Gas-steam-electric power system optimization scheduling method, equipment, medium and product

Abstract

The invention discloses a gas-steam-electric power system optimal dispatching method, device, medium and product, and relates to the technical field of gas-steam-electric power conversion system.The method comprises the steps that a to-be-dispatched gas-steam-electric power system is analyzed through the second law of thermodynamics, and an initial # imgabs0 # efficiency model is obtained; different types of energy media in the initial # imgabs 1 # efficiency model are converted into equivalent electricity under the same unit through an equivalent electricity analysis method, and a simplified # imgabs 2 # efficiency model is obtained; constructing a multi-target model based on a simplified # imgabs3 # efficiency model; solving a Pareto leading edge of the multi-target model by using a traversal weight method; the non-inferior solution set on the Pareto leading edge is decided through the TOPSIS method, the optimal scheduling scheme is obtained, the optimal scheduling scheme of the gas-steam-electric power system is determined based on the simplified # imgabs4 # efficiency model, and the scheduling reasonability of the gas-steam-electric power system can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of gas-steam-electricity conversion systems, and in particular to a method, equipment, medium and product for optimizing and dispatching a gas-steam-electricity system. Background Art

[0002] The steel industry is characterized by large scale, high output, huge energy consumption, and serious pollutant emissions. The by-product gas produced in the steel smelting process is an important secondary energy source, and how to reasonably and efficiently utilize this energy is also an important part of the sustainable development of steel enterprises. The by-product gas is mainly composed of three types of gases, namely coke oven gas (COG), blast furnace gas (BFG) and converter gas (LDG). After meeting the needs of the main process, the by-product gas is distributed to various energy conversion equipment through the conversion system to achieve energy conversion. The main processes include gas to steam conversion and steam to electricity conversion. The energy conversion equipment involved includes various types of fuel boilers, steam turbines and CHP units, namely combined heat and power units (Combined Heat and Power, CHP), etc.

[0003] At present, the optimization scheduling method of the gas-steam-electricity conversion system of steel enterprises only considers the operation results under a single objective, and there is no optimization scheduling method that can take into account the economy, low carbon and high-quality energy utilization of the system. For example, only the operation cost is considered as the optimization target for the scheduling plan of the energy input and output of each energy conversion equipment in the system. However, this scheduling plan can only achieve the optimal economy of the system, ignoring the impact of energy utilization on the comprehensive energy consumption of the enterprise, forming an operation mode that saves money but not energy. Summary of the invention

[0004] The purpose of this application is to provide a gas-steam-electricity system optimization scheduling method, equipment, medium and product, to complete the scheduling optimization of the gas-steam-electricity system based on multiple objectives, and to improve the rationality of the gas-steam-electricity system scheduling.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides a method for optimizing the scheduling of a gas-steam-electricity system, comprising:

[0007] The second law of thermodynamics is used to analyze the gas-steam-electricity system to be dispatched, and the initial Efficiency model;

[0008] Using the equivalent electroanalysis method, the initial In the efficiency model, different types of energy media are converted into equivalent electricity in the same unit, which is simplified. Efficiency model;

[0009] Based on the simplification Efficiency model builds multi-objective model;

[0010] Solving the Pareto frontier of the multi-objective model using an ergodic weight method;

[0011] The TOPSIS (Technique for Order Preference by Similarity to ldeal Solution) method is used to make decisions on the non-inferior solution set on the Pareto frontier and obtain the optimal scheduling plan.

[0012] Optionally, after using the TOPSIS method to make decisions on the non-inferior solution set on the Pareto frontier and obtaining the optimal scheduling solution, the following steps are further included:

[0013] Based on the optimal scheduling plan, the gas-steam-electricity system to be scheduled is scheduled.

[0014] Optionally, the simplification The efficiency model is:

[0015]

[0016] Among them, η ex For the gas-steam-electricity system to be dispatched Efficiency, E out,j is the power of the jth output energy, J is the number of output energy types, ω j is the equivalent electricity coefficient when the j-th output energy is converted into equivalent electricity, E in,i is the power of the i-th input energy, I is the number of input energy types, ω i is the equivalent electricity coefficient when the i-th input energy is converted into equivalent electricity, E P is the self-generated calorific value per unit quantity, ω P is the equivalent electric coefficient of self-generation, t is the time, E S1 is the calorific value of steam per unit quantity S1, ω S1 is the steam S1 equivalent electrical coefficient, E S2 is the calorific value of steam per unit quantity S2, ω S2 is the steam S2 equivalent electrical coefficient, E S3 is the calorific value of steam per unit quantity S3, ω S3 The equivalent electrical coefficient of steam S3 is, E BFG is the calorific value of blast furnace gas steam per unit quantity, ω BFG The equivalent electric coefficient of blast furnace gas is E COG is the calorific value of coke oven gas per unit quantity, ω COGis the equivalent electrical coefficient of coke oven gas, E LDG is the calorific value of converter gas per unit quantity, ω LDG is the converter gas equivalent electrical coefficient, E coal is the calorific value of coal per unit quantity, ω coal is the coal equivalent electricity coefficient.

[0017] Optionally, the multi-objective model includes an overall objective function and constraints.

[0018] Optionally, the overall objective function is:

[0019] F = w1F1 + w2F2;

[0020] Among them, F is the total objective function, w1 is the weight of the carbon emission objective function, F1 is the normalized carbon emission objective function, w2 is the weight of the operating cost objective function, and F2 is the normalized operating cost objective function;

[0021] The objective function of carbon emissions before normalization is:

[0022]

[0023] Among them, f1 is the carbon emission objective function before normalization;

[0024] The operating cost objective function before normalization is:

[0025] f2=C buy +C use +C punish ;

[0026] Among them, f2 is the operating cost objective function before normalization, C buy is the system's energy purchase cost, C use is the cost of gas use, C punish Punishment for gas release;

[0027]

[0028] Where T is the total scheduling period, is the amount of electricity purchased and sold from the grid by the system at time t, is the electricity purchase price of the thermal power system from the grid at time t, is the amount of electricity purchased and sold from the grid by the system at time t, e t sell The price at which the thermal power system sells electricity to the grid at time t is is the coal consumption of the system at time t, is the unit price of coal;

[0029]

[0030] Among them, N is the number of gas consuming equipment, is the consumption of blast furnace gas by gas consuming equipment n at time t, δ BFG is the unit usage fee of blast furnace gas, is the consumption of coke oven gas by gas consuming equipment n at time t, δ COG is the unit usage fee of coke oven gas, is the consumption of converter gas by gas consuming equipment n at time t, δ LDG The unit usage fee of converter gas;

[0031]

[0032] in, is the amount of blast furnace gas released at time t, p BFG is the penalty coefficient for blast furnace gas emission, is the emission of coke oven gas at time t, p COG is the penalty coefficient for coke oven gas emission, is the release amount of converter gas at time t, p LDG It is the penalty coefficient for converter gas emission.

[0033] Optionally, the constraints include: system steam balance constraint, system power balance constraint, system gas balance constraint, fuel boiler energy balance constraint, turbine energy balance constraint, CHP unit energy balance constraint and fuel boiler minimum calorific value constraint.

[0034] Optionally, the system steam balance constraint is:

[0035]

[0036] Among them, K is the number of steam production equipment, The amount of steam of level r produced by the k-th steam-producing equipment at time t, including high pressure, medium pressure and low pressure; is the demand for steam of grade r at time t;

[0037] The system power balance constraint is:

[0038]

[0039] Where M is the number of power generation equipment, is the amount of electricity generated by the mth power generation equipment at time t; is the amount of electricity purchased from the power grid by the gas-steam-electricity system at time t, is the amount of electricity sold by the gas-steam-electricity system to the grid at time t, is the power demand of the gas-steam-electricity system at time t;

[0040] The system gas balance constraint is:

[0041]

[0042] in, is the amount of g-type gas consumed by the n-th fuel boiler equipment at time t; F g，t is the amount of gas released at time t; is the surplus amount of type g gas at time t;

[0043] The energy balance constraint of the fuel boiler is:

[0044]

[0045] in, is the coal consumption of the nth fuel boiler equipment at time t, is the calorific value of type g gas, h coal is the calorific value of coal, is the enthalpy of steam of grade r, η B,n is the efficiency of the fuel boiler n;

[0046] The turbine energy balance constraint is:

[0047]

[0048] in, represents the amount of high-pressure steam consumed by the jth steam turbine at time t, represents the steam extraction capacity of the jth steam turbine at time t, represents the power generation of the jth steam turbine at time t, is the enthalpy of the steam input to the gas turbine, h e is the calorific value of electricity, η T,j is the efficiency of gas turbine j;

[0049] The energy balance constraint of the CHP unit is:

[0050]

[0051] in, is the amount of g-type gas consumed by the k-th CHP unit at time t, η CHP,k is the efficiency of the gas turbine k, is the amount of r-grade steam generated by the k-th CHP unit at time t, is the power generation of the kth CHP unit at time t;

[0052] The minimum calorific value constraint expression of the fuel boiler is:

[0053]

[0054] in, It is the minimum requirement of boiler n for the calorific value of mixed gas combustion.

[0055] In a second aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned gas-steam-electricity system optimization scheduling method.

[0056] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned gas-steam-electricity system optimization scheduling method.

[0057] In a fourth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above-mentioned gas-steam-electricity system optimization scheduling method.

[0058] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0059] The present application provides a method, device, medium and product for optimizing the dispatching of a gas-steam-electricity system. First, the equivalent electrical method is used to The value calculation process is simplified and Efficiency constrains carbon emission indicators and analyzes the reasons why it can reduce carbon emissions. Secondly, the system operation results under different objectives are solved and analyzed. The expression of multi-objective function is constructed based on the single objective function, and the ergodic weight method is used to solve the multi-objective model to obtain the Pareto frontier, and then the TOPSIS method is combined to make decisions to obtain the best operation plan. Combined with case analysis, it can be seen that the present invention is conducive to reducing energy losses in the system, reasonably improving the energy structure of the system, and promoting the coordinated operation of the gas-steam-electricity conversion system of steel enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0061] Figure 1 This is a flow chart of a method for optimizing and scheduling a gas-steam-electricity system in one embodiment of the present application;

[0062] Figure 2A structural diagram of a gas-steam-electricity conversion system for a steel enterprise provided by an embodiment of the present invention;

[0063] Figure 3 A schematic diagram of the principle of a gas-steam-electricity system optimization scheduling method provided by an embodiment of the present invention;

[0064] Figure 4 for Figure 3 The algorithm flow chart involved in the

[0065] Figure 5 For different operating scenarios Comparison chart of efficiency and operating costs;

[0066] Figure 6 This is a schematic diagram of the power generation of each unit in the first scenario;

[0067] Figure 7 This is a schematic diagram of the power generation of each unit in the second scenario;

[0068] Figure 8 This is a schematic diagram of power generation of each unit in the third scenario;

[0069] Fig. 9 The Pareto frontier consisting of non-inferior solution sets under different weights and the schematic diagram of the optimal solution obtained after TOPSIS method decision-making;

[0070] Fig.10 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0071] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0072] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0073] In an exemplary embodiment, Figure 1 As shown, a method for optimizing the dispatching of a gas-steam-electricity system is provided, comprising:

[0074] Step 101: Use the second law of thermodynamics to analyze the gas-steam-electricity system to be dispatched and obtain the initial Efficiency model. The gas-steam-electricity system to be dispatched is as follows Figure 2 shown.

[0075] Step 102: Using the equivalent electroanalysis method, the initial In the efficiency model, different types of energy media are converted into equivalent electricity in the same unit, which is simplified. Efficiency model.

[0076] Step 103: Simplify The efficiency model builds a multi-objective model.

[0077] Step 104: Solve the Pareto frontier of the multi-objective model using the ergodic weight method.

[0078] Step 105: Use the TOPSIS method to make decisions on the non-inferior solution set on the Pareto frontier to obtain the optimal scheduling solution.

[0079] Step 106: Based on the optimal dispatching plan, dispatch the gas-steam-electricity system to be dispatched.

[0080] like Figure 3 As shown, this application introduces the second law of thermodynamics The analysis method conducts a scientific analysis of the quality and quantity of the energy of the gas-steam-electricity conversion system of the steel enterprise, and regards the energy utilization rate of the system as an optimization scheduling target that is equally important as the operating cost. In combination with the equivalent electricity analysis method, different types of energy media are converted into equivalent electricity under the same unit, thereby simplifying The efficiency calculation process is used to obtain the efficiency of the entire conversion system. Efficiency. The relationship between carbon emissions and energy utilization of the gas-steam-electricity conversion system of steel enterprises was analyzed. The efficiency is set as the objective function. This step can impose strong constraints on carbon emissions without using system parameter settings, and can achieve the dual goals of reducing system carbon emissions and reducing energy consumption. Figure 4 , a mixed integer linear programming model is used to establish a scheduling model for the gas-steam-electricity conversion system of a steel enterprise under different economic, energy utilization and carbon emission goals, and then the YALMIP toolbox is called through the MATLAB simulation software, and the CPLEX solver is used to solve it. In order to ensure that the constructed multi-objective function scheduling model has practical significance, the single objective function constructed by S3 is first normalized, and then the ergodic weight method is used to obtain the Pareto frontier of the multi-objective model. S5 uses the TOPSIS method to make decisions on the non-inferior solution set on the obtained Pareto frontier, so as to obtain the optimal decision-making plan. The types of energy media involved in this application and the corresponding equivalent electrical coefficients are shown in Table 1.

[0081] Table 1 Energy medium types and corresponding equivalent electrical coefficients

[0082] Energy Type ω / % E (energy calorific value per unit quantity) <![CDATA[E e (Equivalent electrical value after conversion)]]> Blast furnace gas 0.483 <![CDATA[0.852kW·h·m -3 ]]> <![CDATA[0.412kW·h·m -3 ]]> Converter gas 0.483 <![CDATA[1.982kW·h·m -3 ]]> <![CDATA[0.957kW·h·m -3 ]]> Coke oven gas 0.483 <![CDATA[4.989kW·h·m -3 ]]> <![CDATA[2.410kW·h·m -3 ]]> Electricity 1.000 <![CDATA[1.000kW·h·(kW·h) -1 ]]> <![CDATA[1.000kW·h·(kW·h) -1 ]]> S1 Steam 0.378 <![CDATA[0.917kW·h·kg -1 ]]> <![CDATA[0.347kW·h·kg -1 ]]> S2 Steam 0.342 <![CDATA[0.847kW·h·kg -1 ]]> <![CDATA[0.290kW·h·kg -1 ]]> S3 Steam 0.342 <![CDATA[0.828kW·h·kg -1 ]]> <![CDATA[0.283kW·h·kg -1 ]]> coal 0.483 <![CDATA[6.055kW·h·kg -1 ]]> <![CDATA[2.959kW·h·kg -1 ]]>

[0083] Specific, simplified The efficiency model is:

[0084]

[0085] Among them, η ex For the gas-steam-electricity system to be dispatched Efficiency, E out,j is the power of the jth output energy, J is the number of output energy types, ω j is the equivalent electricity coefficient when the j-th output energy is converted into equivalent electricity, E in,i is the power of the i-th input energy, I is the number of input energy types, ω i is the equivalent electricity coefficient when the i-th input energy is converted into equivalent electricity, E P is the self-generated calorific value per unit quantity, ω P is the equivalent electric coefficient of self-generation, t is the time, E S1 is the calorific value of steam per unit quantity S1, ω S1 is the steam S1 equivalent electrical coefficient, E S2 is the calorific value of steam per unit quantity S2, ω S2 is the steam S2 equivalent electrical coefficient, E S3 is the calorific value of steam per unit quantity S3, ω S3 The equivalent electrical coefficient of steam S3 is, E BFG is the calorific value of blast furnace gas steam per unit quantity, ω BFG The equivalent electric coefficient of blast furnace gas is E COG is the calorific value of coke oven gas per unit quantity, ω COG is the equivalent electrical coefficient of coke oven gas, E LDG is the calorific value of converter gas per unit quantity, ω LDG is the converter gas equivalent electrical coefficient, E coal is the calorific value of coal per unit quantity, ω coal is the coal equivalent electricity coefficient.

[0086] The multi-objective model includes the overall objective function and constraints.

[0087] First, the expression for normalizing each single objective function is:

[0088]

[0089] Among them, F i is the optimization target obtained after normalization, f i is the original optimization objective, f i,max and f i,min f iThe maximum and minimum values ​​obtained when running separately for the optimization target.

[0090] The overall objective function is:

[0091] F=w1F1+w2F2.

[0092] w1+w2=1.

[0093] Among them, F is the total objective function, w1 is the weight of the carbon emission objective function, F1 is the normalized carbon emission objective function, w2 is the weight of the operating cost objective function, and F2 is the normalized operating cost objective function.

[0094] The objective function of carbon emissions before normalization is:

[0095]

[0096] Among them, f1 is the carbon emission objective function before normalization.

[0097] The operating cost objective function before normalization is:

[0098] f2=C buy +C use +C punish .

[0099] Among them, f2 is the operating cost objective function before normalization, C buy is the system's energy purchase cost, C use is the cost of gas use, C punish Punishment for gas venting.

[0100]

[0101] Where T is the total scheduling period, is the amount of electricity purchased and sold from the grid by the system at time t, is the electricity purchase price of the thermal power system from the grid at time t, is the amount of electricity purchased and sold from the grid by the system at time t, The price at which the thermal power system sells electricity to the grid at time t is is the coal consumption of the system at time t, is the unit price of coal.

[0102]

[0103] Among them, N is the number of gas consuming equipment, is the consumption of blast furnace gas by gas consuming equipment n at time t, δ BFG is the unit usage fee of blast furnace gas, is the consumption of coke oven gas by gas consuming equipment n at time t, δCOG is the unit usage fee of coke oven gas, is the consumption of converter gas by gas consuming equipment n at time t, δ LDG It is the unit usage fee of converter gas.

[0104]

[0105] in, is the amount of blast furnace gas released at time t, p BFG is the penalty coefficient for blast furnace gas emission, is the emission of coke oven gas at time t, p COG is the penalty coefficient for coke oven gas emission, is the release amount of converter gas at time t, p LDG It is the penalty coefficient for converter gas emission.

[0106] The carbon emission composition expression of the system is:

[0107]

[0108] in, are the consumption of blast furnace gas, coke oven gas, converter gas and coal by equipment n at time t. BFG , COG , LDG , coal and λ p Carbon emission factors for electricity purchased from different fuels and grids.

[0109] The gas-steam-electricity conversion system of steel enterprises usually contains a variety of different types of steam and power generation equipment, such as fuel boilers, steam turbines, CHP, etc. For each energy conversion equipment in the thermal power system, its energy balance must be guaranteed. Constraints include: system steam balance constraint, system power balance constraint, system gas balance constraint, fuel boiler energy balance constraint, steam turbine energy balance constraint, CHP unit energy balance constraint and fuel boiler minimum calorific value constraint.

[0110] The system steam balance constraint is:

[0111]

[0112] Among them, K is the number of steam production equipment, The amount of steam of level r produced by the k-th steam-producing equipment at time t, including high pressure, medium pressure and low pressure; is the demand for r-grade steam at time t.

[0113] The system power balance constraint is:

[0114]

[0115]

[0116] Where M is the number of power generation equipment, is the amount of electricity generated by the mth power generation equipment at time t; is the amount of electricity purchased from the power grid by the gas-steam-electricity system at time t, is the amount of electricity sold by the gas-steam-electricity system to the grid at time t, is the electricity demand of the gas-steam-electricity system at time t.

[0117] The system gas balance constraint is:

[0118]

[0119] in, F is the amount of gas consumed by the nth fuel boiler equipment at time t. g，t is the amount of gas released at time t. is the surplus amount of type g gas at time t.

[0120] The energy balance constraint of the fuel boiler is:

[0121]

[0122] in, is the coal consumption of the nth fuel boiler equipment at time t, is the calorific value of type g gas, h coal is the calorific value of coal, is the enthalpy of steam of grade r, η B,n is the efficiency of the fuel boiler n.

[0123] The turbine energy balance constraint is:

[0124]

[0125] in, represents the amount of high-pressure steam consumed by the jth steam turbine at time t, represents the steam extraction capacity of the jth steam turbine at time t, represents the power generation of the jth steam turbine at time t, is the enthalpy of the steam input to the gas turbine, h e is the calorific value of electricity, η T,j is the efficiency of gas turbine j.

[0126] The energy balance constraint of the CHP unit is:

[0127]

[0128] in, is the amount of g-type gas consumed by the k-th CHP unit at time t, η CHP,k is the efficiency of the gas turbine k, is the amount of r-grade steam generated by the k-th CHP unit at time t, is the power generation of the kth CHP unit at time t.

[0129] The minimum calorific value constraint expression of fuel boiler:

[0130]

[0131] in, It is the minimum requirement of boiler n for the calorific value of mixed gas combustion.

[0132] This application uses equivalent electrical analysis to The value calculation process is simplified and takes into account Efficiency constrains carbon emission indicators and analyzes the reasons why it can reduce carbon emissions. Secondly, the system operation results under different objectives are solved and analyzed, and a multi-objective function is constructed based on a single objective function. The ergodic weight method is used to solve the multi-objective model to obtain the Pareto frontier, and then the TOPSIS method is combined to make decisions. Finally, the effectiveness of the proposed method is verified through simulation examples. Efficiency model and carbon emission expression analysis: From the perspective of equivalent electrical coefficient, the equivalent electrical coefficient of energy medium is closely related to its own calorific value or enthalpy value. Coal has a high calorific value and a large equivalent electrical coefficient, and its impact on efficiency is the greatest. Therefore, the system will reasonably arrange the use of coal to reduce energy loss. From the perspective of carbon emissions, the carbon emission coefficient of coal is also higher than other energy sources, which has a great impact on carbon emissions. Therefore, by Setting efficiency as the objective function can achieve the limit on carbon emissions.

[0133] The non-inferior solution set obtained by the traversal weight method to solve the multi-objective function is decided by the TOPSIS method, so as to obtain the optimal decision plan. The TOPSIS method is to calculate the distance between the evaluation object and the most ideal point and the least ideal point, and select the most optimal solution that is closest to the most ideal point and farthest from the least ideal point through the evaluation index.

[0134] The expression for calculating the distance between the evaluation object and the optimal point is:

[0135]

[0136] The expression for calculating the distance between the evaluation object and the most undesirable point is:

[0137]

[0138] The expression of the evaluation index is:

[0139]

[0140] in, Respectively represent the positive ideal solution and negative ideal solution of the solution set. i+ , ED i- Represents the Euclidean distance of each solution set to the positive ideal solution and the negative ideal solution. i represents the index of each solution on the Pareto frontier. j represents the index of the considered target. Y i The largest non-inferior solution is the optimal solution to the multi-objective optimization problem.

[0141] This application sets the operation cycle of the gas-steam-electricity conversion system of the steel enterprise under study to 24 hours, with a step interval of 1 hour. At the same time, in order to make the purpose and advantages of the present invention more obvious, this application sets three scenarios for comparative analysis: Scenario 1 only considers the operation cost as the optimization target, which is the original planned production method; Scenario 2 does not consider the operation cost but only considers the impact of energy efficiency on system operation; Scenario 3 considers the impact of both operation cost and energy efficiency on system operation, which is the production method under the method of this application; The operation results analysis of the gas-steam-electricity conversion system of the steel enterprise under different scenarios is shown in Table 2.

[0142] Table 2 Operation results of the gas-steam-electricity conversion system of steel enterprises under different scenarios

[0143]

[0144] As can be seen from Table 2, in Scenario 1, the system's power generation and coal consumption reach the maximum, the power purchase reaches the minimum, and the system operating cost is the lowest. The system's self-generated power and coal consumption reach the maximum, and the externally purchased power reaches the minimum. At this time, the system consumes as much coal as possible to generate electricity and sells it to the power grid to obtain additional revenue, thereby achieving the goal of minimizing system operating costs. The main reason is that when the thermal power system aims to minimize costs, increasing self-generated power is conducive to reducing the system's energy costs. At this time, using coal to generate electricity through units is more economical than purchasing electricity from the power grid, but the energy utilization rate at this time is low, and there is still room for improvement. Scenario 2 will reduce coal consumption and power generation as a way to improve In terms of efficiency, the amount of electricity purchased from the power grid by the system is greater than in other scenarios. The main reason is that the value of coal converted into equivalent electricity is large, and the consumption of coal is Efficiency is negatively correlated, and excessive coal consumption is not conducive to Improved efficiency. By comparing Scenario 1 and Scenario 2, the trade-off between system operating costs and energy efficiency lies mainly in the game between purchased coal and purchased electricity. Efficiency target operation will sacrifice more economy to improve efficiency. Setting efficiency as the objective function can achieve the effect of reducing carbon emissions. The multi-objective function in scenario 3 compromises the advantages of the two single-objective operations and has guiding significance for the optimal scheduling of the system.

[0145] In different operating scenarios Comparison of efficiency and operating costs Figure 5 As shown. Figure 5 It can be seen that the cost target and Negatively correlated with efficiency goals. Efficiency will lead to increased costs, while cost savings will lead to The efficiency is reduced. Compared with single-objective scheduling, multi-objective operation can achieve better system operation cost and The unification of efficiency.

[0146] Figure 5 This is a comparison chart of the power generation of each unit under different scenarios. Figure 6 , Figure 7 , Figure 8 The comparison diagrams show the power generation of each unit under scenario 1, scenario 2, and scenario 3. By comparing the power generation of the units under single-target operation, it is found that the power production of the system has changed during the entire operation cycle. It no longer blindly sells electricity to the grid. Instead, it comprehensively considers system economy, carbon emissions and Under the influence of the three goals of efficiency, the power generation changes with the change of time-of-use electricity price. It can be clearly found that when the electricity price is at a low point at night, the system will reduce its own power generation and meet the demand for electricity by purchasing electricity from the grid. As the time-of-use electricity price increases, the system will no longer purchase electricity from the grid, but increase its own power generation, and sell electricity to the grid after the self-generated power meets the power demand.

[0147] The Pareto frontier composed of the solutions obtained by traversing all weights is as follows Fig. 9 The solution pointed by the arrow is the optimal solution determined by the TOPSIS method. The upper and lower boundary points of the Pareto frontier graph are the operating cost and The results obtained when running with a single efficiency target.

[0148] The present application also provides an application scenario, which applies the above-mentioned gas-steam-electricity system optimization scheduling method. Specifically: the gas-steam-electricity system optimization scheduling method provided in this embodiment can be applied in a steel smelting scenario.

[0149] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Fig.10As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for optimizing the scheduling of a gas-steam-electricity system is implemented.

[0150] Those skilled in the art will understand that Fig.10 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0151] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0152] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0153] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0154] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0155] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0156] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0157] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for optimizing the dispatching of a gas-steam-electricity system, characterized in that: include: The second law of thermodynamics is used to analyze the gas-steam-electricity system to be dispatched, and the initial Efficiency model; Using the equivalent electroanalysis method, the initial In the efficiency model, different types of energy media are converted into equivalent electricity in the same unit, which is simplified. Efficiency model; Based on the simplification Efficiency model builds multi-objective model; Solving the Pareto frontier of the multi-objective model using an ergodic weight method; The TOPSIS method is used to make decisions on the non-inferior solution set on the Pareto frontier and obtain the optimal scheduling plan.

2. The method for optimizing and dispatching a gas-steam-electricity system according to claim 1, characterized in that: After using the TOPSIS method to make decisions on the non-inferior solution set on the Pareto frontier and obtain the optimal scheduling solution, it also includes: Based on the optimal scheduling plan, the gas-steam-electricity system to be scheduled is scheduled.

3. The method for optimizing and dispatching a gas-steam-electricity system according to claim 1, characterized in that: The simplification The efficiency model is: Among them, η ex For the gas-steam-electricity system to be dispatched Efficiency, E out,j is the power of the jth output energy, J is the number of output energy types, ω j is the equivalent electricity coefficient when the j-th output energy is converted into equivalent electricity, E in,i is the power of the i-th input energy, I is the number of input energy types, ω i is the equivalent electricity coefficient when the i-th input energy is converted into equivalent electricity, E P is the self-generated calorific value per unit quantity, ω P is the equivalent electric coefficient of self-generation, t is the time, E S1 is the calorific value of steam per unit quantity S1, ω S1 is the steam S1 equivalent electrical coefficient, E S2 is the calorific value of steam per unit quantity S2, ω S2 is the steam S2 equivalent electrical coefficient, E S3 is the calorific value of steam per unit quantity S3, ω S3 The equivalent electrical coefficient of steam S3 is, E BFG is the calorific value of blast furnace gas steam per unit quantity, ω BFG The equivalent electric coefficient of blast furnace gas is E COG is the calorific value of coke oven gas per unit quantity, ω COG is the equivalent electrical coefficient of coke oven gas, E LDG is the calorific value of converter gas per unit quantity, ω LDG is the converter gas equivalent electric coefficient, E coal is the calorific value of coal per unit quantity, ω coal is the coal equivalent electricity coefficient.

4. The method for optimizing and dispatching a gas-steam-electricity system according to claim 3, characterized in that: The multi-objective model includes an overall objective function and constraints.

5. The method for optimizing and dispatching a gas-steam-electricity system according to claim 4, characterized in that: The total objective function is: F = w1F1 + w2F2; Among them, F is the total objective function, w1 is the weight of the carbon emission objective function, F1 is the normalized carbon emission objective function, w2 is the weight of the operating cost objective function, and F2 is the normalized operating cost objective function; The objective function of carbon emissions before normalization is: Among them, f1 is the carbon emission objective function before normalization; The operating cost objective function before normalization is: f2=C buy +C use +C punish ; Among them, f2 is the operating cost objective function before normalization, C buy is the system's energy purchase cost, C use is the cost of gas use, C punish Punishment for gas release; Among them, T is the total scheduling period, P t buy is the amount of electricity purchased and sold from the grid by the system at time t, is the electricity purchase price of the thermal power system from the grid at time t, P t sell is the amount of electricity purchased and sold from the grid by the system at time t, The price at which the thermal power system sells electricity to the grid at time t is is the coal consumption of the system at time t, is the unit price of coal; Among them, N is the number of gas consuming equipment, is the consumption of blast furnace gas by gas consuming equipment n at time t, δ BFG is the unit usage fee of blast furnace gas, is the consumption of coke oven gas by gas consuming equipment n at time t, δ COG is the unit usage fee of coke oven gas, is the consumption of converter gas by gas consuming equipment n at time t, δ LDG The unit usage fee of converter gas; in, is the amount of blast furnace gas released at time t, p BFG is the penalty coefficient for blast furnace gas emission, is the emission of coke oven gas at time t, p COG is the penalty coefficient for coke oven gas emission, is the release amount of converter gas at time t, p LDG It is the penalty coefficient for converter gas emission.

6. The method for optimizing and dispatching a gas-steam-electricity system according to claim 5, characterized in that: The constraints include: system steam balance constraint, system power balance constraint, system gas balance constraint, fuel boiler energy balance constraint, turbine energy balance constraint, CHP unit energy balance constraint and fuel boiler minimum calorific value constraint.

7. The method for optimizing and dispatching a gas-steam-electricity system according to claim 6, characterized in that: The system steam balance constraint is: Among them, K is the number of steam production equipment, The amount of steam of level r produced by the k-th steam-producing equipment at time t, including high pressure, medium pressure and low pressure; is the demand for steam of grade r at time t; The system power balance constraint is: P t buy ·P t sell =0; Where M is the number of power generation equipment, is the amount of electricity generated by the mth power generation equipment at time t; P t buy is the amount of electricity purchased from the power grid by the gas-steam-electricity system at time t, P t sell is the amount of electricity sold by the gas-steam-electricity system to the grid at time t, is the power demand of the gas-steam-electricity system at time t; The system gas balance constraint is: in, is the amount of g-type gas consumed by the n-th fuel boiler equipment at time t; F g，t is the amount of gas released at time t; is the surplus amount of type g gas at time t; The energy balance constraint of the fuel boiler is: in, is the coal consumption of the nth fuel boiler equipment at time t, is the calorific value of type g gas, h coal is the calorific value of coal, is the enthalpy of steam of grade r, η B,n is the efficiency of the fuel boiler n; The turbine energy balance constraint is: in, represents the amount of high-pressure steam consumed by the jth steam turbine at time t, represents the steam extraction capacity of the jth steam turbine at time t, represents the power generation of the jth steam turbine at time t, is the enthalpy of the steam input to the gas turbine, h e is the calorific value of electricity, η T,j is the efficiency of gas turbine j; The energy balance constraint of the CHP unit is: in, is the amount of g-type gas consumed by the k-th CHP unit at time t, η CHP,k is the efficiency of the gas turbine k, is the amount of r-grade steam generated by the k-th CHP unit at time t, is the power generation of the kth CHP unit at time t; The minimum calorific value constraint expression of the fuel boiler is: in, It is the minimum requirement of boiler n for the calorific value of mixed gas combustion.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the gas-steam-electricity system optimization scheduling method described in any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for optimizing the dispatching of the gas-steam-electricity system described in any one of claims 1 to 7 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for optimizing the dispatching of the gas-steam-electricity system described in any one of claims 1 to 7 is implemented.

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