Electric power is an important energy in steel industry. Electricity accounts for roughly 20% to 30% of the gross energy consumption and costs about 10% of the gross cost of energy. In this paper, under the premise of ensuring the stability of energy supply and the normal production safety, the mathematical programming method and the dynamic mathematical optimization model were used to set up the surplus gas in the optimal allocation among the buffer users and steam production dispatching for the production equipment. The application of this optimization model can effectively improve the energy efficiency and the accuracy of power generation, making full use of secondary energy and residual heat. It also can realize the rationalization of the electricity production structure optimization which can effectively reduce the flare of the gas and steam on one hand, and save energy and decrease production cost on the other.
From the viewpoint of socioeconomic role of steel enterprises in the future, the manufacturing process of steel plants should fulfill three principal functions:
The key problem of selfgenerating was to maximize recovery of secondary energy to generate electricity in iron and steel works, so coordinated optimizing among gases, steam, and electricity was very important. Instead, taking power as an energy medium, study electricity production dispatching optimization using the optimization theory and system energy saving. Under the background of energy management and energy conservation, this paper was undertook through the analysis of the power production side network and purchased status of IISEs, aimed at minimizing cost and optimal benefit of costeffectiveness, the power production dynamic coupling model of surplus byproduct gas, steam was established on the basis of the principle and characteristics of selfgenerating equipment of iron and steel enterprise [
Iron and steel enterprise power system was divided into electric power production side and power consumption side. The power production side included two modes of power production, namely, selfpower plant generation and residual heat and energy generation. The corresponding power link and power users’ situation of electric power production side and consumption side is shown in Figure
The schematic illustration of electrical energy system in iron and steel enterprise.
In Figure
BFG: blast furnace gas, COG: coke oven gas, LDG: Linz Donawitz gas, CCPP: gassteam combined cycle power plant, CHP: combined heat and power, TRT: blast furnace top gas recovery turbine unit, CDQ: coke dry quenching, S1: high pressure steam, S2: medium pressure steam, S3: low pressure steam, BOF: basic oxygen furnace, SPPG: selfpower plant generation, and RHEG: residual heat and energy generating.
Electric power production side of iron and steel enterprises included a variety of power generation and steam production equipment, and its characteristic parameters were different with each other and were both interrelated and influenced by the gas system and steam system causing the complexity of the actual operation, which brings certain difficulties on optimization modeling. Therefore, in this paper, the following assumptions were put forward for the optimization model.
In the given production conditions, gas rigid user consumption was constant; thus, the model only considered the optimization and allocation of distribution between the gas in a buffer users (gas power generating equipment, steam boiler) and did not consider optimal assignment problem of the gas in rigid users of steel production process.
In the given production conditions, the steam demand was considered as a constant. Thus only optimal scheduling problems of steam in relative generation and power items were studied in this paper.
Actual steel production process, boiler efficiency with boiler load changing (if this efficiency changes can be ignored, the more byproduct gas, the more power generation), but the influence of boiler load on the model optimization result was very small, so it was assumed that boiler thermal efficiency of the model was constant in different time, as well as equipment generating efficiency, residual heat recovery efficiency, and steam turbine efficiency was constant; thus, unit system generating (thermal) efficiency also remained unchanging, steam turbine power generation and extraction quantity also thought to be approximate was of linear relationship.
Owing to modeling, different time value would cause different mathematical models; thus, we need to control time interval. The model time interval was determined in a unit of time of 3 minutes.
Due to the output regulation limitations of the generating set, unit output value was presented in the form of every 3 minutes instantaneous value in power generation plan; thus, we chose 3 minutes in this paper, and in order to reflect the power network dynamical change continuously and compactly (the time did not include power turnon time and turnoff time, and in a day work shift. The reason was that the data was more suitable for statistics.) The mathematical expression of this model was reduced a lot of form of juxtaposed, helpful to control model scale.
The model was established considering power reasonable production and outsourcing, with related multicycle mixed integer linear programming (MILP) model of gas and steam. Lee et al. [
The meanings and units of every parameter in the objective function and constraint conditions were listed in Table
Meanings and units of variables in the model.
Nomenclature  

Subscripts  

Unit of time, h 

Power generation and steam production equipment 

Kinds of gas (BFG, COG, and LDG) 

Kinds of steam (S1, S2, and S3) 


Variables  

Price of outsourcing electricity in 

Price of selfgeneration of device 

Price of outsourcing coal, RMB/ 

Price of 

Cost of 

Punishment price of 

Consumption of equipment 

Consumption of 

Production quantity of 

Emission capacity of 

Outsourcing electricity of 

Generation capacity of equipment 
In the condition, the above objective function meets the related constraint. The minimum value of the system comprehensive operation cost was obtained as the optimum value of the function. In the formula, the first item is outsourcing electricity cost, when the selfgenerating electricity could not satisfy production requirements or when generation set malfunction existence from the external power grid electricity outsourcing cost; the second item is selfgenerating cost; the third item is purchased power coal cost, ensuring coalfired boilers or mixed burning gas boiler fuel demand in order to maintain stable heat load; the fourth item is equipment consumption gas cost; the fifth item is comprehensive cost of steam production; the last item is the cost of gas radiation punishment. This paper was argued that gas diffusion cost should be higher than normal use, so the gas punishment cost is larger (the gas diffusion refers to the gas that injection into the atmosphere polluting environment, but power generation with gas was not taken into account, because power generation was beneficial).
Constraint conditions could reflect the actual operation conditions on the system requirements and system internal relationship between various physical quantities. Combined with actual production situation of iron and steel enterprise, the constraints in the model were energy demand constraints, equipment capacity constraints, and thermal balance constraints.
To meet these energy demands and to guarantee normal operation of various processes, each production process in different period on electric power, steam, gas, and other secondary energy demand was different in IISEs. Consider the following.
For power demand constraints, consider
For steam demand constraints, consider
For gas balance constraints, consider
All kinds of energy conversion, storage, and consumption equipment had its rated working range, and for each power generation and steam production equipment working range, its upper and lower limit can be set according to the actual situation. Consider the following.
For equipment rated generating capacity constraints, consider
For equipment steam production capacity constraints, consider the following.
Electric power production was greatly influenced by gas, steam, steam coal, and residual heat resource in iron and steel enterprise, and they were interrelated with and influenced by each other, so through establishing approximate thermal balance relation of equipment to optimal dispatching relationships were obtained. The electric power production system consisted of selfgenerating station power generation equipment and residual heat power generation equipment. Consider the following.
For thermal balance constraint of selfgenerating link, consider
For thermal balance constraint of waste heat and energy recovery generation link, consider
For thermal balance constraint of steam boiler, consider
In the above formulas
Steam turbine is an important means to adjust steam and power balance in the system. The front of assumptions was a linear relationship between steam turbine power generation and extraction quantity. Consider
Boiler is one of the gas buffer users in iron and steel enterprises and its kinds are various. Among them, pure burning gas boiler and coal powder boiler of mixed burning gas are the most typical. Gas stove has the lowest and maximum load limitation, and gas could be only adjusted in this range. Instead, for the coal powder boiler, pulverized coal practical quantity could be adjusted according to gas surplus condition coal utility. Therefore, gas buffer user should meet the following constraint condition. Generally speaking, CCPP is one of the rigid users for the efficiency changed a lot with the load change, and its regulating range is not large. So it was ruled out. Consider
Ensuring that all continuous variables were not less than zero, we can consider
These constraint conditions were all related to each other.
The meanings and units of every parameter in the objective function and constraint conditions were listed in Table
Meanings and units of variables in the model.
Nomenclature  

Subscripts  

Unit of time, h 

Power generation and steam production equipment 

Kinds of gas (BFG, COG, and LDG) 

Kinds of steam (S1, S2, and S3) 


Variables  

Price of outsourcing electricity in 

Price of selfgeneration of device 

Price of outsourcing coal, RMB/ 

Price of 

Cost of 

Punishment price of 

Power demand of 





Rated generating capacity of equipment 

Steam production quantity of equipment 

Adjust lower limit of equipment 

Adjust upper limit of equipment 

Calorific value of the electricity, kJ/kW·h 

Calorific value of 

Calorific value of 

Calorific value of heating coal, kJ/kg 

System generating (thermal) efficiency of equipment 

Caloric value of recovery of residual heat and energy resources of equipment 

Outsourcing electricity of 

Generation capacity of equipment 

Consumption of equipment 

Consumption of 

Production quantity of 

Emission capacity of 

Extraction quantity of equipment 
According to the power mathematical optimization model in the above section, taking an integrated iron and steel enterprise in Northern China, for example, the electric power production optimization model was established on the basis of the actual power equipment situation, as shown in Figure
Optimization model of power production side of an integrated iron and steel enterprise.
To meet the demand of iron and steel enterprise under the premise of electric power production, combined with production side optimization model diagram, the objective function was got as follows:
In the formula, they are, respectively, outsourcing electricity cost, selfgenerating cost, gas radiation punishment cost, equipment consumption gas cost, comprehensive cost of steam production, and purchased power coal cost.
Each parameter meaning and unit of variable in the model was listed in Table
Meanings and units of variables in the model.
Sign  Meaning  Unit 


Unit of time  h 

Power generation and steam production equipment  — 

Equipment of steam production  — 
S1  Equipment of steam S1 production  — 
S2  Equipment of steam S2 production  — 
S3  Equipment of steam S3 production  — 
g1  Equipment of BFG consumption  — 
g2  Equipment of COG consumption  — 
g3  Equipment of LDG consumption  — 

Kinds of gas (BFG, COG, and LDG)  — 

Kinds of steam (S1, S2,and S3)  — 

Outsourcing electricity capacity of 
kW 

Generation capacity of equipment 
kW 

Power coal consumption of equipment 1# in 


BFG consumption of equipment g1 in 
m^{3}/h 

COG consumption of equipment g2 in 
m^{3}/h 

LDG consumption of equipment g3 in 
m^{3}/h 

S1 steam production quantity of equipment S1 in 


S2 steam production quantity of equipment S2 in 


S3 steam production quantity of equipment S3 in 


Emission capacity of 
m^{3}/h 
With ILOG COLEX software, the above model can be solved by taking the related parameters of company electric power, gas, steam data, and equipment into this model. The optimization results were shown in Figure
Comparison of optimal results of the model.
Sign  Meaning  Value of before optimization  Value of after optimization  Unit 


Capacity of CHP generation  11646  13524  kW 

Capacity of CCPP generation  12542  12542  kW 

Capacity of TRT generation  10983  10983  kW 

Capacity of CDQ generation  49500  169500  kW 

Sintering residual heat power generation  101025  133185  kW 

Converter saturation steam power generation  11124  12510  kW 

Residual heat utilization generation of converter rolling mill heating furnace fume  950  1010  kW 

Total selfgenerating  197770  353254  kW 

Outsourcing electricity  199950  44466  kW 

Outsourcing power coal of thermoelectric  79  68 


Combust BFG of thermoelectric  340000  400000  m^{3}/h 

Combust BFG of startup boiler  53000  52000  m^{3}/h 

Combust BFG of 130ton boiler  230000  213300  m^{3}/h 

Combust COG of thermoelectric  28000  30309  m^{3}/h 

Combust COG of startup boiler  2500  2489.7  m^{3}/h 

Combust COG of 130ton boiler  1900  4181.7  m^{3}/h 

Emission capacity of BFG  2300  300  m^{3}/h 

Emission capacity of COG  7480  108  m^{3}/h 

Emission capacity of LDG  3200  350  m^{3}/h 

Steam S1 production of startup boiler  50  11.231 


Steam S1 production of 130ton boiler  20  58.769 


Thermoelectric S2 extraction  0  0 


CDQ S2 extraction  100  30 


Steam S2 production of startup boiler  20  58.769 


Steam S2 production of 130ton boiler  40–50  0 


Steam S2 recovery of sintering  40  60 


Steam S2 recovery of converter  100  100.83 


Steam S2 recovery of rolling mill  60  60.04 


Thermoelectric S3 extraction  0  18.769 


Steam S3 production of 130 ton boiler  220  201.23 


Objective value  1501911  1166712  ¥ per day 
The CHP data of power system network of an iron and steel enterprise in one day.
(In Figure
The example of IISEs datum was shown in Table
Through Figure
In allusion to a specified scale of iron and steel enterprise, when using the optimal model for electric power production and outsourcing optimization analysis, only in the accordance with the specific configuration of power production side, put each power generation equipment and steam production equipment into consideration, clear about productionconsumption relationship of gas, steam coal consumption, electric power, steam production, and other energy medium, make electric power production optimization problems concretization and then specific issue indepth analysis and study, and find out power reasonable production plan and outsourcing strategies in iron and steel enterprise.
The characteristic of the model was a coupling optimization model which includes comprehensive consideration of power, gas, and steam (three kinds of energy medium of iron and steel enterprise); it realized the power network dynamic and continuity; it improved the accuracy of the data and thus can guide iron and steel enterprise reasonable utilization of primary energy (power coal), secondary energy (byproduct gas), and residual heat and energy resource to conduct electricity and steam production; it promoted energy conservation and emission reduction, improved production data accuracy of power network and saved electricity cost to reduce enterprise production cost.
Through the establishment of the electric power generation dynamical optimization model in IISEs, it can be known that the EPS generation optimal dispatching was concerned with gas optimal allocation between the buffer users and steam optimal production in the conditions of production equipment. Through the optimization, the best power production and outsourcing solutions for enterprise can be found out.
EPS generation dynamic optimization model was a coupling optimization model which is based on power, gas, and steam which are three common kinds of energy medium of iron and steel enterprise; the model can realize the power network dynamic and continuity and improve the accuracy of the data, thus it can guide IISEs reasonable utilization of primary energy (power coal), secondary energy (byproduct gas), and residual heat and energy resource to conduct electricity and steam production; it promoted energy conservation and emission reduction improve production data accuracy of power network and saved electricity cost to reduce IISEs production cost.
The authors declare that they have no conflict of interests regarding the publication of this paper.
This research is supported by Scholarship Award for Key Project of Chinese National Programs for Fundamental Research Development Plan (no. 2008AA042901).