A method and system for predicting energy storage frequency modulation instructions based on substitution method

Abstract

A method for predicting energy storage frequency modulation instructions using a substitution method, comprising: obtaining an original frequency modulation instruction signal sequence; finding strong fluctuation points based on the original frequency modulation instruction signal sequence; replacing the strong fluctuation point signals to generate a substitution signal sequence; using the original frequency modulation instruction signal sequence and the substitution signal sequence for prediction to obtain a predicted frequency modulation instruction sequence; and performing power control of different energy storage units in a hybrid energy storage system based on the predicted frequency modulation instruction sequence. The method makes the prediction result of the frequency modulation instruction more accurate, predicts the size of the frequency modulation instruction in advance for energy storage control, and enables the supercapacitor / battery to act in advance, thereby improving response accuracy and frequency modulation benefits.

Description

Technical Field

[0001] The present disclosure relates to the technical field of power grid frequency regulation, and in particular to a method and system for predicting energy storage frequency regulation instructions using a substitution method. Background Art

[0002] Currently, large hydropower and thermal power units (coal-fired and gas-fired) primarily serve as frequency regulation power sources in major regional power grids in my country. These units respond to system frequency fluctuations by adjusting the output of the frequency regulation power source. However, hydropower and thermal power units have certain limitations in frequency regulation. In recent years, the increasing proportion of renewable energy generation, such as wind and solar power, has led to a large number of thermal power units shouldering the heavy burden of AGC regulation, resulting in a series of negative impacts, such as increased coal consumption and severe equipment wear. Existing power frequency regulation resources are no longer able to meet the demand for renewable energy integration. Hybrid energy storage systems with supercapacitors, however, provide coordinated control of primary frequency regulation. By classifying typical operating conditions and converting frequency deviation into power using an optimized decision module, the PCS system control module controls the charge and discharge of the supercapacitor energy storage system, ultimately assisting conventional generators in completing the primary frequency regulation process. Within the rated power range, the system can deliver the specified power output within 1 second with an accuracy exceeding 99%. Its comprehensive response capability fully meets the power conversion requirements within the AGC frequency regulation timescale, significantly exceeding the regulation capabilities of conventional thermal power plants. The more urgent the system regulation needs, the more significant the advantages of this technology.

[0003] Hybrid energy storage systems based on supercapacitor energy storage typically use algorithms such as VMD (Variational Mode Decomposition) to distribute frequency modulation signals. Traditional hybrid energy storage (supercapacitor + lithium battery) methods for assisting thermal power generation units in frequency modulation transfer the difference between the frequency modulation command and the thermal power generation unit to the hybrid energy storage. Using VMD decomposition technology, the original signal is decomposed into K subsequences, IMF1 through IMFK, with the low-frequency components handled by the battery and the high-frequency components handled by the supercapacitor. VMD is an adaptive, fully non-recursive modal variation and signal processing method. This technique has the advantage of being able to determine the number of modal decompositions. Its adaptability is reflected in the fact that it determines the number of modal decompositions for a given sequence based on actual conditions. The subsequent search and solution process adaptively matches the optimal center frequency and limited bandwidth of each mode. This effectively separates the intrinsic mode components (IMFs), partitions the signal in the frequency domain, and ultimately obtains the effective decomposition components of the given signal, ultimately achieving the optimal solution to the variational problem. The decomposition yields relatively stationary subsequences containing multiple frequency scales, making it suitable for non-stationary sequences.

[0004] The traditional hybrid energy storage (supercapacitor + lithium battery) method of assisting thermal power generation units with frequency regulation involves transmitting the difference between the frequency regulation command and the thermal power generation unit to the hybrid energy storage, with the battery handling the low-frequency portion and the supercapacitor handling the high-frequency portion. However, signal transmission (i.e., transmitting the frequency regulation command to the supercapacitor / lithium battery) takes time, as does the response time of the supercapacitor or lithium battery itself. This results in a certain response time difference, further affecting the accuracy of the system response and the power plant's frequency regulation benefits. Summary of the Invention

[0005] In view of the shortcomings of the existing technology, the present invention proposes a prediction method to predict the size of the frequency modulation instruction in advance, so that the supercapacitor / battery can be activated in advance, thereby improving the response accuracy and frequency modulation benefit.

[0006] A method for predicting energy storage frequency modulation instructions using a substitution method is characterized by comprising the steps of:

[0007] S101, obtaining an original frequency modulation instruction signal sequence;

[0008] S102, finding strong fluctuation points based on the original frequency modulation instruction signal sequence;

[0009] S103, replacing the strong fluctuation point signal to generate a replacement signal sequence;

[0010] S104, using the original frequency modulation instruction signal sequence and the replacement signal sequence to perform prediction to obtain a predicted frequency modulation instruction sequence;

[0011] S105. Perform power control of different energy storage units in the hybrid energy storage system based on the predicted frequency modulation instruction sequence.

[0012] Further,

[0013] In step S101,

[0014] Dividing the original frequency modulation command signal sequence into signal sequences and forming groups;

[0015] The frequency modulation command signal sequence is Pt=[X1,X2,X3,.X i ..,X N ], the frequency modulation command signal sequence is divided into two parts according to the length ratio, the front part is used as the input group sequence, and the back part is used as the test group sequence.

[0016] Further,

[0017] If the length ratio is 0.9, then the integer sequence [X1, X2, X3....., X 0.9N ] as the input group, followed by an integer sequence of 10% length [X 0.9N+1 ,X 0.9N+2 ,X0.9N+3 ,...,X N ] as the test group; construct the unknown group sequence, with [X N+1 ,X N+2 ,X N+3 ,...,X N+0.1N ] as the unknown group.

[0018] Further,

[0019] The above-mentioned S102, finding the strong fluctuation point based on the original frequency modulation instruction signal sequence, includes:

[0020] Calculating the change slopes and change angles of adjacent signals, and determining that a number of signal values whose change angles are greater than a first angle threshold form a first change signal sequence;

[0021] After the first change signal sequence is subjected to the exponential factorial operation, the change slopes and change angle values of the adjacent signals are calculated in the same manner, and a number of signal values whose change angle values are greater than the second angle threshold are determined to form a strong change signal sequence [X 1r ,X 2r ,X 3r ,.X ir ..,X rr ].

[0022] Further,

[0023] The above S103, replacing the strong fluctuation point signal to generate a replacement signal sequence, includes:

[0024] Performing a fitting operation on the strongly changing signal sequence to determine a fitting straight line X=Kt+B;

[0025] Using parameters K and B, calculate the strong change signal sequence [X 1r ,X 2r ,X 3r ,.X ir ..,X rr ], each replacement value is inserted into the original frequency modulation instruction signal sequence to obtain the frequency modulation instruction replacement signal sequence Pt 、 .

[0026] Further,

[0027] The replacement value calculation formula is:

[0028] .

[0029] Further,

[0030] The above-mentioned S104, using the original frequency modulation instruction signal sequence and the replacement signal sequence to perform prediction to obtain a predicted frequency modulation instruction sequence, includes:

[0031] Replace the signal sequence Pt with the frequency modulation instruction 、 Input GRU to make predictions and get the first predicted value of the unknown group [X N+1 、 ,X N+2 、 ,X N+3 、 ,...,X N+0.1N 、 ], and the first error W1 of the experimental group was also obtained;

[0032] The difference sequence Pt-Pt between the original FM command signal sequence and the FM command replacement signal sequence 、 Input GRU to make predictions and get the second prediction value of the unknown group [X N+1 、、 ,X N+2 、、 ,X N+3 、、 ,...,X N+0.1N 、、 ], the second error W2 of the experimental group;

[0033] The first prediction value, the second prediction value, the first error, and the second error are integrated to calculate the final prediction result.

[0034] Further,

[0035] The fusion algorithm of the final prediction result is:

[0036]

[0037] Get the predicted frequency modulation instruction sequence [X N+1 ,X N+2 ,X N+3 ,...,X N+0.1N ].

[0038] Further,

[0039] For strong change signal sequence [X 1r ,X 2r ,X 3r ,.X ir ..,X rr ] to perform straight line fitting,

[0040] First, randomly generate a fitting straight line X=Kt+B, and calculate the strong change signal sequence [X 1r ,X 2r ,X 3r ,.X ir ..,X rr] to this straight line is [d1, d2, d3, .., dr], and the sum of [d1, d2, d3, .., dr] is calculated;

[0041] The loop is executed to randomly generate M fitting straight lines X=Kt+B, where K and B are both randomly generated;

[0042] Determine the deviations and the smallest fitting line among the M fitting lines, and use the fitting line X=Kt+B as the selected fitting line.

[0043] Further,

[0044] During the prediction process, the input group sequence is used as input to predict the predicted values of the test group and the unknown group, and the average MAPE of the absolute error is calculated based on the actual value and the predicted value of the test group to generate the first error W1 and the second error W2.

[0045] A replacement method energy storage frequency modulation instruction prediction system is characterized by comprising:

[0046] The command acquisition module obtains the original frequency modulation command signal sequence;

[0047] Search module, finds strong fluctuation points based on the original frequency modulation command signal sequence;

[0048] The replacement module replaces the strong fluctuation point signal to generate a replacement signal sequence;

[0049] The prediction module uses the original frequency modulation instruction signal sequence and the replacement signal sequence to perform prediction and obtain the predicted frequency modulation instruction sequence;

[0050] The power control module performs power control of different energy storage units in the hybrid energy storage system based on the predicted frequency modulation instruction sequence.

[0051] The beneficial effects of the present invention are: providing a replacement method and system for predicting energy storage frequency modulation instructions, which can respond in a timely manner when the power plant grid frequency fluctuates, by finding strong fluctuation points, generating alternative signal sequences, and obtaining predicted frequency modulation instruction sequences, thereby making the prediction results of frequency modulation instructions more accurate, and predicting the size of frequency modulation instructions in advance for energy storage regulation, which can enable supercapacitors / batteries to act in advance, thereby improving response accuracy and frequency modulation benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 A diagram of the method steps.

[0053] Figure 2 、 Figure 3 This is an illustration of the strong fluctuation point search algorithm. DETAILED DESCRIPTION

[0054] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0055] It should be understood that the step numbers used herein are only for convenience of description and are not intended to limit the order in which the steps are to be executed.

[0056] It should be understood that the terms used in this specification are only for the purpose of describing specific embodiments and are not intended to limit this application. As used in this specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0057] The terms “include” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0058] The term "and / or" refers to and includes any and all possible combinations of one or more of the associated listed items.

[0059] The embodiment of the present disclosure provides a method for predicting energy storage frequency modulation instructions by replacing the method, such as Figure 1 Shown, including:

[0060] S101, obtaining an original frequency modulation instruction signal sequence;

[0061] S102, finding strong fluctuation points based on the original frequency modulation instruction signal sequence;

[0062] S103, replacing the strong fluctuation point signal to generate a replacement signal sequence;

[0063] S104, using the original frequency modulation instruction signal sequence and the replacement signal sequence to perform prediction to obtain a predicted frequency modulation instruction sequence;

[0064] S105. Perform power control of different energy storage units in the hybrid energy storage system based on the predicted frequency modulation instruction sequence.

[0065] Wherein, step S101, obtaining the original frequency modulation instruction signal sequence, includes:

[0066] The original frequency modulation instruction signal sequence is divided and constructed into signal sequences.

[0067] The frequency modulation command signal sequence is Pt=[ X1,X2,X3,.X i ..,X N ] The frequency modulation command signal sequence is divided into two parts according to the length ratio, the front part is used as the input group sequence, and the back part is used as the test group sequence.

[0068] If the length ratio value is 0.9, then the integer sequence [X1, X2, X3....., X 0.9N ] as the input group, followed by an integer sequence of 10% length [X 0.9N+1 ,X 0.9N+2 ,X 0.9N+3 ,...,X N ] as the experimental group.

[0069] At the same time, the unknown group sequence is constructed, with [X N+1 ,X N+2 ,X N+3 ,...,X N+0.1N ] is used as the unknown group, and the unknown group has the same sequence length as the test group.

[0070] In the prediction process, the input group sequence is used as the input of the neural network to predict the values of the test group and the unknown group. Finally, the value of the unknown group sequence is used to control the power of the hybrid energy storage system.

[0071] In another embodiment disclosed in this application, Figure 2 As shown,

[0072] The raw frequency modulation command signal sequence exhibits significant volatility, necessitating signal preprocessing. First, identify points of strong fluctuation. For a signal sequence, large changes in the angle between the lines connecting the points indicate significant variations between adjacent points. Large variations indicate poor regularity and high signal volatility.

[0073] The above-mentioned S102, finding the strong fluctuation point based on the original frequency modulation instruction signal sequence, includes:

[0074] Step 1: Calculate the change slopes and change angles of adjacent signals, and determine that a number of signal values whose change angles are greater than a first angle threshold form a first change signal sequence.

[0075] The original frequency modulation command signal sequence is Pt=[ X1,X2,X3,.X i ..,X N ]. The original frequency modulation instruction signal sequence Pt is a function of time t, such as Figure 2 As shown in the coordinate system, the horizontal axis represents time t, and the vertical axis represents signal values X1, X2, X3,.X i ..,X N .

[0076] The slope of the line segment formed by two adjacent signal values is calculated by the horizontal and vertical coordinates of each signal value, and the angle value of the line segment formed by two adjacent signal values is further calculated to obtain the first angle sequence. .

[0077] Each angle value in the first angle sequence is compared with a first angle threshold, and a number of signal values corresponding to angles greater than the first angle threshold are obtained to form a first change signal sequence.

[0078] Set the first angle threshold to 50°, select a number of signal values corresponding to angles greater than 50°, and set the first change signal sequence formed by the several signal values to be [X 1d ,X 2d ,X 3d ,.X id ..,X dd ], where there are d signal values, and the corresponding angle is greater than 50°.

[0079] Step 2: After performing an exponential factorial operation on the first change signal sequence, calculate the change slopes and change angles of adjacent signals, and determine that several signal values whose change angles are greater than a second angle threshold form a strong change signal sequence.

[0080] First, perform an exponential factorial operation on each value in the first change signal sequence to form the following exponential factorial sequence:

[0081] [exp(X 1d ) / 1d!,exp(X 2d ) / 2d!,exp(X 3d ) / 3d!,.exp(X id ) / id!..,exp(X dd ) / dd!].

[0082] Further Figure 3 As shown, the horizontal axis represents time t, and the vertical axis represents the exponential factorial signal value. The slope of the line segment formed by two adjacent signal values is further calculated, and the angle value formed by three adjacent signals is further calculated to obtain a second angle sequence.

[0083] Each angle value in the second angle sequence is compared with the second angle threshold, and a number of signal values corresponding to angles greater than the second angle threshold are obtained to form a strong change signal sequence.

[0084] Set the second angle threshold to 30°, select a number of signal values corresponding to angles greater than 30°, and set the strong change signal sequence formed by the several signal values to be [X 1r ,X 2r ,X 3r ,.Xir ..,X rr ], there are r signal values in total, and the corresponding angle is greater than 30°.

[0085] The r signal values in the strongly changing signal sequence are taken as the points with the strongest volatility in the original sequence.

[0086] The above S103, replacing the strong fluctuation point signal to generate a replacement signal sequence, includes:

[0087] Step 1: performing a fitting operation on the strongly changing signal sequence.

[0088] For strong change signal sequence [X 1r ,X 2r ,X 3r ,.X ir ..,X rr ] to perform straight line fitting.

[0089] First, randomly generate a fitting straight line X=Kt+B, and calculate the strong change signal sequence [X 1r ,X 2r ,X 3r ,.X ir ..,X rr ] to this straight line is [d1, d2, d3, .., dr], and the sum of [d1, d2, d3, .., dr] is calculated;

[0090] Loop and randomly generate M fitting lines X=Kt+B, randomly generate K and B, and repeat M times (M is not less than 100);

[0091] Determine the deviations and the smallest fitting line among the M fitting lines, and use the fitting line X=Kt+B as the selected fitting line.

[0092] Step 2: Replace the signal value in the strongly changing signal sequence.

[0093] Using the parameters K and B in the selected fitting line X=Kt+B, calculate the strong change signal sequence [X 1r ,X 2r ,X 3r ,.X ir ..,X rr For points with strong volatility, the coefficients of the fitting line are used to fit the points with strong volatility, thereby enhancing the regularity of these points.

[0094] The replacement value calculation formula is:

[0095]

[0096] For strong change signal sequence [X1r ,X 2r ,X 3r ,.X ir ..,X rr ] Calculate the strong change replacement signal sequence according to the replacement value calculation formula, insert the strong change replacement signal sequence into the original frequency modulation instruction signal sequence according to the number position, and obtain the frequency modulation instruction replacement signal sequence Pt after replacing the corresponding original signal value 、 .

[0097] The above-mentioned S104, using the original frequency modulation instruction signal sequence and the replacement signal sequence to perform prediction to obtain a predicted frequency modulation instruction sequence, includes:

[0098] Step 1: Replace the signal sequence Pt with the frequency modulation instruction 、 Input GRU (Gated Recurrent Network) for prediction.

[0099] Replace the signal sequence Pt with the frequency modulation instruction 、 It is divided into two parts according to the length ratio, the first part is used as the input group sequence, and the second part is used as the test group sequence.

[0100] If the length ratio value is 0.9, then the integer sequence [X1, X2, X3....., X 0.9N ] as the input group, followed by an integer sequence of 10% length [X 0.9N+1 ,X 0.9N+2 ,X 0.9N+3 ,...,X N ] as the experimental group.

[0101] At the same time, the unknown group sequence is constructed, with [X N+1 ,X N+2 ,X N+3 ,...,X N+0.1N ] as the unknown group.

[0102] During the prediction process, the input group sequence is used as input to predict the values of the test group and the unknown group.

[0103] Get the first predicted value of the unknown group [X N+1 、 ,X N+2 、 ,X N+3 、 ,...,X N+0.1N 、 ], and the predicted value of the experimental group [X 0.9N+1 、 ,X 0.9N+2 、 ,X 0.9N+3 、,...,X N 、 Based on the actual value of the experimental group [X 0.9N+1 ,X 0.9N+2 ,X 0.9N+3 ,...,X N ] and the predicted value [X 0.9N+1 、 ,X 0.9N+2 、 ,X 0.9N+3 、 ,...,X N 、 ] to calculate the first error W1 (mean value of absolute error MAPE) between the two.

[0104] Step 2: The difference sequence Pt-Pt between the original FM command signal sequence and the FM command replacement signal sequence 、 Input GRU (Gated Recurrent Network) for prediction.

[0105] The difference sequence Pt-Pt between the original FM command signal sequence and the FM command replacement signal sequence 、 Put it into GRU for prediction, and use the same prediction algorithm as step 1 above to get the second prediction value of the unknown group [X N+1 、、 ,X N+2 、、 ,X N+3 、、 ,...,X N+0.1N 、、 ], the second error W2 of the experimental group.

[0106] Step 3: Fusion the first prediction value, the second prediction value, the first error, and the second error to calculate the final prediction result.

[0107] The fusion algorithm of the final prediction result is:

[0108]

[0109] Get the predicted frequency modulation instruction sequence [X N+1 ,X N+2 ,X N+3 ,...,X N+0.1N ].

[0110] When the fusion calculation formula is merged, the proportion of the two is balanced according to the principle of coefficient error.

[0111] The above S105 is to perform power control of different energy storage units in the hybrid energy storage system based on the predicted frequency modulation instruction sequence.

[0112] Using the predicted frequency modulation instruction sequence [XN+1 ,X N+2 ,X N+3 ,...,X N+0.1N ] to perform power regulation in hybrid energy storage systems. It monitors grid frequency deviations in real time, dynamically allocates power commands, and quickly smoothes frequency fluctuations.

[0113] With the above Figure 1 Corresponding to the method shown, an embodiment of the present invention further provides an energy storage frequency modulation instruction prediction system using an alternative method, comprising:

[0114] The command acquisition module obtains the original frequency modulation command signal sequence;

[0115] Search module, finds strong fluctuation points based on the original frequency modulation command signal sequence;

[0116] The replacement module replaces the strong fluctuation point signal to generate a replacement signal sequence;

[0117] The prediction module uses the original frequency modulation instruction signal sequence and the replacement signal sequence to perform prediction and obtain the predicted frequency modulation instruction sequence;

[0118] The power control module performs power control of different energy storage units in the hybrid energy storage system based on the predicted frequency modulation instruction sequence.

[0119] Traditional prediction methods often yield inaccurate results due to the high volatility and randomness of frequency modulation signal sequences. Directly applying these signals to a neural network often results in inaccurate predictions. To address these volatilities and randomness, the present invention proposes a novel method that first identifies the fluctuation line based on the characteristics of the original sequence, then replaces it with a replacement sequence. This replacement sequence is then used to predict the original sequence, and the prediction results are then corrected to yield the final prediction. This method significantly reduces the volatility and randomness of the original sequence, further improving prediction accuracy.

[0120] In order to further verify the advantages of the present invention, the present invention uses the method of the present invention and the GRU direct prediction method to predict the frequency modulation instruction sequence, and the comparison results are shown in Table 1.

[0121] Table 1: Comparison of performance indicators.

[0122]

[0123] The four evaluation indicators are described in Table 2.

[0124] Table 2: Description of evaluation indicators.

[0125]

[0126] N represents the sample size, and Represent the actual value and predicted value at time n respectively.

[0127] From the experimental results, we can see that the four evaluation indicators of the algorithm in this application have all been reduced, which shows that the proposed method can well improve the prediction accuracy.

[0128] An embodiment of the present application provides a computer device, comprising: a processor, a memory, and a bus, wherein the memory stores machine-readable instructions executable by the processor. When the computer device is running, the processor and the memory communicate via the bus, and when the machine-readable instructions are executed by the processor, the steps provided in any embodiment of the present application are performed.

[0129] The computer device provided in the embodiment of the present application includes a processor, a memory, and a bus. The memory is used to store and execute instructions, and includes internal memory and external memory. The internal memory here is also called internal memory, which is used to temporarily store the calculation data in the processor and the data exchanged with the external memory such as the hard disk. The processor exchanges data with the external memory through the internal memory. When the electronic device is running, the processor and the memory communicate through the bus, so that the processor executes the following instructions:

[0130] Obtaining the original frequency modulation command signal sequence;

[0131] Find the strong fluctuation point based on the original frequency modulation command signal sequence;

[0132] Replace the strong fluctuation point signal to generate an alternative signal sequence;

[0133] Using the original frequency modulation instruction signal sequence and the replacement signal sequence to perform prediction, a predicted frequency modulation instruction sequence is obtained;

[0134] The power of different energy storage units in the hybrid energy storage system is controlled based on the predicted frequency modulation instruction sequence.

[0135] An embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, executes the steps provided in any embodiment of the present application. The storage medium may be a volatile or non-volatile computer-readable storage medium.

[0136] Through the above description of the embodiments, those skilled in the art will clearly understand that the embodiments of the present disclosure can be implemented through hardware or through software plus the necessary general hardware platform. Based on this understanding, the technical solutions of the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a read-only optical disk, a USB flash drive, a mobile hard disk, etc.) and includes a number of instructions for causing a computer device (such as a personal computer, a server, or a network device) to execute the methods described in the various embodiments of the present disclosure.

[0137] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of a preferred embodiment, and the modules or processes in the accompanying drawings are not necessarily required for implementing the present disclosure.

[0138] Those skilled in the art will appreciate that the modules in the devices of the embodiments may be distributed in the devices of the embodiments as described in the embodiments, or may be located in one or more devices different from the embodiments with corresponding changes. The modules of the above embodiments may be combined into one module or further split into multiple submodules.

[0139] The serial numbers of the above-mentioned embodiments of the present disclosure are for description only and do not represent the advantages or disadvantages of the embodiments.

[0140] Obviously, those skilled in the art may make various changes and modifications to the present disclosure without departing from the spirit and scope of the present disclosure. Thus, if these modifications and variations of the present disclosure fall within the scope of the claims of the present disclosure and their equivalents, the present disclosure is intended to include these modifications and variations.

[0141] Finally, it should be noted that the foregoing description is merely an explanation of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail, those skilled in the art will be able to modify the aforementioned technical solutions or substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A method for predicting energy storage frequency modulation instructions based on substitution method, characterized in that: Including steps: S101, obtaining an original frequency modulation instruction signal sequence; S102, finding strong fluctuation points based on the original frequency modulation instruction signal sequence; S103, replacing the strong fluctuation point signal to generate a replacement signal sequence; S104, using the original frequency modulation instruction signal sequence and the replacement signal sequence to perform prediction to obtain a predicted frequency modulation instruction sequence; S105, performing power control of different energy storage units in the hybrid energy storage system based on the predicted frequency modulation instruction sequence; The above-mentioned S102, finding the strong fluctuation point based on the original frequency modulation instruction signal sequence, includes: Calculating the change slopes and change angles of adjacent signals, and determining that a number of signal values whose change angles are greater than a first angle threshold form a first change signal sequence; After performing exponential factorial operation on the first change signal sequence, similarly calculate the change slope and change angle value of the adjacent signals, and determine that a number of signal values whose change angle value is greater than the second angle threshold form a strong change signal sequence [X 1r ,X 2r ,X 3r ,.X ir ..,X rr ]; The above S103, replacing the strong fluctuation point signal to generate a replacement signal sequence, includes: Performing a fitting operation on the strongly changing signal sequence to determine a fitting straight line X=Kt+B; Using parameters K and B, calculate the strong change signal sequence [X 1r ,X 2r ,X 3r ,.X ir ..,X rr ], each replacement value is inserted into the original frequency modulation instruction signal sequence to obtain the frequency modulation instruction replacement signal sequence Pt 、 .

2. The energy storage frequency modulation instruction prediction method based on the substitution method according to claim 1 is characterized in that: In step S101, Dividing the original frequency modulation command signal sequence into signal sequences and forming groups; The frequency modulation command signal sequence is Pt=[X1,X2,X3,.X i ..,X N ], the frequency modulation command signal sequence is divided into two parts according to the length ratio, the front part is used as the input group sequence, and the back part is used as the test group sequence.

3. The energy storage frequency modulation instruction prediction method based on the substitution method according to claim 2 is characterized in that: If the length ratio is 0.9, the integer sequence [X1, X2, X3....., X 0.9N ] as the input group, followed by an integer sequence of 10% length [X 0.9N+1 ,X 0.9N+2 ,X 0.9N+3 ,...,X N ] as the test group; construct the unknown group sequence, with [X N+1 ,X N+2 ,X N+3 ,...,X N+0.1N ] as the unknown group.

4. The energy storage frequency modulation instruction prediction method based on the substitution method according to claim 1 is characterized in that: The replacement value calculation formula is: 。 5. The energy storage frequency modulation instruction prediction method based on the substitution method according to claim 1 is characterized in that: The above-mentioned S104, using the original frequency modulation instruction signal sequence and the replacement signal sequence to perform prediction to obtain a predicted frequency modulation instruction sequence, includes: Replace the signal sequence Pt with the frequency modulation instruction 、 Input GRU to make predictions and get the first predicted value of the unknown group [X N+1 、 ,X N+2 、 ,X N+3 、 ,...,X N+0.1N 、 ], and the first error W1 of the experimental group was also obtained; The difference sequence Pt-Pt between the original FM command signal sequence and the FM command replacement signal sequence 、 Input GRU to make predictions and get the second predicted value of the unknown group [X N+1 、、 ,X N+2 、、 ,X N+3 、、 ,...,X N+0.1N 、、 ], the second error W2 of the experimental group; The first prediction value, the second prediction value, the first error, and the second error are integrated to calculate the final prediction result.

6. The energy storage frequency modulation instruction prediction method based on the substitution method according to claim 5 is characterized in that: The fusion algorithm of the final prediction result is: Get the predicted frequency modulation instruction sequence [X N+1 ,X N+2 ,X N+3 ,...,X N+0.1N ].

7. The energy storage frequency modulation instruction prediction method based on the substitution method according to claim 1 is characterized in that: For strong change signal sequence [X 1r ,X 2r ,X 3r ,.X ir ..,X rr ] to perform straight line fitting, First, randomly generate a fitting straight line X=Kt+B, and calculate the strong change signal sequence [X 1r ,X 2r ,X 3r ,.X ir ..,X rr ] to this straight line is [d1, d2, d3, .., dr], and the sum of [d1, d2, d3, .., dr] is calculated; The loop is executed to randomly generate M fitting straight lines X=Kt+B, where K and B are both randomly generated; Determine the deviations and the smallest fitting line among the M fitting lines, and use the fitting line y=Kx+B as the selected fitting line.

8. The energy storage frequency modulation instruction prediction method based on the substitution method according to claim 5 is characterized in that: During the prediction process, the input group sequence is used as input to predict the predicted values of the test group and the unknown group, and the average MAPE of the absolute error is calculated based on the actual value and the predicted value of the test group to generate the first error W1 and the second error W2.

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