Thermodynamic system parameter optimization method and system and electronic equipment

Abstract

The invention discloses a thermodynamic system parameter optimization method and system and electronic equipment, and the method comprises the steps: carrying out the configuration modeling of a thermodynamic system, building a thermodynamic system model, and generating a mathematical relation between the economic indexes of the thermodynamic system and the parameters of the thermodynamic system based on the thermodynamic system model; and based on the mathematical relationship, taking the thermodynamic system parameters as optimization variables, taking the thermodynamic system economic indexes as optimization targets, setting optimization control parameters, and optimizing the thermodynamic system through an optimization algorithm. The thermodynamic system is optimized after being subjected to configuration modeling, details can be fully considered, and the optimization effect is better. Optimization variables are not limited to regenerative and reheating parameters, and any parameter in the thermodynamic system can be used as the optimization variable; the optimization target is not limited to the heat consumption rate, all parameters capable of being explicitly calculated in the thermodynamic system can serve as the optimization target, and universality is high; after optimization, parameter values of the thermodynamic system can be directly obtained, application is convenient, and practicability is good.

Description

Technical Field

[0001] The present invention relates to the technical field of thermal power generation, and in particular to a thermal system parameter optimization method, system and electronic equipment. Background Art

[0002] The quality of the thermal system's heat recovery and reheat parameters plays a crucial role in the unit's economic performance. Optimizing these parameters is one of the primary means of improving thermal system economics. Traditional parameter optimization is based on certain assumptions, simplifying the thermal system (for example, ignoring pipeline losses, valve losses, turbine inlet / exhaust pressure drops, and so on, and only considering the basic heat recovery and reheat cycles). This generates a mathematical relationship between the thermal system's economic indicators and the thermal system's parameters. Based on this mathematical relationship, the thermal system is optimized, using the thermal system parameters as optimization variables and the thermal system's economic indicators as the optimization targets.

[0003] In summary, current thermal system optimization is based on relatively simple calculation models, without detailed modeling of the thermal system, making it difficult to fully consider the system. Furthermore, due to the simplification of the thermal system, the optimization variables and optimization objectives used during optimization are significantly limited. Summary of the Invention

[0004] The purpose of the present invention is to provide a method, system and electronic equipment for optimizing thermal system parameters, which are used to address the problems currently existing in the optimization of thermal systems. By configuring and modeling the thermal system before optimizing, it is convenient to fully consider the details and achieve better optimization results. The optimization variables are not limited to heat recovery and reheat parameters, and any parameter in the thermal system can be selected as the optimization variable; the optimization target is not limited to the heat consumption rate, and any result parameter that can be explicitly calculated in the thermal system can be selected as the optimization target, which has strong versatility; after optimization, the parameter value of the thermal system can be directly obtained, which is convenient for application and has good practicality.

[0005] In order to achieve the above objectives, the present invention is implemented through the following technical solutions:

[0006] A first aspect of the present invention provides a method for optimizing thermal system parameters, comprising:

[0007] Performing configuration modeling on the thermal system, establishing a thermal system model, and generating a mathematical relationship between thermal system economic indicators and thermal system parameters based on the thermal system model;

[0008] Based on the mathematical relationship, the thermal system parameters are used as optimization variables, the thermal system economic indicators are used as optimization targets, optimization control parameters are set, and the thermal system is optimized through an optimization algorithm.

[0009] Optionally, performing the configuration modeling on the thermal system and establishing the thermal system model includes:

[0010] Based on the thermal design simulation platform, according to the physical working mechanism of each thermal device in the thermal system, each thermal device is encapsulated as a specified configuration element, the characteristic parameters of the thermal device are set in the corresponding configuration element, the boundary conditions of the thermal system under various working conditions are placed at the ports of the configuration element, and the thermal system model is constructed through the topological connection relationship between the configuration elements.

[0011] Optionally, generating the mathematical relationship between the thermal system economic index and the thermal system parameter based on the thermal system model includes:

[0012] The thermal system parameters of the thermal system are initially set, and the initially set thermal system parameters are input into the thermal system model for calculation to obtain the initial thermal system economic index, thereby obtaining the mathematical relationship between the thermal system parameters and the thermal system economic index.

[0013] Optionally, the setting of the optimization control parameters includes:

[0014] Setting a weight for each of said optimization variables; and

[0015] Set the logical relationship between the different optimization variables.

[0016] Optionally, the optimization algorithm is scalable and includes at least a particle swarm optimization algorithm and a differential evolution algorithm.

[0017] Optionally, the optimization variable is a direct parameter of the thermal system model; and the optimization target is a direct parameter of the thermal system model or a formula composed of multiple direct parameters.

[0018] A second aspect of the present invention provides a thermal system parameter optimization system, comprising a modeling module, an optimization algorithm module, and a display module;

[0019] The modeling module is used to perform configuration modeling on the thermal system, establish a thermal system model, and generate a mathematical relationship between the thermal system economic index and the thermal system parameters based on the thermal system model;

[0020] The optimization algorithm module is connected to the modeling module and is used to set optimization control parameters based on the mathematical relationship, using the thermal system parameters as optimization variables and the thermal system economic indicators as optimization targets, and to optimize the thermal system through the optimization algorithm;

[0021] The display module is connected to the optimization algorithm module and is constructed as a visual interface for defining the optimization target, selecting the optimization variables, setting the optimization control parameters, selecting the optimization algorithm, and displaying the optimization process and optimization scheme of the optimization algorithm module.

[0022] Optionally, the optimization algorithm module is used to sort the multiple optimization schemes according to optimization effects;

[0023] The display module is used to display the values of the optimization variables and the optimization objectives of the multiple optimization solutions according to the sorting results.

[0024] A third aspect of the present invention provides an electronic device, comprising a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, the method according to any one of the above-mentioned first aspects is implemented.

[0025] The present invention has at least the following technical effects:

[0026] By configuring and modeling the thermal system before optimizing it, the mathematical relationship between the optimization variables and the optimization objectives is not simplified, which makes it easier to fully consider the details and achieve better optimization results.

[0027] Optimization variables are not limited to heat recovery and reheat parameters; any parameter in the thermal system can be selected as an optimization variable. Optimization objectives are not limited to heat rate; any result parameter in the thermal system that can be explicitly calculated can be selected as an optimization objective, which is highly versatile.

[0028] After optimization, the thermal system model and parameters can be directly obtained, which is easy to apply and has good practicality. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 A schematic flow chart of a method for optimizing thermal system parameters according to an embodiment of the present invention;

[0030] Figure 2 A logical diagram of an optimization algorithm module provided by an embodiment of the present invention;

[0031] Figure 3 A schematic diagram of an interface of a display module provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0032] The following is a further detailed description of a thermal system parameter optimization method, system and electronic device proposed by the present invention in conjunction with the accompanying drawings and specific embodiments. According to the following description, the advantages and features of the present invention will be clearer. It should be noted that the drawings are in a very simplified form and use non-precise proportions, which are only used to conveniently and clearly assist in explaining the purpose of the embodiments of the present invention. In order to make the purposes, features and advantages of the present invention more obvious and easy to understand, please refer to the accompanying drawings. It should be noted that the structures, proportions, sizes, etc. illustrated in the drawings of this specification are only used to match the contents disclosed in the specification for people familiar with this technology to understand and read, and are not used to limit the limiting conditions for the implementation of the present invention, so they have no technical significance. Any modification of the structure, change in the proportional relationship or adjustment of the size should still fall within the scope of the technical content disclosed by the present invention without affecting the efficacy and purpose that can be achieved by the present invention.

[0033] like Figure 1 As shown, the thermal system parameter optimization method provided in this embodiment includes: performing configuration modeling on the thermal system to establish a thermal system model; generating a mathematical relationship between the thermal system economic index and the thermal system parameters based on the thermal system model; based on the mathematical relationship, using the thermal system parameters as optimization variables and the thermal system economic index as the optimization target, setting optimization control parameters, and optimizing the thermal system using an optimization algorithm.

[0034] In order to implement the above-mentioned thermal system parameter optimization method, this embodiment further provides a thermal system parameter optimization system, including a modeling module, an optimization algorithm module and a display module. The modeling module is used to perform configuration modeling on the thermal system, establish the thermal system model, and generate the mathematical relationship based on the thermal system model. Figure 2 As shown, the optimization algorithm module is connected to the modeling module, and is used to set the optimization control parameters based on the mathematical relationship, with the thermal system parameters as optimization variables, the thermal system economic index as the optimization target, and optimize the thermal system through the optimization algorithm. Figure 3 As shown, the display module is connected to the optimization algorithm module and is constructed as a visual interface for defining the optimization target, selecting the optimization variables, setting the optimization control parameters, selecting the optimization algorithm, and displaying the optimization process of the optimization algorithm module and the generated optimization solution.

[0035] First, in the modeling module, the thermal system can be configured and modeled and calculated using thermodynamic modeling configuration analysis software. The above-mentioned configuration modeling is a technology that configures and sets software functions in a graphical manner. Users can complete the required software functions in a manner similar to "building blocks" without having to write computer programs. Configuration modeling is widely used in many fields such as industrial automation, building automation, energy management, etc., and is mainly used in scenarios such as data acquisition, monitoring and control, and process control. Specifically, in this embodiment, each of the thermal equipment can be encapsulated as a specified configuration element according to the physical working mechanism of each thermal equipment in the thermal system, the characteristic parameters of the thermal equipment can be set in the corresponding configuration element, the boundary conditions of the thermal system under each working condition can be placed at the port of the configuration element, and the thermal system model can be built through the topological connection relationship between the configuration elements.

[0036] After the thermal system model is constructed, the mathematical relationship between the thermal system economic index and the thermal system parameters can be generated based on the thermal system model. Specifically, since the calculation requires a complete thermal boundary to complete, the thermal system parameters of the thermal system (such as the reheat parameter and the reheat parameter) can be initially set based on experience. The initially set thermal system parameters are input into the thermal system model for calculation to obtain the initial thermal system economic index, thereby obtaining the mathematical relationship between the thermal system parameters and the thermal system economic index.

[0037] like Figure 2 As shown, after establishing the mathematical relationship between the thermal system parameters and the thermal system economic indicators based on the thermal system model, the mathematical relationship can be transferred to the optimization algorithm module, and the mathematical relationship between the optimization objective and the optimization variables in the optimization algorithm module can be established accordingly. The mathematical relationship constitutes the mapping relationship between the thermal system economic indicators and the thermal system parameters in each sample (initial sample, intermediate sample, and final sample) in the optimization algorithm.

[0038] like Figure 2 As shown, in the optimization algorithm module, the optimization objective and the optimization variables need to be input. The optimization objective is defined by the user and can be a direct parameter of the thermal system model or a calculation formula composed of multiple direct parameters. For example, in the thermal system of a thermal power plant, the most common thermal system economic indicator is the heat rate. Therefore, the heat rate can be used as the optimization objective, and the optimization objective can be calculated using a formula composed of two direct parameters: generator power and boiler heat release. For another example, when the energy entering the thermal system is determined, the output power of the thermal system, a direct parameter, can be used as the optimization objective.

[0039] The optimization variables are direct parameters of the thermal system, such as reheat pressure, cylinder pressure, heat extraction steam pressure, pressure after the regulating stage, and feedwater temperature. For each optimization sample, the corresponding optimization variables can be mapped to the corresponding boundaries of the thermal system, and the mathematical relationship generated by configuration modeling can be used to calculate the optimization objective for each optimization sample.

[0040] Further, if Figure 2 As shown, in the optimization algorithm module, the optimization algorithm itself also needs to be set, including setting the optimization control parameters and selecting the optimization algorithm. Among them, the optimization control parameters are used to control the weights and mathematical relationships between the optimization variables. Specifically, the weight of each optimization variable can be set. A larger weight can be set for the parameters that are given priority consideration, and a smaller weight can be set for the less important parameters. The logical relationship between two different optimization variables can be set, for example, optimization variable 1> optimization variable 2; the logical relationship between multiple optimization variables can also be set in batches, for example, optimization variable 1> optimization variable 2> optimization variable 3> optimization variable 4; each optimization variable can also be set to be in a free state, that is, without a logical relationship.

[0041] like Figure 3 As shown, the algorithm to be used can be selected in the display module. The types of optimization algorithms can be expanded, for example, to include particle swarm optimization and differential evolution algorithms. After setting the optimization control parameters and selecting the optimization algorithm, the number of optimized populations and the number of iterations can be input, and the optimization algorithm can be started to optimize the thermal system.

[0042] The optimization algorithm module can also be used to sort the multiple optimization schemes according to the optimization effect, that is, to sort the multiple optimization schemes according to the value of the optimization target. For example, when the optimization target is "heat consumption rate" or "minimum value", the multiple optimization schemes can be sorted from small to large according to the heat consumption rate. The display module can display the optimization variables and the values of the optimization targets of the multiple optimization schemes according to the sorting results. Figure 2 As shown, the optimization algorithm module can, for example, output the optimization variables and the optimization objectives of the top-ranked optimization solutions and display them via the display module. For example, when the optimization objectives are "heat rate" and "minimum value," the display module can display the solutions with the lowest heat rate.

[0043] After the optimization is complete, the optimization results can be exported. If you exit the optimization algorithm module directly and return to the modeling module, the parameters of the thermal system will not change and will still be displayed as the pre-optimization solution. If you select an optimization solution and apply it directly to the thermal system before exiting the optimization algorithm module, the parameters of the thermal system (the values of the optimization variables and the optimization target) will be overwritten by the optimization solution and displayed as the post-optimization solution.

[0044] In other aspects, this embodiment further provides an electronic device, including a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, the above-mentioned thermal system parameter optimization method is implemented.

[0045] By configuring and modeling the thermal system before optimizing it, this method allows for full consideration of details and achieves better optimization results. The optimization variables are not limited to heat recovery and reheat parameters; any parameter in the thermal system can be used as an optimization variable. The optimization target is not limited to heat rate; any parameter in the thermal system that can be explicitly calculated can be used as an optimization target, resulting in high versatility. After optimization, the thermal system parameter values can be directly obtained, making it easy to apply and highly practical.

[0046] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.

[0047] It should be noted that the devices and methods disclosed in the embodiments of this document may also be implemented in other ways. The device embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of the devices, methods, and computer program products according to the various embodiments of this document. In this regard, each box in the flowchart or block diagram may represent a module, program, or portion of code, wherein the module, program segment, or portion of code contains one or more executable instructions for implementing a specified logical function, and the module, program segment, or portion of code contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the boxes may also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes may actually be executed substantially in parallel, or they may sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart, may be implemented by a dedicated hardware-based system for performing the specified function or action, or may be implemented by a combination of dedicated hardware and computer instructions.

[0048] In addition, the functional modules in the various embodiments of this document may be integrated together to form an independent part, or each module may exist independently, or two or more modules may be integrated to form an independent part.

[0049] Although the present invention has been described in detail through the above preferred embodiments, it should be understood that the above description is not intended to limit the present invention. After reading the above description, various modifications and substitutions of the present invention will become apparent to those skilled in the art. Therefore, the scope of protection of the present invention should be defined by the appended claims.

Claims

1. A method for optimizing thermal system parameters, characterized in that: include: Performing configuration modeling on the thermal system, establishing a thermal system model, and generating a mathematical relationship between thermal system economic indicators and thermal system parameters based on the thermal system model; Based on the mathematical relationship, the thermal system parameters are used as optimization variables, the thermal system economic indicators are used as optimization targets, optimization control parameters are set, and the thermal system is optimized through an optimization algorithm.

2. The thermal system parameter optimization method according to claim 1, characterized in that: The performing the configuration modeling on the thermal system and establishing the thermal system model comprises: Based on the thermal design simulation platform, according to the physical working mechanism of each thermal device in the thermal system, each thermal device is encapsulated as a specified configuration element, the characteristic parameters of the thermal device are set in the corresponding configuration element, the boundary conditions of the thermal system under various working conditions are placed at the ports of the configuration element, and the thermal system model is constructed through the topological connection relationship between the configuration elements.

3. The thermal system parameter optimization method according to claim 1, characterized in that: Generating the mathematical relationship between the thermal system economic index and the thermal system parameter based on the thermal system model includes: The thermal system parameters of the thermal system are initially set, and the initially set thermal system parameters are input into the thermal system model for calculation to obtain the initial thermal system economic index, thereby obtaining the mathematical relationship between the thermal system parameters and the thermal system economic index.

4. The thermal system parameter optimization method according to claim 1, characterized in that: The setting of the optimization control parameters includes: Setting a weight for each of said optimization variables; and Set the logical relationship between the different optimization variables.

5. The thermal system parameter optimization method according to claim 1, characterized in that: The optimization algorithm is scalable and includes at least a particle swarm algorithm and a differential evolution algorithm.

6. The thermal system parameter optimization method according to claim 1, characterized in that: The optimization variables are direct parameters of the thermal system model; the optimization targets are direct parameters of the thermal system model or a formula composed of multiple direct parameters.

7. A thermal system parameter optimization system, characterized in that: Including modeling module, optimization algorithm module and display module; The modeling module is used to perform configuration modeling on the thermal system, establish a thermal system model, and generate a mathematical relationship between the thermal system economic index and the thermal system parameters based on the thermal system model; The optimization algorithm module is connected to the modeling module and is used to set optimization control parameters based on the mathematical relationship, using the thermal system parameters as optimization variables and the thermal system economic indicators as optimization targets, and to optimize the thermal system through the optimization algorithm; The display module is connected to the optimization algorithm module and is constructed as a visual interface for defining the optimization target, selecting the optimization variables, setting the optimization control parameters, selecting the optimization algorithm, and displaying the optimization process and optimization scheme of the optimization algorithm module.

8. The thermal system parameter optimization system according to claim 7, characterized in that: The optimization algorithm module is used to sort the multiple optimization schemes according to the optimization effect; The display module is used to display the values of the optimization variables and the optimization objectives of the multiple optimization solutions according to the sorting results.

9. An electronic device, characterized in that: The method comprises a processor and a memory, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the method according to any one of claims 1 to 4 is implemented.

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