Steam turbine set steam flow distribution method, equipment, medium and product

Abstract

The invention discloses a steam turbine set steam flow distribution method and device, a medium and a product, and relates to the field of steam turbine optimizing.The method comprises the steps that according to operation data in a thermal power plant, the generating capacity and the external heat supply capacity of each steam turbine in the thermal power plant are determined through a prediction model; the prediction model is obtained by training a neural network by adopting a training sample set in advance; based on the user heat energy demand quantity, the user electric energy demand quantity, the generating capacity of each steam turbine in the thermal power plant and the external heat supply quantity, a steam flow distribution model is established with the minimum sum of the total main steam flow and the total main steam change amplitude of the steam turbines in the thermal power plant as the target; the steam flow distribution model is solved, the main steam flow distributed to each steam turbine in the thermal power plant is determined, and the main steam flow of each steam turbine in the thermal power plant is adjusted. The distribution efficiency and precision of the steam flow of the turboset are improved, and the operation stability of the turboset is guaranteed while the energy consumption of a thermal power plant is reduced.

Description

Technical Field

[0001] The present application relates to the field of steam turbine optimization, and in particular to a method, equipment, medium and product for distributing steam flow of a steam turbine unit based on a neural network. Background Art

[0002] With the development of economy, the demand for industrial heating is increasing day by day, and the energy conversion efficiency of cogeneration has obvious advantages. Therefore, the back-pressure steam turbine unit has been vigorously developed. Its working principle combines the characteristics of pure condensing steam turbine and back-pressure steam turbine, aiming to improve energy utilization efficiency and meet the demand for heating at the same time.

[0003] The steam flow distribution optimization system is an advanced dispatching strategy used in the field of power production, which aims to optimize the economy and efficiency of power plant operation. The system intelligently distributes the steam flow that each steam turbine should bear according to the thermal performance characteristics of each unit in the power plant, the current operating status, and the total steam flow demand of external users. In this way, it can ensure that the coal consumption is minimized throughout the power plant while ensuring the stable operation of the thermal power plant.

[0004] At present, the steam flow distribution of steam turbines is mainly based on manual experience and the characteristic curves at the factory, and is averaged or weighted. The efficiency (heat consumption) curve of the steam turbine unit is usually obtained from the manufacturer's experiments under specific operating conditions. However, due to the influence of the actual operating environment, the characteristic curves provided by the manufacturer are usually not accurate enough. For example, ambient temperature, pressure changes, equipment aging, maintenance status and other operating variables may affect the actual performance of the equipment and cannot be applied to actual working conditions. Moreover, the goal of modeling the distribution scheme is usually to minimize energy consumption, without considering the stability of the steam turbine unit, resulting in low distribution efficiency and easy energy waste. Summary of the invention

[0005] The purpose of the present application is to provide a method, device, medium and product for distributing steam flow of a steam turbine unit, which can improve the distribution efficiency and accuracy of the steam flow of the steam turbine unit.

[0006] To achieve the above objectives, this application provides the following solutions:

[0007] In a first aspect, the present application provides a method for distributing steam flow of a steam turbine unit, comprising:

[0008] Obtaining the operation data of the thermal power plant, the heat energy demand of users and the electricity energy demand of users;

[0009] According to the operation data, a prediction model is used to determine the power generation and external heating supply of each steam turbine in the thermal power plant; the prediction model is obtained by pre-training a neural network using a training sample set, and each training sample in the training sample set includes sample operation data, sample power generation of each steam turbine, and sample external heating supply of each steam turbine;

[0010] Based on the user's heat energy demand, the user's electric energy demand, the power generation of each steam turbine in the thermal power plant and the external heat supply, a steam flow distribution model is established with the goal of minimizing the sum of the total main steam flow and the total main steam change amplitude of the steam turbine in the thermal power plant;

[0011] The steam flow distribution model is solved to determine the main steam flow allocated to each steam turbine in the thermal power plant, and the main steam flow of each steam turbine in the thermal power plant is adjusted.

[0012] In a second aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned method for distributing steam flow of a steam turbine unit.

[0013] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the above-mentioned method for distributing steam flow of a steam turbine unit is implemented.

[0014] In a fourth aspect, the present application provides a computer program product, including a computer program, which implements the above-mentioned steam flow distribution method for a steam turbine unit when executed by a processor.

[0015] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0016] The present application provides a method, device, medium and product for distributing steam flow of a steam turbine unit. By collecting operating data in a thermal power plant, a neural network algorithm is used to predict the power generation and external heating supply of each steam turbine, thereby avoiding the problem of inaccurate measurement of steam turbine efficiency due to the inconsistency between the measurement environment when using the factory characteristic curve and the actual operating environment. Based on the user's thermal energy demand, the user's electrical energy demand, the power generation and external heating supply of each steam turbine in the thermal power plant, a steam flow distribution model is established with the goal of minimizing the sum of the total main steam flow and the total main steam variation amplitude of the steam turbine in the thermal power plant. At the same time, the stability and energy consumption of the steam turbine unit are taken into consideration, thereby improving the distribution efficiency and accuracy of the steam flow of the steam turbine unit, thereby reducing the energy consumption of the thermal power plant while ensuring the operating stability of the steam turbine unit. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 This is an application environment diagram of a steam flow distribution method for a steam turbine unit in one embodiment of the present application;

[0019] Figure 2 A schematic flow chart of a method for distributing steam flow of a steam turbine unit provided in one embodiment of the present application;

[0020] Figure 3 A process block diagram of a steam flow distribution method for a steam turbine unit provided in one embodiment of the present application;

[0021] Figure 4 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0023] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0024] The steam flow distribution method for a steam turbine unit provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, or it can be integrated on the server 104, or it can be placed on the cloud or other servers. The terminal 102 can send the operating data in the thermal power plant to the server 104. After the server 104 receives the operating data in the thermal power plant, it determines the power generation demand value and the external heat demand value based on the neural network, and establishes a steam flow distribution model to determine the main steam flow allocated to each steam turbine in the thermal power plant. The server 104 can feedback the main steam flow allocated to each steam turbine to the terminal 102. In addition, in some embodiments, the steam flow distribution method of the steam turbine unit can also be implemented separately by the server 104 or the terminal 102.

[0025] The terminal 102 may be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, IoT devices, and portable wearable devices. The IoT devices may be smart speakers, smart TVs, smart air conditioners, smart vehicle-mounted devices, etc. The portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or may be a cloud server.

[0026] In an exemplary embodiment, Figure 2 As shown, a method for distributing steam flow of a steam turbine unit is provided. The method is executed by a computer device, and can be executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used as an example to illustrate the method, which includes the following steps 201 to 204. Among them:

[0027] Step 201, obtaining the operation data of the thermal power plant, the heat energy demand of the users and the electric energy demand of the users.

[0028] Specifically, the operating data includes condenser back pressure, main steam pressure, main steam temperature, reheat pressure, reheat steam temperature, feed water temperature, valve opening and main steam flow.

[0029] Step 202, based on the operation data, a prediction model is used to determine the power generation and external heat supply of each steam turbine in the thermal power plant. The prediction model is obtained by pre-training a neural network using a training sample set, and each training sample in the training sample set includes sample operation data, sample power generation of each steam turbine, and sample external heat supply of each steam turbine.

[0030] In a specific example, the prediction model includes a power generation prediction model and a heat prediction model.

[0031] The training sample set includes a first training sample set and a second training sample set.

[0032] Each training sample in the first training sample set includes sample operation data and sample power generation of each steam turbine. The power generation prediction model is obtained by pre-training a neural network using the first training sample set. The present application uses the power generation prediction model to determine the power generation of each steam turbine in the thermal power plant based on the operation data.

[0033] Each training sample in the second training sample set includes sample operation data and the sample external heat supply of each steam turbine. The heat prediction model is obtained by pre-training a neural network with the second training sample set. According to the operation data, this application uses the heat prediction model to determine the external heat supply of each steam turbine in the thermal power plant.

[0034] In a specific example, both the power generation prediction model and the heat prediction model include a multi-layer fully connected neural network connected in sequence.

[0035] The prediction model is:

[0036] A l = g l (W l ·A l-1 + b l );

[0037] Among them, A l is the output value of the l-th layer of the fully connected neural network, W l is the weight matrix of the l-th layer of the fully connected neural network, b l is the bias term matrix of the l-th layer of the fully connected neural network, g l is the activation function of the l-th layer of the fully connected neural network, 0 < l ≤ L, L is the number of layers of the fully connected neural network. When l = 1, A l-1 = A 0 represents the operation data, and the output value of the L-th layer of the fully connected neural network represents the power generation of each steam turbine in the thermal power plant or the external heat supply of each steam turbine in the thermal power plant.

[0038] Taking the first layer of the fully connected neural network as an example, its operation process is: initialize W 1 = [0.02, 0.15, 0.23, 0.05, 0.69, 0.77, 0.12, 0.88] T , A 0 = [-0.03, 0.11, -0.53, 0.15, 0.29, 0.07, -0.32, 0.78] T , b 1 = [0.03], then [W 1 T A 0 + b 1 = 0.02 * -0.03 + 0.15 * 0.11 + 0.23 * -0.53 + 0.05 * 0.15 + 0.69 * 0.29 + 0.77 * 0.07 + 0.12 * -0.32 + 0.88 * 0.78 + 0.03 = 0.8335.

[0039] Specifically, such as Figure 3 ​As shown in the figure, according to the operation data of the distributed control system (DCS) in the thermal power plant, 10 point data including condenser back pressure, main steam pressure, main steam temperature, reheat pressure, reheat steam temperature, feed water temperature, valve opening, main steam flow (i.e. load), power generation, and external heating are obtained and stored in the database as initial data. After data cleaning, conversion, and standardization, the initial data is stored in the database again as a training sample set. The entire prediction model includes an input layer, a hidden layer, and an output layer, and determines the optimal efficiency of each steam turbine under different loads (main steam flow) based on a deep neural network.

[0040] The training goal of the prediction model is to minimize the loss function: Among them, Loss is the loss function value, y i is the actual power generation (or actual external heat supply) of the i-th steam turbine, is the predicted value of the power generation of the i-th steam turbine (or the predicted value of the external heat supply). By minimizing the loss function value, the predicted value of the power generation of the steam turbine (or the predicted value of the external heat supply) is closer to the value under the actual working conditions.

[0041] In this example, the back propagation algorithm is used to l and b l Update, according to the chain rule pass Complete W l and b l The update of l is the intermediate quantity of the l-th layer of the fully connected neural network, z l =W l ·A l-1 +b l , α is the learning rate, usually set to a very small value, dW l W l The partial derivative of l for b l The partial derivative of dA l A l The partial derivative of dz l For z l Repeatedly update W l and b l , until W l and b l When the change value of is less than the set threshold or the change value of Loss is less than the set threshold or the preset number of training times is reached, the model is saved.

[0042] The input data of the prediction model include: ① the actual values ​​of seven variables: condenser back pressure, main steam pressure, main steam temperature, reheat pressure, reheat steam temperature, feed water temperature, and valve opening; ② the main steam flow rate, which is segmented according to the main steam flow rate range. For example, if the main steam flow rate range is 0 to 500, then [0, 1, 2, ..., 500] are predicted for a total of 500 main steam flow rates. The output data of the prediction model are the power generation and external heat supply of the steam turbine under different main steam flow rates.

[0043] Furthermore, the efficiency of the steam turbine can be determined according to the main steam flow rate (turbine efficiency = main steam flow rate / power generation), and multiple main steam flow rates and their corresponding steam turbine efficiencies constitute an efficiency curve. The present application obtains the actual efficiency of the steam turbine under different loads (main steam flow rates) in real time through neural network modeling, avoiding the problem of inaccurate measurement of the steam turbine efficiency due to the inconsistency between the measurement environment when using the factory characteristic curve and the actual operating environment.

[0044] In order to timely capture changes in the on-site environment, the steam flow distribution method for the steam turbine unit also includes: obtaining historical operating data in the thermal power plant, the historical power generation of each steam turbine, and the historical external heating supply of each steam turbine at set time intervals, and using the historical operating data in the thermal power plant, the historical power generation of each steam turbine, and the historical external heating supply of each steam turbine as a training sample set to update the parameters of the prediction model.

[0045] The application updates the parameters of the prediction model once an hour, and predicts the turbine-related data based on the real-time operating data by updating the saved prediction model every hour.

[0046] In another specific example, the prediction model may also use other machine learning algorithms, such as ensemble tree models, linear models, other neural network structures, etc., which can be selected according to actual needs. Among them, ensemble tree models include gradient boosting decision tree (GradientBoosting DecisionTree, GBDT), random forest (Random Forest, RF), distributed gradient boosting library (XGBoost), light gradient boosting machine (Light Gradient Boosting Machine, LightGBM), category feature boosting algorithm (Categoricalboosting, CatBoost), etc.

[0047] Step 203, based on the user's thermal energy demand, the user's electrical energy demand, the power generation of each steam turbine in the thermal power plant and the external heat supply, with the goal of minimizing the sum of the total main steam flow and the total main steam variation amplitude of the steam turbines in the thermal power plant.

[0048] The objective function of the steam flow distribution model is:

[0049]

[0050] Among them, F is the objective function value, n is the number of steam turbines in the thermal power plant, and x i is the main steam flow rate allocated to the i-th steam turbine, λ is a constant, Δx i is the main steam variation amplitude of the i-th steam turbine, Δx i =abs(x i -x i1 ), x i1 is the main steam flow rate allocated to the i-th steam turbine at the previous moment, abs represents the absolute value, through Inhibit x i The change range makes the turbine throttle change relatively stable.

[0051] The objective function of the steam flow distribution model established in this application is to minimize the main steam flow demand of the entire steam turbine unit, thereby reducing energy consumption. It not only minimizes energy consumption, but also takes into account the stable operation of the steam turbine unit.

[0052] The constraints of the steam flow distribution model are:

[0053]

[0054] x i ,Δx i ∈(x imin ,x imax );

[0055] x di ∈(x dimin ,x dimax );

[0056] x pi ∈(x pimin ,x pimax );

[0057] Where n is the number of steam turbines in the thermal power plant, x i is the main steam flow rate allocated to the i-th steam turbine, Δx i is the main steam variation range of the i-th steam turbine, x di The i-th steam turbine has a steam intake of x. i External heating supply at time x pi The i-th steam turbine has a steam intake of x. i The power generation at that time, D is the user's heat demand, that is, the heating demand of all users, P is the user's power demand, that is, the power generation demand of all users, x iminis the minimum steam flow rate of the i-th steam turbine, x imax is the maximum steam flow rate of the i-th steam turbine, x dimin is the minimum heat supply of the i-th steam turbine, x dimax is the maximum heat supply of the i-th steam turbine, x pimin is the minimum power supply of the i-th steam turbine, x pimax is the maximum power supply of the i-th steam turbine.

[0058] Step 204, solving the steam flow distribution model, determining the main steam flow distributed to each steam turbine in the thermal power plant, and adjusting the main steam flow of each steam turbine in the thermal power plant.

[0059] The present application proposes a set of technical solutions that can be applied to actual working conditions. By collecting, storing and processing the operating data of the steam turbine and its related data, and using neural network modeling, the efficiency of the steam turbine under different main steam flow rates is predicted, and the efficiency curve applicable to the actual working conditions is obtained. The steam inlet amount of each steam turbine in the steam turbine unit is optimized and scheduled through integer programming modeling. The optimized scheduling result can reduce the energy consumption of the thermal power plant while ensuring the stable operation of the steam turbine unit, avoid energy waste caused by unreasonable allocation, and improve the overall economic benefits of the steam turbine unit and the thermal power plant.

[0060] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 4 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store operating data in a thermal power plant. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a steam flow distribution method for a steam turbine unit is implemented.

[0061] Those skilled in the art will understand that Figure 4The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0062] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0063] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0064] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0065] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can 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 can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0066] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0067] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0068] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for distributing steam flow of a steam turbine unit, characterized in that: The steam flow distribution method of the steam turbine unit comprises: Obtaining the operation data of the thermal power plant, the heat energy demand of users and the electricity energy demand of users; According to the operation data, a prediction model is used to determine the power generation and external heating supply of each steam turbine in the thermal power plant; the prediction model is obtained by pre-training a neural network using a training sample set, and each training sample in the training sample set includes sample operation data, sample power generation of each steam turbine, and sample external heating supply of each steam turbine; Based on the user's heat energy demand, the user's electric energy demand, the power generation of each steam turbine in the thermal power plant and the external heat supply, a steam flow distribution model is established with the goal of minimizing the sum of the total main steam flow and the total main steam change amplitude of the steam turbine in the thermal power plant; The steam flow distribution model is solved to determine the main steam flow allocated to each steam turbine in the thermal power plant, and the main steam flow of each steam turbine in the thermal power plant is adjusted.

2. The method for distributing steam flow of a steam turbine unit according to claim 1, characterized in that: The operating data include condenser back pressure, main steam pressure, main steam temperature, reheat pressure, reheat steam temperature, feed water temperature, valve opening and main steam flow.

3. The method for distributing steam flow of a steam turbine unit according to claim 1, characterized in that: The prediction model includes a power generation prediction model and a heat prediction model; the training sample set includes a first training sample set and a second training sample set; each training sample in the first training sample set includes sample operation data and sample power generation of each steam turbine; each training sample in the second training sample set includes sample operation data and sample external heat supply of each steam turbine; the power generation prediction model is obtained by pre-training a neural network using the first training sample set; the heat prediction model is obtained by pre-training a neural network using the second training sample set; Based on the operating data, a prediction model is used to determine the power generation and external heat supply of each steam turbine in the thermal power plant, including: Determining the power generation of each steam turbine in the thermal power plant according to the operating data and using the power generation prediction model; The external heat supply of each steam turbine in the thermal power plant is determined according to the operating data and using the heat prediction model.

4. The method for distributing steam flow of a steam turbine unit according to claim 3, characterized in that: The power generation prediction model and the heat prediction model both include multiple layers of fully connected neural networks connected in sequence; The prediction model is: A l =g l (W l ·A l-1 +b l ); Among them, A l is the output value of the fully connected neural network of the l-th layer, W l is the weight matrix of the fully connected neural network of the l-th layer, b l is the bias term matrix of the fully connected neural network of the l-th layer, g l is the activation function of the fully connected neural network of the l-th layer, 0 < l ≤ L, where L is the number of layers of the fully connected neural network. When l = 1, A l-1 = A 0 represents the operation data, and the output value of the fully connected neural network of the L-th layer represents the power generation of each steam turbine in the thermal power plant or the external heat supply of each steam turbine in the thermal power plant.

5. The method for distributing steam flow of a steam turbine unit according to claim 1, characterized in that: The steam turbine unit steam flow distribution method further comprises: The historical operating data in the thermal power plant, the historical power generation of each steam turbine and the historical external heating supply of each steam turbine are obtained at set time intervals, and the historical operating data in the thermal power plant, the historical power generation of each steam turbine and the historical external heating supply of each steam turbine are used as training sample sets to update the parameters of the prediction model.

6. The method for distributing steam flow of a steam turbine unit according to claim 1, characterized in that: The objective function of the steam flow distribution model is: Among them, F is the objective function value, n is the number of steam turbines in the thermal power plant, and x i is the main steam flow rate allocated to the i-th steam turbine, λ is a constant, Δx i is the main steam variation amplitude of the i-th steam turbine.

7. The method for distributing steam flow of a steam turbine unit according to claim 1, characterized in that: The constraints of the steam flow distribution model are: x i ,Δx i ∈(x imin ,x imax ); x di ∈(x dimin ,x dimax ); x pi ∈(x pimin ,x pimax ); Where n is the number of steam turbines in the thermal power plant, x i is the main steam flow rate allocated to the i-th steam turbine, Δx i is the main steam variation range of the i-th steam turbine, x di The i-th steam turbine has a steam intake of x. i External heating supply at time x pi The i-th steam turbine has a steam intake of x. i The power generation at that time, D is the user's thermal energy demand, P is the user's electrical energy demand, x imin is the minimum steam flow rate of the i-th steam turbine, x imax is the maximum steam flow rate of the i-th steam turbine, x dimin is the minimum heat supply of the i-th steam turbine, x dimax is the maximum heat supply of the i-th steam turbine, x pimin is the minimum power supply of the i-th steam turbine, x pimax is the maximum power supply of the i-th steam turbine.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for distributing steam flow of a steam turbine unit according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for distributing steam flow of a steam turbine unit according to any one of claims 1 to 7 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for distributing steam flow of a steam turbine unit according to any one of claims 1 to 7 is implemented.

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