A method for calculating the maximum variable load rate of a coal-fired power generating unit and a related device

Abstract

The present application provides a kind of coal-fired generating unit maximum variable load rate calculation method and related device, including obtaining the actual operating parameter of the unit transient model built, determine the safe operation boundary interval of each constraint parameter;In the safe operation boundary interval, based on the variable load rate of the preset unit transient model is simulated, and the parameter peak value of each constraint parameter is obtained by collection;Linear fitting is carried out to parameter peak value and the variable load rate by least square method, and the fitting model of each constraint parameter is obtained;The safe operation boundary interval is input to the fitting model to obtain the maximum variable load rate.This method determines the safety boundary based on actual parameter, and accurately calculates the rate by simulation, fitting modeling, considers the operation safety and regulation efficiency of unit.

Description

Technical Field

[0001] This invention belongs to the field of coal-fired power generation unit operation optimization technology, specifically to a method and related device for calculating the maximum load change rate of a coal-fired power generation unit. Background Technology

[0002] Coal-fired power generating units, as an important component of traditional thermal power generation, play a crucial role in base load and peak shaving within the power system. With the rapid development of renewable energy sources (such as wind power and photovoltaics), the volatility of the power system has increased significantly. Coal-fired power generating units need to possess greater flexibility to adapt to rapid changes in grid load. The maximum load change rate is one of the key indicators for measuring the flexibility of coal-fired power generating units, reflecting the unit's ability to adapt to load changes per unit time.

[0003] In traditional power systems, coal-fired power generating units typically operate under stable loads, with relatively low requirements for load change rates. However, in modern power systems, these units frequently need to participate in ancillary services such as peak shaving and frequency regulation to balance the supply and demand of the power grid. Therefore, increasing the maximum load change rate of coal-fired power generating units has become an important means to enhance their flexibility and market competitiveness. Summary of the Invention

[0004] To address the problems of insufficient universality and inadequate calculation accuracy in existing methods, this invention proposes a method and related apparatus for calculating the maximum variable load rate of coal-fired power generating units.

[0005] To achieve the above objectives, the present invention proposes the following technical solution: This invention proposes a method for calculating the maximum load change rate of a coal-fired power generating unit, comprising: Obtain the actual operating parameters of the constructed unit transient model and determine the safe operating boundary range of each constraint parameter; Within the safe operating boundary range, the transient model of the unit is simulated based on a preset variable load rate, and the peak values ​​of each constraint parameter are collected. By linearly fitting the peak values ​​of the parameters and the variable load rate using the least squares method, a fitting model for each constraint parameter is obtained. The maximum variable load rate is calculated by inputting the safe operating boundary interval into the fitting model.

[0006] Preferably, the construction of the unit transient model includes: The high-pressure heater, high-pressure cylinder turbine, and intermediate-pressure cylinder turbine are respectively connected to the boiler. The intermediate-pressure cylinder turbine is connected to the low-pressure cylinder turbine. The high-pressure cylinder turbine and the intermediate-pressure cylinder turbine are respectively connected to the high-pressure heater. The high-pressure heater is connected in series with the low-pressure heater through a deaerator. The low-pressure heater is connected to the low-pressure cylinder turbine. The high-pressure cylinder turbine is connected to the deaerator.

[0007] Preferably, the step of obtaining the actual operating parameters of the constructed unit transient model and determining the safe operating boundary range of each constraint parameter includes: Obtain the rated operating parameters of the high-pressure heater, the high-pressure cylinder turbine, the intermediate-pressure cylinder turbine, the low-pressure cylinder turbine, the low-pressure heater, and the boiler in the transient model of the unit; The rated operating parameters are corrected based on the preset dynamic margin to obtain the preliminary boundary range; The boundary values ​​of the preliminary boundary interval are dynamically verified based on the transient model of the unit, and the boundary values ​​of the preliminary boundary interval are corrected based on the verification results to obtain the safe operation boundary interval.

[0008] Preferably, within the safe operating boundary range, the transient model of the unit is simulated multiple times based on a preset variable load rate value, and the peak values ​​of each constraint parameter in each simulation are collected, including: Within the safe operating boundary range, a preset variable load rate range is defined. The variable load rate range is divided into several variable load rate values, and the variable load rate values ​​are sequentially input into the unit transient model for model operation, and the unit operation data corresponding to each variable load rate value is recorded. The unit operation data is arranged according to the time sequence of collection to obtain the operation data time sequence; The absolute deviation value is calculated based on the rated operating parameters and the parameter values ​​of each in the operating data time series; Extract the maximum value from each absolute deviation value to obtain multiple initial parameter peaks; The parameter peak value is obtained by calculating the mean of multiple initial parameter peak values.

[0009] Preferably, the peak value of the parameter and the variable load rate value are linearly fitted using the least squares method to obtain a fitting model for each constraint parameter, including: An independent variable dataset is constructed based on the variable load rate value, and a dependent variable dataset corresponding to the independent variable dataset is constructed based on the parameter peak value. An initial variable dataset pair is obtained, and the Grubbs criterion is used to detect the initial variable dataset pair to obtain an optimized variable dataset pair. The measured peak values ​​of constraint parameters during the operation of the unit transient model are obtained. Based on the measured peak values ​​and the initial parameter peak values, the residuals of the constraint parameters after each simulation are calculated, and the sum of squared residuals is calculated based on the residuals. Based on the sum of squared residuals, the slope parameter and intercept parameter of the linear fitting model are calculated, and an initial linear fitting model is constructed. Based on the optimized variable dataset, the input to the initial linear fitting model is used to calculate the optimal slope parameter and the optimal intercept parameter. The initial linear fitting model is optimized based on the optimal slope parameter and the optimal intercept parameter, and the goodness-of-fit of the optimized initial linear fitting model is verified. If the verification passes, the fitting model is obtained; if the verification fails, the variable load rate value is recalculated.

[0010] Preferably, the goodness-of-fit verification of the optimized initial linear fitting model includes: The measured peak values ​​of the constraint parameters during the operation of the transient model of the unit were collected multiple times, and the average value of the measured peak values ​​was calculated. The determination coefficient is calculated based on the measured peak value, the load gradient rate value, and the average value of the measured values. The determination coefficient is compared with the preset fitting coefficient. If the determination coefficient is greater than or equal to the fitting coefficient, the fitting model is retained; if the determination coefficient is less than the fitting coefficient, the variable load rate value is recalculated.

[0011] Preferably, inputting the safe operating boundary range into the fitting model to calculate the maximum variable load rate includes: Extract the boundary parameters corresponding to the safe operating boundary intervals of all the aforementioned constraint parameters, input the boundary parameters into the fitting model, and calculate the variable load prediction rate corresponding to each constraint parameter; Compare all the predicted load rates and extract the minimum value among them as the maximum predicted load rate.

[0012] This invention proposes a calculation system for the maximum load change rate of a coal-fired power generating unit, used to implement the aforementioned calculation method for the maximum load change rate of a coal-fired power generating unit, comprising: The boundary determination module is configured to obtain the actual operating parameters of the constructed unit transient model and determine the safe operating boundary range of each constraint parameter; The peak calculation module is configured to simulate the transient model of the unit based on a preset variable load rate within the safe operating boundary range, and to collect the peak values ​​of each constraint parameter. The model building module is configured to perform linear fitting of the parameter peak and the variable load rate value using the least squares method to obtain a fitting model for each constraint parameter. The load rate calculation module is configured to input the safe operating boundary interval into the fitting model to calculate the maximum variable load rate.

[0013] The present invention proposes a computer device, characterized in that it includes a memory, a processor, and a computer program stored in the memory and executable in the processor, wherein the processor executes the computer program to implement the steps of the above-described method for calculating the maximum variable load rate of a coal-fired power generation unit.

[0014] The present invention proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described method for calculating the maximum variable load rate of a coal-fired power generation unit.

[0015] Compared with the prior art, the present invention has the following beneficial technical effects: This invention proposes a method for calculating the maximum load change rate of a coal-fired power generating unit. Based on the actual operating parameters of the unit's transient model, this method determines the safe operating boundary range of each constraint parameter, overcoming the limitations of traditional general-purpose calculation models that are only applicable to specific units. It can be flexibly applied to coal-fired power generating units of different capacities and operating conditions, significantly improving the method's universality and avoiding calculation deviations caused by the model deviating from the actual operating characteristics of the unit. Then, within the safe operating boundary, transient simulation of the unit is carried out according to a preset load change gradient rate. The system collects the peak values ​​of each parameter, comprehensively covering the constraint response characteristics under multi-load change scenarios, providing a basis for subsequent calculations. The accurate and complete data source compensates for the incompleteness of data under single operating conditions. By using the least squares method to linearly fit the peak value of the parameters and the variable load gradient rate, a dedicated fitting model for each constraint parameter is constructed to achieve a precise quantitative correspondence between the two. Compared with traditional methods such as empirical estimation and coarse fitting, this significantly improves the calculation accuracy and reliability of the results. Finally, based on the safety boundary parameters, the maximum variable load rate corresponding to each constraint is solved through the fitting model. This takes into account both the safety of unit operation and the efficiency of load regulation, and can provide high-precision technical basis for unit optimization scheduling and variable load strategy formulation, helping the unit to achieve optimal variable load operation within the safety threshold.

[0016] Furthermore, this method accurately constructs a complete transient model of the unit, fully restoring the actual connection links and operational relationships of each core device. This provides a model foundation that closely matches the actual operating state of the unit for the calculation of constraint parameter boundaries, avoiding the boundary judgment deviations caused by the lack of device linkage relationships in simplified models. Then, by obtaining the rated operating parameters of each key device and combining them with preset dynamic margin corrections, a preliminary boundary range is obtained. This fully considers the parameter fluctuation characteristics under transient conditions, avoiding the boundary limitations caused by directly using rated values. Then, based on the transient model of the unit, the preliminary boundary values ​​are dynamically verified and iteratively corrected. This can accurately verify the adaptability of boundary values ​​under different transient scenarios, ensuring that the safe operating boundary range not only meets the equipment safety thresholds but also conforms to the actual dynamic characteristics of operation. The overall process of this method takes into account both parameter benchmarks and transient adaptability, effectively improving the accuracy and reliability of constraint parameter boundary judgments, adapting to units with different equipment configurations, indirectly strengthening the universality of the method, and ensuring that subsequent calculation results conform to engineering realities. Attached Figure Description

[0017] Figure 1 A flowchart illustrating the calculation method for the maximum load change rate of a coal-fired power generating unit proposed in this invention; Figure 2 This is a schematic diagram of the system connection of the transient model of the unit proposed in this invention; Figure 3 A graph showing the relationship between the variable load rate and the peak value of the parameters; Figure 4 This is a schematic diagram of a computer device according to an embodiment of the present invention; Figure 5 This is a block diagram of a chip according to an embodiment of the present invention. Detailed Implementation

[0018] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the spirit or scope of the invention. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive.

[0019] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0020] As a core component of traditional thermal power generation, coal-fired power generating units, with their advantages of large capacity, stable output, and wide peak-shaving range, have long played a crucial dual role in the global power system, serving as both baseload power supply and emergency peak-shaving. They are the "ballast" for ensuring the safe, stable, and continuous power supply of the power system. Whether for industrial production, urban operations, or residential life, the rigid demand for electricity relies on reliable power support from coal-fired power generating units, and their operating status directly affects the power quality and stability of the entire power grid.

[0021] In recent years, with the advancement of "dual carbon" goals and the acceleration of energy structure transformation, renewable energy sources such as wind power and photovoltaics have experienced rapid large-scale development, with their installed capacity and power generation share in the power system continuing to rise. However, renewable energy has significant intermittent, random, and volatile characteristics—wind power output is severely affected by changes in wind speed, while photovoltaic output depends on sunlight conditions and is easily affected by factors such as cloud cover and day-night cycles. This leads to frequent fluctuations in the power output on the generation side of the power system, disrupting the stable state of traditional power grid supply and demand balance and placing higher demands on the grid's regulation and anti-interference capabilities.

[0022] Against this backdrop, coal-fired power generating units, as one of the most controllable power sources with the greatest regulatory potential in the power system, urgently need to break through traditional operating modes and possess greater operational flexibility to quickly respond to dynamic changes in grid load and mitigate supply-demand imbalances caused by renewable energy fluctuations. Maximum load change rate, as one of the core key indicators for measuring the flexibility of coal-fired power generating units, accurately reflects the unit's ability to adjust load within a unit of time. It directly determines the unit's response efficiency and adaptability to changes in grid load, and is a core capability supporting the unit's participation in grid peak shaving and frequency regulation services.

[0023] In traditional power system architectures, renewable energy accounts for a very small percentage, grid load changes are relatively gradual, and coal-fired power generating units mainly operate under constant base load conditions. The core objective is to ensure power supply stability and power generation efficiency, with lower requirements for load change rates. At this time, the unit operation strategy focuses on reducing equipment wear and increased energy consumption caused by load fluctuations. Load change operations are infrequent and slow, and related control technologies and equipment designs are also centered on steady-state operation.

[0024] However, in modern power systems, with the large-scale grid integration of renewable energy, the demand for ancillary services such as peak shaving and frequency regulation has increased dramatically. This has fundamentally changed the operating scenario of coal-fired power generating units, requiring a shift from "steady-state base load operation" to "frequent load adjustment." Units need to rapidly adjust load according to grid commands, increasing power generation during peak hours to fill gaps and reducing load during off-peak hours to create space for renewable energy absorption. Simultaneously, precise frequency regulation is crucial for maintaining grid frequency stability and balancing real-time supply and demand. Therefore, increasing the maximum load adjustment rate of coal-fired power generating units is not only a necessary requirement for adapting to energy structure transformation and ensuring the safe and stable operation of the grid, but also an important means to enhance the market competitiveness of units and expand ancillary service revenue. This has significant practical implications for promoting the power system's transformation towards a clean, low-carbon, safe, and efficient future.

[0025] To address the above problems, this invention proposes a method for calculating the maximum load change rate of a coal-fired power generating unit, such as... Figure 1 As shown, it includes the following steps: Obtain the actual operating parameters of the constructed unit transient model and determine the safe operating boundary range of each constraint parameter; within the safe operating boundary range, simulate the unit transient model based on the preset variable load rate and collect the parameter peak values ​​of each constraint parameter; perform linear fitting between the parameter peak values ​​and the variable load rate using the least squares method to obtain the fitting model of each constraint parameter; input the safe operating boundary range into the fitting model to calculate the maximum variable load rate.

[0026] This method determines the safe operating boundary range of constraint parameters based on the actual operating parameters of the unit's transient model, overcoming the adaptation limitations of traditional general models. It can be flexibly applied to coal-fired generating units of different capacities and operating conditions, significantly improving universality and avoiding computational deviations from reality. Within the safe boundary, transient simulations are conducted according to a preset variable load gradient rate. The system collects parameter peak values, covering the constraint response characteristics of multiple load scenarios, compensating for the accuracy shortcomings of single-condition data. By fitting the parameter peak values ​​and variable load gradient rate using the least squares method, a dedicated model is constructed to achieve precise quantitative correspondence, significantly improving computational accuracy and reliability compared to traditional methods. Finally, the maximum variable load rate is solved based on the model, balancing unit operating safety and regulation efficiency, providing high-precision technical support for optimized scheduling.

[0027] Preferably, in this embodiment, obtaining the actual operating parameters of the constructed unit transient model and determining the safe operating boundary range of each constraint parameter includes: Build a transient model of the unit, such as Figure 2 As shown, the assembly includes: connecting a high-pressure heater, a high-pressure cylinder turbine, and an intermediate-pressure cylinder turbine to a boiler; connecting the intermediate-pressure cylinder turbine to a low-pressure cylinder turbine; connecting the high-pressure cylinder turbine and the intermediate-pressure cylinder turbine to the high-pressure heater; connecting the high-pressure heater to the low-pressure heater via a deaerator; connecting the low-pressure heater to the low-pressure cylinder turbine; and connecting the high-pressure cylinder turbine to the deaerator.

[0028] Obtain the rated operating parameters of the high-pressure heater, high-pressure cylinder turbine, intermediate-pressure cylinder turbine, low-pressure cylinder turbine, low-pressure heater, and boiler in the unit's transient model. Rated operating parameters include main steam temperature, turbine speed, rotor thermal stress, etc. For example, the boiler instruction manual extracts the design upper limit of main steam temperature as 580℃. Combined with the allowable thermal stress characteristics of the pipe material, the theoretical rated operating parameter is initially set to 578℃. The rated value of turbine speed is 3000r / min. According to industry standards, the allowable range of speed fluctuation is set to ±5r / min, that is, the rated operating parameter of turbine speed is 3005r / min.

[0029] Based on the preset dynamic margin, the rated operating parameters are corrected to obtain the preliminary boundary range; for example, if the dynamic margin is 3~5%, then the preliminary boundary range of the main steam temperature is [550℃, 575℃], and the preliminary boundary range of the turbine speed is [3095r / min, 3155r / min].

[0030] The boundary values ​​of the initial boundary interval are dynamically verified based on the unit transient model. The boundary values ​​of the initial boundary interval are then corrected based on the verification results to obtain the safe operating boundary interval. , This refers to a constraint parameter of a thermal power device. and For example, to define the upper and lower boundaries of this constraint parameter, typical steady-state operating conditions of 50%THA, 75%THA, and 100%THA, as well as variable load operating conditions of 55%THA to 50%THA and 75%THA to 100%THA, are selected from the transient model of the unit. Operating data from the transient model is collected continuously for 24 hours at a frequency of 1 second per data point. The actual fluctuation range, peak value, and duration of each constraint parameter are recorded. The field data is compared with the initially defined safety boundaries. If the actual peak value of a parameter repeatedly approaches but does not exceed the corrected boundary, and the equipment operates normally (e.g., no over-temperature alarm, vibration meets standards), then the boundary is confirmed to be feasible. If the actual peak value occasionally exceeds the boundary but does not cause safety issues, the boundary margin can be fine-tuned (e.g., the upper boundary of the main steam temperature is fine-tuned to 576℃). If the boundary is frequently exceeded, the data is reviewed and the boundary value is corrected.

[0031] This method constructs a complete transient model of the unit, encompassing the boiler, turbine cylinders, high and low pressure heaters, and deaerator. This model recreates the actual connections and operational relationships of the equipment, providing a foundation for boundary parameter calculations that closely reflect real-world operating conditions and avoiding the judgment biases inherent in simplified models. By obtaining the rated parameters of key equipment and combining them with dynamic margin corrections, a preliminary boundary range is obtained. Then, the boundary values ​​are dynamically verified and iteratively corrected based on the model to form a safe operating boundary range. This process balances parameter benchmarking with transient adaptability, significantly improving the accuracy and reliability of boundary determination. It provides a scientific basis for subsequent variable load simulation, fitting modeling, and maximum load rate calculation, while also enhancing the method's universality and supporting the safe and efficient operation of the unit.

[0032] Preferably, in this embodiment, within the safe operating boundary range, the transient model of the unit is simulated multiple times based on a preset variable load rate value, and the peak values ​​of each constraint parameter in each simulation are collected, including: Within the safe operating boundary range, a variable load rate range is preset; this range is divided into several variable load rate values, which are then sequentially input into the unit transient model for multiple simulations, recording the unit operating data corresponding to each variable load rate value; the unit operating data is arranged according to the time sequence of acquisition to obtain the operating data time series; based on the rated operating parameters and the parameter values ​​in each operating data time series, the absolute deviation value is calculated; that is, the difference between each parameter value in the operating data time series and the rated operating parameters is calculated, and the difference value is extracted to obtain the absolute deviation value. The maximum value of the absolute deviation value calculated for each simulated unit operating data is extracted to obtain multiple initial parameter peak values; the mean of the multiple initial parameter peak values ​​is calculated to obtain the parameter peak value, i.e. ,in, , , … , Indicates the variable load rate. Indicates the magnitude of different load change rates; These represent different constraint parameters for thermal equipment. Indicates for the first Peak values ​​of the constraint parameters under different load rates Preferably, in this embodiment, the peak value of the parameter and the variable load rate value are linearly fitted using the least squares method to obtain the fitting model for each constraint parameter, including: An independent variable dataset is constructed based on the variable load rate values. A corresponding dependent variable dataset is constructed based on the parameter peak values, resulting in initial variable dataset pairs. The Grubbs criterion is used to test these initial variable dataset pairs, yielding optimized variable dataset pairs. Measured peak values ​​of constraint parameters during the unit's transient model operation are obtained. The residuals of the constraint parameters after each simulation are calculated based on the measured peak values ​​and the initial parameter peak values. The sum of squared residuals is then calculated. The slope and intercept parameters of the linear fitting model are calculated based on the sum of squared residuals, constructing an initial linear fitting model. The optimized variable dataset pairs are input into the initial linear fitting model to calculate the optimal slope and intercept parameters. The initial linear fitting model is then optimized based on the optimal slope and intercept parameters. , , … .

[0033] The goodness-of-fit of the optimized initial linear fitting model is verified. If the verification passes, the fitting model is obtained; if the verification fails, the variable load rate value is recalculated.

[0034] Preferably, in this embodiment, the goodness-of-fit verification of the optimized initial linear fitting model includes: The measured peak values ​​of constraint parameters during the operation of the unit's transient model are collected multiple times, and the average value of the measured peak values ​​is calculated. The coefficient of determination is calculated based on the measured peak values, load gradient rate values, and the average value of the measured values. The coefficient of determination is compared with the preset fitting coefficient. In this example, the fitting coefficient is 0.95. If the coefficient of determination is greater than or equal to the fitting coefficient, the fitted model is retained; if the coefficient of determination is less than the fitting coefficient, the variable load rate value is recalculated.

[0035] Preferably, in this embodiment, the safe operating boundary range is input into the fitting model to calculate the maximum load rate. ,include: Extract the boundary parameters of the safe operating boundary intervals corresponding to all constraint parameters, input the boundary parameters into the fitting model, and calculate the variable load prediction rate corresponding to each constraint parameter. Compare all predicted load rates and extract the minimum value among them as the maximum predicted load rate. ,Right now .

[0036] The example unit model is a supercritical 670MW coal-fired power generating unit with single reheat, and the turbine is a condensing turbine with single intermediate reheat, three cylinders, and four exhaust. Considering the main steam temperature and reheat steam temperature as constraint parameters, through on-site parameter collection, the temperature range of the main steam and reheat steam under safe operating conditions is approximately [550℃, 575℃]. The maximum values ​​of the constraint parameters under different load change rates from 55% THA to 50% THA are recorded, and the load change rate versus parameter peak curve is fitted, as shown below. Figure 3 As shown. Substituting the upper boundary of the safe operating range of the boiler's main steam temperature and reheat steam temperature, the maximum allowable load change rate within the safe operating boundary of the boiler's main steam temperature and reheat steam temperature is obtained, which is the maximum load change rate corresponding to the main steam temperature being at the safe operating boundary, and is 17.473 MW / min.

[0037] This invention proposes a calculation system for the maximum load change rate of a coal-fired power generating unit, which is used to implement the above-mentioned calculation method for the maximum load change rate of a coal-fired power generating unit, including a boundary determination module, a peak value calculation module, a model construction module, and a load rate calculation module; The boundary determination module is configured to obtain the actual operating parameters of the constructed unit transient model and determine the safe operating boundary range of each constraint parameter. The peak calculation module is configured to simulate the transient model of the unit based on a preset variable load rate within the safe operating boundary range, and to collect the peak values ​​of each constraint parameter. The model building module is configured to perform linear fitting of the parameter peak and the variable load rate value using the least squares method to obtain a fitting model for each constraint parameter. The load rate calculation module is configured to input the safe operating boundary interval into the fitting model to calculate the maximum variable load rate.

[0038] In another embodiment of the present invention, a computer device is proposed, comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to implement a corresponding method flow or corresponding function. The processor in this embodiment of the present invention can be used to implement the operation of a method for calculating the maximum variable load rate of a coal-fired power generation unit, including: Obtain the actual operating parameters of the constructed unit transient model and determine the safe operating boundary range of each constraint parameter; within the safe operating boundary range, simulate the unit transient model based on the preset variable load rate and collect the parameter peak values ​​of each constraint parameter; perform linear fitting between the parameter peak values ​​and the variable load rate using the least squares method to obtain the fitting model of each constraint parameter; input the safe operating boundary range into the fitting model to calculate the maximum variable load rate.

[0039] In another embodiment of the present invention, a storage medium is also proposed, specifically a computer-readable storage medium (Memory). A computer-readable storage medium is a memory device in a terminal device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and extended storage media supported by the terminal device. The computer-readable storage medium provides a storage space that stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device.

[0040] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the calculation method for the maximum variable load rate of a coal-fired power generating unit in the above embodiments; one or more instructions in the computer-readable storage medium are loaded and executed by the processor in the following steps: Obtain the actual operating parameters of the constructed unit transient model and determine the safe operating boundary range of each constraint parameter; within the safe operating boundary range, simulate the unit transient model based on the preset variable load rate and collect the parameter peak values ​​of each constraint parameter; perform linear fitting between the parameter peak values ​​and the variable load rate using the least squares method to obtain the fitting model of each constraint parameter; input the safe operating boundary range into the fitting model to calculate the maximum variable load rate.

[0041] Please see Figure 4 The terminal device is a computer device. In this embodiment, the computer device 60 includes a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When executed by the processor 61, the computer program 63 implements a method for calculating the maximum variable load rate of a coal-fired power generation unit according to the embodiment. To avoid repetition, these methods are not described in detail here. Alternatively, when executed by the processor 61, the computer program 63 implements the functions of each model / unit in the calculation system for the maximum variable load rate of a coal-fired power generation unit according to the embodiment. To avoid repetition, these functions are not described in detail here.

[0042] Computer device 60 can be a desktop computer, laptop, handheld computer, cloud server, or other computing device. Computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will understand that... Figure 4This is merely an example of computer device 60 and does not constitute a limitation on computer device 60. It may include more or fewer components than shown, or combine certain components, or different components. For example, computer device may also include input / output devices, network access devices, buses, etc.

[0043] The processor 61 may be a central processing unit (CPU), or other general-purpose processors, CPUs, graphics processing units (GPUs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, quantum computing-based data processing logic units, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0044] The memory 62 can be an internal storage unit of the computer device 60, such as a hard disk or RAM of the computer device 60. The memory 62 can also be an external storage device of the computer device 60, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., provided on the computer device 60.

[0045] Furthermore, the memory 62 may include both internal storage units of the computer device 60 and external storage devices. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 can also be used to temporarily store data that has been output or will be output.

[0046] Any references to memory, database, or other media used in the embodiments of this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may take many forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0047] The databases involved in the embodiments proposed in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchain. The processors involved in the embodiments proposed in this application may be, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc.

[0048] Please see Figure 5 The terminal device is a chip. In this embodiment, the chip 600 includes a processor 622, which may be one or more, and a memory 632 for storing computer programs executable by the processor 622. The computer program stored in the memory 632 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processor 622 may be configured to execute the computer program to perform the aforementioned method for calculating the maximum variable load rate of a coal-fired power generation unit.

[0049] Additionally, chip 600 may also include a power supply component 626 and a communication component 650. The power supply component 626 can be configured to perform power management of chip 600, and the communication component 650 can be configured to enable communication of chip 600, such as wired or wireless communication. Furthermore, chip 600 may also include an input / output interface 658. Chip 600 can operate on an operating system stored in memory 632.

[0050] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered illustrative and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the scope of the invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0051] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can be appropriately combined to form other embodiments that can be understood by those skilled in the art. The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A method for calculating the maximum load change rate of a coal-fired power generating unit, characterized in that, include: Obtain the actual operating parameters of the constructed unit transient model and determine the safe operating boundary range of each constraint parameter; Within the safe operating boundary range, the transient model of the unit is simulated based on a preset variable load rate, and the peak values ​​of each constraint parameter are collected. By linearly fitting the peak values ​​of the parameters and the variable load rate using the least squares method, a fitting model for each constraint parameter is obtained. The maximum variable load rate is calculated by inputting the safe operating boundary interval into the fitting model.

2. The method for calculating the maximum load change rate of a coal-fired power generating unit according to claim 1, characterized in that, The construction of the unit transient model includes: The high-pressure heater, high-pressure cylinder turbine, and intermediate-pressure cylinder turbine are respectively connected to the boiler. The intermediate-pressure cylinder turbine is connected to the low-pressure cylinder turbine. The high-pressure cylinder turbine and the intermediate-pressure cylinder turbine are respectively connected to the high-pressure heater. The high-pressure heater is connected in series with the low-pressure heater through a deaerator. The low-pressure heater is connected to the low-pressure cylinder turbine. The intermediate-pressure cylinder turbine is connected to the deaerator.

3. The method for calculating the maximum load change rate of a coal-fired power generating unit according to claim 2, characterized in that, The process of obtaining the actual operating parameters of the constructed unit transient model and determining the safe operating boundary range of each constraint parameter includes: Obtain the rated operating parameters of the high-pressure heater, the high-pressure cylinder turbine, the intermediate-pressure cylinder turbine, the low-pressure cylinder turbine, the low-pressure heater, and the boiler in the transient model of the unit; The rated operating parameters are corrected based on the preset dynamic margin to obtain the preliminary boundary range; The boundary values ​​of the preliminary boundary interval are dynamically verified based on the transient model of the unit, and the boundary values ​​of the preliminary boundary interval are corrected based on the verification results to obtain the safe operation boundary interval.

4. The method for calculating the maximum load change rate of a coal-fired power generating unit according to claim 3, characterized in that, Within the safe operating boundary range, the transient model of the unit is simulated multiple times based on a preset variable load rate value, and the peak values ​​of each constraint parameter in each simulation are collected, including: Within the safe operating boundary range, a preset variable load rate range is defined. The variable load rate range is divided into several variable load rate values, and the variable load rate values ​​are sequentially input into the unit transient model for model operation, and the unit operation data corresponding to each variable load rate value is recorded. The unit operation data is arranged according to the time sequence of collection to obtain the operation data time sequence; The absolute deviation value is calculated based on the rated operating parameters and the parameter values ​​of each in the operating data time series; Extract the maximum value from each absolute deviation value to obtain multiple initial parameter peaks; The parameter peak value is obtained by calculating the mean of multiple initial parameter peak values.

5. The method for calculating the maximum load change rate of a coal-fired power generating unit according to claim 1, characterized in that, By linearly fitting the peak values ​​of the parameters and the variable load rate values ​​using the least squares method, fitting models for each constraint parameter are obtained, including: An independent variable dataset is constructed based on the variable load rate value, and a dependent variable dataset corresponding to the independent variable dataset is constructed based on the parameter peak value. An initial variable dataset pair is obtained, and the Grubbs criterion is used to detect the initial variable dataset pair to obtain an optimized variable dataset pair. The measured peak values ​​of constraint parameters during the operation of the unit transient model are obtained. Based on the measured peak values ​​and the initial parameter peak values, the residuals of the constraint parameters after each simulation are calculated, and the sum of squared residuals is calculated based on the residuals. Based on the sum of squared residuals, the slope parameter and intercept parameter of the linear fitting model are calculated, and an initial linear fitting model is constructed. Based on the optimized variable dataset, the input to the initial linear fitting model is used to calculate the optimal slope parameter and the optimal intercept parameter. The initial linear fitting model is optimized based on the optimal slope parameter and the optimal intercept parameter, and the goodness-of-fit of the optimized initial linear fitting model is verified. If the verification passes, the fitting model is obtained; if the verification fails, the variable load rate value is recalculated.

6. The method for calculating the maximum load change rate of a coal-fired power generating unit according to claim 5, characterized in that, The goodness-of-fit of the optimized initial linear fitting model is verified, including: The measured peak values ​​of the constraint parameters during the operation of the transient model of the unit were collected multiple times, and the average value of the measured peak values ​​was calculated. The determination coefficient is calculated based on the measured peak value, the load gradient rate value, and the average value of the measured values. The determination coefficient is compared with the preset fitting coefficient. If the determination coefficient is greater than or equal to the fitting coefficient, the fitting model is retained; if the determination coefficient is less than the fitting coefficient, the variable load rate value is recalculated.

7. The method for calculating the maximum load change rate of a coal-fired power generating unit according to claim 1, characterized in that, The maximum variable load rate is calculated by inputting the safe operating boundary range into the fitted model, including: Extract the boundary parameters corresponding to the safe operating boundary intervals of all the aforementioned constraint parameters, input the boundary parameters into the fitting model, and calculate the variable load prediction rate corresponding to each constraint parameter; Compare all the predicted load rates and extract the minimum value among them as the maximum predicted load rate.

8. A calculation system for the maximum load change rate of a coal-fired power generating unit, used to implement the calculation method for the maximum load change rate of a coal-fired power generating unit as described in any one of claims 1 to 7, characterized in that, include: The boundary determination module is configured to obtain the actual operating parameters of the constructed unit transient model and determine the safe operating boundary range of each constraint parameter; The peak calculation module is configured to simulate the transient model of the unit based on a preset variable load rate within the safe operating boundary range, and to collect the peak values ​​of each constraint parameter. The model building module is configured to perform linear fitting of the parameter peak and the variable load rate value using the least squares method to obtain a fitting model for each constraint parameter. The load rate calculation module is configured to input the safe operating boundary interval into the fitting model to calculate the maximum variable load rate.

9. A computer device, characterized in that, The device includes a memory, a processor, and a computer program stored in the memory and executable in the processor. When the processor executes the computer program, it implements the steps of the method for calculating the maximum variable load rate of a coal-fired power generation unit as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for calculating the maximum variable load rate of a coal-fired power generating unit as described in any one of claims 1 to 7.

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