CN112394643B - Scheduling method and system for thermoelectric system of iron and steel enterprise and computer readable storage medium - Google Patents
Scheduling method and system for thermoelectric system of iron and steel enterprise and computer readable storage medium Download PDFInfo
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Abstract
The invention provides a scheduling method, a scheduling system and a computer readable storage medium of a thermoelectric system of an iron and steel enterprise, wherein the scheduling method of the thermoelectric system of the iron and steel enterprise comprises the following steps: s1, obtaining constraint conditions of the iron and steel enterprise thermoelectric system optimization scheduling model; s2, constructing an optimal scheduling model of the iron and steel enterprise thermoelectric system according to the parameters; s3, solving the optimal scheduling model of the iron and steel enterprise thermoelectric system based on a particle swarm optimization algorithm; and S4, scheduling according to the optimal solution output by the optimal scheduling model of the iron and steel enterprise thermoelectric system. The method and the system for dispatching the thermoelectric system of the iron and steel enterprise and the computer readable storage medium can provide a safe and economic dispatching scheme, reduce the cost of the thermoelectric system of the iron and steel enterprise and improve the utilization rate of coal gas.
Description
Technical Field
The invention relates to the technical field of scheduling of a thermoelectric system of an iron and steel enterprise, in particular to a scheduling method and system of the thermoelectric system of the iron and steel enterprise and a computer readable storage medium.
Background
The iron and steel industry is one of the basic prop industries of national economy of China, is the basis and key for developing other industries, but meanwhile, the iron and steel enterprise is also a high-energy-consumption and resource-type industry, and a large amount of energy consumption causes the cost of the enterprise to be increased and causes the waste of resources. The benefits of iron and steel enterprises are influenced by the utilization efficiency of energy, and a large amount of by-product gas including coke oven gas, blast furnace gas and converter gas is generated in the production link of steel making and iron making. The byproduct gas is important secondary energy for enterprises and accounts for about one third of the total energy consumption. Important components of the energy system of the iron and steel enterprise comprise gas, heat and electricity, and the energy is produced independently and restricted mutually. Therefore, on the premise of meeting the safety production of enterprises, a mode is found to jointly produce three kinds of energy, namely coal gas, steam and electric power, so that the utilization rate of the three kinds of energy can be greatly improved, and the national policy of energy conservation and emission reduction is responded.
The common thermoelectric system optimization scheduling of the iron and steel enterprises is a high-dimensional and multi-constraint optimization problem, and related solutions can be divided into two types according to the development process of a scheduling method: (1) the traditional scheduling method mainly comprises a mathematical programming method which is widely applied in the scheduling solution, and the scheduling problem can be implemented by linear programming (I.H. Kim, and C.Han.A. novel MILP model for display with multi-period optimization of bypass, product, pipeline, and system 2001 (Kim, J.H. Han, C.S. third, D.C. third, D.S. fifth, D.C. fifth, D.S. third, D.S. ISIJ.1982, 22:57-65.), mixed integer linear programming (J.H.H.K., and C.H.S. fifth, D.S. fourth, D.S. fifth, D.S. No. fifth, D.S. No. 2001, D.S. 1, D.S. seventh, D.S. 1, D.S. fifth, D.S. 1, D.S. fifth, D.S. 1, D.S. fifth, D.S. 1, D.S. A. of, D.S. A. of the same, D. of the same, A. of the same, D. A. of the same, A. includes, A. of description, A. 1, A. 2, A. 1, A. 2, A. of the same, A. of describing, A, A. of the same, A, A. of the same, A. (2) An intelligent scheduling method, which comprises a method for simulating human solution scheduling, a neural network method, a genetic algorithm (Chang H. genetic algorithms and non-interactive scheduling management system based on environmental distribution for generating units [ J ]. Energy,2011,36(1):181 & 190.), a particle swarm algorithm (Mohammadi-Ivatlo B, Moradi-Dalland M, Rabbit A. com. bound and Power environmental distribution using particle space coefficients [ J ]. Electric Power Systems, 2013,95(1): 9-18).
The traditional scheduling method is the most effective method in the scheduling problem and can obtain the optimal solution, but the complex large-scale optimization problem is often limited by the problem scale, and the solution time is increased along with the increase of the variable number. The intelligent scheduling method has poor solution characteristics, the shorter the solution time is, the less the constraint is, the less the possibility of finding a good solution is, and when the process structure changes, the expandability is poor.
Disclosure of Invention
In view of this, the technical problem to be solved by the present invention is to provide a scheduling method, system and computer readable storage medium for a thermoelectric system of an iron and steel enterprise, which can provide a safe and economic scheduling scheme, reduce the cost of the thermoelectric system of the iron and steel enterprise, and improve the utilization rate of gas.
The technical scheme of the invention is realized as follows:
a scheduling method of a thermoelectric system of a steel enterprise comprises the following steps:
s1, obtaining constraint conditions of the iron and steel enterprise thermoelectric system optimization scheduling model;
s2, constructing a thermoelectric system optimization scheduling model of the iron and steel enterprise according to the parameters;
s3, solving the optimal scheduling model of the iron and steel enterprise thermoelectric system based on a particle swarm optimization algorithm;
and S4, scheduling according to the optimal solution output by the iron and steel enterprise thermoelectric system optimization scheduling model.
Preferably, the S1 specifically includes:
obtaining constraint conditions of the iron and steel enterprise thermoelectric system optimization scheduling model by using a least square support vector machine algorithm;
extracting input energy and output energy sample set from industrial field real-time relational database of iron and steel enterprise (x)i,yi)|xi∈Rn,yi∈R,i＝1,...n}；
xiFor an n-dimensional input energy vector, yiOutputting energy data for one dimension; fitting is performed using a least squares support vector machine algorithm.
Preferably, the obtaining of the constraint condition of the optimal scheduling model of the iron and steel enterprise thermoelectric system by using the least squares support vector machine algorithm specifically includes:
using non-linear mapping
Mapping the sample from an original space to a feature space, and constructing an optimal decision function in a high-dimensional feature space:
wherein w and b are regression parameters to be solved;
according to the structure windRisk minimization principle and addition of relaxation variable eiThe optimization problem is obtained as follows:
constraint conditions are as follows:
wherein: gamma is a punishment factor used for controlling the flatness degree of the equilibrium regression coefficient and the number of large deviation samples;
the Lagrange function is:
wherein a ═ a1,a2...an]TIs Lagrange multiplier;
solving the problem of minimum value:
the compound can be obtained by the formula,
and (2) obtaining an (n +1) dimensional linear equation system by sorting:
wherein: y ═ y1...yn]T，IN∈RNI is a unit matrix, omegaij＝K(xi,xj) Is a kernel function;
the least squares support vector machine estimation function of the LS-SVM is as follows:
preferably, the S2 specifically includes:
s21: optimizing an objective function of a scheduling problem;
wherein, Xi,bfg、Xi,cogRespectively representing the input amounts of blast furnace gas and coke oven gas of the ith boiler; j represents the cost of operation of the iron and steel enterprise thermoelectric system, ccoalRepresents the unit price (Yuan/ton) of the coal purchased from the outsource, cbfgRepresents the unit price (yuan/m) of blast furnace gas BFG3)，cgocRepresenting the unit price (unit/m) of COG of coke oven gas3)，cldgIndicates the unit price (Yuan/m) of the LDG of the converter gas3)，
Representing the unit price (yuan/kwh) of power generation from the self-contained power plant,
representing the unit price of steam (yuan/ton),
represents the total consumption of blast furnace gas BFG of the system,
shows the total consumption of COG of the coke oven gas of the system,
represents the total LDG consumption of the converter gas of the system, PtotalTotal amount of power generated (kwh), F for self-contained power plantssteamIs the total amount of steam generated (ton) in the system.
Preferably, the S2 further includes:
s22, determining constraint conditions of the objective function:
D0i＝f2(Xi,bfg,Xi,cog)
represents the relationship between the input gas and the output steam of the ith boiler, wherein D0iDenotes the amount of steam generated by the ith boiler, Xi,bfgAnd Xi,bfgRespectively representing the input blast furnace gas BFG and coke oven gas quantity of the ith boiler;
representing the total amount of steam produced by the thermoelectric system, wherein D represents the total amount of steam produced by the thermoelectric system;
indicating the range of the blast furnace gas BFG consumed by the ith boiler,
and
respectively representing minimum and maximum constraints of blast furnace gas BFG input into the ith boiler;
shows the range of COG of coke oven gas consumed by i boilers,
and
respectively representing the COG minimum and maximum constraints of the input coke oven gas of the ith boiler;
representing the total blast furnace gas BFG turndown range in the thermoelectric system,
and
respectively representing the maximum and minimum constraints of the blast furnace gas BFG consumed by the system;
shows the adjustable range of the total coke oven gas COG in the thermoelectric system,
and
respectively representing the COG maximum and minimum constraints of the coke oven gas consumed by the system.
Preferably, the S3 specifically includes:
s31: initializing;
setting the maximum iteration times maxgen, the number n of independent variables of the objective function and the maximum speed V of the particlesmaxThe particle swarm size sizepop is characterized in that the maximum and minimum constraints of various coal gases input into each boiler are used as a search space, and the speed and the position are initialized randomly in the search space;
s32: setting a fitness function;
the fitness function consists of an objective function and a penalty function, the objective function is the running cost of the thermoelectric system of the iron and steel enterprise, the penalty function is set as a number a, and when the particles meet various constraint conditions, the value of a is set as a minimum value; when the particle does not satisfy each constraint condition, the value of a is set to be a maximum value;
s33: individual extrema and global optimal solution: the individual extreme value is the optimal solution found for each particle, and a global value is found from the optimal solutions, namely the global optimal solution. And comparing with the historical global optimum, and updating.
S34: update speed formula:
Vid+1＝ωVid+C1random(0,1)(Pid-Xid)+C2random(0,1)(Pgd-Xid)
updating the position formula:
Xid+1＝Xid+Vid
wherein Vid+1For updated speed, VidFor the pre-update velocity, ω is called the inertia factor, C1、C2Referred to as acceleration constant, C1Individual learning factors, C, called per particle2Called the Per particle social learning factor, PidIndividual extreme value, P, called the ith variablegdRepresents the global optimal solution, random (0,1) represents the interval [0,1 ]]A random number of (c); xid+1For updated position, XidIs the position before updating;
s35: setting a termination condition;
and when the set iteration times are reached, stopping iteration updating and outputting an optimal solution.
The invention also provides a scheduling system of the iron and steel enterprise thermoelectric system, which comprises:
the acquisition module is used for acquiring the constraint conditions of the optimal scheduling model of the thermoelectric system of the iron and steel enterprise;
the construction module is used for constructing an optimal scheduling model of the iron and steel enterprise thermoelectric system according to the parameters;
the computing module is used for solving the iron and steel enterprise thermoelectric system optimization scheduling model based on a particle swarm optimization algorithm;
and the scheduling module is used for scheduling according to the optimal solution output by the iron and steel enterprise thermoelectric system optimization scheduling model.
Preferably, the obtaining module comprises an algorithm unit;
the algorithm unit is used for obtaining constraint conditions of the iron and steel enterprise thermoelectric system optimization scheduling model by using a least square support vector machine algorithm;
extracting input energy and output energy sample set from industrial field real-time relational database of iron and steel enterprise (x)i,yi)|xi∈Rn,yi∈R,i＝1,...n}；
xiFor an n-dimensional input energy vector, yiOutputting energy data for one dimension; fitting is performed using a least squares support vector machine algorithm.
Preferably, the building module comprises an optimization unit and a constraint unit;
the optimization unit is used for optimizing an objective function of the scheduling problem, and the constraint unit is used for determining constraint conditions of the objective function.
The invention also provides a computer readable storage medium, wherein the storage medium is stored with a computer program, and when the computer program is executed by a processor, the method for dispatching the thermoelectric system of the iron and steel enterprise is realized.
The invention provides a scheduling method, a system and a computer readable storage medium of a thermoelectric system of an iron and steel enterprise, which apply a least square support vector machine algorithm to fit the functional relationship between the input energy and the output energy of each boiler in the thermoelectric system of the iron and steel enterprise and the relationship between the input energy and the output energy of a generator; by setting a certain weight penalty function, the optimization target of the thermoelectric system of the iron and steel enterprise is described as improving the utilization rate of gas, reducing the diffusion rate of the gas and reducing the operation cost of the system, and an improved particle swarm optimization algorithm is used for solving the thermoelectric system model. The invention can provide a safe and economic dispatching scheme for field personnel of the iron and steel enterprise, thereby reducing the cost of a thermoelectric system of the iron and steel enterprise and improving the utilization rate of coal gas.
Drawings
FIG. 1 is a schematic view of a thermoelectric system of a steel enterprise;
FIG. 2 is a flowchart of a scheduling method of a thermoelectric system of an iron and steel enterprise according to an embodiment of the present invention;
fig. 3 is a block diagram of a scheduling system of a thermoelectric system of an iron and steel enterprise according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 2, an embodiment of the present invention provides a scheduling method for a thermoelectric system of an iron and steel enterprise, including the following steps:
s1, obtaining constraint conditions of the iron and steel enterprise thermoelectric system optimization scheduling model;
s2, constructing a thermoelectric system optimization scheduling model of the iron and steel enterprise according to the parameters;
s3, solving the optimal scheduling model of the iron and steel enterprise thermoelectric system based on a particle swarm optimization algorithm;
and S4, scheduling according to the optimal solution output by the iron and steel enterprise thermoelectric system optimization scheduling model.
Therefore, the scheduling method of the thermoelectric system of the iron and steel enterprise, provided by the invention, is used for fitting the functional relationship between the input energy and the output energy of each boiler in the thermoelectric system of the iron and steel enterprise and the relationship between the input energy and the output energy of the generator by using the least square support vector machine algorithm; by setting a certain weight penalty function, the optimization target of the thermoelectric system of the iron and steel enterprise is described as improving the utilization rate of gas, reducing the diffusion rate of the gas and reducing the operation cost of the system, and an improved particle swarm optimization algorithm is used for solving the thermoelectric system model. The invention can provide a safe and economic dispatching scheme for field personnel of the iron and steel enterprise, thereby reducing the cost of a thermoelectric system of the iron and steel enterprise and improving the utilization rate of coal gas.
Specifically, the method for scheduling the thermoelectric system of the iron and steel enterprise comprises the following steps:
step 1: constraint condition for obtaining iron and steel enterprise thermoelectric system optimization scheduling model by using least square support vector machine algorithm
Thermoelectric system for iron and steel enterpriseThe parameter of the system optimization scheduling is actually the relation between input energy and output energy of each device in the thermoelectric system of the iron and steel enterprise, and a period of input energy and output energy sample set { (x) is extracted from an industrial field real-time relation database of the iron and steel enterprisei,yi)|xi∈Rn,yi∈R,i＝1,...n}，xiFor an n-dimensional input energy vector, yiFor one-dimensional output energy data, a least squares support vector machine algorithm is used for fitting. The specific process is as follows:
using non-linear mapping
Mapping the sample from an original space to a feature space, and constructing an optimal decision function in a high-dimensional feature space:
w and b are regression parameters to be found.
According to the principle of minimizing the structural risk and adding a relaxation variable eiThe optimization problem can be found as follows:
constraint conditions are as follows:
wherein: gamma is a penalty factor and controls the flatness of the equilibrium regression coefficient and the number of large deviation samples.
The Lagrange function is:
wherein a ═ a1,a2...an]TIs Lagrange multiplier.
Solving the problem of minimum value:
the compound can be obtained by the formula,
and (2) obtaining an (n +1) dimensional linear equation system by sorting:
wherein: y ═ y1...yn]T，IN∈RNI is a unit matrix, omegaij＝K(xi,xj) Is a kernel function.
The least squares support vector machine estimation function of the LS-SVM is as follows:
the method for selecting the least square support vector machine kernel function parameters and the penalty factor gamma uses a cross test method, wherein a training sample is divided into m parts, one part is used as a reserved part, and the other m-1 parts are used as a training part. The training part is used as a training set to estimate the probability pr, and the reserved part is used as a test set to test, so that the estimation of the model precision is obtained.
Step 2: iron and steel enterprise thermoelectric system optimization scheduling model construction
1) Optimizing an objective function of a scheduling problem
Wherein, the formula (8) represents that the objective function is taken to minimize the production cost of the thermoelectric system of the iron and steel enterprise, and the decision variables are determined by eachBy-product gas quantity X distributed to boileri,bfg、Xi,cogComposition Xi,bfg、Xi,cogRespectively representing the input quantities of blast furnace gas and coke oven gas of the ith boiler. Wherein J represents the operation cost of the thermoelectric system of the iron and steel enterprise; c. CcoalRepresents the unit price of the outsourced coal, and the unit is: yuan per ton; c. CbfgRepresents the unit price of blast furnace gas BFG, and has the unit of: yuan/m3；ccogThe unit price of the coke oven gas COG is shown as follows: yuan/m3；cldgThe unit price of the converter gas LDG is expressed; the unit is: yuan/m3；cpowerRepresents the unit price of the power generated by the self-contained power plant and has the unit of: yuan/kwh; c. CsteamRepresents the unit price of steam, and the unit is: yuan per ton;
representing the total consumption of blast furnace gas BFG of the system;
representing the total consumption of COG of the coke oven gas of the system;
the total consumption of the system converter gas LDG is shown; ptotalThe unit of the total amount of power generation of the self-contained power plant is as follows: kwh; fsteamThe total steam generation of the system is as follows: ton.
2) Constraint of objective function
D0i＝f2(Xi,bfg,Xi,cog) (9)
Wherein, the formula (9) represents the relation between the input gas and the output steam of the ith boiler, wherein D0iDenotes the amount of steam generated by the ith boiler, Xi,bfgAnd Xi,bfgRespectively representing the input blast furnace gas BFG and coke oven gas quantity of the ith boiler; equation (10) represents the total amount of steam produced by the thermoelectric system, wherein D represents the total amount of steam produced by the thermoelectric system; equation (11) represents the range of the blast furnace gas BFG consumed by the ith boiler,
and
respectively representing minimum and maximum constraints of blast furnace gas BFG input into the ith boiler; the formula (12) represents the range of COG of coke oven gas consumed by i boilers,
and
respectively representing the COG minimum and maximum constraints of the input coke oven gas of the ith boiler; equation (13) represents the total blast furnace gas BFG turndown range in the thermoelectric system,
and
respectively representing the maximum and minimum constraints of the blast furnace gas BFG consumed by the system; the formula (14) represents the total coke oven gas in the thermoelectric systemThe adjustable range of the COG is wide,
and
respectively representing the COG maximum and minimum constraints of the coke oven gas consumed by the system.
And step 3: method for solving thermoelectric system optimization scheduling model of iron and steel enterprise based on particle swarm optimization algorithm
The invention provides a scheduling method of a thermoelectric system of a steel enterprise, which can provide a safe and economic scheduling scheme for field personnel of the steel enterprise, thereby reducing the cost of the thermoelectric system of the steel enterprise and improving the utilization rate of coal gas. The particle swarm optimization algorithm is inspired from the behavior characteristics of the biological population and used for solving the optimization problem, each particle in the algorithm represents a potential solution of the problem, initialization particles are generated based on the upper limit value and the lower limit value of the equipment load capacity, a penalty function is introduced to solve the multi-constraint problem, each particle corresponds to an adaptability value determined by a fitness function, the adaptability value is formed by a target function and the penalty function together, the problem that the conventional particle algorithm cannot process multiple constraints is solved, and when the particles meet the constraints, the smaller the penalty function value is, the larger the penalty function value is as long as the particles do not meet any constraint. The speed of the particles determines the moving direction and distance of the particles, the speed is dynamically adjusted by the moving experience of the particles and other particles, and the optimization of the individual in a solvable space is realized by continuously iteratively updating the speed and the position of the particles, and the steps of the algorithm are as follows:
1) initialization: firstly, setting the maximum iteration times maxgen, the number n of independent variables of an objective function and the maximum speed V of particlesmaxAnd particle swarm size sizepop and the like, the maximum and minimum constraints of various coal gas input into each boiler are used as a search space, and the speed and the position are initialized randomly on the search space.
2) Setting a fitness function: the fitness function consists of an objective function and a penalty function, wherein the objective function is the running cost of the thermoelectric system of the iron and steel enterprise, the penalty function is set as a number a, and when the particles meet various constraint conditions, the value of a is set as a minimum value; when the particle does not satisfy the respective constraints, the value of a is set to a maximum value.
3) Individual extrema and global optimal solution: the individual extreme value is the optimal solution found for each particle, and a global value is found from the optimal solutions, namely the global optimal solution. And comparing with the historical global optimum, and updating.
4) Update formula for speed and position:
Vid+1＝ωVid+C1random(0,1)(Pid-Xid)+C2random(0,1)(Pgd-Xid) (15)
Xid+1＝Xid+Vid (16)
equation (15) is a velocity update equation, where Vid+1For updated speed, VidFor the pre-update velocity, ω is called the inertia factor, and by adjusting the magnitude of ω, the global and local optimization performance can be adjusted, C1、C2Called the acceleration constant, the former called the individual learning factor per particle and the latter called the social learning factor per particle, PidIndividual extreme value, P, called the ith variablegdRepresents the global optimal solution, random (0,1) represents the interval [0,1 ]]A random number of (c); equation (16) is a position update equation, Xid+1For updated position, XidIs the location before update.
5) Termination conditions
And when the set iteration times are reached, stopping iteration updating and outputting an optimal solution.
As shown in fig. 3, an embodiment of the present invention further provides a system for scheduling hot electricity of an iron and steel enterprise, including:
the invention also provides a scheduling system of the iron and steel enterprise thermoelectric system, which comprises:
the system comprises an acquisition module 1, a scheduling module and a scheduling module, wherein the acquisition module is used for acquiring constraint conditions of an optimal scheduling model of a thermoelectric system of a steel enterprise;
the building module 2 is used for building an iron and steel enterprise thermoelectric system optimization scheduling model according to the parameters;
the computing module 3 is used for solving the iron and steel enterprise thermoelectric system optimization scheduling model based on a particle swarm optimization algorithm;
and the scheduling module 4 is used for scheduling according to the optimal solution output by the iron and steel enterprise thermoelectric system optimization scheduling model.
In a preferred embodiment of the invention, the acquisition module 1 comprises an arithmetic unit;
the algorithm unit is used for obtaining constraint conditions of the iron and steel enterprise thermoelectric system optimization scheduling model by using a least square support vector machine algorithm;
extracting input energy and output energy sample set from industrial field real-time relational database of iron and steel enterprise (x)i,yi)|xi∈Rn,yi∈R,i＝1,...n}；
xiFor an n-dimensional input energy vector, yiOutputting energy data for one dimension; fitting is performed using a least squares support vector machine algorithm.
In a preferred embodiment of the invention, the building module 2 comprises an optimization unit and a constraint unit;
the optimization unit is used for optimizing an objective function of the scheduling problem, and the constraint unit is used for determining constraint conditions of the objective function.
The invention also provides a computer readable storage medium, wherein the storage medium is stored with a computer program, and when the computer program is executed by a processor, the method for dispatching the thermoelectric system of the iron and steel enterprise is realized.
The invention provides a scheduling method of a thermoelectric system of an iron and steel enterprise, which is characterized in that a least square support vector machine algorithm is applied to fit the functional relationship between each boiler input energy and each output energy in the thermoelectric system of the iron and steel enterprise and the relationship between the generator input energy and the generator output energy; by setting a certain weight penalty function, the optimization target of the thermoelectric system of the iron and steel enterprise is described as improving the utilization rate of gas, reducing the diffusion rate of the gas and reducing the operation cost of the system, and an improved particle swarm optimization algorithm is used for solving the thermoelectric system model. The invention can provide a safe and economic dispatching scheme for field personnel of the iron and steel enterprise, thereby reducing the cost of a thermoelectric system of the iron and steel enterprise and improving the utilization rate of coal gas.
Taking a thermoelectric system of a certain iron and steel enterprise as an example, the required amount of steam in the thermoelectric system is an actual value, and table 1 shows known amounts in the thermoelectric system. Table 2 shows the comparison of the effect of the scheduling method of the present invention and the effect of the manual scheduling method, and it can be seen from Table 2 that the blast furnace gas BFG consumed by the manual scheduling method is 269.40m3Per, the COG of the consumed coke oven gas is 15.11m3H, production of 310.35m3Steam/h, system cost 7.296 yuan; the optimized scheduling method of the invention consumes 83.12m of blast furnace gas BFG under the condition of the same steam production quantity3H, coke oven gas 30.58m3And the system running cost is 1.923 yuan. From the analysis, the scheduling method consumes less BFG and COG than a manual scheduling method, reduces the operation cost of a thermoelectric system and verifies the effectiveness of the scheduling method.
TABLE 1 known quantities in thermoelectric systems
Table 2 comparison of the effect of the method of the present invention and the manual scheduling method
The invention mainly solves the problem of optimal scheduling of the thermoelectric system of the iron and steel enterprise; aiming at various byproduct gas consumption, steam flow and power generation active power data in the iron and steel enterprise thermoelectric system collected from an industrial field, a least square support vector machine algorithm is applied to fit the functional relationship between the input energy and the output energy of each boiler in the iron and steel enterprise thermoelectric system and the relationship between the input energy and the output energy of a generator. Because the steel enterprise thermoelectric system has a plurality of variables needing to be optimized and the dimensions and the magnitude of the indexes are different, such as the byproduct gas diffusion rate, the quantity of outsourced fuel coal, the equipment stability and the like, the complexity of the objective function solving process is undoubtedly increased due to a plurality of optimization variables, the optimal solution is not easy to obtain, therefore, the invention selects the production cost of the thermoelectric system of the iron and steel enterprise as the optimization target, describes the optimization target of the thermoelectric system of the iron and steel enterprise into the improvement of the utilization rate of the coal gas, the reduction of the diffusion rate of the coal gas and the reduction of the operation cost of the system by setting a certain weighted penalty function, the improvement of the utilization rate of the coal gas means that steam, power generation and the like are generated as much as possible, additional benefits are brought to iron and steel enterprises, the coal gas is diffused to pollute air and cause resource waste, and a penalty function is introduced to the part, which is equivalent to the increase of the system operation cost. And solving the thermoelectric system model by using an improved particle swarm optimization algorithm. The invention can provide a safe and economic dispatching scheme for field personnel of the iron and steel enterprise, thereby reducing the cost of a thermoelectric system of the iron and steel enterprise and improving the utilization rate of coal gas.
Constructing an optimal scheduling model of the thermoelectric system of the iron and steel enterprise: the thermoelectric system diagram of the iron and steel enterprise is shown in fig. 1, the minimum operation cost of the thermoelectric system of the iron and steel enterprise is selected as a target function, and influence factors such as the diffusion of coal gas, the utilization rate of the coal gas and outsourcing coal are converted into the target function or the constraint form by setting punishment of a certain weight. The constraint conditions mainly include equipment capability constraint, energy demand constraint, a relation function between input energy and output energy obtained in the step 1) and the like, and the scheduling object of the model is byproduct Gas, wherein the byproduct Gas comprises Blast Furnace Gas (BFG) and Coke Oven Gas (COke Oven Gas, COG).
The method comprises the steps of obtaining optimal scheduling model parameters of the thermoelectric system of the iron and steel enterprise by utilizing a least square support vector machine algorithm, namely relation functions between input energy and output energy of each device in the thermoelectric system of the iron and steel enterprise, and ensuring that the generation quantity of the output energy meets the demand quantity by using the relation functions as constraint conditions in the modeling process of the thermoelectric system of the iron and steel enterprise.
Solving the optimal scheduling problem by adopting a particle swarm optimization algorithm, wherein firstly, equipment taking byproduct gas as input fuel is limited by an upper limit value and a lower limit value, generating initialized particles based on the upper limit value and the lower limit value, and setting parameters in the particle swarm optimization algorithm; then, a penalty function is introduced to solve the multi-constraint problem, the fitness of the particles is calculated, a global extreme value is found out from the individual extreme values by comparing the fitness values, the position of the global extreme value is updated, the individual extreme values of the particles are updated, a scheduling optimization scheme is obtained, and the effectiveness of the optimized scheduling method is verified through a simulation experiment; and then updating the speed and the position of the particles, updating the individual mechanism and the global extreme value, and terminating the iteration when the iteration times reach the preset maximum value to obtain an optimized scheduling scheme.
In practice, the optimal scheduling of the thermoelectric system of the iron and steel enterprise usually depends on the artificial experience method to perform optimal scheduling on energy, but the production cost is high and the energy utilization rate is low. Therefore, the method aims at modeling the thermoelectric system of the iron and steel enterprise, aims at a plurality of optimization variables with different dimensions and magnitudes in the system, realizes the single-target optimization scheduling of the byproduct gas system by setting a penalty function with a certain weight and taking the system operation cost as an optimization target and converting other factors such as a series of fluctuation factors of the byproduct gas emission, outsourcing fuel, equipment operation and the like into a target function or constraint and other forms, reduces the complexity of the target function and the difficulty of solving problems, in addition, the invention adopts the improved particle swarm optimization algorithm to realize the economy and comprehensive energy efficiency simulation of the scheduling scheme of the thermoelectric system of the iron and steel enterprise, effectively reduces the scheduling blindness of the thermoelectric system of the iron and steel enterprise and reduces the emission of the gas, and experiments prove that the optimization scheduling method of the thermoelectric system of the iron and steel enterprise based on the improved particle swarm optimization algorithm, compared with the scheme of manual scheduling on site, the operation cost of the thermoelectric system is reduced, and the energy is fully utilized.
Through the above description of the embodiments, those skilled in the art will clearly understand that the present application can be implemented by software plus necessary general-purpose hardware, and certainly can also be implemented by special-purpose hardware including special-purpose integrated circuits, special-purpose CPUs, special-purpose memories, special-purpose components and the like. Generally, functions performed by computer programs can be easily implemented by corresponding hardware, and specific hardware structures for implementing the same functions may be various, such as analog circuits, digital circuits, or dedicated circuits. However, for the present application, the implementation of a software program is more preferable. Based on such understanding, the technical solutions of the present application may be substantially embodied in the form of a software product, which is stored in a readable storage medium, such as a floppy disk, a usb disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk of a computer, and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device) to execute the method of the embodiments of the present application.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. The procedures or functions according to the embodiments of the present application are all or partially generated when the computer program instructions are loaded and executed on a computer. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, e.g., the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. A computer-readable storage medium may be any available medium that a computer can store or a data storage device, such as a server, a data center, etc., that is integrated with one or more available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
Finally, it is to be noted that: the above description is only a preferred embodiment of the present invention, and is only used to illustrate the technical solutions of the present invention, and not to limit the protection scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention shall fall within the protection scope of the present invention.
Claims (8)
1. A scheduling method of a thermoelectric system of a steel enterprise comprises the following steps:
s1, obtaining constraint conditions of the iron and steel enterprise thermoelectric system optimization scheduling model;
s2, constructing an optimal scheduling model of the iron and steel enterprise thermoelectric system according to the parameters;
s3, solving the optimal scheduling model of the iron and steel enterprise thermoelectric system based on a particle swarm optimization algorithm;
s4, scheduling according to the optimal solution output by the iron and steel enterprise thermoelectric system optimization scheduling model;
characterized in that, the S2 specifically includes:
s21: optimizing an objective function of a scheduling problem;
wherein, Xi,bfg、Xi,cogRespectively representing the input amounts of blast furnace gas and coke oven gas of the ith boiler; j represents the running cost of the thermoelectric system of the iron and steel enterprise; c. CcoalRepresents the unit price of the outsourced coal, and the unit is: yuan per ton; c. CbfgRepresents the unit price of blast furnace gas BFG, and has the unit of: yuan/m3；ccogThe unit price of the coke oven gas COG is shown as follows: yuan/m3；cldgThe unit price of the converter gas LDG is expressed as follows: yuan/m3；cpowerRepresents the unit price of the power generated by the self-contained power plant and has the unit of: yuan/kwh; c. CsteamRepresents the unit price of steam, and the unit is: yuan per ton;
representing the total consumption of blast furnace gas BFG of the system;
representing the total consumption of COG of the coke oven gas of the system;
the total consumption of the system converter gas LDG is shown; ptotalThe unit of the total amount of power generation of the self-contained power plant is as follows: kwh; fsteamThe total steam generation of the system is as follows: ton.
2. The scheduling method of a thermoelectric system of an iron and steel enterprise as claimed in claim 1, wherein said S1 specifically comprises:
obtaining constraint conditions of the iron and steel enterprise thermoelectric system optimization scheduling model by using a least square support vector machine algorithm;
extracting input energy and output energy sample set from industrial field real-time relational database of iron and steel enterprise (x)i,yi)|xi∈Rn,yi∈R,i＝1,...n}；
xiFor an n-dimensional input energy vector, yiOutputting energy data for one dimension; fitting is performed using a least squares support vector machine algorithm.
3. The scheduling method of a thermoelectric system of an iron and steel enterprise as claimed in claim 2, wherein the obtaining of the constraint condition of the optimal scheduling model of the thermoelectric system of the iron and steel enterprise using the least squares support vector machine algorithm specifically comprises:
using non-linear mapping
Mapping the sample from an original space to a feature space, and constructing an optimal decision function in a high-dimensional feature space:
wherein w and b are regression parameters to be solved;
according to the principle of minimizing the structural risk and adding a relaxation variable eiThe optimization problem is obtained as follows:
constraint conditions are as follows:
wherein: gamma is a punishment factor used for controlling the flatness degree of the equilibrium regression coefficient and the number of large deviation samples;
the Lagrange function is:
wherein a ═ a1,a2...an]TIs Lagrange multiplier;
solving the problem of minimum value:
the compound can be obtained by the formula,
and (2) obtaining an (n +1) dimensional linear equation system by sorting:
wherein: y ═[y1...yn]T，IN∈RNI is a unit matrix, omegaij＝K(xi,xj) Is a kernel function;
the least squares support vector machine estimation function of the LS-SVM is as follows:
4. the steel enterprise thermoelectric system scheduling method of claim 1, wherein the S2 further comprises:
s22, determining constraint conditions of the objective function:
D0i＝f2(Xi,bfg,Xi,cog)
represents the relationship between the input gas and the output steam of the ith boiler, wherein D0iDenotes the amount of steam generated by the ith boiler, Xi,bfgAnd Xi,bfgRespectively representing the input blast furnace gas BFG and coke oven gas quantity of the ith boiler;
representing the total amount of steam produced by the thermoelectric system, wherein D represents the total amount of steam produced by the thermoelectric system;
indicating the range of the blast furnace gas BFG consumed by the ith boiler,
and
respectively indicating minimum BFG of blast furnace gas input by the ith boilerA maximum constraint;
shows the range of COG of coke oven gas consumed by i boilers,
and
respectively representing the COG minimum and maximum constraints of the input coke oven gas of the ith boiler;
representing the total blast furnace gas BFG turndown range in the thermoelectric system,
and
respectively representing the maximum and minimum constraints of the blast furnace gas BFG consumed by the system;
shows the adjustable range of the total coke oven gas COG in the thermoelectric system,
and
respectively representing the COG maximum and minimum constraints of the coke oven gas consumed by the system.
5. The scheduling method of a thermoelectric system of an iron and steel enterprise as claimed in claim 1, wherein said S3 specifically comprises:
s31: initializing;
setting the maximum iteration times maxgen, the number n of independent variables of the objective function and the maximum speed V of the particlesmaxThe particle swarm size sizepop is characterized in that the maximum and minimum constraints of various coal gases input into each boiler are used as a search space, and the speed and the position are initialized randomly in the search space;
s32: setting a fitness function;
the fitness function consists of an objective function and a penalty function, the objective function is the running cost of the thermoelectric system of the iron and steel enterprise, the penalty function is set as a number a, and when the particles meet various constraint conditions, the value of a is set as a minimum value; when the particle does not satisfy each constraint condition, the value of a is set to be a maximum value;
s33: individual extrema and global optimal solution: the individual extreme value is the optimal solution found for each particle, and a global value is found from the optimal solutions, namely the global optimal solution; comparing with the historical global optimum, and updating;
s34: update speed formula:
Vid+1＝ωVid+C1random(0,1)(Pid-Xid)+C2random(0,1)(Pgd-Xid)
updating the position formula:
Xid+1＝Xid+Vid
wherein Vid+1For updated speed, VidFor the pre-update velocity, ω is called the inertia factor, C1、C2Referred to as acceleration constant, C1Individual learning factors, C, called per particle2Called the Per particle social learning factor, PidIndividual extreme value, P, called the ith variablegdRepresents the global optimal solution, random (0,1) represents the interval [0,1 ]]A random number of (c); xid+1For updated position, XidIs the position before updating;
s35: setting a termination condition;
and when the set iteration times are reached, stopping iteration updating and outputting an optimal solution.
6. The scheduling system of the thermoelectric system of the iron and steel enterprise is applied to the scheduling method of claims 1 to 5, and is characterized by comprising the following steps:
the acquisition module is used for acquiring the constraint conditions of the optimal scheduling model of the thermoelectric system of the iron and steel enterprise;
the construction module is used for constructing an optimal scheduling model of the thermoelectric system of the iron and steel enterprise according to the parameters;
the computing module is used for solving the iron and steel enterprise thermoelectric system optimization scheduling model based on a particle swarm optimization algorithm;
and the scheduling module is used for scheduling according to the optimal solution output by the iron and steel enterprise thermoelectric system optimization scheduling model.
7. The steel enterprise thermoelectric system dispatching system of claim 6, wherein the obtaining module comprises an algorithmic unit;
the algorithm unit is used for obtaining constraint conditions of the iron and steel enterprise thermoelectric system optimization scheduling model by using a least square support vector machine algorithm;
extracting input energy and output energy sample set from industrial field real-time relational database of iron and steel enterprise (x)i,yi)|xi∈Rn,yi∈R,i＝1,...n}；
xiFor an n-dimensional input energy vector, yiOutputting energy data for one dimension; fitting is performed using a least squares support vector machine algorithm.
8. The steel enterprise thermoelectric system scheduling system of claim 6, wherein the building module comprises an optimization unit and a constraint unit;
the optimization unit is used for optimizing an objective function of the scheduling problem, and the constraint unit is used for determining constraint conditions of the objective function.
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