A method for constructing a digital twin model of a combined heat and power system

Abstract

The application discloses a kind of cogeneration system digital twin model construction methods, comprising clear trusted parameter, selecting benchmark condition, establishing equation, solving equation correct solution and constructing digital twin model.The complex cogeneration heat system is analyzed, the reliability of thermal characteristic parameter is clear, and the equation set is established for the heat system using the parameter with higher reliability, the correct solution of equation set is solved by iterative correction idea, and finally the digital twin model is established using the correct solution, to accurately reflect the purpose of complex cogeneration heat system characteristics by digital twin model, representing.The method provided by the application can establish high-fidelity digital twin model.

Description

Technical Field

[0001] This invention belongs to the field of energy technology, and in particular relates to a method for constructing a digital twin model of a combined heat and power system. Background Technology

[0002] A digital twin is a concept that transcends reality. It can be viewed as a digital mapping system of one or more important, interdependent equipment systems representing the actual physical systems. Once a relatively complete digital twin model of a device or system is established, it can be considered that the digital twin accurately describes the attributes, characteristics, and behaviors of the components and the system as a whole. A digital twin mainly consists of three components: a physical product in physical space, a virtual product in virtual space, and a data and information interaction interface between the physical and virtual spaces.

[0003] Digital twins create virtual models of physical entities through digital means, reflecting their attributes, simulating their behavior, and predicting their trends. Creating a complete virtual model includes geometric, physical, behavioral, and rule-based models. It's important to note that the core of any digital twin is a high-fidelity virtual model. Therefore, a thorough understanding of the physical world is crucial for building an effective digital twin model; otherwise, the virtual model will fail to accurately and effectively reflect the physical characteristics of the physical world.

[0004] Patent document CN113822496A discloses a method for online optimization of heating modes and parameters in a multi-unit cogeneration plant, including: step S1, setting up a multi-heating mode combination scheme for heating units to participate in deep peak shaving and coordinated operation. Step S2: Construct a digital twin model of the unit's multi-heating mode coordinated operation using mechanism modeling and data identification methods; Step S3: Construct an evaluation model for the unit's multi-mode coordinated operation; Step S4: Predict the heating load and power generation load of the power plant; Step S5: Calculate the economic indicators and peak-shaving capacity under the power plant-level multi-heating mode combination; Step S6: For the peak-shaving capacity, economic indicators, and evaluation indicators under different operating schemes of the multi-mode peak-shaving of the heating unit, conduct optimization analysis and optimization calculations of the multi-heating modes, select the optimal multi-mode coordinated operation mode and the best combination scheme of multi-mode peak-shaving coordinated operation, and provide online guidance for the unit to optimize power generation and heating operation. This method evaluates components at all levels based on pollutant emissions.

[0005] Patent document CN113266869A discloses a real-time optimization and control method for a combined heat and power (CHP) heating system based on digital twin technology. The method includes steps S1: establishing a digital twin platform for the CHP heating system, used for information exchange between the digital twin model and the physical entities of the CHP heating system; S2: constructing the digital twin model of the CHP heating system based on the operating data of the physical entities; and S3: placing the digital twin model constructed in step S2 onto the digital twin platform built in step S1. Under the premise of ensuring user heating needs, and with the goal of minimizing the carbon emissions of the CHP heating system, the method uses the digital twin model constructed in step S2 to perform optimization and solution based on predicted heat load and intermittent renewable energy prediction data, and performs real-time optimization and control of the CHP heating system based on the solution results. However, this method does not consider the different relationships between known and unknown parameters at different stages, resulting in inaccurate final model results. Summary of the Invention

[0006] To address the aforementioned problems, this invention provides a method for constructing a digital twin model of a combined heat and power system, which can establish a high-fidelity digital twin model.

[0007] A method for constructing a digital twin model of a combined heat and power system includes the following steps: Obtain all operating conditions and corresponding heat balance diagrams during the operation of a thermal power plant; The operating condition that accounts for the longest period in a thermal power plant's operating cycle is used as the basic operating condition. Based on the operating parameters of all modules in the heat balance diagram corresponding to the basic operating condition, a multi-level thermodynamic equation system including all modules is constructed along the steam-water flow direction, with the steam turbine as the starting point. The operating parameters include physical parameters that can be directly collected by sensors and estimated parameters obtained by conversion based on physical parameters. The corresponding training set of estimated parameters is generated by using empirical estimation and actual operating data, and the multi-level thermodynamic equations are corrected step by step based on thermo-economic indicators. The thermodynamic equations are then solved to obtain the best result within the preset error accuracy. Based on the aforementioned optimal thermodynamic equations, a digital twin model of the corresponding cogeneration system is constructed using a digital twin modeling tool.

[0008] This invention is based on the basic working mechanism, component attributes, and relevant physical laws of a combined heat and power system. When the information about the physical system is incomplete, it fills in the unknown parts of the information required to establish a digital twin model, thereby establishing a high-fidelity digital twin model.

[0009] Specifically, the multi-level thermodynamic equations are constructed based on existing limited information and the laws of conservation of energy, the first and second laws of thermodynamics, heat transfer, and the law of conservation of mass.

[0010] Specifically, the modules include, but are not limited to, turbine components, boiler components, feedwater heat exchanger components, condenser components, water pump components, pipelines, and confluence and diversion components.

[0011] Specifically, the construction process of the multi-level thermodynamic equation system is as follows: Under the aforementioned basic operating conditions, the calculation is performed based on the turbine extraction stages. If there are k extractions, the turbine is divided into k+1 stages. Establish a set of thermodynamic equations for each stage, including the laws of conservation of energy and mass.

[0012] Specifically, the expression for the law of conservation of energy is as follows: Regarding the steam turbine section: Regarding the heater section: In the formula, This represents the total power generation capacity of the steam turbine. The steam inlet flow rate for turbine stage i. This represents the enthalpy ratio of steam entering turbine stage i. The steam output of turbine stage i is the steam output. This is the specific enthalpy of the steam leaving stage i of the turbine. This indicates the amount of steam extracted from stage i. Let i be the ideal stage efficiency of turbine stage i. This represents the condensate flow rate from the previous heater in the heater corresponding to the i-th stage of the steam turbine. This represents the hydrophobic specific enthalpy value from the previous heater in the heater corresponding to the i-th stage of the steam turbine. This represents the condensate flow rate of the heater corresponding to the i-th stage of the steam turbine. This represents the hydrophobic specific enthalpy value of the heater corresponding to the i-th stage of the steam turbine. This represents the feedwater flow rate through the heater corresponding to the i-th stage of the steam turbine. This represents the feedwater specific enthalpy value of the heater corresponding to the i-th stage of the steam turbine. This represents the feedwater specific enthalpy value flowing into the heater corresponding to the i-th stage of the steam turbine. This represents the heat exchange efficiency of the heater corresponding to the i-th stage of the steam turbine.

[0013] Specifically, the expression for the law of conservation of mass is as follows: Regarding the steam turbine section: Regarding the heater section: In the formula, This represents the feedwater flow rate into the heater corresponding to the i-th stage of the steam turbine. This represents the feedwater flow rate exiting the heater corresponding to the i-th stage of the steam turbine. This indicates the amount of steam extracted from stage i.

[0014] Specifically, the empirical estimation includes conversion based on empirical formulas, conversion based on the equipment's design formulas, and assumptions made by consulting historical calculation parameters.

[0015] Specifically, the step-by-step correction is based on calibration of the thermal components of the power plant, including: Corrections were made to address the specific relationship between the estimated parameters and evaluation indicators obtained from the thermal calculations; There is no specific formula to correct the relationship between the estimated parameters and evaluation indicators obtained from the thermal calculations.

[0016] Specifically, the stepwise correction includes correction based on thermodynamic balance, correction based on correction curves, correction for estimated parameters, and three types of thermodynamic correction.

[0017] Specifically, the three types of thermodynamic corrections include: Corrections for deviations of the turbine's operating boundary conditions from the baseline operating conditions; Corrections for variables affecting the water supply heating system; Corrections for relevant generator operating conditions.

[0018] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) The method of completing the physical model information in the process of building a high-precision digital twin model of the present invention can not only complete the unknown thermal characteristic parameters of the components in the heat balance diagram in the design stage, solving the predicament that some components are subject to the manufacturer and it is difficult to have a comprehensive understanding of the component; at the same time, it can also establish a digital twin model of the thermal system of complex cogeneration in the design stage through the solution, and evaluate the model.

[0019] (2) By supplementing the thermodynamic characteristic parameters of each component in the complex cogeneration system, this invention provides scientific theoretical support for the construction of a digital twin model of the complex cogeneration system, making the calculation results more credible. Attached Figure Description

[0020] Figure 1 A flowchart illustrating a method for constructing a digital twin model of a combined heat and power system provided in this embodiment; Figure 2 This is a flowchart of the construction and solution of a multi-level thermodynamic equation system provided in this embodiment; Figure 3 This is a flowchart illustrating the correction of initial values ​​for the initial iterations provided in this embodiment. Figure 4 This is a flowchart illustrating the correction of the numerical optimization algorithm in the iterative calculation provided in this embodiment; Figure 5 This is a flowchart illustrating the modification of the evolutionary optimization algorithm in the iterative calculation provided in this embodiment. Detailed Implementation

[0021] like Figure 1 As shown, based on the incomplete information and the working mechanism of the combined heat and power (CHP) system, a method for constructing a digital twin model of the CHP system is presented. The incomplete information manifests in several ways: the complex CHP system has not been built or put into operation; equipment manufacturers have not fully disclosed the characteristics of their equipment; and the information deviation arises from significant discrepancies between the operating CHP system and its original design state after a period of operation. The digital twin model includes a geometric model, a physical model, a rule model, and a behavioral model. Constructing a digital twin model of the CHP system under conditions of incomplete information includes the following steps: Obtain all operating conditions and corresponding heat balance diagrams during the operation of a thermal power plant; The operating condition that accounts for the longest period in a thermal power plant's operating cycle is used as the basic operating condition. Based on the operating parameters of all modules in the heat balance diagram corresponding to the basic operating condition, a multi-level thermodynamic equation system including all modules is constructed along the steam-water flow direction, with the steam turbine as the starting point. The operating parameters include physical parameters that can be directly collected by sensors and estimated parameters obtained by conversion based on physical parameters. A training set of corresponding estimated parameters is generated using empirical estimation and actual operating data. The multi-level thermodynamic equations are then corrected step by step based on thermoeconomic indicators. Finally, the multi-level thermodynamic equations are solved to obtain the best result within the preset error accuracy. Based on the aforementioned optimal thermodynamic equations, a digital twin model of the corresponding cogeneration system is constructed using a digital twin modeling tool.

[0022] More specifically, due to the operating mechanism of steam turbines, the actual state parameters of the steam during operation do not perfectly match the state parameters marked on the heat balance diagram. This is especially true for complex combined heat and power (CHP) systems in actual operation. While high-precision sensors may be able to measure steam temperature and pressure, enthalpy and humidity, equally important parameters of steam in the system, cannot be guaranteed to be accurate. Therefore, it is crucial to identify highly reliable parameter data precisely and accurately by utilizing heat balance diagrams, design values ​​of each component in complex CHP systems, instruction manuals for each piece of equipment, and sensor measurements from actual operation.

[0023] As the benchmark for the thermal system in the digital twin model, the thermal characteristics of the benchmark operating condition are used as the thermal characteristics in the digital twin model. Specifically, the operating condition that occupies the largest proportion of time in the actual operating cycle is used as the benchmark operating condition, or the rated operating condition in the heat balance diagram is used as the benchmark operating condition. After the benchmark operating condition is determined, the thermal characteristic parameters of the benchmark operating condition are listed one by one.

[0024] Using the baseline operating condition as the target, through operating condition analysis, and utilizing the laws of conservation of energy and mass, as well as continuity equations, a set of equations corresponding to the baseline operating condition is constructed based on clearly achievable parameters. Simultaneously, the relationship between the number of equations and the number of unknown parameters is checked to ensure that the set of equations can be solved. Specifically, the part with the most reliable parameters is identified. Generally speaking, the steam parameters at the turbine inlet section have high reliability; therefore, it can be assumed that the steam parameters at this point are completely reliable. Equations are constructed step-by-step using the laws of conservation of energy and mass, and then, following the direction of the steam-water flow, equations are constructed one by one until the listed thermodynamic characteristic parameters are fully encompassed. These equations are then combined to form the set of equations for the baseline operating condition.

[0025] During the construction process, not all thermodynamic characteristic parameters are known. Therefore, for some parameters, it is necessary to make assumptions based on empirical formulas, design parameters, and assumption methods, and then substitute them into the equations one by one to solve them. In order to ensure that the solution results can accurately reflect the thermodynamic characteristics of the complex combined heat and power production system, the solution results need to be checked. When the calculation results meet the error accuracy, it is considered that the solution can accurately reflect the thermodynamic characteristics of the system. If the calculation results exceed the error accuracy, it is necessary to select an appropriate correction method according to different situations.

[0026] Digital twin models are built using digital twin modeling tools. Specifically, digital twin models are constructed by utilizing the geometric parameters and high-reliability parameters of each component in a complex combined heat and power (CHP) thermal system, which can accurately reflect the correct solution of the thermal characteristics of the thermal system, the operating rules of the complex CHP thermal system, expert knowledge, and predefined rules.

[0027] like Figure 2 As shown, the construction and solution of a multi-level thermodynamic equation system includes the following steps: Determining the reliability of thermodynamic parameters: Since the data marked on the heat balance diagram and some sensor measurements are not all 100% reliable, it is necessary to assess the reliability of existing data and identify those with higher reliability. Furthermore, the reliability of these thermodynamic parameters is primarily due to the difficulty in determining the working fluid characteristics during unit operation. Because some stages of the turbine operate in the wet steam region, the extraction steam humidity of each stage cannot be determined due to the characteristics of wet steam, resulting in the inability to determine the specific enthalpy of the steam at that extraction point. Therefore, the highly reliable state parameters refer to those in which all thermodynamic state parameters can be completely confirmed under that state. If all parameters cannot be completely confirmed under that state, then the parameters in that state are classified as unreliable parameters. For example, the thermodynamic state parameters of steam under known humidity conditions are reliable parameters, while those of wet steam with unknown humidity are unreliable parameters.

[0028] Determine the baseline operating condition for calculation: When a thermal system of a complex combined heat and power plant is in operation, the operating condition is not constant. Therefore, the operating condition that accounts for the largest proportion of the operating cycle during normal operation is selected, or the rated operating condition in the heat balance diagram is directly selected as the baseline operating condition that reflects the thermal characteristics of the equipment contained in the complex combined heat and power plant thermal system.

[0029] Establish a set of equations that conform to basic laws: Based on the established reference operating conditions, establish a set of equations that conform to basic objective laws, according to the law of conservation of energy, the first and second laws of thermodynamics, heat transfer, and the law of conservation of mass.

[0030] Substitute highly reliable thermodynamic parameters into the equation system: Determine reliable thermodynamic parameters and substitute them into the established equation system that conforms to objective laws. Then, rearrange the equation system for subsequent solution.

[0031] Based on empirical assumptions, some unknown parameters are used as initial values ​​for iteration: Select the more suitable parameters and make reasonable assumptions as initial values ​​for iteration, which are then used for subsequent solutions; the empirical assumptions include: empirical formula assumptions, empirical data assumptions, engineering design empirical assumptions, etc.; some of the assumed parameters can be determined according to the specific objectives, such as turbine stage efficiency, heat exchanger heat transfer coefficient, steam state parameters, etc.

[0032] Calculate the thermodynamic characteristic parameters of each stage of the steam turbine module: Since the section with the highest feasibility of steam parameters in the heat balance diagram or previous data is from the boiler outlet to the steam turbine inlet, the temperature and pressure can be obtained relatively accurately through sensors, and the dryness is close to 100%, and the enthalpy value can also be obtained by looking up the temperature and pressure in the table; therefore, it is assumed that the parameters are known and reliable. Starting from this, and using the assumed parameters as the initial values ​​for iteration, the thermodynamic characteristic parameters of each stage of the steam turbine module are solved stage by stage.

[0033] Calculate the thermodynamic characteristic parameters of the remaining modules: Calculate the thermodynamic characteristic parameters of each of the remaining modules in the thermodynamic system one by one, following the flow direction of the steam and water after passing through the steam turbine.

[0034] Calculate the evaluation index parameters of the thermal system: solve for the evaluation index parameters of the thermal system by using the thermal characteristic parameters; among which the evaluation indexes are mainly thermal efficiency, heat consumption, unit energy consumption, steam consumption rate, heat consumption rate and other thermal economic indicators.

[0035] Compare the calculated evaluation index errors With set error Size: where the evaluation index error is the maximum relative error between the evaluation index parameters of the thermal system and the given evaluation index values ​​of the thermal system. The set error is a given allowable calculation error. If Then proceed to the next step; if Then, the initial iteration values ​​are corrected using an appropriate correction method, and the thermodynamic characteristic parameters of each stage of the turbine module are recalculated.

[0036] To determine whether the calculation results conform to objective laws, and to determine whether the set error is met. The calculation results are then evaluated to determine whether they conform to objective laws. This evaluation includes assessing whether the turbine efficiency is less than 1, whether a certain part of a node conforms to the laws of conservation of mass, energy, and continuity equations, and whether each module violates the laws of thermodynamics.

[0037] If the initial values ​​of the iteration are not in accordance with objective laws, the initial values ​​will be appropriately modified, and the thermodynamic characteristic parameters of each stage of the turbine module will be recalculated.

[0038] If the results conform to objective laws, then the thermal verification calculations are performed using the resulting data. These thermal verification calculations include two fundamental methods for calculating thermal systems: "constant power calculation" and "constant flow rate calculation." Different calculation methods can be adopted depending on different needs.

[0039] Compare the error between the results of the thermal verification calculation and the set value. and the size of the given precision :like If so, proceed to the next step; if The initial values ​​of the iteration are then appropriately modified, and the thermodynamic characteristic parameters of each stage of the turbine module are recalculated.

[0040] like Figure 3 As shown, the correction process for the initial values ​​of the initial iteration is as follows: First, determine the parameters that need to be corrected. Generally speaking, the parameters that need to be corrected include the steam state parameters, the turbine stage efficiency, and the heat exchanger thermodynamic performance. Furthermore, the steam state parameters include steam pressure, steam temperature, steam flow rate, and steam dryness fraction. The heat exchanger thermodynamic performance includes the heat transfer coefficient and heat transfer efficiency.

[0041] Determine evaluation index parameters Furthermore, the evaluation index parameters should include design evaluation index parameters under the design parameters. Actual evaluation index parameters under actual operating (calculated results) parameters Furthermore, other evaluation indicators under the design parameters include thermal efficiency, heat rate, electrical power, heat consumption, steam consumption rate, and other thermal economic indicators.

[0042] Obtain data under design parameters or find other thermal system state parameter data by referring to relevant charts through thermal calculation results. Furthermore, the step of obtaining other parameter data by consulting relevant charts based on the results includes consulting the unknown part of the thermal parameters through a steam meter based on the thermal state parameters provided by the steam.

[0043] Determine evaluation index parameters With the parameter to be corrected The relationship between the design parameters: Furthermore, the evaluation index parameters under the design parameters With the parameter to be corrected The relationship between them can be obtained through mechanistic formulas or fitting after data analysis, denoted as Furthermore, the evaluation index parameters With the parameter to be corrected The relationship between them can be a mapping relationship that directly or indirectly reflects the relationship between them, such as a mechanism formula, a fitting formula obtained by fitting data, or an accurate simulation model obtained by modeling with other simulation software.

[0044] Judgment evaluation index parameters With the parameter to be corrected Does the relationship between them have a specific mathematical expression?

[0045] Based on the judgment results, different optimization methods are selected for correction calculations: further, the selected optimization algorithms include numerical optimization algorithms and evolutionary algorithms.

[0046] like Figure 4 As shown, the process of correcting the numerical optimization algorithm in the iterative computation of constructing a digital twin model is as follows: Given initialization parameters and initial iteration point Meanwhile, let k:=0.

[0047] By solving A subproblem at a certain point to determine the direction of descent .

[0048] The step size factor is determined using a certain search method. , making .

[0049] make , k:=k+1.

[0050] The parameters to be corrected were obtained by consulting relevant charts. Other key parameter data after optimization calculation Furthermore, the determination of other key data... This refers to determining the parameters to be corrected obtained from the solution. In step S3 of the iterative calculation process for parameter correction in constructing a digital twin model based on incomplete information, the calculation is as follows: Other key state parameter data that correspond one-to-one are denoted as .

[0051] Other key data after optimization calculation Key data under design parameters Error between Furthermore, the error calculation method is as follows: using the formula... The error values ​​of the corresponding state parameters for each stage were calculated. Let the maximum error be . The maximum error can be used as one of the termination conditions for the optimization algorithm.

[0052] Determine whether the error of the result after optimization calculation is within the expected error range. Furthermore, if the maximum error... Less than expected error If the answer is yes, proceed to the next step; otherwise, solve the problem again. A subproblem at a certain point to determine the direction of descent .

[0053] Calculate the change in index parameters at each level caused by the parameters that need to be corrected. Furthermore, the change in indicators caused by the parameters that need to be corrected at each stage. To infer parameters from incomplete information during the iterative calculations for constructing a digital twin model, the change in specific evaluation index parameters should be selected, based on these parameters. With the parameter to be corrected The relationship between them was obtained .

[0054] Determine whether the correction result meets the requirements. Less than the error precision Then output the correction coefficient. and the corrected parameters .

[0055] Conversely, solve the problem again. A subproblem at a certain point to determine the direction of descent .

[0056] like Figure 5 As shown, the computational process for correcting the evolutionary optimization algorithm in the iterative computation of constructing a digital twin model is as follows: Population initialization: Design appropriate initialization operations based on the characteristics of the problem, and perform initialization operations on N individuals in the population.

[0057] Population fitness assessment: Calculate the fitness value of individuals in the population based on the optimized objective function.

[0058] Mutation operation: The mutation operation is to determine whether the parent individual needs to be mutated based on the mutation probability (pre-specified, usually 0.1); the main function of the mutation operator is to maintain the diversity of the population and prevent the population from getting trapped in local optima, and it is generally designed as a random transformation.

[0059] Crossover operation: The crossover operation is determined based on the crossover probability (pre-specified, typically 0.9) to decide whether the parent individuals need to undergo crossover. The crossover operator must be designed according to the characteristics of the problem being optimized; it is the core of the entire algorithm, and its design directly determines the performance of the entire algorithm.

[0060] Selection Operation: The selection operation should design appropriate selection operators to select individuals in the population. The selected individuals will enter the mating pool to form the parent population, used for crossover to generate new individuals. The selection strategy should be based on the fitness value of the individuals; commonly used selection strategies include roulette wheel selection and tournament selection.

[0061] The parameters to be corrected were obtained by consulting relevant charts. Other key parameter data after optimization calculation The determination of other key data This refers to determining the parameters to be corrected obtained from the solution. In step S3 of the iterative calculation process for parameter correction in constructing a digital twin model based on incomplete information, the calculation is as follows: Other key state parameter data that correspond one-to-one are denoted as .

[0062] Other key data after optimization calculation Key data under design parameters Error between Furthermore, the error calculation method is as follows: using the formula... The error values ​​of the corresponding state parameters for each stage were calculated. Let the maximum error be . The maximum error can be used as one of the termination conditions for the optimization algorithm.

[0063] Determine whether the error of the result after optimization calculation is within the expected error: if the maximum error Less than expected error Then proceed to the next step; Conversely, the fitness values ​​of individuals in the population are recalculated based on the optimized objective function.

[0064] Calculate the change in index parameters at each level caused by the parameters that need to be corrected. Furthermore, the change in indicators caused by the parameters that need to be corrected at each stage. This should refer to the change in the selected evaluation index parameters during the iterative calculation. Specifically, the change in the index caused by the parameters requiring correction at each stage can be calculated through parameter correction during the iterative calculation. With the parameter to be corrected The relationship between them was obtained .

[0065] Determine if the correction result meets the requirements. Furthermore, if... Less than the error precision Then output the correction coefficient. and the corrected parameters If the condition is met, proceed to the next round of calculation; otherwise, recalculate the fitness values ​​of individuals in the population based on the optimized objective function.

Claims

1. A method for constructing a digital twin model of a combined heat and power system, characterized in that, Includes the following steps: Obtain all operating conditions and corresponding heat balance diagrams during the operation of a thermal power plant; The operating condition that accounts for the longest period in a thermal power plant's operating cycle is used as the basic operating condition. Based on the operating parameters of all modules in the heat balance diagram corresponding to the basic operating condition, a multi-level thermodynamic equation system including all modules is constructed along the steam-water flow direction, with the steam turbine as the starting point. The operating parameters include physical parameters that can be directly collected by sensors and estimated parameters obtained by conversion based on physical parameters. The corresponding training set of estimated parameters is generated by using empirical estimation and actual operation data. The multi-level thermodynamic equations are corrected step by step based on thermoeconomic indicators, and the multi-level thermodynamic equations are solved to obtain the best result that meets the preset evaluation indicators. Based on the aforementioned optimal thermodynamic equations, a digital twin model of the corresponding cogeneration system is constructed using a digital twin modeling tool.

2. The method for constructing a digital twin model of a cogeneration system according to claim 1, characterized in that, The multi-level thermodynamic equations are constructed based on existing limited information and the laws of energy conservation, the first and second laws of thermodynamics, heat transfer, and the law of mass conservation.

3. The method for constructing a digital twin model of a cogeneration system according to claim 1, characterized in that, The modules include, but are not limited to, turbine components, boiler components, feedwater heat exchanger components, condenser components, water pump components, piping, and confluence and diversion components.

4. The method for constructing a digital twin model of a cogeneration system according to claim 1, characterized in that, The construction process of the multi-level thermodynamic equation system is as follows: Under the aforementioned basic operating conditions, the calculation is performed based on the turbine extraction stages. If there are k extractions, the turbine is divided into k+1 stages. Establish a set of thermodynamic equations for each stage, including the laws of conservation of energy and mass.

5. The method for constructing a digital twin model of a cogeneration system according to claim 4, characterized in that, The expression for the law of conservation of energy is as follows: Regarding the steam turbine section: ； Regarding the heater section: ； In the formula, This represents the total power generation capacity of the steam turbine. The steam inlet flow rate for turbine stage i. This represents the enthalpy ratio of steam entering turbine stage i. The steam output of turbine stage i is the steam output. This is the specific enthalpy of the steam leaving stage i of the turbine. This indicates the amount of steam extracted from stage i. Let i be the ideal stage efficiency of turbine stage i. This represents the condensate flow rate from the previous heater in the heater corresponding to the i-th stage of the steam turbine. This represents the hydrophobic specific enthalpy value from the previous heater in the heater corresponding to the i-th stage of the steam turbine. This represents the condensate flow rate of the heater corresponding to the i-th stage of the steam turbine. This represents the hydrophobic specific enthalpy value of the heater corresponding to the i-th stage of the steam turbine. This represents the feedwater flow rate through the heater corresponding to the i-th stage of the steam turbine. This represents the feedwater specific enthalpy value of the heater corresponding to the i-th stage of the steam turbine. This represents the feedwater specific enthalpy value flowing into the heater corresponding to the i-th stage of the steam turbine. This represents the heat exchange efficiency of the heater corresponding to the i-th stage of the steam turbine.

6. The method for constructing a digital twin model of a cogeneration system according to claim 4, characterized in that, The expression for the law of conservation of mass is as follows: Regarding the steam turbine section: ； ； ； Regarding the heater section: ； ； In the formula, This represents the feedwater flow rate into the heater corresponding to the i-th stage of the steam turbine. This represents the feedwater flow rate exiting the heater corresponding to the i-th stage of the steam turbine. This indicates the amount of steam extracted from stage i.

7. The method for constructing a digital twin model of a cogeneration system according to claim 1, characterized in that, The empirical estimation includes conversion based on empirical formulas, conversion based on the equipment's design formulas, and assumptions made by consulting historical calculation parameters.

8. The method for constructing a digital twin model of a cogeneration system according to claim 1, characterized in that, The stepwise correction is based on calibration of the thermal components of the power plant, including: Corrections were made to address the specific relationship between the estimated parameters and evaluation indicators obtained from the thermal calculations; There is no specific formula to correct the relationship between the estimated parameters and evaluation indicators obtained from the thermal calculations.

9. The method for constructing a digital twin model of a cogeneration system according to claim 1 or 8, characterized in that, The stepwise correction includes correction based on thermodynamic balance, correction based on correction curves, correction for estimated parameters, and three types of thermodynamic correction.

10. The method for constructing a digital twin model of a cogeneration system according to claim 9, characterized in that, The three types of thermodynamic corrections include: Corrections for deviations of the turbine's operating boundary conditions from the baseline operating conditions; Corrections for variables affecting the water supply heating system; Corrections for the relevant generator operating conditions.

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