CN117996787B - Frequency modulation method of fused salt coupling thermal power generating unit based on bidirectional prediction - Google Patents
Frequency modulation method of fused salt coupling thermal power generating unit based on bidirectional prediction Download PDFInfo
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- 150000003839 salts Chemical class 0.000 title claims abstract description 62
- 238000000034 method Methods 0.000 title claims abstract description 39
- 230000002457 bidirectional effect Effects 0.000 title claims abstract description 22
- 230000008878 coupling Effects 0.000 title claims description 9
- 238000010168 coupling process Methods 0.000 title claims description 9
- 238000005859 coupling reaction Methods 0.000 title claims description 9
- 238000000354 decomposition reaction Methods 0.000 claims abstract description 140
- 238000004146 energy storage Methods 0.000 claims abstract description 120
- 238000012937 correction Methods 0.000 claims abstract description 87
- 230000004044 response Effects 0.000 claims abstract description 77
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 39
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 claims abstract description 21
- 229910052744 lithium Inorganic materials 0.000 claims abstract description 21
- 238000013528 artificial neural network Methods 0.000 claims description 3
- 230000006870 function Effects 0.000 description 6
- 238000001914 filtration Methods 0.000 description 4
- 238000012549 training Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 3
- 238000003491 array Methods 0.000 description 2
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- 238000004590 computer program Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 229910002651 NO3 Inorganic materials 0.000 description 1
- NHNBFGGVMKEFGY-UHFFFAOYSA-N Nitrate Chemical compound [O-][N+]([O-])=O NHNBFGGVMKEFGY-UHFFFAOYSA-N 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
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Classifications
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
- H02J3/241—The oscillation concerning frequency
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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Abstract
The application provides a frequency modulation method of a fused salt coupled thermal power generating unit based on bidirectional prediction, which comprises the steps of determining a hybrid energy storage response requirement based on a frequency modulation instruction received in real time; determining an aliasing degree correction value based on the hybrid energy storage response requirement, the decomposition layer number of the VMD algorithm, the trained prediction module and the trained reverse prediction module; updating the decomposition layer number based on the set decomposition layer number range to obtain an aliasing degree correction value corresponding to different decomposition layer numbers; selecting the decomposition layer number corresponding to the minimum value in all the aliasing degree correction values as a target decomposition layer number; decomposing the hybrid energy storage response requirement by utilizing a VMD algorithm based on the target decomposition layer number to obtain a target modal component and a residual component; dividing the target modal component and the residual component to obtain a high-frequency component and a low-frequency component; the molten salt energy storage device is controlled to respond according to the high-frequency component and the lithium battery is controlled to respond according to the low-frequency component.
Description
Technical Field
The application relates to the technical field of power grid frequency modulation, in particular to a frequency modulation method of a fused salt coupling thermal power generating unit based on bidirectional prediction.
Background
The fire-storage combined frequency modulation can obviously improve the frequency modulation performance of the thermal power generating unit, and can quickly and effectively reduce the shortage of the frequency modulation capacity of the system. At present, the fire-storage combined frequency modulation technology comprises battery energy storage, super-capacitor energy storage, flywheel energy storage, fused salt energy storage, hybrid energy storage formed by various forms and the like, the cycle life of thermal power pond energy storage is low, certain potential safety hazards exist, super-capacitor energy storage and flywheel energy storage are used as the representation of power type energy storage devices, fire has the defects of high cost, low energy density and the like, the fused salt energy storage uses raw materials such as nitrate as a heat storage medium, energy is stored and released through the conversion of heat energy of a heat transfer working medium and the internal energy of fused salt, and the fire-storage combined frequency modulation technology has the advantages of low cost, high safety, large capacity, long service life and the like.
In the prior art, a fused salt energy storage and variation modal decomposition (variational mode decomposition, VMD) technology is utilized to assist in frequency modulation, however, a plurality of modal components (INTRINSIC MODE FUNCTION, IMF) obtained by simply decomposing signals through VMD have the problem of serious modal aliasing, and the accuracy of subsequent frequency modulation response is affected.
Disclosure of Invention
The present application aims to solve at least one of the technical problems in the related art to some extent.
Therefore, a first object of the present application is to provide a frequency modulation method for a fused salt coupled thermal power generating unit based on bidirectional prediction, so as to improve accuracy of frequency modulation response.
The second aim of the application is to provide a frequency modulation system of a fused salt coupled thermal power generating unit based on bidirectional prediction.
A third object of the present application is to propose an electronic device.
A fourth object of the present application is to propose a computer readable storage medium.
In order to achieve the above object, an embodiment of a first aspect of the present application provides a frequency modulation method for a fused salt coupled thermal power generating unit based on bidirectional prediction, a hybrid energy storage device configured in a thermal power plant includes fused salt energy storage equipment and a lithium battery, and the frequency modulation method includes the following steps:
determining a hybrid energy storage response requirement based on the frequency modulation instruction received in real time;
Determining an aliasing degree correction value based on the hybrid energy storage response requirement, the decomposition layer number of the VMD algorithm, the trained prediction module and the trained reverse prediction module;
Updating the decomposition layer number based on the set decomposition layer number range to obtain an aliasing degree correction value corresponding to different decomposition layer numbers;
selecting the decomposition layer number corresponding to the minimum value in all the aliasing degree correction values as a target decomposition layer number;
decomposing the hybrid energy storage response requirement by utilizing a VMD algorithm based on a target decomposition layer number to obtain a target modal component and a residual component;
dividing the target modal component and the residual component to obtain a high-frequency component and a low-frequency component;
And controlling the molten salt energy storage equipment to respond according to the high-frequency component and controlling the lithium battery to respond according to the low-frequency component.
In the method of the first aspect of the present application, the determining the aliasing degree correction value based on the hybrid energy storage response requirement, the decomposition layer number of the VMD algorithm, the trained prediction module and the trained inverse prediction module includes: acquiring the aliasing degree based on the hybrid energy storage response requirement, the decomposition layer number of the VMD algorithm and the trained prediction module; determining a decomposition layer number predicted value based on the hybrid energy storage response requirement, the aliasing degree and the trained reverse prediction module; and determining a correction coefficient based on the decomposition layer number predicted value and the decomposition layer number, and correcting the aliasing degree by using the correction coefficient to obtain an aliasing degree correction value.
In the method of the first aspect of the present application, the correction coefficient satisfies:
where a is a correction coefficient, k is a decomposition layer number, and k' is a decomposition layer number prediction value.
To achieve the above objective, an embodiment of a second aspect of the present application provides a frequency modulation system of a fused salt coupled thermal power generating unit based on bidirectional prediction, a hybrid energy storage device configured in a thermal power plant includes fused salt energy storage equipment and a lithium battery, the frequency modulation system includes:
the acquisition module is used for determining the hybrid energy storage response requirement based on the frequency modulation instruction received in real time;
The aliasing degree correction module is used for determining an aliasing degree correction value based on the hybrid energy storage response requirement, the decomposition layer number of the VMD algorithm, the trained prediction module and the trained reverse prediction module;
The updating module is used for updating the decomposition layer number based on the set decomposition layer number range so as to obtain an aliasing degree correction value corresponding to different decomposition layer numbers;
the selection module is used for selecting the decomposition layer number corresponding to the minimum value in all the aliasing degree correction values as a target decomposition layer number;
the decomposition module is used for decomposing the hybrid energy storage response requirement by utilizing a VMD algorithm based on the target decomposition layer number to obtain a target modal component and a residual component;
the dividing module is used for dividing the target modal component and the residual component to obtain a high-frequency component and a low-frequency component;
And the control module is used for controlling the molten salt energy storage equipment to respond according to the high-frequency component and controlling the lithium battery to respond according to the low-frequency component.
In the system of the second aspect of the present application, the aliasing degree correction module is specifically configured to: acquiring the aliasing degree based on the hybrid energy storage response requirement, the decomposition layer number of the VMD algorithm and the trained prediction module; determining a decomposition layer number predicted value based on the hybrid energy storage response requirement, the aliasing degree and the trained reverse prediction module; and determining a correction coefficient based on the decomposition layer number predicted value and the decomposition layer number, and correcting the aliasing degree by using the correction coefficient to obtain an aliasing degree correction value.
In the system of the second aspect of the present application, the correction coefficient satisfies:
where a is a correction coefficient, k is a decomposition layer number, and k' is a decomposition layer number prediction value.
To achieve the above object, an embodiment of a third aspect of the present application provides an electronic device, including: a processor, and a memory communicatively coupled to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method according to the first aspect of the present application.
To achieve the above object, an embodiment of a fourth aspect of the present application provides a computer-readable storage medium having stored therein computer-executable instructions for implementing the method set forth in the first aspect of the present application when executed by a processor.
According to the frequency modulation method, the frequency modulation system, the electronic equipment and the storage medium of the fused salt coupling thermal power generating unit based on the bidirectional prediction, which are provided by the application, the hybrid energy storage response requirement is determined based on the frequency modulation instruction received in real time; determining an aliasing degree correction value based on the hybrid energy storage response requirement, the decomposition layer number of the VMD algorithm, the trained prediction module and the trained reverse prediction module; updating the decomposition layer number based on the set decomposition layer number range to obtain an aliasing degree correction value corresponding to different decomposition layer numbers; selecting the decomposition layer number corresponding to the minimum value in all the aliasing degree correction values as a target decomposition layer number; decomposing the hybrid energy storage response requirement by utilizing a VMD algorithm based on the target decomposition layer number to obtain a target modal component and a residual component; dividing the target modal component and the residual component to obtain a high-frequency component and a low-frequency component; the molten salt energy storage device is controlled to respond according to the high-frequency component and the lithium battery is controlled to respond according to the low-frequency component. Under the condition, the aliasing degree correction value is determined based on the hybrid energy storage response requirement, the decomposition layer number of the VMD algorithm, the trained prediction module and the trained reverse prediction module, wherein the trained prediction module and the trained reverse prediction module are comprehensively used for obtaining the aliasing degree correction value so as to improve the accuracy of the obtained aliasing degree, and then the minimum value in all the aliasing degree correction values is selected, at the moment, the aliasing degree correction value is the minimum, so that the aliasing condition among target modal components can be better avoided when the hybrid energy storage response requirement is decomposed by the VMD algorithm based on the target decomposition layer number, and the accuracy of the frequency modulation response is improved.
Additional aspects and advantages of the application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the application.
Drawings
The foregoing and/or additional aspects and advantages of the application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:
FIG. 1 is a schematic flow chart of a frequency modulation method of a fused salt coupled thermal power generating unit based on bidirectional prediction, which is provided by the embodiment of the application;
Fig. 2 is a flow chart of a method for obtaining a corresponding aliasing degree correction value according to each decomposition layer number provided in the embodiment of the present application;
Fig. 3 is a block diagram of a frequency modulation system of a fused salt coupled thermal power generating unit based on bidirectional prediction provided by the embodiment of the application.
Detailed Description
Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative and intended to explain the present application and should not be construed as limiting the application.
The frequency modulation method and system of the fused salt coupling thermal power generating unit based on bidirectional prediction in the embodiment of the application are described below with reference to the accompanying drawings.
The embodiment of the application provides a frequency modulation method of a fused salt coupling thermal power generating unit based on bidirectional prediction, so as to improve the accuracy of frequency modulation response.
In the application, a hybrid energy storage device configured in a thermal power plant comprises molten salt energy storage equipment and a lithium battery. The hybrid energy storage device assists the thermal power generating unit to participate in frequency modulation. The molten salt energy storage equipment is connected to a power grid through a thermal power unit, and the lithium battery is connected to the power grid through a converter. Before receiving the frequency modulation instruction, a connecting wire between the molten salt energy storage equipment and the thermal power generating unit is disconnected, if the frequency modulation instruction is received, the connecting wire between the molten salt energy storage equipment and the thermal power generating unit is conducted, and the thermal power generating unit supplies power to the molten salt energy storage equipment to convert electric energy into heat energy or release heat energy through the molten salt energy storage equipment to convert the electric energy into electric energy at a generator and send the electric energy to a power grid to perform frequency modulation response. Wherein the molten salt energy storage output P C of the molten salt energy storage device for frequency modulation response is determined based on a frequency modulation instruction and a thermal power unit load P G of the thermal power unit when a connecting wire is disconnected, specifically, when the power grid issues the frequency modulation instruction, the frequency modulation instruction carries a load response demand P T, at the moment, the thermal power unit responds with the thermal power unit load P G, and the rest is responded by the hybrid energy storage device, so the hybrid energy storage device is calculated to respond with the energy storage output of P J(PT-PG=PJ), wherein the lithium battery responds with the battery power P L, the molten salt energy storage device will participate in the response with the molten salt energy storage output P C. After the battery power P L and the molten salt energy storage output P C are obtained through calculation, a connecting wire between the molten salt energy storage device and the thermal power generating unit is controlled to be conducted, the molten salt energy storage device is controlled to respond with the calculated value of the molten salt energy storage output P C, and the lithium battery is controlled to respond with the calculated value of the battery power P L, so that frequency modulation is completed. The frequency modulation method can be used for more accurately determining the values of the battery power P L and the molten salt energy storage output P C in the hybrid energy storage device.
Fig. 1 is a flow chart of a frequency modulation method of a fused salt coupled thermal power generating unit based on bidirectional prediction, which is provided by the embodiment of the application. As shown in fig. 1, the frequency modulation method of the fused salt coupled thermal power generating unit based on bidirectional prediction comprises the following steps:
step S101, determining a hybrid energy storage response requirement based on the frequency modulation command received in real time.
In step S101, since the frequency modulation command carries the load response requirement P T, the load response requirement P T can be obtained based on the frequency modulation command received in real time, the thermal power unit load P G is obtained, and the difference between the load response requirement P T and the thermal power unit load P G is calculated to obtain the hybrid energy storage response requirement P J (also referred to as energy storage output), i.e. P T-PG=PJ.
Step S102, determining an aliasing degree correction value based on the hybrid energy storage response requirement, the decomposition layer number of the VMD algorithm, the trained prediction module and the trained reverse prediction module.
In step S102, determining an aliasing degree correction value based on the hybrid energy storage response requirement, the number of decomposition layers of the VMD algorithm, the trained prediction module, and the trained inverse prediction module, includes: obtaining the aliasing degree based on the hybrid energy storage response requirement, the decomposition layer number of the VMD algorithm and the trained prediction module; determining a decomposition layer number predicted value based on the hybrid energy storage response requirement, the aliasing degree and the trained reverse prediction module; and determining a correction coefficient based on the decomposition layer number predicted value and the decomposition layer number, and correcting the aliasing degree by using the correction coefficient to obtain an aliasing degree correction value.
And training the prediction module by using the first historical data set to obtain a trained prediction module. The prediction module generally adopts a neural network which is mature in engineering, such as GRU (Gate Recurrent Unit, gate control circulation unit) and the like. The first historical data set includes a historical hybrid energy storage response demand, a number of decomposition levels, and a degree of aliasing of the historical hybrid energy storage response demand. The input of the prediction module is the historical hybrid energy storage response requirement and the number of decomposition layers, the output is the aliasing degree, the samples of the first historical data set are for example > 20000, and the prediction module is trained after training is completed. In addition, the aliasing degree of the historical hybrid energy storage response requirement is obtained by decomposing the historical hybrid energy storage response requirement into k modal components IMFs by using a VMD algorithm and then mapping the k IMFs to a frequency domain. The degree of aliasing satisfies: wherein k is the number of decomposition layers, Representing the frequency overlap interval of two adjacent modal components,/>Representing the frequency interval in which two adjacent modal components are located,/>Is the frequency maximum of the latter (i.e. the i +1 th modality component) of the two adjacent modality components,Is the frequency minimum of the previous (i.e., i-th) of the two adjacent modal components.
And training the prediction module by using the second historical data set to obtain a trained reverse prediction module. The backward prediction module may also employ a GRU model. The second historical data set includes a historical hybrid energy storage response demand, a number of decomposition levels, and a degree of aliasing of the historical hybrid energy storage response demand. The inputs of the reverse prediction module are the aliasing degree of the historical hybrid energy storage response requirement and the historical hybrid energy storage response requirement, and the output is the decomposition layer number k. Samples of the second historical data set are, for example, > 20000, and after training is completed are referred to as a trained reverse prediction module.
Wherein the correction coefficient satisfies:
Where a is a correction coefficient, k is a decomposition layer number, k' is a prediction value of the decomposition layer number, and else is expressed.
In step S102, the correction coefficient is multiplied by the aliasing degree output by the trained prediction module to obtain an aliasing degree correction value.
Taking any decomposition layer number within the set decomposition layer number range as an example, fig. 2 is a flow chart of a method for obtaining a corresponding aliasing degree correction value by using each decomposition layer number according to an embodiment of the present application. As shown in fig. 2, the method for obtaining the corresponding aliasing correction value of a certain decomposition layer number includes:
The input signal (i.e. the hybrid energy storage response requirement) is used for determining the decomposition layer number k of the VMD algorithm; inputting the input signal and the number of decomposition layers into a trained prediction module to predict the aliasing degree Dk under the current k, inputting the aliasing degree Dk and the input signal into a trained reverse prediction module to predict k ' (namely, obtaining a predicted value of the number of decomposition layers), obtaining a correction coefficient a based on k ' and k, and correcting the aliasing degree Dk by using the correction coefficient a to obtain a corrected Dk ' (namely, an aliasing degree correction value).
Step S103, updating the decomposition layer number based on the set decomposition layer number range to obtain the aliasing degree correction value corresponding to different decomposition layer numbers.
In step S103, taking the case where the set decomposition layer number range is [4,12], the aliasing degree correction value corresponding to each decomposition layer number is obtained by processing in step S102 for each decomposition layer number in the set decomposition layer number range [4,12 ].
Step S104, selecting the decomposition layer number corresponding to the minimum value in all the aliasing degree correction values as a target decomposition layer number.
In step S104, the minimum aliasing degree correction value is selected from among the aliasing degree correction values corresponding to all the decomposition levels, and the decomposition level corresponding to the minimum aliasing degree correction value is the target decomposition level.
Step S105, decomposing the hybrid energy storage response requirement by utilizing a VMD algorithm based on the target decomposition layer number to obtain a target modal component and a residual component.
As will be readily appreciated, the VMD algorithm is a completely non-recursive modal variation method that decomposes the original signal into a plurality of modal components with certain sparsity. The number of modal components obtained by decomposition is equal to the number of decomposition layers.
In step S105, the VMD algorithm is used to decompose the hybrid energy storage response requirement to obtain a plurality of target modal components, where the number of target modal components is equal to the number of target decomposition layers. And (3) carrying out difference on the sum of the hybrid energy storage response requirement and all the target modal components to obtain a residual component.
Step S106, dividing the target modal component and the residual component to obtain a high-frequency component and a low-frequency component.
In step S106, high-frequency and low-frequency reconstruction is performed on the target modal component and the residual component according to the characteristic of stabilizing power fluctuation of the molten salt and the lithium battery.
Specifically, in step S106, the target modal component and the residual component are divided to obtain a high-frequency component and a low-frequency component, including: rounding based on half of the target decomposition layer number to obtain a filtering order; and carrying out high-low frequency reconstruction on the target modal component and the residual component based on the filtering order, wherein the sum of the target modal components smaller than or equal to the filtering order is a high-frequency component, and the sum of all the target modal components larger than the filtering order and the residual component is a low-frequency component.
And step S107, controlling the molten salt energy storage device to respond according to the high-frequency component and controlling the lithium battery to respond according to the low-frequency component.
Considering that the high frequency component is stabilized by the molten salt and the low frequency component is stabilized by the lithium battery, specifically, in step S107, the molten salt energy storage device is controlled to respond according to the high frequency component and the lithium battery is controlled to respond according to the low frequency component. That is, the molten salt energy storage output P C of the molten salt energy storage device is equal to the high frequency component and the battery power P L is equal to the low frequency component.
To further verify the advantages of the algorithm of the present application, the algorithm of the present application and the conventional prediction method were used to decompose the fm instruction, respectively, and the results of the conventional model prediction D MAPE (Mean Absolute Percentage Error ) and the VMD prediction D MAPE of the present application are shown in table 1.
Table 1 error table
As can be seen from Table 1, the average prediction error of the algorithm provided by the application is 9.3, and the average prediction error of the traditional model is 14.7, so that the algorithm provided by the application greatly improves the prediction precision.
In order to achieve the embodiment, the application further provides a frequency modulation system of the fused salt coupling thermal power unit based on bidirectional prediction, and the hybrid energy storage device of the thermal power plant comprises fused salt energy storage equipment and a lithium battery.
Fig. 3 is a block diagram of a frequency modulation system of a fused salt coupled thermal power generating unit based on bidirectional prediction provided by the embodiment of the application. As shown in fig. 3, the frequency modulation system of the fused salt coupled thermal power generating unit based on bidirectional prediction comprises an acquisition module 11, an aliasing degree correction module 12, an updating module 13, a selection module 14, a decomposition module 15, a division module 16 and a control module 17, wherein:
an acquisition module 11, configured to determine a hybrid energy storage response requirement based on the frequency modulation instruction received in real time;
The aliasing degree correction module 12 is configured to determine an aliasing degree correction value based on the hybrid energy storage response requirement, the decomposition layer number of the VMD algorithm, the trained prediction module, and the trained reverse prediction module;
An updating module 13, configured to update the decomposition layer number based on the set decomposition layer number range to obtain an aliasing degree correction value corresponding to the different decomposition layer numbers;
A selecting module 14, configured to select a decomposition layer number corresponding to a minimum value in all the aliasing degree correction values as a target decomposition layer number;
the decomposition module 15 is configured to decompose the hybrid energy storage response requirement by using a VMD algorithm based on the target decomposition layer number to obtain a target modal component and a residual component;
A dividing module 16, configured to divide the target modal component and the residual component to obtain a high-frequency component and a low-frequency component;
the control module 17 is used for controlling the molten salt energy storage device to respond according to the high-frequency component and controlling the lithium battery to respond according to the low-frequency component.
Further, in one possible implementation manner of the embodiment of the present application, the aliasing degree correction module 12 is specifically configured to: obtaining the aliasing degree based on the hybrid energy storage response requirement, the decomposition layer number of the VMD algorithm and the trained prediction module; determining a decomposition layer number predicted value based on the hybrid energy storage response requirement, the aliasing degree and the trained reverse prediction module; and determining a correction coefficient based on the decomposition layer number predicted value and the decomposition layer number, and correcting the aliasing degree by using the correction coefficient to obtain an aliasing degree correction value.
Further, in one possible implementation of the embodiment of the present application, the correction coefficient satisfies:
where a is a correction coefficient, k is a decomposition layer number, and k' is a decomposition layer number prediction value.
It should be noted that the explanation of the foregoing embodiment of the frequency modulation method of the fused salt coupled thermal power generating unit based on bidirectional prediction is also applicable to the frequency modulation system of the fused salt coupled thermal power generating unit based on bidirectional prediction in this embodiment, and will not be repeated here.
In the embodiment of the application, the hybrid energy storage response requirement is determined based on the frequency modulation instruction received in real time; determining an aliasing degree correction value based on the hybrid energy storage response requirement, the decomposition layer number of the VMD algorithm, the trained prediction module and the trained reverse prediction module; updating the decomposition layer number based on the set decomposition layer number range to obtain an aliasing degree correction value corresponding to different decomposition layer numbers; selecting the decomposition layer number corresponding to the minimum value in all the aliasing degree correction values as a target decomposition layer number; decomposing the hybrid energy storage response requirement by utilizing a VMD algorithm based on the target decomposition layer number to obtain a target modal component and a residual component; dividing the target modal component and the residual component to obtain a high-frequency component and a low-frequency component; the molten salt energy storage device is controlled to respond according to the high-frequency component and the lithium battery is controlled to respond according to the low-frequency component. Under the condition, the aliasing degree correction value is determined based on the hybrid energy storage response requirement, the decomposition layer number of the VMD algorithm, the trained prediction module and the trained reverse prediction module, wherein the trained prediction module and the trained reverse prediction module are comprehensively used for obtaining the aliasing degree correction value so as to improve the accuracy of the obtained aliasing degree, and then the minimum value in all the aliasing degree correction values is selected, at the moment, the aliasing degree correction value is the minimum, so that the aliasing condition among target modal components can be better avoided when the hybrid energy storage response requirement is decomposed by the VMD algorithm based on the target decomposition layer number, and the accuracy of the frequency modulation response is improved. In addition, the bi-directional prediction (i.e., using a trained prediction module and a trained reverse prediction module) better solves the problem of large prediction errors relative to conventional single prediction.
In order to achieve the above embodiment, the present application further provides an electronic device, including: a processor, a memory communicatively coupled to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the methods provided by the previous embodiments.
In order to implement the above embodiment, the present application also proposes a computer-readable storage medium having stored therein computer-executable instructions, which when executed by a processor are configured to implement the method provided in the foregoing embodiment.
In order to implement the above embodiments, the present application also proposes a computer program product comprising a computer program which, when executed by a processor, implements the method provided by the above embodiments.
In the foregoing description of embodiments, reference has been made to the terms "one embodiment," "some embodiments," "example," "a particular example," or "some examples," etc., meaning that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present application, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and additional implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order from that shown or discussed, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present application.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. As with the other embodiments, if implemented in hardware, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules may also be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product.
The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like. While embodiments of the present application have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the application, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the application.
Claims (6)
1. The frequency modulation method of the fused salt coupling thermal power unit based on the bidirectional prediction is characterized in that a hybrid energy storage device configured in a thermal power plant comprises fused salt energy storage equipment and a lithium battery, and the frequency modulation method comprises the following steps:
determining a hybrid energy storage response requirement based on the frequency modulation instruction received in real time;
Determining an aliasing degree correction value based on the hybrid energy storage response requirement, the decomposition layer number of the VMD algorithm, the trained prediction module and the trained reverse prediction module;
Updating the decomposition layer number based on the set decomposition layer number range to obtain an aliasing degree correction value corresponding to different decomposition layer numbers;
selecting the decomposition layer number corresponding to the minimum value in all the aliasing degree correction values as a target decomposition layer number;
decomposing the hybrid energy storage response requirement by utilizing a VMD algorithm based on a target decomposition layer number to obtain a target modal component and a residual component;
dividing the target modal component and the residual component to obtain a high-frequency component and a low-frequency component;
controlling the molten salt energy storage device to respond according to the high-frequency component and controlling the lithium battery to respond according to the low-frequency component;
The determining of the aliasing degree correction value based on the hybrid energy storage response requirement, the decomposition layer number of the VMD algorithm, the trained prediction module and the trained reverse prediction module comprises the following steps:
obtaining an aliasing degree based on the hybrid energy storage response requirement, the decomposition layer number of the VMD algorithm and the trained prediction module, wherein the trained prediction module is trained by utilizing a first historical data set, the prediction module adopts a GRU neural network, the input of the prediction module is the historical hybrid energy storage response requirement and the decomposition layer number, and the output is the aliasing degree;
determining a decomposition layer number predicted value based on the hybrid energy storage response requirement, the aliasing degree and the trained reverse prediction module, wherein the trained reverse prediction module is obtained by utilizing the second historical data set to train the prediction module, the reverse prediction module adopts a GRU model, the input of the reverse prediction module is the aliasing degree of the historical hybrid energy storage response requirement and the historical hybrid energy storage response requirement, and the output is the decomposition layer number;
and determining a correction coefficient based on the decomposition layer number predicted value and the decomposition layer number, and correcting the aliasing degree by using the correction coefficient to obtain an aliasing degree correction value.
2. The frequency modulation method of a bidirectional prediction-based molten salt coupled thermal power generating unit according to claim 1, wherein the correction coefficient satisfies:
where a is a correction coefficient, k is a decomposition layer number, and k' is a decomposition layer number prediction value.
3. Frequency modulation system of fused salt coupling thermal power generating unit based on two-way prediction, its characterized in that, the mixed energy storage device of thermal power plant configuration includes fused salt energy storage equipment and lithium cell, and frequency modulation system includes:
the acquisition module is used for determining the hybrid energy storage response requirement based on the frequency modulation instruction received in real time;
The aliasing degree correction module is used for determining an aliasing degree correction value based on the hybrid energy storage response requirement, the decomposition layer number of the VMD algorithm, the trained prediction module and the trained reverse prediction module;
The updating module is used for updating the decomposition layer number based on the set decomposition layer number range so as to obtain an aliasing degree correction value corresponding to different decomposition layer numbers;
the selection module is used for selecting the decomposition layer number corresponding to the minimum value in all the aliasing degree correction values as a target decomposition layer number;
the decomposition module is used for decomposing the hybrid energy storage response requirement by utilizing a VMD algorithm based on the target decomposition layer number to obtain a target modal component and a residual component;
the dividing module is used for dividing the target modal component and the residual component to obtain a high-frequency component and a low-frequency component;
the control module is used for controlling the molten salt energy storage equipment to respond according to the high-frequency component and controlling the lithium battery to respond according to the low-frequency component;
the aliasing degree correction module is specifically configured to:
obtaining an aliasing degree based on the hybrid energy storage response requirement, the decomposition layer number of the VMD algorithm and the trained prediction module, wherein the trained prediction module is trained by utilizing a first historical data set, the prediction module adopts a GRU neural network, the input of the prediction module is the historical hybrid energy storage response requirement and the decomposition layer number, and the output is the aliasing degree;
determining a decomposition layer number predicted value based on the hybrid energy storage response requirement, the aliasing degree and the trained reverse prediction module, wherein the trained reverse prediction module is obtained by utilizing the second historical data set to train the prediction module, the reverse prediction module adopts a GRU model, the input of the reverse prediction module is the aliasing degree of the historical hybrid energy storage response requirement and the historical hybrid energy storage response requirement, and the output is the decomposition layer number;
and determining a correction coefficient based on the decomposition layer number predicted value and the decomposition layer number, and correcting the aliasing degree by using the correction coefficient to obtain an aliasing degree correction value.
4. A bi-predictive molten salt coupled thermal power plant frequency modulation system as claimed in claim 3 wherein the correction factors satisfy:
where a is a correction coefficient, k is a decomposition layer number, and k' is a decomposition layer number prediction value.
5. An electronic device, comprising: a processor, and a memory communicatively coupled to the processor;
The memory stores computer-executable instructions;
the processor executes computer-executable instructions stored in the memory to implement the method of any one of claims 1-2.
6. A computer readable storage medium having stored therein computer executable instructions which when executed by a processor are adapted to carry out the method of any of claims 1-2.
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