Real-time evaluation method and device for peak capacity of coal power unit

Abstract

The invention discloses a real-time evaluation method and device for the peak capacity of a coal power unit, and relates to the technical field of power system operation and control. According to the main technical scheme, coal power unit load capacity associated parameters are divided into two types, different differentiation methods are adopted, and load upper limits of different types of parameters on unit limitation are evaluated, and the method comprises the steps that on the basis of real-time working condition detection, a preset step-by-step approximation load prediction method is adopted, and the real-time load upper limit corresponding to the first type of parameters is determined; based on the environmental protection real-time emission constraint, determining a first load upper limit of environmental protection parameter limitation and a second load upper limit (coal mill real-time output constraint) of quantitative coal mill limitation; and finally, multi-dimensional constraints (such as target induced draft fan output, target blower output, target primary fan output, target air pre-heater pressure difference, a first load upper limit and a second load upper limit) are integrated in real time, and the minimum value is taken to serve as a real-time evaluation result of the peak capacity of the coal power unit.

Description

Technical Field

[0001] This application relates to the field of power system operation and control technology, and in particular to a method and apparatus for real-time assessment of the peak capacity of coal-fired power units. Background Technology

[0002] With the large-scale integration of new energy sources such as wind and solar power into the power grid, their inherent intermittent, random, and volatile characteristics are continuously consuming the power system's flexible adjustment resources. Currently, the total installed capacity of solar and wind power nationwide has significantly exceeded the planned scale, further exacerbating the shortage of peak-shaving resources in the power system and the problem of new energy consumption, making related issues increasingly prominent. At the same time, the daily peak-valley difference rate of the power grid continues to widen, while new energy sources, due to their inherent characteristics, have limited capacity to support power supply during the evening peak hours. Coupled with the impact of "calm and windless conditions," "no sunlight at night," and extreme weather such as rain, snow, and freezing, some regions frequently experience power supply and demand tensions during peak hours, posing a severe challenge to energy supply security. Against this backdrop, the "Blue Book on the Development of New Power Systems" clearly states that coal-fired power will remain a crucial support for ensuring my country's power supply security for a considerable period. Crucially, as coal-fired power units transition to a dual role as both "basic backup power sources" and "system regulation power sources," their functions of providing a safety net for power supply and regulating grid peak and frequency are irreplaceable and must continue to play a vital role. This is both a practical necessity to address the current supply challenges brought about by the integration of new energy sources and a core guarantee for the stable operation of the new power system. Currently, the analysis of the power output obstruction and actual adjustment capacity of coal-fired power units still mainly relies on post-event review and manual judgment. These methods have obvious lag and cannot capture changes in the unit's operating status in real time. As a result, the assessment of peak capacity lacks timely support and is difficult to reflect the dynamic adjustment potential of the unit. Therefore, how to provide a real-time assessment method for the peak capacity of coal-fired power units is an urgent technical problem to be solved. Summary of the Invention

[0003] This application provides a real-time assessment method and apparatus for the peak capacity of coal-fired power units. The main purpose is to provide a real-time assessment solution that transforms the traditional logic of "analyzing historical data after the fact" into the logic of "real-time perception of the current state and dynamic calculation of peak potential". This enables the real-time capture of changes in the unit's operating status, fully reflects the unit's dynamic adjustment potential, and provides more timely and effective real-time assessment results of the peak capacity of coal-fired power units.

[0004] To achieve the above objectives, this application mainly provides the following technical solutions: The first aspect of this application provides a method for real-time assessment of the peak capacity of a coal-fired power unit, the method comprising: Determine the load-carrying capacity correlation parameters of coal-fired power units. The parameters include a first type of parameters and a second type of parameters. The first type of parameters are load-sensitive parameters that are strongly correlated with the load. The first type of parameters includes at least: induced draft fan current, forced draft fan current, primary air fan current, and air preheater differential pressure. The second type of parameters are load-insensitive parameters. The second type of parameters includes at least: coal mill output and environmental emission parameters. Based on the real-time parameter values ​​of the first type of parameters corresponding to the actual operating conditions of the coal-fired power unit, a pre-set step-by-step load prediction method is adopted to determine the real-time load upper limit corresponding to the first type of parameters, which is used to reverse characterize the output of the target induced draft fan, the output of the target forced draft fan, the output of the target primary air fan, and the pressure difference of the target air preheater. Based on the rigid constraints of environmental protection standards, the first load limit of the environmental protection parameters is determined; Based on the number of coal mills, reserve margin, and rated output constraints of the units, the second load limit of the coal mills is quantified. By integrating the target induced draft fan output, the target forced draft fan output, the target primary air fan output, the target air preheater differential pressure, the first load limit, and the second load limit, the minimum value among them is taken as the real-time evaluation result of the peak capacity of the coal-fired power unit.

[0005] A second aspect of this application provides a real-time assessment device for the peak capacity of a coal-fired power unit, the device comprising: The first determining unit is used to determine the load-carrying capacity related parameters of the coal-fired power unit. The parameters include a first type of parameters and a second type of parameters. The first type of parameters are load-sensitive parameters that are strongly correlated with the load. The first type of parameters includes at least: induced draft fan current, forced draft fan current, primary air fan current, and air preheater differential pressure. The second type of parameters are load-insensitive parameters. The second type of parameters includes at least coal mill output and environmental emission parameters. The first processing unit is used to determine the real-time load upper limit corresponding to the first type of parameters based on the real-time parameter values ​​of the first type of parameters corresponding to the actual operating conditions of the coal-fired power unit, using a preset step-by-step load prediction method, and is used to reverse characterize the output of the target induced draft fan, the output of the target forced draft fan, the output of the target primary air fan, and the pressure difference of the target air preheater. The second processing unit is used to determine the first load limit of the environmental protection parameters based on the rigid constraints of environmental protection standards. The third processing unit is used to quantify the second load limit of the coal mill based on the configuration of the number of coal mills, the reserve margin, and the rated output constraint of the unit. The second determining unit is used to integrate the target induced draft fan output, the target forced draft fan output, the target primary air fan output, the target air preheater differential pressure, the first load limit, and the second load limit, and take the minimum value among them as the real-time evaluation result of the peak capacity of the coal-fired power unit.

[0006] A third aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the real-time assessment method for the peak capacity of a coal-fired power unit as described above.

[0007] A fourth aspect of this application provides an electronic device, the device including at least one processor, and at least one memory and bus connected to the processor; The processor and the memory communicate with each other via the bus. The processor is used to call program instructions in the memory to execute the real-time assessment method for the peak capacity of coal-fired power units as described above.

[0008] By employing the above-described technical solution, the technical solution provided in this application has at least the following advantages: This application provides a method and apparatus for real-time assessment of the peak capacity of coal-fired power units. Based on the differences in the sensitivity of parameters to load, the load-carrying capacity-related parameters of coal-fired power units are divided into two categories (such as first-class parameters and second-class parameters). Different differentiated methods are employed to assess the load limits imposed on the unit by different types of parameters, taking into account the different patterns of load variation for each category of parameters. This includes: for the first-class parameters, based on real-time operating condition detection, a pre-set stepwise load prediction method is used to determine the real-time load limits corresponding to the first-class parameters, obtaining parameters such as target induced draft fan output, target forced draft fan output, target primary air fan output, and target air preheater differential pressure; for the second-class parameters, such as determining the first load limit limited by environmental parameters based on real-time environmental emission constraints, and quantifying the second load limit limited by the coal mills (real-time output constraints of the coal mills) based on the number of coal mills, reserve margin, and rated output constraints of the unit. Finally, by real-time integration of multi-dimensional constraints (such as target induced draft fan output, target forced draft fan output, target primary air fan output, target air preheater differential pressure, and first and second load limits), the minimum value among them is taken as the real-time assessment result of the peak capacity of the coal-fired power unit.

[0009] Compared to existing technologies that rely primarily on post-event review and manual assessment, leading to significant delays in evaluation, the real-time evaluation solution provided in this application transforms the traditional logic of "post-event analysis of historical data" into the logic of "real-time perception of the current state and dynamic calculation of peak potential." This enables real-time capture of changes in unit operating status, fully reflects the dynamic adjustment potential of the unit, and provides more timely and effective real-time evaluation results of the peak capacity of coal-fired power units. This facilitates early warning of unit output obstruction, allows power control agencies to adjust plans in a timely manner, optimize load allocation, and rationally arrange unit start-up, shutdown, and standby status, ensuring sufficient reserve capacity in the event of emergencies and improving the safety margin of the power system.

[0010] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0011] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 A flowchart illustrating a real-time assessment method for the peak capacity of a coal-fired power unit, provided as an embodiment of this application. Figure 2 This application demonstrates a step-by-step approximation load forecasting process in its embodiments. Figure 3 This is a schematic diagram illustrating the model processing flow provided in the embodiments of this application; Figure 4 A schematic diagram of the relationship model between coal mill output and upper load limit provided in the embodiments of this application; Figure 5 This is a block diagram of a real-time assessment device for the peak capacity of a coal-fired power unit, provided in an embodiment of this application. Detailed Implementation

[0012] Exemplary embodiments of the present application will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this application will be thorough and complete, and will fully convey the scope of the present application to those skilled in the art.

[0013] The load-carrying capacity of a coal-fired power unit refers to the range of loads that a coal-fired power unit can continuously and stably bear under normal operating conditions, based on factors such as equipment condition, coal quality, and environmental protection requirements. It emphasizes "routine" and "continuous," representing the basic output capacity demonstrated in the unit's daily operation, covering the normal operating range from low load to rated load. Unlike the load-carrying capacity of a unit, the peak capacity of a coal-fired power unit refers to the maximum short-term output level that a coal-fired power unit can achieve by adjusting its operating parameters during emergency or critical periods such as peak electricity demand in the power grid or insufficient output from new energy sources (e.g., "static and windless" or "no light at night"). It emphasizes "emergency response" and "limit values" and aims to quickly fill the power grid's power supply gap and ensure the balance between power supply and demand. It is usually a short-term and non-continuous output state.

[0014] It is evident that the peak capacity of a coal-fired power unit is a manifestation of its load-carrying capacity under special circumstances. In essence, peak capacity is the "extraordinary output" achieved by the unit through short-term adjustments (such as improving boiler combustion efficiency or briefly exceeding the limits of some non-core parameters) on the basis of its conventional load-carrying capacity. It is a temporary breakthrough of the upper limit of load-carrying capacity.

[0015] The inventors discovered through research that the analysis of the power output obstruction and actual adjustment capacity of coal-fired power units still mainly relies on post-event review and manual judgment. This method has obvious lag and cannot capture changes in the unit's operating status in real time, resulting in a lack of timely support for the assessment of peak capacity and difficulty in reflecting the dynamic adjustment potential of the unit.

[0016] Therefore, this application provides a method for real-time assessment of the peak capacity of coal-fired power units, such as... Figure 1 As shown, the following specific steps are provided in this embodiment of the invention: Step 101: Determine the load-carrying capacity related parameters of the coal-fired power unit. The parameters include the first type of parameters and the second type of parameters. The first type of parameters are load-sensitive parameters that are strongly correlated with the load. The first type of parameters include at least the induced draft fan current, forced draft fan current, primary air fan current, and air preheater differential pressure. The second type of parameters are load-insensitive parameters. The second type of parameters include at least the coal mill output and environmental emission parameters.

[0017] Conventional coal-fired power units mainly consist of three main components: a boiler, a steam turbine generator set (referred to as "steam turbine"), and a generator, as well as major auxiliary components such as a coal mill, forced draft fan, induced draft fan, primary air fan, and air preheater. They are also equipped with environmental protection systems such as desulfurization and denitrification. When the operating parameters of the main and auxiliary equipment exceed safety limits, it will trigger the tripping of the main or corresponding equipment, directly affecting the unit's normal load-carrying capacity. In actual operation, the phenomenon of power output obstruction in coal-fired power units often occurs under specific conditions such as high load and high back pressure. The core influencing factors include the operating status of the main and auxiliary equipment, the coal quality characteristics fed into the furnace, and compliance requirements for environmental emission indicators.

[0018] It is evident that, based on the working principle that "unit output is the result of the coordinated operation of main and auxiliary equipment," the unit's load-carrying capacity is jointly constrained by the boiler, turbine, auxiliary equipment, and environmental protection system. Therefore, the core of assessing the peak capacity of coal-fired power units is to identify "key variables affecting unit output." In this application, the embodiments may, but are not limited to, select parameters directly related to load changes from historical operating data (reflecting long-term operating patterns) or existing logical relationships (based on equipment design principles). The purpose is to ensure that subsequent assessments of the peak capacity of coal-fired power units can cover the limiting factors of the entire "heat source-transmission-environmental protection" chain.

[0019] Furthermore, in this embodiment, based on the "difference in parameter sensitivity to load," the aforementioned "parameters directly related to load changes" are divided into two categories. For ease of distinction, this embodiment uses the terms "first" and "second" to identify them, resulting in the first category of parameters and the second category of parameters. Specific explanations are as follows: The first category of parameters are load-sensitive parameters. The principle behind their classification is that "the parameters are strongly correlated with the load." For example, when the load increases, the boiler needs to burn more fuel and the auxiliary equipment needs to deliver the medium with greater power, which causes parameters such as fan current and air preheater pressure difference to change synchronously. The limits of these parameters directly determine the "dynamic upper limit" of load increase.

[0020] The second category of parameters are those that are insensitive to load. Their classification is based on the principle that "parameters are more affected by their own adjustment margin or operating strategy." For example, the output of a coal mill can be flexibly adapted to the load by adjusting the number of operating mills (e.g., starting a standby coal mill when the load increases). Environmental emission parameters need to be maintained at fixed standard values ​​(they have no direct linear relationship with load levels; they only need to meet the standards). Although these parameters do not change synchronously with the load, once an anomaly occurs (such as a coal mill malfunction or excessive emissions), they become a "rigid constraint" on load increases.

[0021] In the above embodiments of this application, the "parameters directly related to load changes" are first divided into a first category of parameters and a second category of parameters. Then, based on the different patterns of load changes in different categories of parameters, different differentiated evaluation methods are used in a targeted manner (such as step 102 below, steps 103-104 below), thereby avoiding evaluation bias caused by using a single evaluation method.

[0022] Step 102: Based on the real-time parameter values ​​of the first type of parameters corresponding to the actual operating conditions of the coal-fired power unit, a pre-set step-by-step load prediction method is adopted to determine the real-time load upper limit corresponding to the first type of parameters, which is used to reverse evaluate the output of the target induced draft fan, the output of the target forced draft fan, the output of the target primary air fan, and the differential pressure of the target air preheater.

[0023] In this embodiment of the application, the working principle of the pre-set progressively approximating load forecasting method is as follows: gradually probe the upper limit of the load to avoid the equipment tripping due to a one-time exceedance of the first type of parameter limit. Essentially, it is to calculate the peak capacity of the first type of parameter limit in real time.

[0024] For example, the execution process of the pre-set stepwise approximation load prediction method includes: increasing the unit load by a certain percentage (e.g., 3%, 5%, 10%) of the rated load, simulating the operating conditions after the load increase, predicting the target parameter value of the first type of parameter, comparing it with the safety limit of each first type of parameter, and if it does not exceed the limit, continuing the stepwise approximation of the unit load increase operation until any of the new target parameter values ​​of the multiple predicted first type of parameters exceeds the limit, at which point the iteration operation stops. Since, under the premise that the "new target parameter value of the predicted first type of parameter" meets the condition of "not exceeding the limit", each iteration operation is equivalent to reversely determining the unit load to be increased, the maximum load is selected as the prediction result of the real-time load upper limit of the first type of parameter, and based on this selected "real-time load upper limit of the first type of parameter" (i.e., the maximum load), the "target induced draft fan output, target forced draft fan output, target primary air fan output, and target air preheater differential pressure" can also be obtained accordingly.

[0025] In some modified embodiments, step 102 can be further refined to include the following implementation steps (A1-A62), and combined with, for example Figure 2 The demonstrated step-by-step load forecasting process is explained in detail as follows: A1. If the deviation ratio between the current actual load and the rated load of the unit exceeds the preset percentage (a%), the load and the corresponding coal quantity shall be adjusted according to the preset range (b%).

[0026] Calculate the deviation ratio between the current actual load and the unit's rated load. If the deviation exceeds a certain percentage (e.g., 10%), it indicates that the load deviates significantly from the rated value, requiring adjustments to the load and coal quantity to gradually approach the rated value, thus entering subsequent adjustment branches of different magnitudes.

[0027] For example, such as Figure 2 As shown, “+10% load / coal quantity”, “+5% load / coal quantity”, and “+3% load / coal quantity” are different preset ranges provided in the embodiments of this application.

[0028] For example, depending on whether the load deviates from the rated load by more than 10%, different adjustments are made to the load and the corresponding coal quantity (because load and coal quantity are related, and coal quantity is a key input factor affecting load). A +10% adjustment is suitable for situations with larger deviations, while +5% and +3% are more refined adjustments used in the subsequent gradual approximation process, covering different stages from larger deviations to approaching the target through adjustments of different magnitudes.

[0029] A2. Based on the adjusted load and coal quantity values, retrieve at least one corresponding historical operating condition data from the historical operating database. The historical operating database records the performance of various parameters of the unit under different load and coal quantity inputs.

[0030] Based on the adjusted load and coal quantity values, at least one historical operating condition data point matching the adjusted values ​​is retrieved from the historical operating database. Because historical operating condition data records the performance of various parameters of the unit under different load and coal quantity inputs, matching data from similar operating conditions provides a reliable input basis for subsequent A4 model predictions, ensuring that the predictions are based on real and similar operating scenarios.

[0031] A3. Average at least one historical operating condition data to obtain target operating condition data. The target historical operating condition data shall include at least historical average data based on adjusted load and coal quantity.

[0032] For each historical operating condition data point retrieved from the historical operating database that matches at least one target historical operating condition data point, its average value is calculated. This step aims to reduce the randomness and error of individual sets of historical operating condition data, making the data subsequently input into the A4 model more representative and stable, and improving the reliability of subsequent prediction results.

[0033] A4. Input the target operating condition data into the pre-built unit load and first type of parameter mapping relationship analysis model for processing, so as to predict and output the target parameter value of the first type of parameter.

[0034] The core requirement of step A4 is to "predict the operating status of the first type of parameter after load increase". To this end, this application embodiment constructs a model (such as "Unit Load and First Type of Parameter Mapping Relationship Analysis Model") to provide a quantitative mapping relationship between "load and first type of parameter". In model application, the processing flow of this model is essentially "input key control variables → model calculation → output predicted value of D1 type parameter", combined with... Figure 3 The illustrated model processing flow diagram shows the specific flow provided by the model as follows: Model Input Layer: The "core control variables" in the real-time operation of the receiving unit include the current load (the starting point for evaluation), coal quantity (fuel input, which determines combustion intensity), and oxygen quantity (an indirect manifestation of total air volume, which determines combustion completeness). These three variables are key factors affecting the first type of parameters and are also core parameters that can be actively adjusted during unit operation.

[0035] Model computation layer: By training and optimizing the LSTM / CNN model, temporal and correlation features are extracted from the input variables. For example, LSTM processes the time dependence of variables (such as the lag effect of the current coal input on the main steam pressure 1-5 minutes later), and CNN extracts the local correlation between variables (such as the nonlinear matching relationship between oxygen and fan current). Finally, through the weight calculation inside the model, the predicted value of the first type of parameter corresponding to the current input variable is output.

[0036] Model output layer: Outputs predicted values ​​of the first type of parameters, such as induced draft fan current, forced draft fan current, primary air fan current, and air preheater differential pressure. These output values ​​are the core basis for the "parameter limit comparison" in Figure 3, directly reflecting the "theoretical operating state of the first type of parameters under the current control variables".

[0037] In order to make the model achieve the following Figure 3 The processing flow demonstrated in this application embodiment provides a method for training the model, including the following: (1)-(3); (1) The reason for using historical operating condition data for at least one year is that the operation of the unit is affected by factors such as season (such as winter heating and summer high temperature), coal quality fluctuations, and equipment status changes. The data for the whole year can cover different operating conditions and avoid the model limitations caused by single operating condition data. (2) Long Short-Term Memory Network (LSTM) was chosen because it is good at processing time-series data (unit operating parameters change continuously over time and have a dependency relationship, such as the current main steam pressure being affected by the coal input in the previous 10 minutes); Convolutional Neural Network (CNN) was chosen because it can extract local correlation features between parameters (such as the nonlinear correlation between fan current and air volume).

[0038] (3) By comparing the deviation between the model's predicted values ​​and the historical actual values, the model parameters (such as the number of hidden layer nodes in LSTM and the size of the convolution kernel in CNN) are adjusted, and finally the model with the smallest error and the highest stability is selected to ensure the accuracy of subsequent real-time predictions.

[0039] A5. By comparing the target parameter value of the first type of parameter with the safe operating limits of each parameter of the unit, it can be determined whether the current adjustment range is within the safe operating range of the unit.

[0040] After adjusting the load / coal quantity for different preset ranges (+10%, +5%, +3%), and using deviation matrix correction, the corresponding unit parameters (such as induced draft fan current, forced draft fan current, primary air fan current, air preheater differential pressure, etc.) are calculated. This step is to obtain the predicted results of each type I parameter of the unit under different adjustment ranges, so as to compare them with the parameter limits later.

[0041] Under each adjustment range, the calculated target parameter values ​​of each first-class parameter are compared with the safe operating limits of each parameter of the unit (such as the rated current of the fan, the maximum allowable differential pressure of the air preheater, the upper limit of the main steam pressure, etc., which are the respective safe limits of each first-class parameter). If the target parameter value of any first-class parameter exceeds the limit, it means that the adjustment range is not feasible. However, if the target parameter values ​​of each first-class parameter do not exceed the limit, the unit load can be increased by a certain adjustment range, and the A1-A5 operation can be performed again to determine whether the adjustment range is within the safe operating range of the unit. The above embodiment of this application adopts such an iterative operation to gradually approach the maximum load limit within the "safe operating range of the unit".

[0042] It should be noted that this application also introduces a deviation matrix to correct the model's prediction results. The deviation matrix records the deviation between the model's historical predictions and the actual values. By combining the deviation matrix with the output of the large number model, the predicted values ​​can be adjusted to compensate for prediction deviations caused by factors such as data limitations or incomplete fitting of complex working conditions, making the results closer to reality.

[0043] Furthermore, A1-A5 above actually represent the implementation process of "determining whether the adjustment range is within the safe operating range of the unit" under a single adjustment range. Based on this implementation process, the embodiments of this application provide an achievable iterative implementation process, which, for example, includes the following steps A61 and A62, and the specific explanation is as follows: In order to gradually test the load limit, the embodiments of this application will gradually increase the load (such as using different preset ranges to increase the load), and iteratively execute the above A1-A5 operations until it is determined that the new adjustment range is not within the safe operating range of the unit, as shown in step A61 below.

[0044] If A61 is within the safe operating range of the unit, the operation of predicting the new target parameter value of the first type of parameter is performed iteratively by gradually increasing the preset adjustment range until it is determined that the new adjustment range is no longer within the safe operating range of the unit. Based on the iterative operation of gradually increasing the preset adjustment range, the maximum load that the unit can reach is predicted, which is used as the load limit corresponding to the first type of parameter.

[0045] If A62 is not within the safe operating range of the unit, the preset adjustment range is gradually reduced to iteratively execute the operation of predicting the new target parameter value of the first type of parameter until it is determined that the new adjustment range is within the safe operating range of the unit. Based on the new target parameter value predicted corresponding to the final determined new adjustment range, the maximum load that the unit can achieve is predicted as the real-time load limit corresponding to the first type of parameter.

[0046] However, if the first adjustment range is executed (as shown in A1-A5 above), the result is that the current adjustment range is not within the safe operating range of the unit. This indicates that the first load increase may have been too high. In this case, a lower preset adjustment range is used, and the load forecasting process is executed again, as shown in step A62. That is, by reducing the adjustment range in the opposite direction, the upper limit of the load is gradually tested.

[0047] As shown in A61 and A62 above, the different adjustment directions are caused by the preset amplitude of different gears, but the essential purpose is to gradually explore the upper limit of the load in order to finally obtain the real-time upper limit of the load corresponding to the first type of parameter.

[0048] Furthermore, in some specific embodiments, such as Figure 3 The output prediction values ​​for the first type of parameters (such as induced draft fan current, forced draft fan current, primary air fan current, and air preheater differential pressure) are shown. These parameters can be referred to as "smoke and air parameters," which have a direct "causal relationship" with the load. Examples include the following: When the load increases, the boiler needs to burn more fuel, which requires the forced draft fan to deliver more air (to aid combustion), the induced draft fan to discharge more flue gas (to maintain furnace pressure), and the primary air fan to deliver more pulverized coal (for fuel supply). At the same time, the air preheater needs to heat more air (to improve combustion efficiency).

[0049] The "output" or "operating parameters" of these devices (such as the differential pressure of the air preheater reflecting air flow resistance) all have physical limits (such as the rated current of the fan motor and the maximum allowable differential pressure of the air preheater). When the load increases to a certain level, the air and flue gas parameters will first reach the limit, at which point the load cannot continue to increase. The limits of the air and flue gas parameters directly determine their corresponding "load ceiling".

[0050] Therefore, based on such Figure 2 The demonstrated load forecasting process, which gradually approaches the target load, is the highest load that the unit can achieve under the combined constraints of air and flue gas parameters such as "induced draft fan output, forced draft fan output, primary air fan output, and air preheater differential pressure". Correspondingly, it can also inversely represent the target induced draft fan output, target forced draft fan output, target primary air fan output, and target air preheater differential pressure.

[0051] For example, for the four parameters "induced draft fan current, forced draft fan current, primary air fan current, and air preheater differential pressure", the "stepwise approximation load prediction method" (e.g.) is executed once for each parameter. Figure 2 This yields the load limit calculated independently for each parameter, resulting in four "load limit values", which correspond to "target induced draft fan output, target forced draft fan output, target primary air fan output, and target air preheater differential pressure".

[0052] Step 103: Determine the first load limit of environmental protection parameters based on the rigid constraints of environmental protection standards.

[0053] For the second type of parameter, which has low load sensitivity, a model needs to be built based on the equipment's operating mechanism rather than data fitting to ensure that the assessment conforms to the actual operating logic of the equipment. Taking environmental parameters as an example, the principle is "rigid constraints of environmental standards." The environmental protection system needs to adjust its operating strategy according to the load (such as increasing the amount of desulfurizing agent injected when the load increases), but the emission indicators (such as NOx and SO2 concentrations) need to be maintained within fixed limits (regardless of the load level). Therefore, real-time emission data is directly compared with the limits: if the real-time value does not exceed the standard, it means that the environmental protection system has no constraints; if it exceeds the standard, the load needs to be reduced to the level where emissions meet the standard, thereby determining the upper limit of the load restricted by the environmental parameters.

[0054] It should be noted that, in order to distinguish different second-category parameters, such as environmental protection parameters and the different load limits imposed on coal mills, the embodiments of this application use the terms "first" and "second" as identifiers for differentiation, such as "the first load limit limited by environmental protection parameters," Step 104: Based on the number of coal mills, reserve margin, and rated output constraints of the units, quantify the second load limit of the coal mills.

[0055] The coal mill is a core piece of equipment for fuel preparation, and its output directly determines the boiler's fuel supply. This step can quantify the limitation of the coal mill's peak capacity through "number of units configured + reserve margin". The working principle is as follows: In this application embodiment, the logic of "total number of units N - number of units in operation n - number of units not in operation Nn - number of units in reserve m" is clarified, where m≤Nn, because the coal mills not in operation may have faults, maintenance, or other situations, and not all of them can be started at any time. The actual number of available reserve units m needs to be determined through equipment status monitoring (such as coal mill lubricating oil temperature and motor insulation).

[0056] The load limit correlation logic is as follows: the output of the coal mill is positively correlated with the number of operating mills (the more operating mills, the greater the total output), and the number of standby mills, m, determines the "extra output increase space". Specifically, if the currently operating coal mills have reached full output, starting m standby coal mills can increase fuel supply, thereby increasing the load; if there are no standby mills or the standby coal mills cannot be started, the coal mill output reaches the limit, and the load cannot be increased further.

[0057] Accordingly, this application provides a schematic diagram of the relationship between coal mill output and load limit, as shown in the embodiments. Figure 4 As shown, the calculation of the coal mill output margin is as follows: Output margin of operating coal mill = (Rated output of a single coal mill × Total number of available mills N) - (Current output of a single coal mill × Number of operating mills n), the core of which is "Total available output - Current actual output", reflecting the additional fuel supply capacity that the coal mill can provide. For example, it can be expressed as the following formula (1): ; in, To ensure sufficient output margin for operating the coal mill, For the first i The maximum allowable output of the operating coal mill (usually close to) (Limited by equipment safety, coal quality, etc.) For the first i The current actual output of each of the n operating coal mills is summed to obtain the total output margin of all operating coal mills. This margin represents the increased fuel supply that can be achieved by simply increasing the output of the current coal mills without starting the standby coal mills.

[0058] The provision of backup power is defined as: Backup power = Rated output of a single coal mill × Number of backup mills (m), that is, the additional output that can be added by starting a backup coal mill. For example, backup power can be expressed as... Based on the coal mill's output margin and reserve output, the peak capacity of the unit, which is limited by the coal mill's output, is expressed by the following formula (2): ; in, The unit's peak capacity, limited by the output of the coal mill, is a variable. The current actual output of the unit represents the "actual load currently being borne by the unit before calculating peak capacity". It is the benchmark value for subsequent calculation of the "peak capacity increase", that is, the peak capacity is the additional output of the coal mill on the basis of the current output.

[0059] Among them, variables For the total output of existing coal mills, This is the output margin for operating the coal mill.

[0060] in, The ratio means that the total amount of pulverized coal (original rated + additional margin) that the coal mill system can provide is how many times the original rated amount of pulverized coal. This multiple directly corresponds to the "proportion by which the unit output can be increased" (because the amount of pulverized coal and the unit output are approximately linearly related). Therefore, the "current actual unit output" is used as the basis for this ratio. Multiply by the "coal powder quantity increase ratio", and then combine it with the "current actual output of the unit". "The maximum output that the unit can achieve under the permission of the coal mill."

[0061] In addition, the embodiments of this application also provide "additional constraints", namely "if..." "If the output is greater than the rated output of the unit, then the rated output of the unit shall be used" is a safety constraint in engineering practice, and the specific explanation is as follows: No matter how great the output potential of the coal mill, the unit's own "rated output limit" (such as the upper limit of boiler evaporation capacity and the upper limit of turbine power) will limit the final peak capacity. At the same time, if the coal quality deteriorates (such as a decrease in pulverized coal combustion efficiency), even if the coal mill provides more pulverized coal, the actual output of the unit may not be able to increase proportionally. In this case, the "maximum output that the unit can actually achieve" (rated output) should be used as the standard to ensure that the assessment results are consistent with the actual operating limitations.

[0062] The above, step 104, finally quantifies the second load limit of the coal mill.

[0063] Step 105: By integrating the target induced draft fan output, target forced draft fan output, target primary air fan output, target air preheater differential pressure, first load limit, and second load limit, the minimum value among them is taken as the real-time assessment result of the peak capacity of the coal-fired power unit.

[0064] In this application embodiment, the final peak capacity of the unit is determined by "multi-dimensional constraint integration". The working principle is that "the peak output of the unit is determined by the weakest link". The operation of the unit is the result of the coordinated action of the main and auxiliary equipment and the environmental protection system. If any link reaches the limit, it will limit the load increase.

[0065] Therefore, the "multi-factor coverage" provided in this application embodiment is as follows: all limiting factors such as the output of the induced draft fan / forced draft fan / primary air fan (first type of parameter), the differential pressure of the air preheater (first type of parameter), the output of the coal mill (first type of parameter), and environmental emissions (first type of parameter) are included in the evaluation to ensure that nothing is omitted.

[0066] Furthermore, it provides a "minimum value selection logic": calculate the peak output corresponding to each limiting factor, and take the minimum value as the final evaluation result, because the factor corresponding to the minimum value is the "bottleneck" of the current unit load increase. Only by solving the bottleneck can the peak capacity be further improved. It is expressed by formula (3): ; in , , , , , These are the peak outputs that the unit can achieve under limiting factors such as air preheater differential pressure, coal mill output, induced draft fan output, forced draft fan output, primary air fan output, and environmental emissions.

[0067] Below, using steps 101-105 as an example, we provide an example, assuming the following load limits: air preheater differential pressure limit: 90% of rated load; induced draft fan output limit: 85% of rated load; forced draft fan output limit: 92% of rated load; primary air fan output limit: 88% of rated load; coal mill output limit: 80% of rated load; environmental emission limit: 86% of rated load. In this case, the final peak capacity of the coal-fired power unit is 80% of the rated load (with the coal mill output limit being the lowest).

[0068] Further analysis of steps 101-105 above reveals that the "real-time" nature of the real-time assessment method for peak capacity of coal-fired power units provided in this application embodiment is reflected in several aspects, which are illustrated below: The "preset step-by-step load prediction method" used in step 102 starts with the unit's current actual load, and accesses real-time operating data such as current coal and oxygen quantities. It simulates a load increase by a certain percentage step, and simultaneously predicts the "first type of parameter value after the load increase under the current operating conditions" through the model, and compares it with the real-time updated parameter limits. The entire process does not require waiting for historical data accumulation, but rather "dynamically detects the upper limit of the load based on the current state." For example, when the power grid experiences a sudden peak in electricity consumption, this step can immediately calculate "the maximum load that the unit can increase at this moment," providing an immediate basis for dispatching decisions, rather than analyzing "whether it could reach the peak just now" afterward.

[0069] Example 1: In step 102, "model deviation correction" is used to realize real-time deviation perception and dynamic model adaptation collaboration. This is a key guarantee for "real-time evaluation accuracy" and solves the problem of "difficulty in capturing parameter changes" in manual judgment.

[0070] Example 2: In step 103, for environmental emission parameters, real-time monitoring data (such as SO2 and NOx concentration data at the second / minute level) are directly accessed without waiting for historical statistics: if the current emission value does not exceed the standard, it means that the environmental protection system has no real-time constraints; if there is a sudden exceedance (such as insufficient desulfurization agent injection), it can be immediately determined that "the current load needs to be reduced to the emission standard level", and this constraint is included in the real-time peak capacity assessment to avoid the risk of "continuing to peak despite exceeding the emission standard" due to waiting for a post-event report.

[0071] Example 3: In step 104, real-time output constraints on the coal mills are implemented. For example, if one coal mill is suddenly reduced to standby, step 104 updates the "peak capacity of the unit with coal mill output constraints" in real time. ".

[0072] Finally, for example, in step 105, as the final output of the "real-time assessment," the final peak capacity of the unit is determined through "multi-dimensional constraint integration." The working principle behind this is that "the peak output of the unit is determined by the weakest link." Therefore, the "assessment result of the peak capacity of the coal-fired power unit" obtained in step 105 is affected by examples 1-3 above, and it ultimately achieves the real-time integration of multi-dimensional constraints.

[0073] In summary, this application provides a real-time assessment method for the peak capacity of coal-fired power units. Based on the differences in the sensitivity of parameters to load, this application divides the load-carrying capacity-related parameters of coal-fired power units into two categories (such as the first category and the second category). Different differentiated methods are employed to assess the load limits imposed on the unit by different categories of parameters, taking into account the different patterns of load variation for each category. This includes: processing the first category of parameters by using a pre-set, step-by-step load prediction method based on real-time operating condition detection to determine the real-time load limit corresponding to the first category of parameters, obtaining parameters such as target induced draft fan output, target forced draft fan output, target primary air fan output, and target air preheater differential pressure; and processing the second category of parameters by determining the first load limit limited by environmental parameters based on real-time environmental emission constraints, and quantifying the second load limit limited by the coal mills (real-time output constraints of the coal mills) based on the number of coal mills, reserve margin, and rated output constraints of the unit. Finally, by real-time integration of multi-dimensional constraints (such as target induced draft fan output, target forced draft fan output, target primary air fan output, target air preheater differential pressure, and first and second load limits), the minimum value among them is taken as the real-time assessment result of the peak capacity of the coal-fired power unit.

[0074] Compared to existing technologies that rely primarily on post-event review and manual assessment, leading to significant delays in evaluation, the real-time evaluation solution provided in this application transforms the traditional logic of "post-event analysis of historical data" into the logic of "real-time perception of the current state and dynamic calculation of peak potential." This enables real-time capture of changes in unit operating status, fully reflects the dynamic adjustment potential of the unit, and provides more timely and effective real-time evaluation results of the peak capacity of coal-fired power units. This facilitates early warning of unit output obstruction, allows power control agencies to adjust plans in a timely manner, optimize load allocation, and rationally arrange unit start-up, shutdown, and standby status, ensuring sufficient reserve capacity in the event of emergencies and improving the safety margin of the power system.

[0075] Furthermore, as a response to the above Figure 1 The implementation of the method shown in this application provides a real-time assessment device for the peak capacity of coal-fired power units. This device embodiment corresponds to the aforementioned method embodiment. For ease of reading, this device embodiment will not repeat the details of the aforementioned method embodiment, but it should be clear that the device in this embodiment can implement all the contents of the aforementioned method embodiment. This device is used to provide more timely and effective real-time assessment results of the peak capacity of coal-fired power units, specifically as follows... Figure 5 As shown, the device includes: The first determining unit 21 is used to determine the load-carrying capacity related parameters of the coal-fired power unit. The parameters include a first type of parameters and a second type of parameters. The first type of parameters are load-sensitive parameters that are strongly correlated with the load. The first type of parameters include at least: induced draft fan current, forced draft fan current, primary air fan current, and air preheater differential pressure. The second type of parameters are load-insensitive parameters. The second type of parameters include at least coal mill output and environmental emission parameters. The first processing unit 22 is used to determine the real-time load upper limit corresponding to the first type of parameters based on the real-time parameter values ​​of the first type of parameters corresponding to the actual operating conditions of the coal-fired power unit, using a preset step-by-step load prediction method, and is used to reverse characterize the output of the target induced draft fan, the output of the target forced draft fan, the output of the target primary air fan, and the pressure difference of the target air preheater. The second processing unit 23 is used to determine the first load limit of the environmental protection parameters based on the rigid constraints of environmental protection standards. The third processing unit 24 is used to quantify the second load limit of the coal mill based on the configuration of the number of coal mills, the reserve margin, and the rated output constraint of the unit. The second determining unit 25 is used to integrate the target induced draft fan output, the target forced draft fan output, the target primary air fan output, the target air preheater differential pressure, the first load limit, and the second load limit, and take the minimum value among them as the real-time evaluation result of the peak capacity of the coal-fired power unit.

[0076] Furthermore, the first processing unit 22 is specifically used for: If the deviation between the current actual load and the unit's rated load exceeds a preset percentage, the load and the corresponding coal quantity will be adjusted according to the preset range. Based on the adjusted load and coal quantity values, at least one corresponding historical operating condition data is retrieved from the historical operating database, which records the performance of various parameters of the unit under different load and coal quantity inputs. The historical operating condition data is averaged to obtain the target operating condition data, which includes at least historical average data based on adjusted load and coal quantity. The target operating condition data is input into a pre-built analysis model of the mapping relationship between unit load and first type of parameters for processing, so as to predict and output the target parameter value of the first type of parameters. By comparing the target parameter value of the first type of parameter with the safe operating limit value of each parameter of the unit, it is determined whether the current adjustment range is within the safe operating range of the unit; If it is within the safe operating range of the unit, the operation of predicting the new target parameter value of the first type of parameter is iteratively executed by gradually increasing the preset adjustment range until it is determined that the new adjustment range is no longer within the safe operating range of the unit; based on the iterative operation of gradually increasing the preset adjustment range, the maximum load that the unit can reach is predicted, which is used as the load limit corresponding to the first type of parameter. If it is not within the safe operating range of the unit, the preset adjustment range is gradually reduced to iteratively execute the operation of predicting the new target parameter value of the first type of parameter until it is determined that the new adjustment range is within the safe operating range of the unit; based on the new target parameter value predicted corresponding to the final determined new adjustment range, the maximum load that the unit can reach is predicted as the real-time load limit corresponding to the first type of parameter.

[0077] Furthermore, during the execution of the first processing unit 22, a correction operation is provided for the analysis model of the mapping relationship between unit load and first type of parameters, including: after accumulating multiple iterations to predict the target parameter value of the first type of parameters, taking the target parameter value of the first type of parameters obtained in each iteration; if it is detected that the deviation between the target parameter value of the first type of parameters and the actual parameter value at the same time is greater than a preset threshold, using the deviation matrix to correct the analysis model of the mapping relationship between unit load and first type of parameters.

[0078] Furthermore, the third processing unit 24 is specifically used for: The output margin of a coal mill is obtained by calculating the rated output of a single coal mill with the total number of available mills, and the current output of a single coal mill with the number of currently operating mills. The standby output of the coal mill is calculated by using the rated output of a single coal mill and the number of standby mills. Based on the coal mill output margin and the coal mill standby output, the peak capacity of the unit with the coal mill output limit is calculated using a preset coal mill output formula. The second load limit of the coal mill is obtained by constraining the peak capacity of the unit by the rated output of the unit.

[0079] Furthermore, during the execution of the third processing unit 24, obtaining the second load limit of the coal mill based on the peak capacity of the unit constrained by the rated output of the unit and the output limit of the coal mill includes: If the peak capacity of the unit limited by the output of the coal mill is greater than the rated output of the unit, then the rated output of the unit shall be used as the second load limit of the coal mill. If the peak capacity of the unit limited by the output of the coal mill is not greater than the rated output of the unit, then the peak capacity of the unit limited by the output of the coal mill shall be used as the second load limit of the coal mill.

[0080] Furthermore, the preset coal mill output formula includes: ; in, The unit's peak capacity, limited by the output of the coal mill, is a variable. For the actual output of the current generating units, To ensure sufficient output margin for operating the coal mill, This is the total output of the existing coal mills.

[0081] In summary, the real-time assessment device for the peak capacity of coal-fired power units includes a processor and a memory. The first determining unit 21, the first processing unit 22, the second processing unit 23, the third processing unit 24, and the second determining unit 25 are all stored in the memory as program units. The processor executes the program units stored in the memory to achieve the corresponding functions.

[0082] The processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured, and by adjusting kernel parameters, a real-time evaluation solution can be provided. This transforms the traditional logic of "analyzing historical data after the fact" into a logic of "real-time perception of the current state and dynamic calculation of peak potential," thereby enabling real-time capture of changes in unit operating status, fully reflecting the unit's dynamic adjustment potential, and providing more timely and effective real-time evaluation results of the peak capacity of coal-fired power units.

[0083] This application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the real-time evaluation method for the peak capacity of coal-fired power units as described above.

[0084] This application provides an electronic device, which includes at least one processor, at least one memory and a bus connected to the processor; wherein the processor and the memory communicate with each other through the bus; the processor is used to call program instructions in the memory to execute the real-time evaluation method for the peak capacity of coal-fired power units as described above.

[0085] This application also provides a computer program product that, when executed on a data processing device, is suitable for performing steps of a real-time assessment method for initializing the peak capacity of coal-fired power units.

[0086] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0087] In a typical configuration, the device includes one or more processors (CPUs), memory, and a bus. The device may also include input / output interfaces, network interfaces, etc.

[0088] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and memory includes at least one memory chip. Memory is an example of computer-readable media.

[0089] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0090] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0091] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0092] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for real-time evaluation of the peak capacity of a coal-fired power unit, characterized in that, The method includes: Determine the load-carrying capacity correlation parameters of coal-fired power units. The parameters include a first type of parameters and a second type of parameters. The first type of parameters are load-sensitive parameters that are strongly correlated with the load. The first type of parameters includes at least: induced draft fan current, forced draft fan current, primary air fan current, and air preheater differential pressure. The second type of parameters are load-insensitive parameters. The second type of parameters includes at least: coal mill output and environmental emission parameters. Based on the real-time parameter values ​​of the first type of parameters corresponding to the actual operating conditions of the coal-fired power unit, a pre-set step-by-step load prediction method is adopted to determine the real-time load upper limit corresponding to the first type of parameters, which is used to reverse characterize the output of the target induced draft fan, the output of the target forced draft fan, the output of the target primary air fan, and the pressure difference of the target air preheater. Based on the rigid constraints of environmental protection standards, the first load limit of the environmental protection parameters is determined; Based on the number of coal mills, reserve margin, and rated output constraints of the units, the second load limit of the coal mills is quantified. By integrating the target induced draft fan output, the target forced draft fan output, the target primary air fan output, the target air preheater differential pressure, the first load limit, and the second load limit, the minimum value among them is taken as the real-time evaluation result of the peak capacity of the coal-fired power unit.

2. The method according to claim 1, characterized in that, The real-time parameter values ​​of the first type of parameters based on the actual operating conditions of the coal-fired power units are used to determine the real-time load upper limit corresponding to the first type of parameters using a pre-set step-by-step approximation load prediction method, including: If the deviation between the current actual load and the unit's rated load exceeds a preset percentage, the load and the corresponding coal quantity will be adjusted according to the preset range. Based on the adjusted load and coal quantity values, at least one corresponding historical operating condition data is retrieved from the historical operating database, which records the performance of various parameters of the unit under different load and coal quantity inputs. The historical operating condition data is averaged to obtain the target operating condition data, which includes at least historical average data based on adjusted load and coal quantity. The target operating condition data is input into a pre-built analysis model of the mapping relationship between unit load and first type of parameters for processing, so as to predict and output the target parameter value of the first type of parameters. By comparing the target parameter value of the first type of parameter with the safe operating limit value of each parameter of the unit, it is determined whether the current adjustment range is within the safe operating range of the unit; If it is within the safe operating range of the unit, the operation of predicting the new target parameter value of the first type of parameter is iteratively executed by gradually increasing the preset adjustment range until it is determined that the new adjustment range is no longer within the safe operating range of the unit; based on the iterative operation of gradually increasing the preset adjustment range, the maximum load that the unit can reach is predicted, which is used as the load limit corresponding to the first type of parameter. If it is not within the safe operating range of the unit, the preset adjustment range is gradually reduced to iteratively execute the operation of predicting the new target parameter value of the first type of parameter until it is determined that the new adjustment range is within the safe operating range of the unit; based on the new target parameter value predicted corresponding to the final determined new adjustment range, the maximum load that the unit can reach is predicted as the real-time load limit corresponding to the first type of parameter.

3. The method according to claim 2, characterized in that, After accumulating multiple iterations to predict the target parameter values ​​of the first type of parameters, the method further includes: Obtain the target parameter value of the first type of parameter obtained in each iteration operation; If the deviation between the target parameter value of the first type of parameter and the actual parameter value at the same time is found to be greater than a preset threshold, the deviation matrix is ​​used to correct the analysis model of the mapping relationship between the unit load and the first type of parameter.

4. The method according to any one of claims 1 to 3, characterized in that, The second load limit for the coal mills, quantified based on the number of coal mills, reserve margin, and rated output constraints, includes: The output margin of a coal mill is obtained by calculating the rated output of a single coal mill with the total number of available mills, and the current output of a single coal mill with the number of currently operating mills. The standby output of the coal mill is calculated by using the rated output of a single coal mill and the number of standby mills. Based on the coal mill output margin and the coal mill standby output, the peak capacity of the unit with the coal mill output limit is calculated using a preset coal mill output formula. The second load limit of the coal mill is obtained by constraining the peak capacity of the unit by the rated output of the unit.

5. The method according to claim 4, characterized in that, The process of determining the second load limit for the coal mill based on the rated output of the unit and the peak capacity of the coal mill, including: If the peak capacity of the unit limited by the output of the coal mill is greater than the rated output of the unit, then the rated output of the unit shall be used as the second load limit of the coal mill. If the peak capacity of the unit limited by the output of the coal mill is not greater than the rated output of the unit, then the peak capacity of the unit limited by the output of the coal mill shall be used as the second load limit of the coal mill.

6. The method according to claim 4, characterized in that, The preset coal mill output formula includes: ； in, The unit's peak capacity, limited by the output of the coal mill, is a variable. For the actual output of the current generating units, To ensure sufficient output margin for the coal mill, This is the total output of the existing coal mills.

7. A real-time assessment device for the peak capacity of a coal-fired power unit, characterized in that, The device includes: The first determining unit is used to determine the load-carrying capacity related parameters of the coal-fired power unit. The parameters include a first type of parameters and a second type of parameters. The first type of parameters are load-sensitive parameters that are strongly correlated with the load. The first type of parameters includes at least: induced draft fan current, forced draft fan current, primary air fan current, and air preheater differential pressure. The second type of parameters are load-insensitive parameters. The second type of parameters includes at least coal mill output and environmental emission parameters. The first processing unit is used to determine the real-time load upper limit corresponding to the first type of parameters based on the real-time parameter values ​​of the first type of parameters corresponding to the actual operating conditions of the coal-fired power unit, using a preset step-by-step load prediction method, and is used to reverse characterize the output of the target induced draft fan, the output of the target forced draft fan, the output of the target primary air fan, and the pressure difference of the target air preheater. The second processing unit is used to determine the first load limit of the environmental protection parameters based on the rigid constraints of environmental protection standards. The third processing unit is used to quantify the second load limit of the coal mill based on the number of coal mills, the reserve margin, and the rated output constraint of the unit. The second determining unit is used to integrate the target induced draft fan output, the target forced draft fan output, the target primary air fan output, the target air preheater differential pressure, the first load limit, and the second load limit, and take the minimum value among them as the real-time evaluation result of the peak capacity of the coal-fired power unit.

8. The apparatus according to claim 7, characterized in that, The third processing unit is specifically used for: The output margin of a coal mill is obtained by calculating the rated output of a single coal mill with the total number of available mills, and the current output of a single coal mill with the number of currently operating mills. The standby output of the coal mill is calculated by using the rated output of a single coal mill and the number of standby mills. Based on the coal mill output margin and the coal mill standby output, the peak capacity of the unit with the coal mill output limit is calculated using a preset coal mill output formula. The second load limit of the coal mill is obtained by constraining the peak capacity of the unit by the rated output of the unit.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements a real-time assessment method for the peak capacity of a coal-fired power unit as described in any one of claims 1-6.

10. An electronic device, characterized in that, The device includes at least one processor, and at least one memory and bus connected to the processor; The processor and the memory communicate with each other via the bus. The processor is used to call program instructions in the memory to execute the real-time assessment method for the peak capacity of coal-fired power units as described in any one of claims 1-6.

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