.D. dissertation], Kunming University of Science and Technology, China, 2013.
    [19] 张琦, 提威, 杜涛, 蔡九菊.钢铁企业富余煤气--蒸汽--电力耦合模型及其应用.化工学报, 2011, 62(3): 753-758 http://www.cnki.com.cn/Article/CJFDTOTAL-HGSZ201103023.htm

    Zhang Qi, Ti Wei, Du Tao, Cai Jiu-Ju. Coupling model of gas-steam-electricity and its application in steel works. CIESC Journal, 2011, 62(3): 753-758 http://www.cnki.com.cn/Article/CJFDTOTAL-HGSZ201103023.htm
    [20] 孙彦广. 钢铁企业能量流网络信息模型及多种能源介质动态调控. 见: 2010年全国能源环保生产技术会议论文集. 九江, 江西, 中国: 中国金属学会, 2010.

    Sun Yang-Guang. Energy flow network information model in iron and steel enterprise and dynamic control of various energy. In: Proceedings of the 2010 National Energy and Environmental Protection Production Technology Conference. Jiujiang, Jiangxi, China: Chinese Society of Metal, 2010.
    [21] 曾玉娇. 钢铁企业电力系统有功和无功优化调度问题的研究[博士学位论文], 钢铁研究总院, 中国, 2015.

    Zeng Yu-Jiao. Research on Active and Reactive Power Optimization Scheduling of the Power System in Iron and Steel Enterprise [Ph.D. dissertation], Central Iron & Steel Research Institute, China, 2015.
    [22] 曾玉娇, 孙彦广. 钢铁企业蒸汽--电力系统多时段优化调度. 见: 第25届中国过程控制会议论文集. 大连, 辽宁, 中国: 中国自动化学会过程控制专业委员会, 2014.

    Zeng Yu-Jiao, Sun Yan-Guang. Multi-period optimal scheduling of steam power system for iron and steel industry. In: Proceedings of the 25th China Process Control Conference. Dalian, Liaoning, China: China Automation Society Process Control Specialized Committee, 2014.
    [23] 何佳毅, 纪扬, 李文兵, 张云利.钢铁企业能源系统网络模型仿真及组态的研究与实现.冶金自动化, 2012, 36(1): 7-12 http://www.cnki.com.cn/Article/CJFDTOTAL-YJZH201201003.htm

    He Jia-Yi, Ji Yang, Li Wen-Bing, Zhang Yun-Li. Research and software realization of network model and configuration on energy simulation in iron and steel enterprises. Metallurgical Industry Automation, 2012, 36(1): 7-12 http://www.cnki.com.cn/Article/CJFDTOTAL-YJZH201201003.htm
    [24] Mardan N. Combining Simulation and Optimization for Improved Decision Support on Energy Efficiency in Industry [Ph.D. dissertation], Linköping University, Sweden, 2012.
    [25] Yamamoto T, Nakagawa T. A vision of energy structure for integrated steel works of future. Transactions of the Iron and Steel Institute of Japan, 1983, 23(10): 862-892 doi: 10.2355/isijinternational1966.23.862
    [26] Ohkuma R, Ikegami K, Yasunaga S. Energy problems and energy control system in the Japanese steel industry. A I I E Transactions, 1981, 13(2): 164-174 doi: 10.1080/05695558108974549
    [27] Gou H, Olynyk S. A corporate mass and energy simulation model for an integrated steel plant. Iron & Steel Technology, 2007, 4(4): 141-150 http://cat.inist.fr/?aModele=afficheN&cpsidt=19180666
    [28] 刘浏, 干勇, 张江玲, 李菁.钢铁联合企业能源循环利用的分析研究.钢铁, 2006, 41(6): 1-4 http://www.cnki.com.cn/Article/CJFDTOTAL-GANT200606000.htm

    Liu Liu, Gan Yong, Zhang Jiang-Ling, Li Jing. Research on energy recycling at integrated steel companies. Iron & Steel, 2006, 41(6): 1-4 http://www.cnki.com.cn/Article/CJFDTOTAL-GANT200606000.htm
    [29] 王建军, 蔡九菊, 张琦, 吴复忠, 陈春霞.钢铁企业能量流模型化研究.中国冶金, 2006, 16(5): 48-52 http://cpfd.cnki.com.cn/Article/CPFDTOTAL-ZGJS201006001007.htm

    Wang Jian-Jun, Cai Jiu-Ju, Zhang Qi, Wu Fu-Zhong, Chen Chun-Xia. Study on energy-flow modelling in iron and steel enterprise. China Metallurgy, 2006, 16(5): 48-52 http://cpfd.cnki.com.cn/Article/CPFDTOTAL-ZGJS201006001007.htm
    [30] 仇晓磊, 孟庆玉, 洪新.钢铁生产长流程工序能耗数学模型研究.冶金能源, 2007, 26(3): 3-6, 53 http://www.cnki.com.cn/Article/CJFDTOTAL-YJLY200703000.htm

    Qiu Xiao-Lei, Meng Qing-Yu, Hong Xin. Study on mathematical model of process energy consumption of BF-LD process. Energy for Metallurgical Industry, 2007, 26(3): 3-6, 53 http://www.cnki.com.cn/Article/CJFDTOTAL-YJLY200703000.htm
    [31] Zeng Y J, Sun Y G. An improved particle swarm optimization for the combined heat and power dynamic economic dispatch problem. Electric Power Components and Systems, 2014, 42(15): 1700-1716 doi: 10.1080/15325008.2014.949913
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出版历程
  • 收稿日期:  2017-04-06
  • 录用日期:  2017-05-11
  • 刊出日期:  2017-06-20

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