A low-carbon operation method, system, device and medium for a high-load energy industrial park

Abstract

This invention discloses a low-carbon operation method, system, equipment, and medium for high-energy-consuming industrial parks, relating to the fields of power system operation optimization and carbon emission control technology. It includes establishing production energy consumption characteristic models for traditional high-energy-consuming enterprises within the park, and establishing production energy consumption characteristic models for large-volume energy-consuming enterprises within the park. Dynamic carbon emission factors for electricity consumption nodes within the park are obtained. Based on the production energy consumption characteristic models, dynamic carbon emission factors, and the planned electricity consumption and planned output of each enterprise within the park, an energy consumption-output-carbon emission co-optimization model is constructed. Solving the energy consumption-output-carbon emission co-optimization model yields low-carbon operation strategies for each enterprise within the park. This invention systematically reduces carbon emissions from electricity consumption without significantly affecting the output and electricity demand of enterprises within the park, providing quantifiable and executable technical support for the construction of zero-carbon industrial parks.

Description

Technical Field

[0001] This invention relates to the field of power system operation optimization and carbon emission control technology, specifically to a low-carbon operation method, system, equipment and medium for high-energy-consuming industrial parks. Background Technology

[0002] Industrial parks, as core carriers for the clustered development of various industries, have become key units for the concentrated consumption of energy resources and the concentrated emission of carbon dioxide in my country. Low-carbon transformation of these parks has become an important lever for promoting green and high-quality industrial development. Currently, high-energy-consuming industrial parks simultaneously house traditional high-energy-consuming industries such as electrolytic aluminum and cement, as well as large-scale energy-consuming industries such as electro-hydrogen production. Their production electricity consumption accounts for a high proportion, and their energy consumption processes are highly continuous, possessing potential for flexible adjustment. Existing methods for optimizing park operation mainly focus on comprehensive energy flow optimization around economic objectives. Some studies have begun to introduce regional annual average electricity consumption carbon emission factors to assess the long-term electricity consumption carbon emission levels of the parks. However, existing technologies have failed to fully consider the differences in energy consumption characteristics of different industrial production processes, nor have they effectively analyzed the flexible adjustment characteristics of heterogeneous users such as electrolytic aluminum, cement, and electro-hydrogen production in terms of production sequence and energy demand, resulting in a lack of quantitative basis for low-carbon operation decisions in the parks.

[0003] The shortcomings of existing technologies are as follows: First, the annual average electricity consumption carbon emission factor is generally used to assess the carbon emission level of industrial parks, which cannot reflect the differences in carbon content per kilowatt-hour at different times, making it difficult to support flexible optimization and control aimed at carbon reduction targets. Second, the modeling of different types of enterprises in the park is mostly limited to the level of external energy consumption characteristics, failing to deeply decompose the power-current-temperature coupling relationship of electrolytic aluminum, the multi-stage warehousing and collaborative supply mechanism of cement, and the energy consumption characteristics of the entire process of hydrogen production, compression, and storage in electro-hydrogen production, thus failing to accurately quantify the flexible carbon reduction adjustment capabilities of each enterprise. Third, existing optimization models mostly take minimizing economic costs as the single objective, lacking a collaborative optimization framework that uses dynamic carbon emission factors as guiding signals and takes into account the satisfaction of enterprises' electricity consumption and output deviations, making it difficult to fully explore the collaborative carbon reduction potential of multiple types of users while ensuring the production experience of enterprises. Summary of the Invention

[0004] In view of the above-mentioned existing problems, the present invention provides a low-carbon operation method, system, equipment and medium for high-energy-consuming industrial parks, in order to solve the problems in the existing technology that are difficult to support flexible optimization and control for carbon reduction targets, cannot accurately quantify the flexible carbon reduction adjustment capabilities of each enterprise, and cannot fully explore the collaborative carbon reduction potential of multiple types of users while ensuring the production experience of enterprises.

[0005] To address the aforementioned technical challenges, a low-carbon operation method for energy-intensive industrial parks is proposed, including: Establish production energy consumption characteristic models for traditional high-energy-consuming enterprises in the park, and establish production energy consumption characteristic models for large-volume energy-consuming enterprises in the park; obtain dynamic carbon emission factors for electricity consumption nodes in the park, and based on the production energy consumption characteristic models, dynamic carbon emission factors, and the planned electricity consumption and planned output of each enterprise in the park, construct an energy consumption-output-carbon emission co-optimization model with the goal of minimizing the total carbon emissions in the park and taking into account the low-carbon operation satisfaction of each enterprise in the park; solve the energy consumption-output-carbon emission co-optimization model to obtain the low-carbon operation strategies of each enterprise in the park.

[0006] As a preferred embodiment of the low-carbon operation method for high-energy-consuming industrial parks described in this invention, the establishment of production energy consumption characteristic models for traditional high-energy-consuming enterprises within the park includes establishing production energy consumption characteristic models for electrolytic aluminum enterprises and cement enterprises within the park. Establishing a production energy consumption characteristic model for electrolytic aluminum enterprises within the park includes constructing constraints on the range of values ​​for power consumption, current, and electrolytic cell temperature based on the electrolytic cell equipment parameters of the electrolytic aluminum enterprises. Based on the equivalent circuit parameters of the aluminum electrolysis enterprise, a coupling relationship constraint between power consumption and current is constructed, and based on the thermodynamic parameters of the electrolytic cell of the aluminum electrolysis enterprise, a dynamic change constraint between power consumption and electrolytic cell temperature is constructed. Based on the equipment lifespan and process continuity requirements of electrolytic aluminum enterprises, operational state constraints are constructed to limit the adjustment status and number of adjustments of electrolytic aluminum load power. Based on the chemical equivalent and production temperature of electrolytic aluminum enterprises, correlation constraints between production efficiency and output are constructed. All constraints are used together as the production energy consumption characteristic model of electrolytic aluminum enterprises.

[0007] As a preferred embodiment of the low-carbon operation method for high-energy-consuming industrial parks described in this invention, the establishment of a production energy consumption characteristic model for cement enterprises within the park includes constructing an upper limit constraint on the production power of each production stage based on the rated power of each production stage of the cement enterprise. Based on the single production time of each production link in a cement enterprise, work status constraints representing the start-up and shutdown status of each production link are constructed. Based on the initial inventory and maximum storage capacity of materials in each production link of a cement enterprise, storage adjustment capacity constraints that ensure the daily output and storage capacity of each link and meet the daily production target are constructed. Based on the material conversion coefficient determined by the production process of cement enterprises, a flexible material supply constraint is constructed for the material flow between each production link, and all constraints are used together as a production energy consumption characteristic model of cement enterprises.

[0008] As a preferred embodiment of the low-carbon operation method for high-energy-consuming industrial parks described in this invention, the step of establishing a production energy consumption characteristic model for large-scale energy-consuming enterprises within the park includes: establishing a production energy consumption characteristic model for hydrogen production enterprises within the park; constructing operational characteristic constraints for the hydrogen production system based on the correlation between hydrogen production efficiency and operating power of water electrolysis; and constructing operational characteristic constraints for the hydrogen compression system based on the correlation between the compression ratio and power consumption of the hydrogen compression system. Based on the correlation between the pressure of the hydrogen storage tank and the quality of hydrogen in the hydrogen storage system, operational characteristic constraints of the hydrogen storage system are constructed that relate the tank pressure, hydrogen filling capacity, and hydrogen release capacity. All constraints are then used together as a production energy consumption characteristic model for electro-hydrogen production enterprises.

[0009] As a preferred embodiment of the low-carbon operation method for high-energy-consuming industrial parks described in this invention, the step of minimizing the total carbon emissions of the park includes multiplying the power consumption of each enterprise in the park at each time period with the dynamic carbon emission factor of the corresponding time period and summing the results, and minimizing the summation result as the optimization objective function. Among them, the dynamic carbon emission factor, as a quantitative indicator of the carbon content of electricity consumption in the park, is input into the optimization objective function.

[0010] As a preferred embodiment of the low-carbon operation method for high-energy-consuming industrial parks described in this invention, the energy consumption-output-carbon emission collaborative optimization model includes setting low-carbon operation dissatisfaction constraints for electrolytic aluminum enterprises, cement enterprises, and electro-hydrogen production enterprises, respectively. Among them, the dissatisfaction with low-carbon operation is calculated by combining the deviation between each enterprise's actual electricity consumption and planned electricity consumption, the deviation between actual output and planned output, and the corresponding total electricity consumption deviation penalty coefficient and output deviation penalty coefficient. The low-carbon operation dissatisfaction constraint and the optimization objective function are combined to form an energy consumption-output-carbon emission collaborative optimization model. In the low-carbon operation dissatisfaction constraint, for each enterprise, the sum of the total electricity consumption deviation penalty coefficient and the output deviation penalty coefficient is 1. The total electricity consumption deviation penalty coefficient and the output deviation penalty coefficient respectively characterize the enterprise's sensitivity to the deviation of total electricity consumption and output, and are used as known parameters input into the low-carbon operation dissatisfaction constraint.

[0011] As a preferred embodiment of the low-carbon operation method for high-energy-consuming industrial parks described in this invention, the method of obtaining the low-carbon operation strategies of each enterprise in the park includes collecting hourly dynamic carbon emission factor prediction values ​​of the park's electricity consumption nodes within a future preset time period, and obtaining the planned electricity consumption curves and planned output of each enterprise in the park. The predicted value of dynamic carbon emission factor, the planned electricity consumption curve and the planned output are used as input parameters and substituted into the energy consumption-output-carbon emission collaborative optimization model for solution. The solution results will be used as a collaborative low-carbon production strategy, i.e., a low-carbon operation strategy, for electrolytic aluminum enterprises, cement enterprises, and electro-hydrogen enterprises within the park.

[0012] The beneficial effects of this preferred technical solution are: it integrates traditional high-energy-consuming enterprises, large-volume energy-consuming enterprises, dynamic carbon signals, and satisfaction constraints into a collaborative optimization framework, reducing carbon emissions from electricity consumption without significantly affecting the output and electricity demand of enterprises in the park, thus providing quantifiable and executable technical support for the construction of zero-carbon parks.

[0013] As a preferred embodiment of the low-carbon operation system for a high-energy-consuming industrial park as described in this invention, it is characterized by including a modeling module for the production energy consumption characteristics of traditional high-energy-consuming enterprises, a modeling module for the production energy consumption characteristics of large-scale energy-consuming enterprises, a dynamic carbon emission factor acquisition module, and a collaborative optimization and strategy generation module.

[0014] The traditional high-energy-consuming enterprise production energy consumption characteristic modeling module is used to obtain the production energy consumption characteristic model of electrolytic aluminum enterprises by constructing constraints on the value range of power consumption, current, and electrolytic cell temperature, the coupling relationship between power and current, the dynamic change of power and electrolytic cell temperature, the operating status, and the correlation constraints between production efficiency and output. For cement enterprises, it is used to obtain the production energy consumption characteristic model by constructing constraints on the upper limit of production power, operating status, storage adjustment capacity, and flexible material supply between each production stage.

[0015] The energy consumption characteristic modeling module for large-scale energy-consuming enterprises is used to obtain the energy consumption characteristic model of electrolytic hydrogen production enterprises by constructing the operating characteristic constraints of the water electrolysis hydrogen production system, the operating characteristic constraints of the hydrogen compression system, and the operating characteristic constraints of the hydrogen storage system.

[0016] The dynamic carbon emission factor acquisition module is used to obtain dynamic carbon emission factors based on power system carbon emission flow analysis and calculation through simulation results of the park's regional power system operation or power flow data released by the power grid company, or directly obtained from the power grid company's release.

[0017] The collaborative optimization and strategy generation module is used to construct a collaborative optimization model based on the output energy consumption characteristic models of electrolytic aluminum enterprises, cement enterprises, and electric hydrogen production enterprises, as well as dynamic carbon emission factors, combined with the planned electricity consumption and planned output of each enterprise in the park. The model aims to minimize the total carbon emissions of the park and takes into account the low-carbon operation satisfaction of each enterprise.

[0018] A computer device includes a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of a method for low-carbon operation of a high-energy-consuming industrial park.

[0019] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a method for low-carbon operation of a high-energy-consuming industrial park.

[0020] The beneficial effects of this invention are as follows: This invention establishes a refined production energy consumption model for electrolytic aluminum, cement, and electro-hydrogen production enterprises, transforming the traditionally perceived rigid high-energy-consuming load into a quantifiable and flexible adjustment resource, providing a mathematical basis for subsequent power reduction and storage time-shifting capabilities; by introducing hourly dynamic carbon emission factors to replace the traditional annual average, the park can identify the actual carbon content of electricity consumption at different times, providing a time-dimensional guiding signal for load shifting; by constructing a collaborative optimization model with the goal of minimizing total carbon emissions while taking into account the satisfaction of enterprise electricity consumption and output deviation, a quantifiable balance is achieved between carbon reduction targets and production benefits through configurable penalty coefficients; by inputting the predicted value of dynamic carbon factors and the planning curves of each enterprise into the solver, specific execution strategies for the next 24 hours are output, reducing carbon emissions from electricity consumption in the park without significantly affecting enterprise output and electricity demand, providing quantifiable and executable technical support for the construction of zero-carbon parks. Attached Figure Description

[0021] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 The present invention provides an overall flowchart of a low-carbon operation method for a high-energy-consuming industrial park according to an embodiment of the present invention.

[0023] Figure 2 The present invention provides a system scheme flowchart for a low-carbon operation system of a high-energy-consuming industrial park according to one embodiment of the present invention. Detailed Implementation

[0024] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0025] Example 1, referring to Figure 1 As one embodiment of the present invention, a low-carbon operation method for a high-energy-consuming industrial park is provided, comprising: S100: Establish production energy consumption characteristic models for traditional high-energy-consuming enterprises in the park, and establish production energy consumption characteristic models for large-scale energy-consuming enterprises in the park.

[0026] S200: Obtain the dynamic carbon emission factors of the electricity consumption nodes in the park, and based on the production energy consumption characteristic model, dynamic carbon emission factors, and the planned electricity consumption and planned output of each enterprise in the park, construct an energy consumption-output-carbon emission collaborative optimization model with the goal of minimizing the total carbon emissions of the park and taking into account the low-carbon operation satisfaction of each enterprise in the park.

[0027] S300: Solve the energy consumption-output-carbon emission synergistic optimization model to obtain the low-carbon operation strategies of each enterprise in the park.

[0028] It should be noted that this invention refines the energy consumption characteristics of electrolytic aluminum, cement, and electro-hydrogen production enterprises, introduces hourly dynamic carbon emission factors as guiding signals, and constructs a collaborative optimization model that balances carbon emission minimization with enterprise satisfaction regarding electricity consumption and output deviations. Solving this model yields specific operational strategies, systematically reducing carbon emissions from electricity consumption in industrial parks without significantly impacting normal production. Example 2, refer to Figure 1 This is a second embodiment of the present invention, which provides a low-carbon operation method for a high-energy-consuming industrial park, including: In step S100, establishing the production energy consumption characteristic model of traditional high-energy-consuming enterprises in the industrial park includes the following: traditional energy-intensive enterprises, represented by electrolytic aluminum and cement, are important load types for industrial parks and power systems, characterized by high energy consumption and high carbon emissions. Considering that electricity carbon emissions account for an important component of the carbon footprint of high-energy-consuming enterprises, the production energy consumption characteristic models of electrolytic aluminum and cement enterprises in the industrial park are established, specifically including steps S101~S102: S101: Establishing a production energy consumption characteristic model for electrolytic aluminum enterprises within the industrial park includes steps A1~A5: A1: Based on the electrolytic cell equipment parameters of aluminum electrolysis enterprises, constraints are established on the range of values ​​for power consumption, current, and electrolytic cell temperature. The power-current-temperature limit constraints are expressed as follows: in, Let t be the electrical power consumed by the electrolytic aluminum load. Let t be the current of the electrolytic aluminum load. This represents the minimum power consumption of the electrolytic aluminum load. This represents the maximum electrical power consumption of the electrolytic aluminum load. This represents the minimum current for the electrolytic aluminum load. This represents the maximum current of the electrolytic aluminum load. Let t be the temperature of the electrolytic cell at time t, which corresponds to the electrolytic aluminum load. This represents the minimum temperature of the electrolytic cell under the load of electrolytic aluminum. The maximum value of the electrolytic cell temperature for the electrolytic aluminum load is defined as the upper and lower limits of the electrolytic aluminum load power, current, and electrolytic cell temperature in engineering practice. These limits are determined by the actual technical parameters of the electrolytic aluminum equipment.

[0029] A2: Based on the equivalent circuit parameters of electrolytic aluminum enterprises, a coupling constraint between power consumption and current is constructed. In engineering applications, an equivalent circuit model of the electrolytic aluminum load can be built, and operating voltage and current data of the electrolytic aluminum load under two or more operating conditions can be collected to calculate the equivalent resistance and equivalent back electromotive force. The power-current coupling constraint is expressed as follows: in, Let t be the electrical power consumed by the electrolytic aluminum load. Let t be the current of the electrolytic aluminum load. The equivalent resistance of the electrolytic aluminum load. This is the equivalent back electromotive force of the electrolytic aluminum load.

[0030] A3: Based on the thermodynamic parameters of the electrolytic cells in aluminum electrolysis enterprises, dynamic variation constraints on power consumption and electrolytic cell temperature are constructed, which are expressed as power-temperature coupling constraints: in, Let t be the temperature of the electrolytic cell at time t, which corresponds to the electrolytic aluminum load. The heat injected at time t, For time intervals, This represents the change in the electrolytic aluminum load power at time t compared to the previous time. The effective heat used in the electrolytic aluminum production at time t. Let be the heat dissipation of the electrolytic cell at time t. This represents the specific heat capacity of the electrolyte in the electrolytic cell. The mass of the electrolyte in the electrolytic cell. Let be the change in temperature of the electrolytic cell at time t. The electrolytic cell temperature at time t-1 represents the electrolytic aluminum load. Let t-1 be the electrical power consumed by the electrolytic aluminum load.

[0031] A4: Based on the equipment lifespan and process continuity requirements of electrolytic aluminum enterprises, operational state constraints are constructed to limit the power adjustment status and number of adjustments for electrolytic aluminum loads. The power dynamic adjustment constraint is expressed as: in, The variable is 0-1; a value of 1 indicates that the electrolytic aluminum load is in a power-up state at time t. This is a 0-1 variable; a value of 1 indicates that the electrolytic aluminum load is in a power reduction state at time t. The variable is 0-1; a value of 1 indicates that the electrolytic aluminum load is in a power-maintaining state at time t. The rate of downward ramp-up of the electrolytic aluminum load. The upward ramp rate of the electrolytic aluminum load is constrained by the thermal inertia of the electrolytic cell and the critical material inventory of alumina within the cell. It can be given based on the actual technical parameters of the electrolytic cell, or obtained by measuring the upward or downward adjustment of power per unit time under normal operating conditions of the electrolytic aluminum load. It is a very large positive number.

[0032] The operating state constraint formula is expressed as: in, The total timeframe for low-carbon operation decisions is taken as 24 hours. This setting represents the maximum daily power adjustment frequency for electrolytic aluminum load. It limits the lifespan of electrolytic aluminum equipment and ensures process continuity. A value of 3 to 5 times is acceptable, or it can be set based on the technical parameters of the production equipment, product production requirements, and the willingness to adjust for low-carbon energy consumption. The stronger the production line's resistance to lifespan loss, the lower the requirements for product production continuity, and the stronger the willingness to adjust for low-carbon energy consumption, the higher the maximum daily power adjustment frequency. Let be the power down-adjustment state variable at time t-1. Let be the power up-adjustment state variable at time t-1.

[0033] A5: Based on the stoichiometry and production temperature of electrolytic aluminum enterprises, construct the correlation constraints between production efficiency and output. Combine all constraints into a production energy consumption characteristic model for electrolytic aluminum enterprises. The production efficiency and output constraints are expressed as follows: in, Let be the production efficiency of the electrolytic aluminum load at time t. Let be the rated efficiency of the electrolytic aluminum load at time t. The rated production temperature for electrolytic aluminum load. Let t be the output of electrolytic aluminum. This is the chemical equivalent of electrolytic aluminum, with a value of 0.3356.

[0034] S102: Establishing a production energy consumption characteristic model for cement enterprises within the industrial park includes steps B1~B4: B1: Low-carbon energy consumption regulation in cement enterprises is achieved through warehousing, i.e., storing materials during high-carbon periods and engaging in production during low-carbon periods. Based on the rated power of each production stage of the cement enterprise, an upper limit constraint on the production power of each production stage is constructed. The production power limit constraint is expressed as follows: in, Let t be the total power consumption of the cement plant. Let t be the basic power consumption of the cement plant, that is, the power required even without starting any major production processes. As an index for the production process, This is a 0-1 variable, representing whether the i-th production step is in the started state at time t, with 1 indicating the started state and 0 indicating the not started state. The rated power of the i-th production stage is directly given by the technical parameters of the cement production equipment. This is the upper limit of the total power consumption of cement enterprises, which is limited by the capacity of the enterprise's transformer or the current carrying capacity of the power supply line.

[0035] Cement production includes five main stages: raw material crushing, raw meal grinding, fuel preparation, clinker calcination, and cement grinding. The products from the first four stages can be stored in the raw material warehouse, raw material grinding warehouse, fuel warehouse, and clinker warehouse, respectively.

[0036] B2: Based on the single production time of each production stage in a cement enterprise, construct working state constraints to represent the start-up and shutdown states of each production stage. The working state constraints are expressed as follows: in, For time indexing, Let i be the time consumed in a single production run of the i-th production stage. For the current moment, It is a 0-1 variable, indicating whether the i-th production step starts at time k. A value of 1 indicates that it starts at the current time, and a value of 0 indicates that it does not start.

[0037] B3: Based on the initial material inventory and maximum storage capacity of each production stage of a cement enterprise, construct storage adjustment capacity constraints to ensure the daily output and storage capacity of each stage and to meet the daily production target. The storage adjustment capacity constraints are expressed as follows: in, Let i be the amount of material produced in a single production run at the i-th stage. Let be the initial inventory level of the material corresponding to the i-th stage. Let i be the daily production target for the i-th stage. Let represent the amount of material transferred out for the i-th stage. Let represent the initial inventory of the material corresponding to the i-th stage. Let be the maximum storage capacity of the material corresponding to the i-th stage.

[0038] B4: Based on the material conversion coefficient determined by the cement enterprise's production process, construct flexible material supply constraints for material flow between each production stage. Combine all constraints into a production energy consumption characteristic model for the cement enterprise. The flexible material supply constraints are expressed as: in, This refers to the material conversion coefficient from the raw material crushing stage to the raw meal grinding stage. The material conversion coefficient from the raw material grinding stage to the fuel preparation stage. The material conversion coefficient from fuel preparation to clinker calcination. The material conversion coefficient from clinker calcination to cement grinding. , , , and These refer to the amount of material produced in a single production run, specifically in the stages of raw material crushing, raw meal grinding, fuel preparation, clinker calcination, and cement grinding. , , , and Let t represent the storage volume of materials at time t for each of the following stages: raw material crushing, raw meal grinding, fuel preparation, clinker calcination, and cement grinding. , , , and This refers to the maximum storage capacity of materials corresponding to the processes of raw material crushing, raw meal grinding, fuel preparation, clinker calcination, and cement grinding. Let t represent the cement shipment demand at time t. , , , and This refers to the initial inventory of materials corresponding to the processes of raw material crushing, raw meal grinding, fuel preparation, clinker calcination, and cement grinding.

[0039] Furthermore, in step S100, establishing the production energy consumption characteristic model of large energy-consuming enterprises within the park includes establishing the production energy consumption characteristic model of hydrogen production enterprises within the park, specifically including steps S111~113: S111: Hydrogen production by water electrolysis is a key link connecting renewable energy and industrial decarbonization. It is a typical representative of new large-scale energy-consuming enterprises in industrial parks and power systems. Currently, the mainstream processes for hydrogen production by water electrolysis include alkaline water electrolysis (ALK) and proton exchange membrane water electrolysis (PEM). The main production links for hydrogen production by water electrolysis include three parts: hydrogen production by water electrolysis, hydrogen compression, and hydrogen storage.

[0040] Based on the correlation between hydrogen production efficiency and operating power in water electrolysis, operational characteristic constraints for the water electrolysis hydrogen production system are constructed. These constraints are expressed as follows: in, Let be the volume of hydrogen produced by the water electrolysis hydrogen production system at time t. Let be the hydrogen production efficiency of the water electrolysis hydrogen production system at time t. Let t be the operating electrical power of the water electrolysis hydrogen production system. The operating electrical power of the water electrolysis hydrogen production system at time t-1 is... This refers to the calorific value of hydrogen, which is the heat released when one standard cubic meter of hydrogen is completely burned. The rated power of the water electrolysis hydrogen production system is given by the technical parameters of the electrolyzer equipment. The load factor is the ratio of actual operating power to rated power. Let t be the hydrogen output power of the water electrolysis hydrogen production system at time t. This represents the minimum operating power of the water electrolysis hydrogen production system. This represents the maximum operating power of the water electrolysis hydrogen production system. To limit the downhill ramp rate of the water electrolysis hydrogen production system Limiting the uphill ramp rate of the water electrolysis hydrogen production system. , and The dynamic hydrogen production efficiency polynomial coefficients are obtained by collecting energy consumption data under different load rates provided by electrolyzer manufacturers and performing quadratic polynomial fitting.

[0041] S112: Based on the relationship between the compression ratio and power consumption of the hydrogen compression system, the operating characteristic constraints of the hydrogen compression system are constructed. The operating characteristic constraints of the hydrogen compression system are expressed as follows: in, Let t be the work consumed by the compression system to compress one mole of hydrogen at time t. Let be the ideal gas constant, taken as 8.314 J / (mol·K), and let be the temperature of the compressed hydrogen gas, in Kelvin. The heat capacity ratio of hydrogen. The compression ratio of a compression system is the ratio of the outlet pressure to the inlet pressure. Let be the electrical power consumed by the hydrogen compression system at time t. The intake velocity of the compression system is the molar flow rate of hydrogen entering the compressor per unit time. The relative molecular mass of hydrogen is 2.016 g / mol.

[0042] S113: Based on the correlation between the pressure of the hydrogen storage tank and the quality of hydrogen in the hydrogen storage system, construct constraints on the operating characteristics of the hydrogen storage system that relate the pressure of the hydrogen storage tank, the amount of hydrogen added, and the amount of hydrogen released, and use all constraints together as a production energy consumption characteristic model for the electro-hydrogen production enterprise.

[0043] The operating characteristic constraints of a hydrogen storage system are expressed as follows: in, Let t be the pressure inside the hydrogen storage tank. Let be the volume of the hydrogen storage tank at time t. Let t be the amount of hydrogen in the hydrogen storage tank at time t. The temperature of the hydrogen gas inside the hydrogen storage tank. Let be the mass of hydrogen in the hydrogen storage tank at time t. The molar mass of hydrogen is 0.002016 kg / mol. Let be the mass of hydrogen in the hydrogen storage tank at time t+1. Let t be the amount of hydrogen added to the hydrogen storage tank at time t, that is, the mass of hydrogen gas input from the compression system into the storage tank. Let t be the amount of hydrogen released from the hydrogen storage tank at time t, that is, the mass of hydrogen discharged from the storage tank for subsequent use. This is the minimum permissible pressure for the hydrogen storage tank. This is the maximum permissible pressure of the hydrogen storage tank. This is a 0-1 variable, representing whether the hydrogen storage tank is being filled with hydrogen at time t. A value of 1 indicates that it is being filled with hydrogen, and a value of 0 indicates that it is not being filled with hydrogen. This is a 0-1 variable, representing whether the hydrogen storage tank is releasing hydrogen at time t. A value of 1 indicates that hydrogen is being released, and a value of 0 indicates that no hydrogen is being released. This represents the maximum hydrogen charging rate for the hydrogen storage tank. This represents the maximum hydrogen release rate of the hydrogen storage tank.

[0044] In step S200, constructing the energy consumption-output-carbon emission synergistic optimization model includes steps S201~ S201: Multiply the electricity consumption of each enterprise in the park at each time period by the corresponding dynamic carbon emission factor, sum the results, and minimize the summation as the optimization objective function. The formula is expressed as: in, This represents the total carbon emissions from electricity consumption within the park during the scheduling period. The total timeframe for low-carbon operation decisions is 24 hours, denoted as t. The dynamic carbon emission factor at the park's electricity consumption nodes at time t represents the carbon content per kilowatt-hour of electricity consumed in the park. This factor can be calculated through power system operation simulation or power system carbon emission flow analysis methods in the area where the park is located, or directly provided by hourly forecasts published by the power grid company. Let t be the power consumption of the electrolytic aluminum enterprise. Let t be the power consumption of the cement plant. Let t be the electrical power consumption of the water electrolysis hydrogen production system. Let t be the electrical power consumption of the hydrogen compression system at time t.

[0045] Among them, the dynamic carbon emission factor, as a quantitative indicator of the carbon content of electricity consumption in the park, is input into the optimization objective function.

[0046] S202: Set low-carbon operation dissatisfaction constraints for electrolytic aluminum enterprises, cement enterprises, and electrolytic hydrogen production enterprises respectively, expressed by the formula: in, Dissatisfaction with low-carbon operations among electrolytic aluminum enterprises is a dimensionless metric; a higher value indicates a worse experience for the enterprise. This is a dimensionless penalty coefficient for the deviation of total electricity consumption by electrolytic aluminum enterprises, reflecting the enterprise's sensitivity to deviations from planned total electricity consumption. This is the penalty coefficient for the output deviation of electrolytic aluminum enterprises. Let t be the actual power consumption of the electrolytic aluminum enterprise. The planned power consumption of an electrolytic aluminum enterprise at time t is declared in advance by the enterprise based on its production plan. This represents the planned output of an electrolytic aluminum enterprise at time t. This represents the actual output of the electrolytic aluminum enterprise at time t. Dissatisfaction with the low-carbon operation of cement enterprises The penalty coefficient for the total electricity consumption deviation of cement enterprises. This is the penalty coefficient for the output deviation of cement enterprises. Let t be the actual power consumption of the cement plant. Let t be the planned power consumption of the cement plant. Let t be the planned output of the cement company at time t. This represents the actual output of the cement company at time t. Dissatisfaction with the low-carbon operation of electro-hydrogen production enterprises, dimensionless. The penalty coefficient for the total electricity consumption deviation of electro-hydrogen production enterprises is dimensionless. This is the penalty coefficient for the output deviation of electro-hydrogen production enterprises. Let t be the operating electrical power of the water electrolysis hydrogen production system. Let t be the actual power consumption of the electro-hydrogen production system. Let t be the planned hydrogen production of the electro-hydrogen production system. This represents the actual output of the electro-hydrogen production company at time t. The threshold for dissatisfaction with low-carbon operation of electrolytic aluminum enterprises. The threshold for dissatisfaction with low-carbon operation of cement enterprises. For low-carbon operation dissatisfaction thresholds for electro-hydrogen production enterprises, in engineering practice, the threshold can be set according to the combination of the largest deviations that actually occurred under historical no-penalty conditions, or the maximum electricity consumption deviation and output deviation can be agreed upon in advance with park users through service contracts.

[0047] It should be noted that the dissatisfaction with low-carbon operation is calculated by combining the deviation between the actual electricity consumption and the planned electricity consumption, the deviation between the actual output and the planned output of each enterprise, and the corresponding penalty coefficients for the total electricity consumption deviation and the output deviation. The constraint of dissatisfaction with low-carbon operation and the optimization objective function together constitute the energy consumption-output-carbon emission collaborative optimization model. It should also be noted that in the low-carbon operation dissatisfaction constraint, for each enterprise, the sum of the total electricity consumption deviation penalty coefficient and the output deviation penalty coefficient is 1. The deviation penalty coefficient is calculated with normalized weights based on the unit electricity demand response compensation cost and the unit output deviation economic loss of the user's location. The total electricity consumption deviation penalty coefficient and the output deviation penalty coefficient respectively characterize the enterprise's sensitivity to the deviation of total electricity consumption and output, and are used as known parameters input into the low-carbon operation dissatisfaction constraint.

[0048] In step S300, obtaining the low-carbon operation strategies of each enterprise in the park includes steps S301 to S303: S301: Obtain the hourly dynamic carbon emission factor prediction value of the power supply node in the park for the next 24 hours, and obtain the planned electricity consumption curve and planned output curve of electrolytic aluminum, cement and electro-hydrogen production enterprises (submitted by each enterprise according to its own production plan).

[0049] S302: The predicted value of dynamic carbon emission factor, planned electricity consumption curve and planned output are used as input parameters and substituted into the energy consumption-output-carbon emission co-optimization model for solution. That is, a commercial optimization solver (such as Gurobi or CPLEX) is called to solve the model. The result returned by the solver is the set value of electricity consumption for each enterprise in each period of the next 24 hours.

[0050] S303: The solution results will be used as a collaborative low-carbon production strategy for electrolytic aluminum, cement and electro-hydrogen production enterprises, i.e., a low-carbon operation strategy. The park operator will distribute the low-carbon operation strategy to each enterprise for implementation, so as to minimize the overall carbon emissions of electricity consumption in the park without significantly affecting the satisfaction of enterprises.

[0051] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0052] Example 3, referring to Figure 2 This is the third embodiment of the present invention, which provides a low-carbon operation system for high-energy-consuming industrial parks, including a modeling module for the production energy consumption characteristics of traditional high-energy-consuming enterprises, a modeling module for the production energy consumption characteristics of large-scale energy-consuming enterprises, a dynamic carbon emission factor acquisition module, and a collaborative optimization and strategy generation module.

[0053] The traditional high-energy-consuming enterprise production energy consumption characteristic modeling module is used to obtain the production energy consumption characteristic model of electrolytic aluminum enterprises by constructing constraints on the value range of power consumption, current, and electrolytic cell temperature, the coupling relationship between power and current, the dynamic change of power and electrolytic cell temperature, the operating status, and the correlation constraints between production efficiency and output. For cement enterprises, it is used to obtain the production energy consumption characteristic model by constructing constraints on the upper limit of production power, operating status, storage adjustment capacity, and flexible material supply between each production stage.

[0054] The energy consumption characteristic modeling module for large-scale energy-consuming enterprises is used to obtain the energy consumption characteristic model of electrolytic hydrogen production enterprises by constructing the operating characteristic constraints of the water electrolysis hydrogen production system, the operating characteristic constraints of the hydrogen compression system, and the operating characteristic constraints of the hydrogen storage system.

[0055] The dynamic carbon emission factor acquisition module is used to obtain dynamic carbon emission factors based on power system carbon emission flow analysis and calculation through simulation results of the park's regional power system operation or power flow data released by the power grid company, or directly obtained from the power grid company's release.

[0056] The collaborative optimization and strategy generation module is used to construct a collaborative optimization model based on the output energy consumption characteristic models of electrolytic aluminum enterprises, cement enterprises, and electric hydrogen production enterprises, as well as dynamic carbon emission factors, combined with the planned electricity consumption and planned output of each enterprise in the park. The model aims to minimize the total carbon emissions of the park and takes into account the low-carbon operation satisfaction of each enterprise.

[0057] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0058] Example 4, the fourth embodiment of the present invention, differs from the previous three embodiments in that: If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0059] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0060] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0061] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

Claims

1. A method for low-carbon operation of a high-load energy industrial park, characterized in that: The application relates to a method for optimizing low-carbon operation of enterprises in a park. The method comprises the following steps: establishing a production energy consumption characteristic model of a traditional high-load energy consumption enterprise in the park and a production energy consumption characteristic model of a bulk energy consumption enterprise in the park; obtaining a dynamic carbon emission factor of a power consumption node in the park, and based on the production energy consumption characteristic model, the dynamic carbon emission factor and the planned power consumption and planned output of each enterprise in the park, constructing an energy consumption-output-carbon emission collaborative optimization model with the minimum total carbon emission of the park as the target and the low-carbon operation satisfaction of each enterprise in the park being taken into account; solving the energy consumption-output-carbon emission collaborative optimization model to obtain the low-carbon operation strategy of each enterprise in the park.

2. The low-carbon operation method of a high-load energy industrial park according to claim 1, characterized in that: The method comprises the following steps: establishing a production energy consumption characteristic model of a traditional high-load energy consumption enterprise in the park and a production energy consumption characteristic model of a bulk energy consumption enterprise in the park; The method comprises the following steps: establishing a production energy consumption characteristic model of a traditional high-load energy consumption enterprise in the park and a production energy consumption characteristic model of a bulk energy consumption enterprise in the park; The method comprises the following steps: establishing a production energy consumption characteristic model of a traditional high-load energy consumption enterprise in the park and a production energy consumption characteristic model of a bulk energy consumption enterprise in the park; The method comprises the following steps: establishing a production energy consumption characteristic model of a traditional high-load energy consumption enterprise in the park and a production energy consumption characteristic model of a bulk energy consumption enterprise in the park; 3. The low-carbon operation method of a high-load energy industrial park according to claim 2, characterized in that: The method comprises the following steps: establishing a production energy consumption characteristic model of a traditional high-load energy consumption enterprise in the park and a production energy consumption characteristic model of a bulk energy consumption enterprise in the park; The method comprises the following steps: establishing a production energy consumption characteristic model of a traditional high-load energy consumption enterprise in the park and a production energy consumption characteristic model of a bulk energy consumption enterprise in the park; The method comprises the following steps: establishing a production energy consumption characteristic model of a traditional high-load energy consumption enterprise in the park and a production energy consumption characteristic model of a bulk energy consumption enterprise in the park; 4. The low-carbon operation method of a high-load energy industrial park according to claim 3, characterized in that: The method comprises the following steps: establishing a production energy consumption characteristic model of a traditional high-load energy consumption enterprise in the park and a production energy consumption characteristic model of a bulk energy consumption enterprise in the park; The method comprises the following steps: establishing a production energy consumption characteristic model of a traditional high-load energy consumption enterprise in the park and a production energy consumption characteristic model of a bulk energy consumption enterprise in the park; 5. The low-carbon operation method of a high-load energy industrial park according to claim 4, characterized in that: The method comprises the following steps: establishing a production energy consumption characteristic model of a traditional high-load energy consumption enterprise in the park and a production energy consumption characteristic model of a bulk energy consumption enterprise in the park; The method comprises the following steps: establishing a production energy consumption characteristic model of a traditional high-load energy consumption enterprise in the park and a production energy consumption characteristic model of a bulk energy consumption enterprise in the park; The method for optimizing low-carbon operation of enterprises in a park comprises the following steps: establishing a production energy consumption characteristic model of a traditional high-load energy consumption enterprises in the park and a production energy consumption characteristic model of a bulk energy consumption enterprise in the park; The dynamic carbon emission factor is taken as a quantitative index of carbon content level of power consumption of the park and is input into the optimization objective function.

6. The low-carbon operation method of a high-load energy industrial park according to claim 5, characterized in that: The energy-consumption-yield-carbon emission collaborative optimization model comprises, for the electrolytic aluminum enterprise, the cement enterprise and the hydrogen production enterprise, a low-carbon operation dissatisfaction constraint is set respectively. The low-carbon operation dissatisfaction is calculated by the offset amount of actual power consumption and planned power consumption, the offset amount of actual yield and planned yield of each enterprise, and the corresponding total power consumption offset degree penalty coefficient and yield offset degree penalty coefficient. The low-carbon operation dissatisfaction constraint and the optimization objective function jointly constitute the energy-consumption-yield-carbon emission collaborative optimization model. In the low-carbon operation dissatisfaction constraint, the sum of the total power consumption offset degree penalty coefficient and the yield offset degree penalty coefficient is 1 for each enterprise. The total power consumption offset degree penalty coefficient and the yield offset degree penalty coefficient represent the sensitivity of the enterprise to the total power consumption offset and the yield offset respectively, and are input into the low-carbon operation dissatisfaction constraint as known parameters.

7. The low-carbon operation method of a high-load energy industrial park according to claim 6, characterized in that: The low-carbon operation strategy of each enterprise in the park is obtained by collecting the predicted value of the dynamic carbon emission factor of the park power consumption node at the hour level in the future preset period, and obtaining the planned power consumption curve and the planned yield of each enterprise in the park. The predicted value of the dynamic carbon emission factor, the planned power consumption curve and the planned yield are input into the energy-consumption-yield-carbon emission collaborative optimization model for solving. The solving result is taken as the collaborative low-carbon production strategy of the electrolytic aluminum enterprise, the cement enterprise and the hydrogen production enterprise in the park, i.e. the low-carbon operation strategy.

8. A low-carbon operation system of a high-energy-load industrial park, applying a low-carbon operation method of a high-energy-load industrial park according to any one of claims 1-7, characterized in that, The system comprises a traditional high-energy-consuming enterprise production energy consumption characteristic modeling module, a bulk energy-consuming enterprise production energy consumption characteristic modeling module, a dynamic carbon emission factor acquisition module and a collaborative optimization and strategy generation module. The traditional high-energy-consuming enterprise production energy consumption characteristic modeling module is used to obtain the production energy consumption characteristic model of the electrolytic aluminum enterprise by constructing the value range constraint among the power consumption, current and electrolytic cell temperature, the coupling relationship constraint of power and current, the dynamic change constraint of power and electrolytic cell temperature, the operation state constraint and the correlation constraint of production efficiency and yield, and obtain the production energy consumption characteristic model of the cement enterprise by constructing the production power upper limit constraint of each production link, the working state constraint, the storage adjustment capability constraint and the flexible feeding constraint between production links. The bulk energy-consuming enterprise production energy consumption characteristic modeling module is used to obtain the production energy consumption characteristic model of hydrogen production enterprise by constructing the operation characteristic constraint of the electrolytic water hydrogen production system, the operation characteristic constraint of the hydrogen compression system and the operation characteristic constraint of the hydrogen storage system. The dynamic carbon emission factor acquisition module is used to obtain the dynamic carbon emission factor based on power system carbon emission flow analysis and calculation through the park regional power system operation simulation result or the power flow data released by the power grid company, or directly obtain the dynamic carbon emission factor from the power grid company. The synergistic optimization and strategy generation module is configured to, based on the output electrolytic aluminum enterprise production energy consumption characteristic model, the cement enterprise production energy consumption characteristic model, the electric hydrogen production enterprise production energy consumption characteristic model, and the dynamic carbon emission factor, combine planned power consumption and planned output of each enterprise in the park to construct a synergistic optimization model with the minimum total carbon emission of the park as the target and taking into account the low-carbon operation satisfaction of each enterprise. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-8 when the computer program is executed by the processor. The processor executes the computer program to implement the steps of the low-carbon operation method of the high-load industrial park in any one of claims 1 to 7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the low-carbon operation method of the high-load industrial park in any one of claims 1 to 7.

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