A Method for Evaluating the Active Power Regulation Capacity of Thermal Power Units Considering Deep Peak Regulation

Abstract

The present invention discloses a method for evaluating the active power regulation ability of a thermal power unit considering deep peak shaving, including: determining key parameters affecting the active power regulation ability of the thermal power unit based on the measured parameters under the normal active power output level and the deep peak shaving active power output level of the thermal power unit; determining the parameter sensitivity analysis boundary of each key parameter according to the key parameters and the difference parameters of the thermal power unit at the normal level and the deep peak shaving level; constructing a parameter change sequence for each key parameter according to the parameter sensitivity analysis boundary of each key parameter; constructing a normalized comprehensive quantification index for the active power regulation of the thermal power unit according to the parameter change sequence of each key parameter; substituting the measured values of the key parameters of the thermal power unit into the normalized comprehensive quantification index to obtain the real-time normalized comprehensive quantification index of the thermal power unit, and determining the active power regulation ability of the thermal power unit based on the real-time normalized comprehensive quantification index.

Description

Technical Field

[0001] The present invention relates to the technical field of power system operation mode screening, and more specifically, to a method for evaluating the active power regulation ability of thermal power units considering deep peak shaving. Background Art

[0002] The continuous increase in the penetration rate of renewable energy has compressed the power generation space of thermal power units. Especially during the period of large-scale renewable energy generation, the thermal power units in the system are often in the deep peak shaving condition, resulting in a reduction in the regulation ability of thermal power units. The reduction in active power regulation caused by large-scale deep peak shaving of thermal power will significantly weaken the active power regulation level of the power system and threaten the safe and stable operation of the power system. However, the current research on the active power regulation ability of thermal power units under deep peak shaving is still relatively preliminary, lacking a method to evaluate the change in the active power regulation ability of thermal power units under different conditions. Therefore, proposing a method for evaluating the active power regulation ability of thermal power units under deep peak shaving is of great significance for evaluating the safe and stable ability of the power grid and then maintaining the safe and stable operation of the power grid.

[0003] Based on the measured parameters of the deep peak shaving units for identification, the regulation characteristics of the units under specific conditions can be obtained. However, there are various types of units, and the parameter changes of different units under different conditions are different. Restricted by experimental costs and operating conditions, it is difficult to comprehensively evaluate the active power regulation characteristics of deep peak shaving units through refined experiments. The deep peak shaving thermal power condition has changed the operating characteristics of the system. Since the measured parameters of deep peak shaving thermal power units are missing in the PSD Power Tools calculation data, it seriously affects the accuracy of power grid safety and stability analysis work and dispatching control schemes, and it is difficult to meet the requirements of future work and research. With a large number of conventional power sources participating in deep peak shaving, the safe and stable characteristics of the power grid will undergo major changes. Evaluating the change in the active power regulation ability of thermal power units under different conditions is of great significance for evaluating the safe and stable ability of the power grid and then maintaining the safe and stable operation of the power grid. In order to accurately grasp the active power regulation ability of the units after deep peak shaving operation, it is urgent to conduct research on the evaluation of the active power regulation ability of deep peak shaving units. Summary of the Invention

[0004] In view of the deficiencies of the prior art, the present invention provides a method for evaluating the active power regulation ability of thermal power units considering deep peak shaving.

[0005] According to one aspect of the present invention, there is provided a method for evaluating the active power regulation ability of thermal power units considering deep peak shaving, including:

[0006] Based on the measured parameters of the thermal power unit under normal active power output level and deep peak shaving active power output level, determine the key parameters affecting the active power regulation ability of the thermal power unit;

[0007] Determine the parameter sensitivity analysis boundaries of each key parameter according to the key parameters and the difference parameters of the thermal power unit under normal level and deep peak shaving level;

[0008] Construct the parameter change sequences of each key parameter according to the parameter sensitivity analysis boundaries of each key parameter;

[0009] Construct the normalized comprehensive quantization index for the active power regulation of the thermal power unit according to the parameter change sequences of each key parameter;

[0010] Substitute the measured values of the key parameters of the thermal power unit into the normalized comprehensive quantization index to obtain the real-time normalized comprehensive quantization index of the thermal power unit, and determine the active power regulation ability of the thermal power unit based on the real-time normalized comprehensive quantization index.

[0011] Optionally, construct the parameter change sequences of each key parameter according to the parameter sensitivity analysis boundaries of each key parameter, including:

[0012] Construct the single-machine infinite-bus model of the thermal power unit;

[0013] Perform power flow calculation on the single-machine infinite-bus model to obtain the power flow calculation file of the thermal power unit;

[0014] Build the thermal power unit model of a typical thermal power unit;

[0015] Based on the power flow calculation file, take values at equal intervals within the parameter sensitivity analysis boundary, modify the parameters in order from small to large, and generate the dynamic parameter files of each key parameter according to the thermal power unit model;

[0016] Construct the parameter change sequences of each key parameter according to the dynamic parameter files of each key parameter, where the parameter change sequences include the key parameter change sequence, the maximum value sequence of the active power change of the thermal power unit, and the maximum value time sequence of the active power change of the thermal power unit.

[0017] Optionally, construct the normalized comprehensive quantization index for the active power regulation of the thermal power unit according to the parameter change sequences of each key parameter, including:

[0018] Fit the parameter change sequences based on the polynomial fitting method to obtain the time-domain fitting curve functions of each key parameter;

[0019] Construct the amplitude index and speed index for the active power regulation of the thermal power unit according to the time-domain fitting curve functions;

[0020] Construct the normalized comprehensive quantization index for the active power regulation of the thermal power unit according to the amplitude index and speed index.

[0021] Optionally, fit the parameter change sequences based on the polynomial fitting method to obtain the time-domain fitting curve functions, including:

[0022] Based on the polynomial fitting method, fit the parameter change sequence to obtain the first time-domain fitting curve function of the maximum active power of the thermal power unit with respect to the key parameter change and the second time-domain fitting region function of the maximum active power moment with respect to the key parameter change.

[0023] Optionally, the expression of the amplitude index is:

[0024]

[0025] The expression of the speed index is:

[0026]

[0027] In the formula, is the measured value of the key parameter, is the first time-domain fitting curve function, is the second time-domain fitting region function, and m is the number of key parameters.

[0028] Optionally, according to the amplitude index and the speed index, construct the normalized comprehensive quantization index for the active power regulation of the thermal power unit, including:

[0029] Construct the comprehensive quantization index for the active power regulation of the thermal power unit according to the amplitude index and the speed index;

[0030] Normalize the comprehensive quantization index to obtain the normalized comprehensive quantization index.

[0031] Optionally, the expression of the comprehensive quantization index is:

[0032]

[0033] In the formula, F A is the amplitude index, and F S is the speed index;

[0034] The expression of the normalized comprehensive quantization index is:

[0035]

[0036] Among them,

[0037]

[0038] In the formula, F A,max is the maximum value of the amplitude index, and F S,min is the minimum value of the speed index, and f y,i,max (x i (t)) is the maximum value of the i-th parameter fitting function, and f z,i,min (xi $(t)$ is the minimum value of the fitting function of the $i$-th parameter is the measured value of the key parameter, and $m$ is the number of key parameters.

[0039] According to another aspect of the present invention, there is provided an active power regulation ability evaluation device for a thermal power unit considering deep peak shaving, including:

[0040] A first determination module, configured to determine key parameters affecting the active power regulation ability of the thermal power unit based on measured parameters under the normal active power output level and the deep peak shaving active power output level of the thermal power unit;

[0041] A second determination module, configured to determine the parameter sensitivity analysis boundary of each key parameter according to the key parameter and the difference parameter of the thermal power unit under the normal level and the deep peak shaving level;

[0042] A first construction module, configured to construct a parameter change sequence of each key parameter according to the parameter sensitivity analysis boundary of each key parameter;

[0043] A second construction module, configured to construct a normalized comprehensive quantization index for the active power regulation of the thermal power unit according to the parameter change sequence of each key parameter;

[0044] An acquisition module, configured to substitute the measured value of the key parameter of the thermal power unit into the normalized comprehensive quantization index, obtain the real-time normalized comprehensive quantization index of the thermal power unit, and determine the active power regulation ability of the thermal power unit according to the real-time normalized comprehensive quantization index.

[0045] According to yet another aspect of the present invention, there is provided a computer-readable storage medium storing a computer program for executing the method described in any of the above aspects of the present invention.

[0046] According to yet another aspect of the present invention, there is provided an electronic device including: a processor; a memory for storing executable instructions of the processor; the processor is configured to read the executable instructions from the memory and execute the instructions to implement the method described in any of the above aspects of the present invention.

[0047] Therefore, a method for evaluating the active power regulation ability of thermal power units considering deep peak shaving provided by the present invention aims to address the problem of insufficient understanding of the active power regulation ability of thermal power units under the current deep peak shaving state. By measuring the parameters of normal operating conditions and extreme deep peak shaving operating conditions, the difference parameters between deep peak shaving conditions and normal conditions and the possible change ranges of the difference parameters are determined. Based on the parameter sensitivity analysis, the influence trend of unit parameters on the change of active power regulation ability is fitted, and a quantitative index of active power regulation ability is constructed based on the regulation amplitude index and the regulation speed index. Based on the quantitative index of active power regulation ability proposed in this paper, it can provide a reference for evaluating the active power regulation ability of deep peak shaving units and support for the safety and stability analysis of power systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The exemplary embodiments of the present invention can be more fully understood by referring to the following drawings:

[0049] Figure 1 is a schematic flow chart of a method for evaluating the active power regulation ability of thermal power units considering deep peak shaving provided by an exemplary embodiment of the present invention;

[0050] Figure 2 is a schematic diagram of the extreme value time sensitivity analysis curve and its polynomial fitting of the PID proportional link coefficient provided by an exemplary embodiment of the present invention;

[0051] Figure 3 is a schematic diagram of the power extreme value sensitivity analysis curve and its polynomial fitting of the PID proportional link coefficient provided by an exemplary embodiment of the present invention;

[0052] Figure 4 is a schematic diagram of the extreme value time sensitivity analysis curve and its polynomial fitting of the PID integral link coefficient provided by an exemplary embodiment of the present invention;

[0053] Figure 5 is a schematic diagram of the power extreme value sensitivity analysis curve and its polynomial fitting of the PID integral link coefficient provided by an exemplary embodiment of the present invention;

[0054] Figure 6 is a schematic structural diagram of an apparatus for evaluating the active power regulation ability of thermal power units considering deep peak shaving provided by an exemplary embodiment of the present invention;

[0055] Figure 7 is the structure of an electronic device provided by an exemplary embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0056] Below, the exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments of the present invention, and it should be understood that the present invention is not limited to the exemplary embodiments described herein.

[0057] It should be noted that the relative arrangement of components and steps, the numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.

[0058] Those skilled in the art will understand that the terms "first" and "second" in the embodiments of the present invention are only used to distinguish different steps, devices or modules, and neither represent any specific technical meaning nor indicate the necessary logical order between them.

[0059] It should also be understood that, in the embodiments of the present invention, “a plurality of” may refer to two or more than two, and “at least one” may refer to one, two or more than two.

[0060] It should also be understood that any component, data or structure mentioned in the embodiments of the present invention can generally be understood as one or more, unless explicitly limited or otherwise indicated in the context.

[0061] In addition, the term "and / or" in this invention merely describes an association relationship between related objects, indicating that three possible relationships exist. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Furthermore, the character " / " in this invention generally indicates that the related objects are in an "or" relationship.

[0062] It should also be understood that the description of the various embodiments of the present invention focuses on the differences between the various embodiments, and the same or similar aspects thereof can be referenced with each other. For the sake of brevity, they will not be described one by one.

[0063] At the same time, it should be understood that for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship.

[0064] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.

[0065] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered part of the specification.

[0066] It should be noted that like reference numerals and letters refer to similar items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0067] Embodiments of the present invention can be applied to electronic devices such as terminal devices, computer systems, servers, etc., which can operate together with many other general or special computing system environments or configurations. Examples of well-known terminal devices, computing systems, environments, and / or configurations suitable for use with electronic devices such as terminal devices, computer systems, servers, etc. include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, network personal computers, minicomputer systems, mainframe computer systems, and distributed cloud computing technology environments including any of the above systems, and so on.

[0068] Terminal devices, computer systems, servers and other electronic devices can be described in the general context of computer system executable instructions (such as program modules) executed by a computer system. Generally, program modules can include routines, programs, target programs, components, logics, data structures, etc., which perform specific tasks or implement specific abstract data types. The computer system / server can be implemented in a distributed cloud computing environment, where tasks are executed by remote processing devices linked through a communication network. In a distributed cloud computing environment, program modules can be located on local or remote computing system storage media including storage devices.

[0069] Exemplary method

[0070] Figure 1 is a schematic flow chart of a method for evaluating the active power regulation ability of a thermal power unit considering deep peak shaving provided by an exemplary embodiment of the present invention. This embodiment can be applied to an electronic device, such as Figure 1 As shown, the method 100 for evaluating the active power regulation ability of a thermal power unit considering deep peak shaving includes the following steps:

[0071] Step 101, based on the measured parameters under the normal active power output level and the deep peak shaving active power output level of the thermal power unit, determine the key parameters affecting the active power regulation ability of the thermal power unit;

[0072] Step 102, according to the key parameters and the difference parameters of the thermal power unit at the normal level and the deep peak shaving level, determine the parameter sensitivity analysis boundary of each key parameter;

[0073] Step 103, according to the parameter sensitivity analysis boundary of each key parameter, construct a parameter change sequence of each key parameter;

[0074] Step 104, according to the parameter change sequence of each key parameter, construct a normalized comprehensive quantization index for the active power regulation of the thermal power unit;

[0075] Step 105: Substitute the measured values of the key parameters of the thermal power unit into the normalized comprehensive quantification index to obtain the real-time normalized comprehensive quantification index of the thermal power unit, and determine the active power regulation ability of the thermal power unit based on the real-time normalized comprehensive quantification index.

[0076] Specifically, a method for evaluating the active power regulation ability of a thermal power unit considering deep peak shaving provided by the present invention aims to address the problem of insufficient understanding of the active power regulation ability of thermal power units under the current deep peak shaving state. By measuring the parameters of the conventional operating conditions and the extreme deep peak shaving operating conditions, the difference parameters between the deep peak shaving conditions and the conventional conditions and the possible change ranges of the difference parameters are determined. Based on the parameter sensitivity analysis, the influence trend of the unit parameters on the change of the active power regulation ability is clarified. Based on polynomial fitting, an approximate functional formula for the active power extreme value and the extreme value moment is obtained, so as to establish a mapping relationship between the deep peak shaving unit parameters and the active power regulation ability, and establish an evaluation index for the active power regulation ability of the deep peak shaving unit. The specific implementation process of this method is as follows:

[0077] Step 1: Based on the measured parameter identification results of the GJ / GJ+ / GA / TB card models in PSD PowerTools under the normal active power output level and the deep peak shaving active power output level of the thermal power unit, compare the parameter differences of the prime mover, steam turbine governing system, actuator and other models, and clarify the key parameters affecting the active power regulation ability of the thermal power unit.

[0078] Step 2: For the difference parameters among the key parameters under the normal level and the deep peak shaving level, take the model parameter values under the normal active power output level and the deep peak shaving active power output level as the upper and lower bounds of the parameters respectively, and appropriately expand them to both sides to fully cover the parameter change range. Take the expanded parameter values as the parameter sensitivity analysis boundaries, and each parameter should have upper and lower bounds.

[0079] Step 3: Generate a large amount of operation mode data of the thermal power unit:

[0080] Step 3.1: Based on the PSD Power Tools standard parameter library, construct a single-machine infinite-bus model of the thermal power unit, perform power flow calculation, and obtain a power flow calculation file.

[0081] Step 3.2: Build a typical thermal power unit model in PSD Power Tools. Based on the power flow calculation file, sample and take values at equal intervals within the sensitivity analysis boundaries of the difference parameters, modify the parameters in an orderly manner from small to large, and generate dynamic parameter files respectively to construct a large amount of unit parameter data.

[0082] Step 3.3: Set the frequency difference disturbance, calculate the dynamic parameter files generated by changing each difference parameter one by one, and extract the extreme values and extreme value moments of the active power change curve of the thermal power unit. Each parameter file generates a power change curve.

[0083] Step 3.4: Based on the above steps, generate three groups of parameter sequences for each differential parameter, namely the differential parameter change sequence X i ={x i,1 , x i,2 , …, x i,j , …x i,n}, the maximum active power change sequence Y i ={y i,1 , y i,2 , …, y i,j , …y i,n}, and the maximum active power change time sequence Z i ={z i,1 , z i,2 , …, z i,j , …z i,n}. Where the subscript i is the i-th parameter, the subscript j is the j-th parameter for equally spaced sensitivity analysis, and n represents the number of equally spaced values for a single parameter.

[0084] Step 4: Based on the polynomial fitting method, obtain the time-domain fitting curve function of the extreme active power output and the extreme time of the thermal power unit with respect to the change of the differential parameter Where the superscript “^” represents an approximate value.

[0085] Step 5: Based on the fitting curve function of each differential parameter, construct the active power regulation amplitude index F A as follows:

[0086]

[0087] In the formula: the superscript “*” represents the measured value of the differential parameter; m is the number of differential parameters.

[0088] Step 6: Based on the fitting curve function of each differential parameter, construct the active power regulation speed index F S as follows:

[0089]

[0090] Step 7: Based on the regulation speed amplitude and the regulation speed index, construct the comprehensive quantitative index η of the active power regulation ability of the thermal power unit as:

[0091]

[0092] Step 8: Based on the normalization of the comprehensive quantitative index, obtain the normalized index η of the comprehensive quantitative index of the active power regulation ability of the thermal power unit * :

[0093]

[0094] Where:

[0095]

[0096] Among them, F A,max is the maximum value of the active power regulation amplitude index of the thermal power unit, F S,min is the minimum value of the active power regulation speed index of the thermal power unit, f y,i,max (x i (t)) is the maximum value of the i-th parameter fitting function, f z,i,min (x i (t)) is the minimum value of the fitting function of the i-th parameter.

[0097] Step 9: Substitute the actual parameters of the thermal power unit into equations (1) to (5) to obtain the normalized index of the comprehensive quantitative index of the active power regulation capability of the thermal power unit. The closer this index is to 1, the stronger the active power regulation capability of the thermal power unit.

[0098] In order to provide a general process for implementing a method to evaluate the active power regulation capability of thermal power units considering deep peak regulation and verify its effectiveness, an analysis was conducted based on the measured parameters of an actual thermal power unit under normal operation and deep peak regulation.

[0099] An evaluation model for the active power regulation capability of thermal power units was constructed. The measured parameters under normal operation and deep regulation were compared. The differential parameters that affect the regulation capability were the PID proportional link coefficient and the PID integral link coefficient.

[0100] Based on the measured parameters under normal operation and deep adjustment operation, the value range of each difference parameter is clarified. In this example, the PID proportional link coefficient range is [0,2], and the PID integral link coefficient range is [0,0.4]. The values of each difference parameter are changed in steps of 0.01 to generate a dynamic parameter file. Based on PSD Power Tools simulation analysis, the extreme values and extreme moments of active power changes in the simulation results of each dynamic file are extracted to form the difference parameter change sequence Xi = {xi,1,xi,2,…,xi,j,…xi,n}, and the maximum value sequence of active power change of thermal power units Y i ={y i,1 ,y i,2 ,…,y i,j ,…y i,n}, and the maximum value of the active power change time sequence Z of the thermal power unit i ={z i,1 ,z i,2 ,…,z i,j ,…z i,n}.

[0101] Based on the polynomial fitting method, the time domain fitting curve function of the active output extreme value and extreme value time of the thermal power unit with respect to the change of difference parameters is obtained. like Figures 2 to 4 shown.

[0102] Table 1 Polynomial fitting expression results

[0103]

[0104] Then, the comprehensive quantitative index is calculated based on the proposed formula:

[0105]

[0106] The maximum value of the comprehensive quantitative index is:

[0107]

[0108] There are two sets of thermal power unit parameters. The first set of parameters has a PID proportional coefficient of 0.5 and a PID integral coefficient of 0.1; the second set of parameters has a PID proportional coefficient of 0.7 and a PID integral coefficient of 0.3:

[0109]

[0110] It can be seen from the indicators that the regulation ability of the thermal power units under the first set of parameters is slightly better.

[0111] Therefore, the present invention provides a method for evaluating the active power regulation capability of thermal power units taking deep peak regulation into consideration. The method aims to address the problem of insufficient understanding of the active power regulation capability of thermal power units under the current deep peak regulation state. By measuring the parameters of conventional operating conditions and extreme deep peak regulation operating conditions, the method determines the difference parameters under deep regulation conditions and conventional conditions and the possible range of variation of the difference parameters. The method fits the trend of the influence of unit parameters on the change of active power regulation capability based on parameter sensitivity analysis, and constructs a quantitative index of active power regulation capability based on the regulation amplitude index and the regulation speed index. Based on the quantitative index of active power regulation capability proposed in this article, a reference can be provided for evaluating the active power regulation capability of deep regulation units, and support can be provided for the safety and stability analysis of the power system.

[0112] Exemplary device

[0113] Figure 6 FIG. 1 is a schematic diagram of a structure of a device for evaluating the active power regulation capability of a thermal power unit considering deep peak regulation provided by an exemplary embodiment of the present invention. Figure 6 As shown, the apparatus 600 includes:

[0114] A first determining module 610 is configured to determine key parameters that affect the active power regulation capability of the thermal power unit based on measured parameters of the thermal power unit at a normal active power output level and a deep peak load active power output level;

[0115] The second determination module 620 is configured to determine the parameter sensitivity analysis boundary of each key parameter based on the key parameters and the difference parameters of the thermal power units at the normal level and the deep peak regulation level;

[0116] A first construction module 630 is configured to construct a parameter change sequence of each key parameter based on a parameter sensitivity analysis boundary of each key parameter;

[0117] The second construction module 640 is used to construct a normalized comprehensive quantitative index of active power regulation of the thermal power unit according to the parameter change sequence of each key parameter;

[0118] The acquisition module 650 is used to bring the measured values of the key parameters of the thermal power unit into the normalized comprehensive quantitative index, obtain the real-time normalized comprehensive quantitative index of the thermal power unit, and determine the active power regulation capability of the thermal power unit based on the real-time normalized comprehensive quantitative index.

[0119] Optionally, the first building block 630 includes:

[0120] The first construction submodule is used to construct a single-machine infinite model of a thermal power unit;

[0121] The calculation submodule is used to perform power flow calculation on the single-machine infinite model and obtain the power flow calculation file of the thermal power unit;

[0122] Build submodules to build thermal power plant models of typical thermal power plants;

[0123] The generation submodule is used to adopt the values at equal intervals in the parameter sensitivity analysis boundary based on the power flow calculation file, modify the parameters in an orderly manner from small to large, and generate dynamic parameter files of each key parameter according to the thermal power unit model;

[0124] The second construction submodule is used to construct the parameter change sequence of each key parameter according to the dynamic parameter file of each key parameter, wherein the parameter change sequence includes the key parameter change sequence, the maximum value sequence of the active power change of the thermal power unit, and the maximum value moment sequence of the active power change of the thermal power unit.

[0125] Optionally, the second building block 640 includes:

[0126] The fitting submodule is used to fit the parameter change sequence based on the polynomial fitting method to obtain the time domain fitting curve function of each key parameter;

[0127] The third construction submodule is used to construct the amplitude index and speed index of the active power regulation of the thermal power unit according to the time domain fitting curve function;

[0128] The fourth construction sub-module is used to construct a normalized comprehensive quantization index for the active power regulation of a thermal power unit according to the amplitude index and the speed index.

[0129] Optionally, the fitting sub-module includes:

[0130] A fitting unit is used to fit the parameter change sequence based on the polynomial fitting method to obtain the first time-domain fitting curve function of the maximum active power of the thermal power unit with respect to the key parameter change and the second time-domain fitting region function of the maximum active power time with respect to the key parameter change.

[0131] Optionally, the expression of the amplitude index is:

[0132]

[0133] The expression of the speed index is:

[0134]

[0135] In the formula, is the measured value of the key parameter, is the first time-domain fitting curve function, is the second time-domain fitting region function, and m is the number of key parameters.

[0136] Optionally, the fourth construction sub-module includes:

[0137] A construction unit is used to construct a comprehensive quantization index for the active power regulation of the thermal power unit according to the amplitude index and the speed index;

[0138] A normalization unit is used to normalize the comprehensive quantization index to obtain a normalized comprehensive quantization index.

[0139] Optionally, the expression of the comprehensive quantization index is:

[0140]

[0141] In the formula, F A is the amplitude index, F S is the speed index;

[0142] The expression of the normalized comprehensive quantization index is:

[0143]

[0144] Among them,

[0145]

[0146] In the formula, F A,max is the maximum value of the amplitude index, F S,minis the minimum value of the speed index, f y,i,max (x i (t)) is the maximum value of the fitting function of the i-th parameter, f z,i,min (x i (t)) is the minimum value of the fitting function of the i-th parameter, is the measured value of the key parameter, and m is the number of key parameters.

[0147] Exemplary electronic device

[0148] Figure 7 is the structure of an electronic device provided by an exemplary embodiment of the present invention. As Figure 7 shown, the electronic device 70 includes one or more processors 71 and a memory 72.

[0149] The processor 71 may be a central processing unit (CPU) or other forms of processing units with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

[0150] The memory 72 may include one or more computer program products, and the computer program products may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory, etc. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 71 may run the program instructions to implement the methods of the software programs of the various embodiments of the present invention described above and / or other desired functions. In one example, the electronic device may further include: an input device 73 and an output device 74, and these components are interconnected through a bus system and / or other forms of connection mechanisms (not shown).

[0151] In addition, the input device 73 may further include, for example, a keyboard, a mouse, and so on.

[0152] The output device 74 may output various information to the outside. The output device 74 may include, for example, a display, a speaker, a printer, and a communication network and its connected remote output devices, and so on.

[0153] Of course, for simplicity, Figure 7 only some of the components related to the present invention in the electronic device are shown in

[0154] Exemplary computer program product and computer-readable storage medium

[0155] In addition to the above methods and devices, an embodiment of the present invention may also be a computer program product, which includes computer program instructions that, when executed by a processor, cause the processor to execute the steps in the methods according to various embodiments of the present invention described in the "Exemplary Methods" section above in this specification.

[0156] The computer program product may be written in any combination of one or more programming languages for programming code to perform the operations of the embodiments of the present invention. The programming languages include object-oriented programming languages such as Java, C++, etc., and also include conventional procedural programming languages such as the "C" language or similar programming languages. The programming code may be executed entirely on the user's computing device, partially on the user's device, executed as a stand-alone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0157] Furthermore, an embodiment of the present invention may also be a computer-readable storage medium, on which computer program instructions are stored, and the computer program instructions, when executed by a processor, cause the processor to execute the steps in the methods according to various embodiments of the present invention described in the "Exemplary Methods" section above in this specification.

[0158] The computer-readable storage medium may adopt any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may include, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (non-exhaustive list) of the readable storage medium include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0159] The basic principles of the present invention have been described above in conjunction with specific embodiments. However, it should be noted that the advantages, benefits, effects, etc. mentioned in the present invention are only examples and not limitations, and it cannot be considered that these advantages, benefits, effects, etc. are essential for each embodiment of the present invention. In addition, the above-disclosed specific details are only for illustrative and easy-to-understand purposes, rather than limitations. The above details do not limit the present invention to necessarily adopt the above specific details for implementation.

[0160] Each embodiment in this specification is described in a progressive manner, with each embodiment focusing on its differences from the other embodiments. References to the same or similar parts between the various embodiments are sufficient. For system embodiments, since they largely correspond to method embodiments, their description is relatively simple. For relevant parts, references to the description of the method embodiments are sufficient.

[0161] The block diagrams of the devices, systems, equipment, and systems involved in the present invention are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As will be appreciated by those skilled in the art, these devices, systems, equipment, and systems can be connected, arranged, or configured in any manner. Words such as "including," "comprising," "having," and the like are open-ended words, meaning "including but not limited to," and can be used interchangeably therewith. The words "or" and "and" used herein refer to the words "and / or" and can be used interchangeably therewith, unless the context clearly indicates otherwise. The word "such as" used herein refers to the phrase "such as but not limited to," and can be used interchangeably therewith.

[0162] The method and system of the present invention may be implemented in many ways. For example, the method and system of the present invention may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above sequence of steps for the method is for illustration only, and the steps of the method of the present invention are not limited to the sequence specifically described above, unless otherwise specified. In addition, in some embodiments, the present invention may also be implemented as a program recorded in a recording medium, which includes machine-readable instructions for implementing the method according to the present invention. Thus, the present invention also covers recording media that store programs for executing the method according to the present invention.

[0163] It should also be noted that, in the system, device and method of the present invention, each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. The above description of the disclosed aspects is provided to enable any technician in this field to make or use the present invention. Various modifications to these aspects will be very obvious to those skilled in the art, and the general principles defined here can be applied to other aspects without departing from the scope of the present invention. Therefore, the present invention is not intended to be limited to the aspects shown here, but according to the widest scope consistent with the principles disclosed here and novel features.

[0164] The above description has been presented for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of the present invention to the forms disclosed herein. Although a number of example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A method for evaluating the active power regulation ability of a thermal power unit considering deep peak shaving, characterized in that include: Based on the measured parameters of the thermal power units at normal active power output levels and deep peak load regulation levels, determine the key parameters that affect the active power regulation capability of the thermal power units; Determining parameter sensitivity analysis boundaries of each key parameter based on the key parameters and difference parameters of the thermal power unit at a normal level and a deep peak regulation level; Constructing a parameter change sequence for each key parameter according to the parameter sensitivity analysis boundary of each key parameter; Constructing a normalized comprehensive quantitative index for active power regulation of the thermal power unit according to a parameter change sequence of each key parameter; Substituting the measured values of the key parameters of the thermal power unit into the normalized comprehensive quantitative index to obtain the real-time normalized comprehensive quantitative index of the thermal power unit, and determining the active power regulation capability of the thermal power unit according to the real-time normalized comprehensive quantitative index; According to the parameter change sequence of each key parameter, a normalized comprehensive quantitative index of the active power regulation of the thermal power unit is constructed, including: Fitting the parameter change sequence based on a polynomial fitting method to obtain a time-domain fitting curve function of each key parameter; Constructing an amplitude index and a speed index for active power regulation of a thermal power unit according to the time domain fitting curve function; According to the amplitude index and the speed index, a normalized comprehensive quantitative index for active power regulation of the thermal power unit is constructed.

2. The method according to claim 1, wherein According to the parameter sensitivity analysis boundary of each key parameter, a parameter change sequence of each key parameter is constructed, including: Construct a single-unit infinite-scale model of a thermal power plant; Performing power flow calculation on the single-machine infinite model to obtain a power flow calculation file of the thermal power unit; Build a thermal power plant model of a typical thermal power plant; Based on the power flow calculation file, adopting values at equal intervals within the parameter sensitivity analysis boundary, modifying parameters in an orderly manner from small to large, and generating a dynamic parameter file of each key parameter according to the thermal power unit model; According to the dynamic parameter files of each key parameter, a parameter change sequence of each key parameter is constructed respectively, wherein the parameter change sequence includes a key parameter change sequence, a maximum value sequence of active power change of thermal power units, and a maximum value time sequence of active power change of thermal power units.

3. The method according to claim 1, characterized in that Fitting the parameter change sequence based on a polynomial fitting method to obtain a time domain fitting curve function includes: The parameter change sequence is fitted based on a polynomial fitting method to obtain a first time domain fitting curve function of the maximum active power of the thermal power unit with respect to the key parameter change and a second time domain fitting area function of the key parameter change at the moment of the maximum active power.

4. The method according to claim 3, characterized in that The expression of the amplitude index is: The expression of the speed index is: wherein, is the measured value of the key parameter, is the first time-domain fitting curve function, is the second time-domain fitting region function, and m is the number of key parameters.

5. The method according to claim 1, wherein According to the amplitude index and the speed index, a normalized comprehensive quantitative index of active power regulation of the thermal power unit is constructed, including: Constructing a comprehensive quantitative index for active power regulation of the thermal power unit based on the amplitude index and the speed index; Normalizing the comprehensive quantitative index to obtain the normalized comprehensive quantitative index.

6. The method according to claim 5, characterized in that The expression of the comprehensive quantitative index is: In the formula, F A is the amplitude index, F S is the speed indicator; The expression of the normalized comprehensive quantitative index is: in, where F A,max is the maximum value of the amplitude index, F S,min is the minimum value of the speed index, f y,i,max (x i (t)) is the maximum value of the fitting function of the i-th parameter, f z,i,min (x i (t)) is the minimum value of the fitting function of the i-th parameter, is the measured value of the key parameter, and m is the number of key parameters.

7. An active power regulation ability evaluation device for thermal power units considering deep peak shaving, characterized in that include: The first determination module is configured to determine key parameters affecting the active power regulation ability of the thermal power unit based on the measured parameters under the normal active power output level and the deep peak shaving active power output level of the thermal power unit; The second determination module is configured to determine the parameter sensitivity analysis boundary of each key parameter according to the key parameter and the difference parameter of the thermal power unit under the normal level and the deep peak shaving level; The first construction module is configured to construct a parameter change sequence of each key parameter according to the parameter sensitivity analysis boundary of each key parameter; The second construction module is configured to construct a normalized comprehensive quantization index for the active power regulation of the thermal power unit according to the parameter change sequence of each key parameter; The acquisition module is configured to substitute the measured value of the key parameter of the thermal power unit into the normalized comprehensive quantization index, obtain the real-time normalized comprehensive quantization index of the thermal power unit, and determine the active power regulation ability of the thermal power unit according to the real-time normalized comprehensive quantization index; The second construction module includes: Fitting the parameter change sequence based on the polynomial fitting method to obtain the time-domain fitting curve function of each key parameter; Constructing an amplitude index and a speed index for the active power regulation of the thermal power unit according to the time-domain fitting curve function; Constructing a normalized comprehensive quantization index for the active power regulation of the thermal power unit according to the amplitude index and the speed index.

8. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, and the computer program is used to execute the method according to any one of claims 1-6 above.

9. An electronic device, characterized in that: The electronic device includes: A processor; A memory for storing executable instructions of the processor; The processor is configured to read the executable instructions from the memory and execute the instructions to implement the method according to any one of claims 1-6 above.

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