Multi-objective optimal strategy for steam power system in steel industry based on electricity equivalent calculation

doi:10.11949/j.issn.0438-1157.20151584

Abstract

Abstract:

The steam power system in steel industries is featured by a variety of energy and various energy producers. In this paper, a method to convert energy into electricity equivalent is introduced to analyze the efficiency of energy conversion in the system, and mathematical programming and optimization are employed to improve energy management based on the background of steam power system in a large iron and steel company. With the establishment of mixed integer nonlinear programming model which is multi-objective, the global optimal solution will be achieved by solve the multi-objective-constrained model by LINGO step by step on condition that the objectives, the minimum cost of energy hourly and the maximum exergy efficiency, and the constraints including the capacity of power plants, energy demand and operating cost are set. Pareto front is to be achieved by solving the objective function-the maximum exergy efficiency-with cost constraint by the minimum cost of energy hourly multiplying an over-relaxation which is little bigger than 1. Rationality, feasibility and efficiency of ultimate optimization solution by stepwise multi-objective optimization is to be demonstrated in comparison with solutions by single-objective optimization and multi-objective genetic algorithm. The schedule of effective operation at low cost of steam power system is to be well-founded in theory in accordance with the optimal solution.

Key words: steam system, electricity equivalent calculation, multi-objective optimization, mathematical modeling, optimization, exergy

摘要：

针对钢铁企业蒸汽系统汽源设备多、能源品种多的特点，以某大型钢铁企业实际运行的蒸汽系统为背景，运用等效电方法对蒸汽系统的能量转换作出科学分析，采用数学规划方法，建立多目标的混合整数非线性规划模型（MINLP）。采用分步优化方法，先以蒸汽系统小时能源总成本最低为目标，将得到的结果乘以松弛系数建立成本约束并以(火用)效率最高为目标，利用LINGO软件求得多目标-约束优化模型的全局最优解，再通过改变松弛系数得到一组Pareto前沿。最后与单目标优化和多目标遗传算法结果相比较，证明分步优化方法所得的结果是可行的综合最优结果，能够实现低成本和高(火用)效率的双目标，并为生产调度提供依据。

关键词: 蒸汽系统, 等效电方法, 多目标优化, 数学模型, 优化, (火用)

CLC Number:

TQ028.8

CHEN Jun, ZHOU Weiguo, WANG Hai, LI Su. Multi-objective optimal strategy for steam power system in steel industry based on electricity equivalent calculation[J]. CIESC Journal, 2016, 67(9): 3804-3811.

陈骏, 周伟国, 王海, 李苏. 基于等效电法的钢铁企业蒸汽系统多目标运行优化策略[J]. 化工学报, 2016, 67(9): 3804-3811.

References 33

[1]	GUO Z C, FU Z X. Current situation of energy consumption and measures taken for energy saving in the iron and steel industry in China[J]. Energy, 2009, 35:4356-4360.
[2]	张立宏, 蔡九菊, 杜涛, 等. 钢铁企业蒸汽系统的现状分析及改进措施[J]. 中国冶金, 2007, 17(1):50-53. ZHANG L H, CAI J J, DU T, et al. Present status analysis of steel enterprise steam system and its improvement measures[J]. China Metallurgy, 2007, 17(1):50-53.
[3]	李德芳, 索寒生, 刘暄. 石化企业能源管理系统的研发与应用[J]. 化工学报, 2015, 66(1):7-13. LI D F, SUO H S, LIU X. Development and application of energy management system for petrochemical enterprises[J]. CIESC Journal, 2015, 66(1):7-13.
[4]	YANG J H, CAI J J, SUN W Q, et al. Optimization and scheduling of byproduct gas system in steel plant[J]. Journal of Iron and Steel Research, International, 2015, 22(5):408-413.
[5]	XIA X, ELAIW A M. Optimal dynamic economic dispatch of generation:a review[J]. Electric Power Systems Research, 2010, 80:975-986.
[6]	江亿, 杨秀. 在能源分析中采用等效电方法[J]. 中国能源, 2010, 32(5):5-11. JIANG Y, YANG X. Electricity equivalent conversion application in energy system analysis[J]. Energy of China, 2010, 32(5):5-11.
[7]	KONG H N. A green mixed integer linear programming model for optimization of byproduct gases in iron and steel industry[J]. Journal of Iron and Steel Research, International, 2015, 22(8):681-685.
[8]	李国俊, 郁鸿凌, 李瑞阳, 等. 大型蒸汽管网系统的运行优化调度[J]. 化工进展, 2007, 26(1):77-81. LI G J, YU H L, LI R Y, et al. Optimal operational planning for steam pipes system[J]. Chemical Industry and Engineering Progress, 2007, 26(1):77-81.
[9]	张琦, 提威, 杜涛, 等. 钢铁企业富余煤气-蒸汽-电力耦合模型及其应用[J]. 化工学报, 2011, 62(3):753-758. ZHANG Q, TI W, DU T, et al. Coupling model of gas-steam-electricity and its application in steel works[J]. CIESC Journal, 2011, 62(3):753-758.
[10]	游夏竹, 杜文莉, 赵亮, 等. 乙烯装置蒸汽管网用能配置与实时优化[J]. 化工学报, 2012, 64(2):641-648. YOU X Z, DU W L, ZHAO L, et al. Energy configuration and real-time optimization for polyethylene unit steam piping network[J]. CIESC Journal, 2012, 64(2):641-648.
[11]	MADDALONI A, PORZIO G F, NASTASI G, et al. Exploitation of multi-objective optimization in retrofit analysis:a case study for the iron and steel production[J]. Energy Procedia, 2014, 61:2297-2300.
[12]	MADDALONI A, PORZIO G F, NASTASI G. Multi-objective optimization applied to retrofit analysis:a case study for the iron and steel industry[J]. Applied Thermal Engineering, 2015, 91:638-646.
[13]	LI D P, DAS S, PAHWA A, et al. A multi-objective evolutionary approach for generator scheduling[J]. Expert Systems with Applications, 2013, 40:7647-7655.
[14]	CARLOS A, COELLO C. Evolutionary multi-objective optimization[J]. European Journal of Operational Research, 2007, 181:1617-1619.
[15]	都键, 陈静, 刘琳琳, 等. 基于可靠性分析的用水网络优化设计[J]. 化工学报, 2013, 64(12):4491-4495. DU J, CHEN J, LIU L L, et al. Water-using network synthesis involving reliability analysis[J]. CIESC Journal, 2013, 64(12):4491-4495.
[16]	ALARCON-RODRIGUEZ A, AULT G, GALLOWAY S. Multi-objective planning of distributed energy resources:a review of state-of-the-art[J]. Renewable and Sustainable Energy Reviews, 2010, 14:1353-1366.
[17]	FADAEE M, RADZI M A M. Multi-objective optimization of a stand-alone hybrid renewable energy system by using evolutionary algorithms:a review[J]. Renewable and Sustainable Energy Reviews, 2012, 132:3364-3369.
[18]	周章玉, 曾敏刚, 成思危, 等. 化工企业供应链长期规划与投资决策体系[J]. 化工学报, 2003, 54(5):659-664. ZHOU Z Y, ZENG M G, CHENG S W, et al. Supply chain long range planning and investment decision making system in chemical industry[J]. Journal of Chemical Industry and Engineering (China), 2003, 54(5):659-664.
[19]	吴双应, 易甜甜, 肖兰. 基于多目标函数的亚临界有机朗肯循环的参数优化和性能分析[J]. 化工学报, 2014, 65(10):4078-4085. WU S Y, YI T T, XIAO L. Parametric optimization and performance analysis of subcritical organic Rankine cycle based on multi-objective function[J]. CIESC Journal, 2014, 65(10):4078-4085.
[20]	MIRRZAZVI S K, JONES D F, TAMIZ M. A comparison of genetic and conventional methods for the solution of integer goal programmes[J]. European Journal of Operational Research, 2001, 132:594-602.
[21]	JONES D F, MIRRZAZVI S K, TAMIZ M. Multi-objective meta-heuristics:an overview of the current state-of-the-art[J]. European Journal of Operational Research, 2002, 137:1-9.
[22]	卢海, 鄢烈祥, 史彬,等. 并行多家族遗传算法解多目标优化问题[J]. 化工学报, 2012, 63(12):3985-3990. LU H, YANG L X, SHI B, et al. Multi-objective optimization based on parallel multi-families genetic algorithm[J].CIESC Journal, 2012, 63(12):3985-3990.
[23]	MAVROTAS G. Effective implementation of the ε-constraint method in multi-objective mathematical programming problems[J]. Applied Mathematics and Computation, 2009, 213:455-465.
[24]	SEN S, KOTHARI D P. Optimal thermal generating unit commitment:a review[J]. Electric Power & Energy Systems, 1998, 20(7):443-451.
[25]	JASZKIEWICZ A. Genetic local search for multi-objective combinatorial optimization[J]. European Journal of Operational Research, 2002, 137:50-71.
[26]	KONAK A, COIT D W, SMITH A E. Multi-objective optimization using genetic algorithms:a tutorial[J]. Reliability Engineering and System Safety, 2006, 91:992-1007.
[27]	BANSAL R C. Optimization methods for electric power systems:an overview[J]. International Journal of Emerging Electric Power Systems, 2005, 2(1):1-23.
[28]	SARAVANAN B, DAS S, SIKRI S, et al. A solution to the unit commitment problem-a review[J]. Front. Energy, 2013, 7(2):223-236.
[29]	FARHAT I A, EL-HAWARY M E. Optimization methods applied for solving the short-term hydrothermal coordination problem[J]. Electric Power Systems Research, 2009, 79:1308-1320.
[30]	ZITZLER E, LAUMANNS M, THIELE L. SPEA2:improving the strength Pareto evolutionary algorithm[R]. Zurich, Switzerland:Swiss Federal Institute Technology, 2005.
[31]	LU H, YEN G G. Rank-density-based multiobjective genetic algorithm and benchmark test function study[J]. IEEE Trans. Evol. Comput., 2003, 7(4):325-343.
[32]	YEN G G, LU H. Dynamic multiobjective evolutionary algorithm:adaptive cell-based rand and density estimation[J]. IEEE Trans. Evol. Comput., 2003, 7(3):253-274.
[33]	NEUMAIER A, SHCHERBINA O, HUYER W, et al. A comparison of complete global optimization solvers[J]. Math. Program, Ser.B, 2005, 103:335-356.