Energy system optimization scheduling method and system considering variable working condition characteristics of equipment

Abstract

The invention discloses an energy system optimization scheduling method and system considering variable working condition characteristics of equipment, and the method comprises the steps: building a coupling model based on an energy system network topology structure; based on historical equipment data, constructing an equipment variable working condition characteristic model; on the basis of the coupling model, an initial optimization scheduling model of the energy system is constructed with the purpose of minimizing the operation cost and the carbon emission cost; constructing constraint conditions based on the coupling model and the equipment variable working condition characteristic model, and constraining the initial optimization scheduling model by adopting the constraint conditions to obtain a target optimization scheduling model; and solving the target optimization scheduling model to obtain an optimal surplus gas distribution strategy. The variable working condition characteristics of the equipment are considered, namely, the influence of the load rate on the efficiency of the energy conversion equipment is considered, the accuracy of the optimal scheduling model is improved, and economic, efficient and environment-friendly operation of the energy system of the iron and steel enterprise is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of energy optimization scheduling, and in particular to an energy system optimization scheduling method and system taking into account the variable operating condition characteristics of equipment. Background Art

[0002] With the rapid development of the global economy, the continuous growth of energy consumption, the increasing amount of greenhouse gas emissions, and the increasingly serious problem of global warming. To alleviate global warming, it is necessary to reduce carbon emissions in the industrial sector and improve energy efficiency. In particular, carbon dioxide emissions from the steel industry account for a large proportion of the industrial sector. Therefore, studying the optimal scheduling of the energy system of steel enterprises is of great significance for reducing carbon dioxide emissions from energy consumption in the steel industry.

[0003] At present, the traditional energy system optimization scheduling model cannot accurately describe the actual operation of the equipment. The load of the energy system's boilers, steam turbines, cogeneration units and other equipment fluctuates during production, resulting in the equipment's efficiency also fluctuating. Therefore, it is necessary to establish a more accurate equipment model in the optimization scheduling model. Summary of the invention

[0004] The purpose of the present invention is to achieve coordinated energy management and optimal allocation of the energy system of a steel enterprise, ensure the lowest operating cost of each system in the steel enterprise, and reduce carbon emissions. In order to achieve the above purpose, the present invention provides an energy system optimization scheduling method and system considering the variable operating characteristics of equipment.

[0005] In a first aspect, an embodiment of the present invention provides an energy system optimization scheduling method considering the variable operating condition characteristics of equipment, including:

[0006] Based on the network topology of the energy system, a coupling model is constructed; wherein the coupling model is used to characterize the generation, conversion, storage, transmission and distribution of gas, steam and electricity;

[0007] Based on historical equipment data, a variable operating condition characteristic model of the equipment is constructed; wherein the variable operating condition characteristic model of the equipment is used to characterize the nonlinear relationship between equipment efficiency and equipment load rate;

[0008] Based on the coupling model, an initial optimization scheduling model of the energy system is constructed with the goal of minimizing operating costs and carbon emission costs;

[0009] Constructing constraint conditions based on the coupling model and the equipment variable operating condition characteristic model, and using the constraint conditions to constrain the initial optimization scheduling model to obtain a target optimization scheduling model;

[0010] The target optimization scheduling model is solved to obtain the optimal surplus gas allocation strategy.

[0011] Preferably, the construction of the equipment variable operating condition characteristic model based on historical equipment data includes:

[0012] Acquire historical equipment data; wherein the historical equipment data includes energy conversion equipment efficiency data and energy conversion equipment load rate data;

[0013] The historical equipment data is fitted to obtain a relational expression between equipment efficiency and equipment load rate, and the relational expression is characterized as an equipment variable operating condition characteristic model; wherein the relational expression is as follows:

[0014]

[0015] in, represents the efficiency correction coefficient of energy conversion equipment i at time t, m represents the fitting order, k i,n Represents the n-order fitting coefficient, N i,t Represents the load rate of energy conversion device i at time t.

[0016] Preferably, the initial optimization scheduling model of the energy system is constructed based on the coupling model with the goal of minimizing the operating cost and the carbon emission cost, including:

[0017] Based on the coupling model, an objective function is constructed with the goal of minimizing the operating cost and the carbon emission cost, and the objective function is characterized as an initial optimization scheduling model of the energy system; wherein the objective function is as follows:

[0018]

[0019] C e =C coa +C elegen +C elebuy -C elesale +C gasrel

[0020] in, represents the carbon emission cost, C e represents the operating cost, C tax represents the carbon tax cost, C BFG represents the general carbon emission factor of blast furnace gas, C COG represents the general carbon emission factor of coke oven gas, C coal represents the general carbon emission factor of coal, represents the consumption of blast furnace gas in energy conversion equipment i during period t, represents the consumption of coke oven gas in energy conversion equipment i during period t, represents the coal consumption in energy conversion equipment i during period t, C coarepresents the coal purchase cost, C elegen represents the cost of self-generated electricity, C elebuy represents the cost of purchased electricity, C elesale represents the revenue from electricity sales, C gasrel Represents the gas dissipation penalty cost.

[0021] Preferably, the constraining conditions are constructed based on the coupling model and the equipment variable operating condition characteristic model, and the initial optimization scheduling model is constrained by the constraints to obtain the target optimization scheduling model, including:

[0022] Characterizing the equipment variable operating condition characteristic model as a first constraint condition;

[0023] Constructing a second constraint condition based on the coupling model; wherein the second constraint condition includes an energy supply and demand balance constraint, an energy conversion balance constraint, and an equipment operation capacity constraint;

[0024] The first constraint condition and the second constraint condition are used to constrain the initial optimization scheduling model to obtain a target optimization scheduling model.

[0025] Preferably, the energy supply and demand balance constraints include gas supply and demand balance constraints, steam supply and demand balance constraints and electricity supply and demand balance constraints;

[0026] The following formula is used to characterize the gas supply and demand balance constraint:

[0027]

[0028] The steam supply and demand balance constraint is characterized by the following formula:

[0029]

[0030] The power supply and demand balance constraint is characterized by the following formula:

[0031]

[0032] in, represents the gas consumption of energy conversion equipment i in period t, Indicates the gas dissipation, Indicates the gas demand, represents the steam generation of energy conversion equipment r during period t, represents the steam demand, represents the amount of electricity generated by the energy conversion device e during period t, Indicates the power demand.

[0033] Preferably, the energy conversion balance constraints include gas-to-steam energy balance constraints, steam-to-electricity energy balance constraints, gas-to-steam energy balance constraints and gas-to-electricity energy balance constraints;

[0034] The following formula is used to characterize the gas-to-steam energy balance constraint:

[0035]

[0036] The steam-to-electricity energy balance constraint is represented by the following formula:

[0037]

[0038] The following formula is used to characterize the gas-to-steam energy balance constraint:

[0039]

[0040] The following formula is used to characterize the energy balance constraint of gas-to-electricity conversion:

[0041]

[0042] Among them, η GB Indicates the efficiency of gas boiler, represents the gas consumption of energy conversion equipment i in period t, Indicates the calorific value of gas. represents the steam generation of energy conversion equipment r during period t, Indicates the calorific value of steam, η T represents the turbine efficiency, represents the amount of electricity generated by energy conversion equipment e during period t, h ele Indicates the calorific value of electricity, η CHP,h is the thermal efficiency of the combined heat and power unit, represents the amount of coal purchased by energy conversion equipment i in period t, h coal Indicates the calorific value of coal, η CHP,e It indicates the power generation efficiency of the cogeneration unit.

[0043] Preferably, the equipment operation capacity constraints include energy conversion equipment operation capacity constraints and coal-gas mixed combustion equipment operation capacity constraints;

[0044] The following formula is used to characterize the operating capacity constraint of the energy conversion equipment:

[0045]

[0046] The following formula is used to characterize the operating capacity constraint of the coal-gas mixed combustion equipment:

[0047]

[0048] in, represents the gas consumption of energy conversion equipment i in period t, Indicates the upper limit of gas consumption of energy conversion equipment i, Indicates the lower limit of gas consumption of energy conversion equipment i, represents the steam generation of energy conversion equipment r during period t, represents the upper limit of the steam generation capacity of the energy conversion equipment r, Indicates the lower limit of steam generation of energy conversion equipment r, Indicates the calorific value of gas. Indicates the minimum calorific value of mixed gas required by gas mixed combustion equipment i.

[0049] Preferably, solving the target optimization scheduling model to obtain the optimal surplus gas allocation strategy includes:

[0050] The active target point particle swarm algorithm is used to solve the target optimization scheduling model to obtain the optimal surplus gas allocation strategy.

[0051] In a second aspect, an embodiment of the present invention provides an energy system optimization scheduling system that takes into account the variable operating conditions of equipment, including:

[0052] A coupling model building module is used to build a coupling model based on the energy system network topology; wherein the coupling model is used to characterize the generation, conversion, storage, transmission and distribution of gas, steam and electricity;

[0053] An equipment variable operating condition characteristic model construction module is used to construct an equipment variable operating condition characteristic model based on historical equipment data; wherein the equipment variable operating condition characteristic model is used to characterize the nonlinear relationship between equipment efficiency and equipment load rate;

[0054] An initial optimization scheduling model building module is used to build an initial optimization scheduling model of the energy system based on the coupling model with the goal of minimizing operating costs and carbon emission costs;

[0055] A target optimization scheduling model construction module is used to construct constraint conditions based on the coupling model and the equipment variable operating condition characteristic model, and use the constraint conditions to constrain the initial optimization scheduling model to obtain a target optimization scheduling model;

[0056] The model solving module is used to solve the target optimization scheduling model to obtain the optimal surplus gas allocation strategy.

[0057] Preferably, the equipment variable operating condition characteristic model building module includes:

[0058] A historical data acquisition unit, used to acquire historical equipment data; wherein the historical equipment data includes energy conversion equipment efficiency data and energy conversion equipment load rate data;

[0059] The data fitting unit is used to perform data fitting on the historical equipment data to obtain a relationship expression between equipment efficiency and equipment load rate, and characterize the relationship expression as an equipment variable operating condition characteristic model; wherein the relationship expression is as follows:

[0060]

[0061] in, represents the efficiency correction coefficient of energy conversion equipment i at time t, m represents the fitting order, k i,n Represents the n-order fitting coefficient, N i,t Represents the load rate of energy conversion device i at time t.

[0062] Compared with the prior art, the energy system optimization scheduling method and system of the embodiment of the present invention that considers the variable operating conditions of equipment have the following beneficial effects: the production, storage, conversion and use processes of three energy media, namely gas, steam and electricity, are described through a coupling model, so as to more effectively realize the coordinated optimization among the energy media in the energy system of the steel enterprise; the energy system optimization scheduling model of the steel enterprise that considers the variable operating conditions of the energy conversion equipment efficiency can improve the equipment operation efficiency, reduce carbon dioxide emissions, and is conducive to the economical, efficient and environmentally friendly operation of the energy system of the steel enterprise, and is more conducive to the rational optimization allocation and utilization of resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 It is a flow chart of an energy system optimization scheduling method considering the variable operating condition characteristics of equipment according to an embodiment of the present invention;

[0064] Figure 2 This is a schematic diagram of the network topology structure of the energy system of a steel enterprise according to an embodiment of the present invention;

[0065] Figure 3 It is a schematic diagram of a process for constructing a variable operating condition characteristic model of equipment according to an embodiment of the present invention;

[0066] Figure 4 is a schematic diagram of an initial optimization scheduling model according to an embodiment of the present invention;

[0067] Figure 5 It is a schematic diagram of a process of obtaining a target optimization scheduling model according to an embodiment of the present invention;

[0068] Figure 6 is a schematic diagram of the surplus gas, steam demand and electricity demand of an embodiment of the present invention;

[0069] Figure 7 is a schematic diagram of power generation equipment output in scenario 2 of an embodiment of the present invention;

[0070] Figure 8 is a schematic diagram of power generation equipment output in scenario 3 of an embodiment of the present invention;

[0071] Fig. 9 It is a structural schematic diagram of an energy system optimization scheduling system considering the variable operating condition characteristics of equipment according to an embodiment of the present invention;

[0072] Fig.10 It is a structural schematic diagram of a module for constructing a variable operating condition characteristic model of equipment according to an embodiment of the present invention. DETAILED DESCRIPTION

[0073] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0074] In the description of the present invention, it should be understood that the terms "first" and "second" etc. are used in the present invention to distinguish different objects rather than to describe a specific order.

[0075] In the embodiment of the present invention, the steel enterprise has a startup boiler, a 130t boiler, a cogeneration unit, a waste heat and waste pressure unit, and a steam turbine.

[0076] like Figure 1 As shown, an embodiment of the present invention provides an energy system optimization scheduling method considering the variable operating condition characteristics of equipment, including the steps of:

[0077] S1. Construct a coupling model based on the energy system network topology;

[0078] There are a variety of energy conversion equipment and energy media within the energy system of steel enterprises. These energy media interact with each other and can be converted into each other. At the same time, they are closely related to the steel manufacturing process. Therefore, the energy system of steel enterprises has a complex network structure.

[0079] like Figure 2 As shown in the figure, it is the network topology of the energy system of the steel enterprise. As can be seen from the figure, the input of the energy system is mainly a variety of surplus gas, waste heat and waste pressure resources, purchased coal and purchased electricity generated by the main processes of the steel manufacturing process. The surplus gas and waste heat and waste pressure resources are respectively used to generate steam and electricity through gas boilers (GB), combined heat and power units (CHP), waste heat boilers (CDQ) and waste pressure turbine power generation devices (TRT). Most of these steam and electricity are used in various processes of the steel manufacturing process, and a small part of the steam can be used to generate electricity through steam turbines (TG).

[0080] In the energy system of steel enterprises, since surplus gas can be stored in gas tanks, but waste heat and waste pressure resources cannot be stored, they must be used in a timely manner. Therefore, the energy use principle of the energy system of steel enterprises is to use waste heat and waste pressure resources first, and then use surplus gas on the basis of fully using waste heat and waste pressure resources. Surplus gas can be converted into steam and electricity through energy conversion equipment to meet the heat load demand and electricity load demand of each production process in the steel manufacturing process. Since steam cannot be stored, electricity can be purchased or sold from the external power grid to achieve dynamic balance. Therefore, the energy use principle of the energy system is to meet the heat load demand of each process in the steel manufacturing process. After meeting the heat load demand, consider factors such as the time-of-use electricity price of the power grid to achieve a balance between surplus gas and electricity purchased from the power grid.

[0081] Specifically, the coupling model is used to characterize the generation, conversion, storage, transmission and distribution of gas, steam and electricity. The coupling model describes the production, storage, conversion and use of the three energy media of gas, steam and electricity, and more effectively realizes the coordinated optimization of the energy media in the energy system of steel enterprises.

[0082] S2. Build a variable operating condition characteristic model of equipment based on historical equipment data;

[0083] In the traditional optimization and scheduling process of the energy system of steel enterprises, the variable operating characteristics of equipment efficiency changing with load rate are often ignored, which affects the accuracy of the equipment model and thus reduces the economic efficiency of the optimization and scheduling plan of the energy system of steel enterprises. In view of this, this step establishes an equipment variable operating characteristic model, which is used to characterize the nonlinear relationship between equipment efficiency and equipment load rate. Specifically, Figure 3 As shown, step S2 includes:

[0084] S201, obtaining historical equipment data;

[0085] The historical equipment data includes energy conversion equipment efficiency data and energy conversion equipment load rate data, which can be provided by steel enterprises.

[0086] S202 , performing data fitting on historical equipment data to obtain a relationship expression between equipment efficiency and equipment load rate, and representing the relationship expression as an equipment variable operating condition characteristic model.

[0087] By fitting polynomial data on the historical equipment data of steel enterprises, we obtained the relationship expression between equipment efficiency and equipment load rate, which will be used to correct the equipment efficiency parameters in the traditional energy system optimization scheduling model.

[0088] Specifically, the relational expression is as follows:

[0089]

[0090] in, represents the efficiency correction coefficient of energy conversion equipment i at time t, m represents the fitting order, k i,n Represents the n-order fitting coefficient, N i,t It represents the load rate of energy conversion device i at time t, that is, the ratio of output power to capacity.

[0091] Furthermore, the electrical efficiency of the combined heat and power unit is as follows:

[0092]

[0093] Among them, η CHP,e represents the electrical efficiency of the combined heat and power unit, k CHP,n Represents the n-order fitting coefficient, N CHP Indicates the electrical load rate of the cogeneration unit.

[0094] Furthermore, the thermal efficiency of the combined heat and power unit is as follows:

[0095]

[0096] η CHP,h =α CHP η CHP,e

[0097] Among them, α CHP represents the heat-to-electricity ratio of the cogeneration unit, k CHP,α,n represents the n-order fitting coefficient, η CHP,h It represents the thermal efficiency of the combined heat and power unit.

[0098] Further, the gas boiler efficiency is as follows:

[0099]

[0100] Among them, η GB represents the gas boiler efficiency, k GB,n Represents the n-order fitting coefficient, N GB Indicates the gas boiler load rate.

[0101] Further, the turbine efficiency is given by:

[0102]

[0103] Among them, η T represents the turbine efficiency, k T,n Represents the n-order fitting coefficient, N T Indicates the turbine load factor.

[0104] S3. Based on the coupling model, the initial optimization scheduling model of the energy system is constructed with the goal of minimizing the operating cost and carbon emission cost;

[0105] Based on the coupling model, the objective function is constructed with the goal of minimizing the operating cost and carbon emission cost, and the objective function is represented as the initial optimization scheduling model of the energy system. Specifically, the model diagram of the initial optimization scheduling model can be found in Figure 4 . Further, the objective function is as follows:

[0106]

[0107] C e =C coa +C elegen +C elebuy -C elesale +C gasrel

[0108] in, represents the carbon emission cost, C e represents the operating cost, C tax represents the carbon tax cost, C BFG represents the general carbon emission factor of blast furnace gas, C COG represents the general carbon emission factor of coke oven gas, C coal represents the general carbon emission factor of coal, represents the consumption of blast furnace gas in energy conversion equipment i during period t, represents the consumption of coke oven gas in energy conversion equipment i during period t, represents the coal consumption in energy conversion equipment i during period t, C coa represents the coal purchase cost, C elegen represents the cost of self-generated electricity, C elebuy represents the cost of purchased electricity, C elesale represents the revenue from electricity sales, C gasrel Represents the gas dissipation penalty cost.

[0109] Furthermore, there is the following formula:

[0110]

[0111] in, represents the price of purchased coal, represents the amount of coal purchased by energy conversion equipment i in period t, represents the cost of self-generated electricity per unit time of power generation equipment, represents the power generation of energy conversion equipment i in period t, represents the purchased electricity price during period t, represents the amount of electricity purchased during period t, represents the electricity price sold to outside during period t, It represents the electricity sold during period t. represents the gas dissipation penalty coefficient, Represents the amount of gas dissipated in period t.

[0112] S4. Based on the coupling model and the equipment variable operating condition characteristic model, constraint conditions are constructed, and the initial optimization scheduling model is constrained by the constraint conditions to obtain the target optimization scheduling model;

[0113] Specifically, Figure 5 As shown, step S4 includes:

[0114] S401, characterizing the equipment variable operating condition characteristic model as a first constraint condition;

[0115] S402, constructing a second constraint condition based on the coupling model;

[0116] Specifically, the second constraint condition includes energy supply and demand balance constraint, energy conversion balance constraint and equipment operation capacity constraint.

[0117] In the production process of steel enterprises, gas, steam and electricity are always in a dynamic state of change. The gas, steam and electricity of steel enterprises must meet the needs of the main steel production process, that is, the energy supply and demand balance constraint. Specifically, the energy supply and demand balance constraint includes the gas supply and demand balance constraint, the steam supply and demand balance constraint and the electricity supply and demand balance constraint.

[0118] The following formula is used to characterize the gas supply and demand balance constraint:

[0119]

[0120] The following formula is used to characterize the steam supply and demand balance constraint:

[0121]

[0122] The following formula is used to characterize the power supply and demand balance constraint:

[0123]

[0124] in, represents the gas consumption of energy conversion equipment i in period t, Indicates the gas dissipation, Indicates the gas demand, represents the steam generation of energy conversion equipment r during period t, represents the steam demand, represents the amount of electricity generated by the energy conversion device e during period t, Indicates the power demand.

[0125] In the energy conversion equipment of steel enterprises, different energy conversion processes must satisfy the energy conversion balance relationship, that is, the energy conversion balance constraint. Specifically, the energy conversion balance constraint includes the energy balance constraint of gas to steam, the energy balance constraint of steam to electricity, the energy balance constraint of gas to steam, and the energy balance constraint of gas to electricity.

[0126] The following formula is used to characterize the energy balance constraint of gas-to-steam conversion:

[0127]

[0128] The following formula is used to characterize the energy balance constraint of steam-to-electricity:

[0129]

[0130] The following formula is used to characterize the energy balance constraint of gas-to-steam conversion:

[0131]

[0132] The following formula is used to characterize the energy balance constraint of gas-to-electricity conversion:

[0133]

[0134] Among them, η GB Indicates the efficiency of gas boiler, represents the gas consumption of energy conversion equipment i in period t, Indicates the calorific value of gas. represents the steam generation of energy conversion equipment r during period t, Indicates the calorific value of steam, η T represents the turbine efficiency, represents the amount of electricity generated by energy conversion equipment e during period t, h ele Indicates the calorific value of electricity, η CHP,h is the thermal efficiency of the combined heat and power unit, represents the amount of coal purchased by energy conversion equipment i in period t, h coal Indicates the calorific value of coal, η CHP,e It indicates the power generation efficiency of the cogeneration unit.

[0135] In order to ensure safe production, there are certain restrictions on the gas consumption, steam production and calorific value of mixed gas of energy conversion equipment, namely equipment operation capacity constraints. Specifically, equipment operation capacity constraints include energy conversion equipment operation capacity constraints and gas mixed combustion equipment operation capacity constraints.

[0136] The following formula is used to characterize the operating capacity constraints of energy conversion equipment:

[0137]

[0138] The following formula is used to characterize the operating capacity constraint of coal-gas mixed combustion equipment:

[0139]

[0140] in, represents the gas consumption of energy conversion equipment i in period t, Indicates the upper limit of gas consumption of energy conversion equipment i, Indicates the lower limit of gas consumption of energy conversion equipment i, represents the steam generation of energy conversion equipment r during period t, represents the upper limit of the steam generation capacity of the energy conversion equipment r, Indicates the lower limit of steam generation of energy conversion equipment r, Indicates the calorific value of gas. Indicates the minimum calorific value of mixed gas required by gas mixed combustion equipment i.

[0141] S403: Use the first constraint condition and the second constraint condition to constrain the initial optimization scheduling model to obtain a target optimization scheduling model.

[0142] The optimal scheduling model of the energy system of a steel enterprise, which takes into account the efficiency of energy conversion equipment with variable operating conditions, can improve equipment operating efficiency, reduce carbon dioxide emissions, and is conducive to the economical, efficient and environmentally friendly operation of the energy system of the steel enterprise, and is more conducive to the rational optimization allocation and utilization of resources.

[0143] S5. Solve the target optimization scheduling model to obtain the optimal surplus gas allocation strategy.

[0144] Specifically, the active target point particle swarm algorithm is used to solve the target optimization scheduling model and obtain the optimal surplus gas allocation strategy to minimize the carbon emission cost and operating cost of steel enterprises. Among them, the active target point particle swarm algorithm (APSO) is an improved algorithm of the particle swarm algorithm. It introduces a new active target point, which is a point in the search space other than all positions that the current population individuals can search. After adding this point, it searches for subsequent positions together with the previous historical optimal individual position and group position, which can avoid the particles being constrained by the historical optimal position and falling into the local optimal solution. The addition of new active points makes the particles more random in the process of updating speed and position, and can jump out of multiple local extreme points in time, ensuring that the solution finally obtained by the algorithm is the global optimal solution.

[0145] In order to verify the effectiveness of the energy system optimization scheduling method considering the variable operating characteristics of the equipment in the embodiment of the present invention, in a specific embodiment, the above method is used to simulate the gas-steam-electricity system optimization scheduling model of the steel enterprise considering the variable operating characteristics of the equipment, and the improved particle swarm optimization algorithm is used to solve this model, and programming is performed in the MATLAB2021a environment. Among them, the gas surplus (COG surplus and BFG surplus), steam demand (S1 steam demand, S2 steam demand and S3 steam demand) and electricity demand in the energy system can be found in Figure 6 It can be understood that COG surplus is coke oven gas surplus, BFG surplus is blast furnace gas surplus, S1 steam is high pressure steam, S2 steam is medium pressure steam, and S3 steam is low pressure steam. The operating capacity of the energy conversion equipment is shown in Table 1, and the main parameters of the energy conversion equipment are shown in Table 2.

[0146] Table 1 Operational capabilities of energy conversion equipment

[0147]

[0148]

[0149] Table 2 Main parameters of energy conversion equipment

[0150]

[0151] According to the demand of the energy system's electricity load, the power grid needs to participate in the balance of electricity supply and demand. The time-of-use electricity price of the power grid in the area where the steel enterprises are located is shown in Table 3.

[0152] Table 3 Time-of-use electricity prices

[0153]

[0154] The following three scenarios are constructed, and the superiority of the proposed model is verified by comparing and analyzing the optimization scheduling results in different scenarios:

[0155] Scenario 1: The energy system of the steel enterprise is not optimized.

[0156] Scenario 2: Optimal scheduling without considering the variable operating characteristics of the equipment.

[0157] Scenario 3: Optimal scheduling considering the variable operating characteristics of equipment.

[0158] The solution and analysis of step S5 are as follows:

[0159] According to the simulation of step S4, the optimization results under three scenarios are shown in Table 4.

[0160] Table 4 Optimization results under three scenarios

[0161] Scenario Operating cost / 10,000 yuan Carbon emission cost / 10,000 yuan Total cost / 10,000 yuan Scenario 1 630.40 213.72 844.12 Scenario 2 572.00 188.92 760.92 Scene 3 531.32 152.78 684.10

[0162] As shown in Table 4, the total cost is the highest under scenario 1, followed by scenario 2, and the lowest under scenario 3. Moreover, compared with scenario 1, the total cost of optimized scheduling in scenario 2 is reduced by 9.8%, while the total cost of optimized scheduling in scenario 3 is reduced by 19.0%. This is because when scenario 1 optimizes the scheduling of the energy system of the steel enterprise, the surplus gas is directly dissipated into the air, and the heat load required by the energy system is all obtained by converting coal and electricity, while most of the electricity load required by the energy system is directly purchased from the external power grid. After optimizing the scheduling of scenarios 2 and 3, the surplus gas can provide part of the heat load and electricity load through gas boilers and cogeneration units, reducing part of the operating cost. However, since scenario 2 does not consider the variable operating characteristics of the equipment, the optimization scheduling process is somewhat irrational. Therefore, after considering the variable operating characteristics of the equipment in scenario 3, the operating cost of the energy system is further reduced.

[0163] like Figure 7 The figure shows the power generation equipment output diagram for scenario 2. Figure 7 From the analysis, it can be seen that in scenario 2, the energy conversion equipment is considered to be operating at rated efficiency. If the equipment operates at rated efficiency, the economic efficiency of the CHP unit will be improved. At this time, the CHP unit is used as the main power generation equipment. When the electricity price is low (0:00-8:00), it is more economical to purchase electricity directly from the external power grid, but due to the limitation of thermal load, it cannot supply all the electricity load, and most of the electricity load still needs to be generated by the CHP unit; when the electricity price is peak (8:00-13:00, 19:00-21:00), it is more economical to use the CHP unit to supply electricity than to purchase electricity from the external power grid. At this time, the output of the CHP unit is increased. However, during the peak period of electricity price, the load rate of the CHP unit fluctuates, and the CHP unit operates at an operating efficiency lower than the rated power during some periods. At this time, the economic efficiency of the CHP unit is not as good as purchasing electricity directly from the external power grid, and the actual total cost of the energy system will become higher. Therefore, there is a certain irrationality in the scheduling scheme under the scenario 2 model, which will generate more carbon dioxide and higher operating costs.

[0164] like Figure 8 The figure shows the power generation equipment output diagram for scenario 3. Figure 8 Analysis shows that in scenario 3, due to the consideration of the variable operating characteristics of the equipment, the load and capacity of the equipment will affect the equipment load rate, that is, the operating efficiency of the equipment, and thus affect the output of the cogeneration unit. Therefore, in scenario 3, the efficiency of the cogeneration unit is less than the rated efficiency, its actual output is smaller, and more electrical load is purchased directly from the external power grid. At this time, the optimized scheduling plan of the steel enterprise's gas-steam-electricity system is more reasonable.

[0165] The embodiment of the present invention is an energy system optimization scheduling method that takes into account the variable operating conditions of equipment. The production, storage, conversion and use processes of three energy media, namely gas, steam and electricity, are described through a coupling model, so as to more effectively realize the coordinated optimization among the energy media in the energy system of the steel enterprise. The steel enterprise energy system optimization scheduling model that takes into account the variable operating conditions of the energy conversion equipment efficiency can improve the equipment operation efficiency, reduce carbon dioxide emissions, and is conducive to the economical, efficient and environmentally friendly operation of the energy system of the steel enterprise, and is more conducive to the rational optimization allocation and utilization of resources.

[0166] Based on the above energy system optimization scheduling method, such as Fig. 9 As shown, an embodiment of the present invention provides an energy system optimization scheduling system considering the variable operating condition characteristics of equipment, including:

[0167] A coupling model building module 1 is used to build a coupling model based on the energy system network topology; wherein the coupling model is used to characterize the generation, conversion, storage, transmission and distribution of gas, steam and electricity;

[0168] The equipment variable operating condition characteristic model construction module 2 is used to construct the equipment variable operating condition characteristic model based on historical equipment data; wherein the equipment variable operating condition characteristic model is used to characterize the nonlinear relationship between equipment efficiency and equipment load rate;

[0169] The initial optimization scheduling model construction module 3 is used to construct the initial optimization scheduling model of the energy system based on the coupling model with the goal of minimizing the operating cost and the carbon emission cost;

[0170] The target optimization scheduling model construction module 4 is used to construct constraint conditions based on the coupling model and the equipment variable operating condition characteristic model, and use the constraint conditions to constrain the initial optimization scheduling model to obtain the target optimization scheduling model;

[0171] The model solving module 5 is used to solve the target optimization scheduling model to obtain the optimal surplus gas allocation strategy.

[0172] Specifically, Fig.10 As shown, the equipment variable operating condition characteristic model construction module 2 includes:

[0173] A historical data acquisition unit 21 is used to acquire historical equipment data; wherein the historical equipment data includes energy conversion equipment efficiency data and energy conversion equipment load rate data;

[0174] The data fitting unit 22 is used to perform data fitting on the historical equipment data to obtain a relationship expression between the equipment efficiency and the equipment load rate, and characterize the relationship expression as an equipment variable operating condition characteristic model; wherein the relationship expression is as follows:

[0175]

[0176] in, represents the efficiency correction coefficient of energy conversion equipment i at time t, m represents the fitting order, k i,n Represents the n-order fitting coefficient, N i,t Represents the load rate of energy conversion device i at time t.

[0177] It should be noted that each module in the above-mentioned energy system optimization and scheduling system considering the variable operating characteristics of the equipment can be fully or partially implemented by software, hardware and their combination. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules. For the specific definition of an energy system optimization and scheduling system considering the variable operating characteristics of the equipment, please refer to the above definition of an energy system optimization and scheduling method considering the variable operating characteristics of the equipment. The two have the same functions and effects, which will not be repeated here.

[0178] To sum up, the embodiment of the present invention is an energy system optimization scheduling method and system that takes into account the variable operating conditions of equipment. The coupling model describes the production, storage, conversion and use processes of three energy media, namely gas, steam and electricity, to more effectively achieve the coordinated optimization of the energy media in the energy system of the steel enterprise. The steel enterprise energy system optimization scheduling model that takes into account the variable operating conditions of the energy conversion equipment efficiency can improve the equipment operation efficiency, reduce carbon dioxide emissions, and is conducive to the economical, efficient and environmentally friendly operation of the energy system of the steel enterprise, and is more conducive to the rational optimization allocation and utilization of resources.

[0179] Each embodiment in this specification is described in a progressive manner, and the same or similar parts of each embodiment can be directly referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. It should be noted that the technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, all possible combinations of the technical features in the above-mentioned embodiments are not 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.

[0180] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and substitutions can be made without departing from the technical principles of the present invention. These improvements and substitutions should also be regarded as the scope of protection of the present invention.

Claims

1. An energy system optimization scheduling method considering the variable operating conditions of equipment, characterized in that: include: Based on the network topology of the energy system, a coupling model is constructed; wherein the coupling model is used to characterize the generation, conversion, storage, transmission and distribution of gas, steam and electricity; Based on historical equipment data, a variable operating condition characteristic model of the equipment is constructed; wherein the variable operating condition characteristic model of the equipment is used to characterize the nonlinear relationship between equipment efficiency and equipment load rate; Based on the coupling model, an initial optimization scheduling model of the energy system is constructed with the goal of minimizing operating costs and carbon emission costs; Constructing constraint conditions based on the coupling model and the equipment variable operating condition characteristic model, and using the constraint conditions to constrain the initial optimization scheduling model to obtain a target optimization scheduling model; The target optimization scheduling model is solved to obtain the optimal surplus gas allocation strategy.

2. The energy system optimization scheduling method according to claim 1 is characterized in that: The device variable operating condition characteristic model is constructed based on historical device data, including: Acquire historical equipment data; wherein the historical equipment data includes energy conversion equipment efficiency data and energy conversion equipment load rate data; The historical equipment data is fitted to obtain a relational expression between equipment efficiency and equipment load rate, and the relational expression is characterized as an equipment variable operating condition characteristic model; wherein the relational expression is as follows: in, represents the efficiency correction coefficient of energy conversion equipment i at time t, m represents the fitting order, k i,n Represents the n-order fitting coefficient, N i,t Represents the load rate of energy conversion device i at time t.

3. The energy system optimization scheduling method according to claim 1 is characterized in that: Based on the coupling model, an initial optimization scheduling model of the energy system is constructed with the goal of minimizing operating costs and carbon emission costs, including: Based on the coupling model, an objective function is constructed with the goal of minimizing the operating cost and the carbon emission cost, and the objective function is characterized as an initial optimization scheduling model of the energy system; wherein the objective function is as follows: C e =C coa +C elegen +C elebuy -C elesale +C gasrel in, represents the carbon emission cost, C e represents the operating cost, C tax represents the carbon tax cost, C BFG represents the general carbon emission factor of blast furnace gas, C COG represents the general carbon emission factor of coke oven gas, C coal represents the general carbon emission factor of coal, represents the consumption of blast furnace gas in energy conversion equipment i during period t, represents the consumption of coke oven gas in energy conversion equipment i during period t, represents the coal consumption in energy conversion equipment i during period t, C coa represents the coal purchase cost, C elegen represents the cost of self-generated electricity, C elebuy represents the cost of purchased electricity, C elesale represents the revenue from electricity sales, C gasrel Represents the gas dissipation penalty cost.

4. The energy system optimization scheduling method according to claim 1, characterized in that: The step of constructing constraint conditions based on the coupling model and the equipment variable operating condition characteristic model, and using the constraint conditions to constrain the initial optimization scheduling model to obtain a target optimization scheduling model includes: Characterizing the equipment variable operating condition characteristic model as a first constraint condition; Constructing a second constraint condition based on the coupling model; wherein the second constraint condition includes an energy supply and demand balance constraint, an energy conversion balance constraint, and an equipment operation capacity constraint; The first constraint condition and the second constraint condition are used to constrain the initial optimization scheduling model to obtain a target optimization scheduling model.

5. The energy system optimization scheduling method according to claim 4 is characterized in that: The energy supply and demand balance constraints include gas supply and demand balance constraints, steam supply and demand balance constraints and electricity supply and demand balance constraints; The following formula is used to characterize the gas supply and demand balance constraint: The steam supply and demand balance constraint is characterized by the following formula: The power supply and demand balance constraint is characterized by the following formula: in, represents the gas consumption of energy conversion equipment i in period t, Indicates the gas dissipation, Indicates the gas demand, represents the steam generation of energy conversion equipment r during period t, represents the steam demand, represents the amount of electricity generated by the energy conversion device e during period t, Indicates the power demand.

6. The energy system optimization scheduling method according to claim 4 is characterized in that: The energy conversion balance constraints include gas-to-steam energy balance constraints, steam-to-electricity energy balance constraints, gas-to-steam energy balance constraints and gas-to-electricity energy balance constraints; The following formula is used to characterize the gas-to-steam energy balance constraint: The steam-to-electricity energy balance constraint is represented by the following formula: The following formula is used to characterize the gas-to-steam energy balance constraint: The following formula is used to characterize the energy balance constraint of gas-to-electricity conversion: Among them, η GB Indicates the efficiency of gas boiler, represents the gas consumption of energy conversion equipment i in period t, Indicates the calorific value of gas. represents the steam generation of energy conversion equipment r during period t, Indicates the calorific value of steam, η T represents the turbine efficiency, represents the amount of electricity generated by energy conversion equipment e during period t, h ele Indicates the calorific value of electricity, η CHP,h is the thermal efficiency of the combined heat and power unit, represents the amount of coal purchased by energy conversion equipment i in period t, h coal Indicates the calorific value of coal, η CHP,e It indicates the power generation efficiency of the cogeneration unit.

7. The energy system optimization scheduling method according to claim 4 is characterized in that: The equipment operation capacity constraints include energy conversion equipment operation capacity constraints and coal-gas mixed combustion equipment operation capacity constraints; The following formula is used to characterize the operating capacity constraint of the energy conversion equipment: The following formula is used to characterize the operating capacity constraint of the coal-gas mixed combustion equipment: in, represents the gas consumption of energy conversion equipment i in period t, Indicates the upper limit of gas consumption of energy conversion equipment i, Indicates the lower limit of gas consumption of energy conversion equipment i, represents the steam generation of energy conversion equipment r during period t, represents the upper limit of the steam generation capacity of the energy conversion equipment r, Indicates the lower limit of steam generation of energy conversion equipment r, Indicates the calorific value of gas. Indicates the minimum calorific value of mixed gas required by gas mixed combustion equipment i.

8. The energy system optimization scheduling method according to claim 1 is characterized in that: Solving the target optimization scheduling model to obtain the optimal surplus gas allocation strategy includes: The active target point particle swarm algorithm is used to solve the target optimization scheduling model to obtain the optimal surplus gas allocation strategy.

9. An energy system optimization scheduling system considering the variable operating conditions of equipment, characterized in that: include: A coupling model building module is used to build a coupling model based on the energy system network topology; wherein the coupling model is used to characterize the generation, conversion, storage, transmission and distribution of gas, steam and electricity; The equipment variable operating condition characteristic model construction module is used to construct the equipment variable operating condition characteristic model based on historical equipment data; wherein the equipment variable operating condition characteristic model is used to characterize the nonlinear relationship between equipment efficiency and equipment load rate; An initial optimization scheduling model building module is used to build an initial optimization scheduling model of the energy system based on the coupling model with the goal of minimizing operating costs and carbon emission costs; A target optimization scheduling model construction module is used to construct constraint conditions based on the coupling model and the equipment variable operating condition characteristic model, and use the constraint conditions to constrain the initial optimization scheduling model to obtain a target optimization scheduling model; The model solving module is used to solve the target optimization scheduling model to obtain the optimal surplus gas allocation strategy.

10. The energy system optimization and dispatching system according to claim 9, characterized in that: The equipment variable operating condition characteristic model construction module includes: A historical data acquisition unit, used to acquire historical equipment data; wherein the historical equipment data includes energy conversion equipment efficiency data and energy conversion equipment load rate data; The data fitting unit is used to perform data fitting on the historical equipment data to obtain a relationship expression between equipment efficiency and equipment load rate, and characterize the relationship expression as an equipment variable operating condition characteristic model; wherein the relationship expression is as follows: in, represents the efficiency correction coefficient of energy conversion equipment i at time t, m represents the fitting order, k i,n Represents the n-order fitting coefficient, N i,t Represents the load rate of energy conversion device i at time t.

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