Steel Industry Multi-type Energy Optimized Scheduling with Energy Flow Network Simulation
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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.Abstract: Energy optimized scheduling is one of the key techniques for system energy saving in steel industry. In this paper, firstly, the energy optimized scheduling method with energy flow network simulation is introduced in the context of mass flow and energy flow coupled steel process. Secondly, some techniques including energy flow network model, steel process and energy system integrated energy simulation, multi-type energy optimized scheduling in steel works are addressed. In the end, some examples are given to illustrate the application results of the new energy optimized scheduling method.
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Key words:
- Steel industry /
- energy flow network /
- process simulation /
- energy optimization
1) 本文责任编委 王伟 -
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) 1 535.51 53.53 706.28 68.04 232.02 72.88 2 559.04 54.92 695.72 65.46 233.63 86.54 3 596.72 53.19 687.07 68.50 244.10 86.51 4 601.08 54.33 688.21 68.00 241.65 87.06 5 603.81 50.49 683.91 69.29 232.46 87.34 6 591.34 41.40 692.70 66.46 227.09 75.43 7 519.22 41.01 687.92 67.75 212.86 88.88 8 535.57 39.67 671.71 68.88 222.10 92.51 9 551.91 37.43 645.14 68.46 231.19 95.83 10 485.58 29.66 621.28 67.25 223.98 95.74 11 450.53 29.85 606.15 68.25 213.31 96.46 12 434.83 41.19 590.32 69.25 211.23 95.31 13 526.87 49.38 598.75 63.96 210.01 96.10 14 574.09 51.22 588.72 64.46 207.68 95.13 15 575.12 50.32 580.63 64.96 211.26 94.86 16 579.06 50.39 590.40 66.46 208.83 94.80 17 585.32 49.34 614.26 67.41 204.75 94.98 18 490.65 49.33 627.63 65.41 204.20 95.43 19 465.11 50.53 630.09 64.58 204.95 96.00 20 479.29 45.10 633.68 68.68 204.07 95.14 21 555.11 42.69 639.92 68.67 199.99 95.60 22 588.46 42.71 661.87 68.17 210.39 95.31 23 583.30 44.64 665.51 67.67 207.11 94.44 24 544.32 47.94 672.49 66.00 216.08 90.16 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) 233.6498 234.1791 外购天然气费用(万元) 0 0 给水费用(万元) 7.5127 7.6305 缓冲用户煤气使用费用(万元) 122.0902 123.1707 外购电费用(万元) 23.8638 9.18634 外送电收益(万元) 10.6401 18.7683 设备运行维护费用(万元) 98.9796 101.4965 总费用(万元) 475.4562 456.8951 放散率 0 0 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19:00~23:00 0.7188 0.3394 平时段 7:00~8:00和11:00~19:00 0.4917 0.3394 谷时段 0:00~7:00和23:00~24:00 0.2796 0.3394 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) 253.68 205.82 外购天然气费用(万元) 2.58 5.78 缓冲用户煤气使用费用(万元) 100.15 97.5 外购电费用(万元) 36.32 37.56 外送电收益(万元) 0 80.07 设备运行维护费用(万元) 82.22 82.04 总费用(万元) 474.96 348.64 放散率 0 0 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) 213.17 193.497 外购天然气费用(万元) 2.41 2.38 缓冲用户煤气使用费用(万元) 118.63 96.119 外购电费用(万元) 29.17 38.84 外送电收益(万元) 0 24.69 设备运行维护费用(万元) 81.73 81.23 总费用(万元) 445.13 387.388 放散率 0 0 -
[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.htmWang 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.htmWang 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.shtmlLiu 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.htmZhang 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.2012.11.012Xiao 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.htmZhang 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.htmGao 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.htmLiu 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.htmZhang 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.htmLi 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.htmZhang 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.htmHe 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.htmLiu 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.htmWang 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.htmQiu 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