Composite Energy Storage Adaptive Power Dynamic Allocation Method, System, Device and Medium

Abstract

The present application relates to the field of energy storage technologies, and discloses a composite energy storage adaptive power dynamic distribution method, system, device and medium, including determining a composite energy storage reference power based on the obtained load power and grid-side output power; performing modal decomposition on the composite energy storage reference power to obtain multiple IMF components from high frequency to low frequency and a residual, and determining a preliminary power-type reference power and a preliminary energy-type reference power based on the adjacent IMF components and the permutation entropy corresponding to the residual; generating a power-type adjustment power corresponding to the preliminary power-type reference power and an energy-type adjustment power corresponding to the preliminary energy-type reference power; determining a target power-type reference power based on the preliminary power-type reference power, the power-type adjustment power and the energy-type adjustment power, and determining a target energy-type reference power based on the preliminary energy-type reference power, the energy-type adjustment power and the power-type adjustment power. The beneficial effect is to optimize the charging and discharging process and extend the cycle life of the energy-type energy storage.

Description

Technical Field

[0001] The present application relates to the technical field of energy storage, and particularly to a composite energy storage adaptive power dynamic allocation method, system, device and medium. Background Art

[0002] With the wide application of wind energy and solar energy, energy storage technology has become the key to solving their volatility and instability problems. The composite energy storage technology combines the advantages of energy-type and power-type energy storage, and can effectively improve the grid connection ability of wind-solar power generation. However, existing power allocation methods, such as low-pass filtering (LPF), wavelet transform (WT) and empirical mode decomposition (EMD), have errors and instability. Although the CEEMD algorithm can effectively solve the mode mixing problem, most methods do not consider the current SOC state of the energy storage system, resulting in overcharging and over-discharging, which affects the device life.

[0003] The current problem to be solved is how to achieve adaptive dynamic power allocation of composite energy storage to improve the reliability and efficiency of the composite energy storage system. Summary of the Invention

[0004] The present application provides a composite energy storage adaptive power dynamic allocation method, system, device and medium, which solves the technical problem of how to achieve adaptive dynamic power allocation of composite energy storage, and achieves the technical effects of optimizing the charge-discharge process, improving the utilization rate of power-type energy storage, reducing the high-frequency high-power charge-discharge of energy-type energy storage, and prolonging the cycle life of energy-type energy storage.

[0005] To achieve the above object, the main technical solutions adopted in the present application include:

[0006] In a first aspect, an embodiment of the present application provides a composite energy storage adaptive power dynamic allocation method, the method including:

[0007] Determine a composite energy storage reference power based on the obtained load power and grid-side output power;

[0008] Perform mode decomposition on the composite energy storage reference power to obtain multiple IMF components and a residual from high frequency to low frequency, and determine a preliminary power-type reference power and a preliminary energy-type reference power based on the adjacent IMF components and the permutation entropy corresponding to the residual;

[0009] Generate a power-type adjustment power corresponding to the preliminary power-type reference power and an energy-type adjustment power corresponding to the preliminary energy-type reference power;

[0010] Determine the target power-type reference power based on the preliminary power-type reference power, the power-type adjustment power, and the energy-type adjustment power, and determine the target energy-type reference power based on the preliminary energy-type reference power, the energy-type adjustment power, and the power-type adjustment power.

[0011] A composite energy storage adaptive power dynamic allocation method provided in this embodiment accurately determines the composite energy storage reference power by combining the load power and the grid-side output power in real time, providing a reliable data basis for subsequent dynamic adjustment. Then, the power signal is decomposed into multiple IMF components and residuals from high frequency to low frequency through modal decomposition, covering power characteristics in different frequency bands. At the same time, permutation entropy analysis is applied to identify the complexity and dynamic changes of the power signal. This efficient frequency-domain analysis method enables a fine grasp of the multi-level characteristics of power fluctuations, providing a scientific basis for generating the preliminary reference power. On this basis, by dynamically adjusting the power-type and energy-type reference powers, it can adapt to load and grid-side fluctuations in real time, avoiding over-response or slowness. Finally, by comprehensively considering the influence of the preliminary reference power and the adjustment power, the target power-type reference power and the target energy-type reference power are refined, thus realizing dynamic power allocation and recovery in the short term, optimizing the charge and discharge process, reducing high-frequency high-power charge and discharge of the energy-type energy storage, and extending the cycle life of the energy-type energy storage.

[0012] Optionally, perform modal decomposition on the composite energy storage reference power to obtain multiple IMF components from high frequency to low frequency, including:

[0013] Use the CEEMD algorithm to perform modal decomposition on the composite energy storage reference power to obtain multiple IMF components and residuals from high frequency to low frequency.

[0014] Optionally, the determining the preliminary power-type reference power and the preliminary energy-type reference power based on the permutation entropy corresponding to the adjacent IMF components and the residuals includes:

[0015] Determine the permutation entropy of the IMF components and the residuals;

[0016] According to the permutation entropy, determine the permutation entropy difference between adjacent IMF components;

[0017] Determine the adjacent IMF components with the largest permutation entropy difference as the demarcation point for high-low frequency reconstruction;

[0018] Determine the IMF components before the demarcation point as high-frequency components, and reconstruct the high-frequency components to obtain the preliminary power-type reference power;

[0019] Determine the IMF components after the demarcation point and the residuals as low-frequency components, and reconstruct the low-frequency components to obtain the preliminary energy-type reference power.

[0020] In this embodiment, by calculating the permutation entropy of the IMF components and their residuals, basic data is provided for subsequent frequency analysis. According to the permutation entropy, the difference in permutation entropy between adjacent IMF components is calculated, which reflects the difference in frequency between these components. By selecting the adjacent IMF components with the largest difference in permutation entropy, the demarcation point between the high-frequency and low-frequency components is accurately determined. Then, the IMF components before the demarcation point are determined as high-frequency components, and these high-frequency components are reconstructed to obtain a preliminary power-type reference power. The IMF components and residuals after the demarcation point are regarded as low-frequency components, and by reconstructing these low-frequency components, a preliminary energy-type reference power is obtained. This embodiment can effectively divide the signal into high-frequency and low-frequency components, thereby providing a more accurate basis for the calculation of the power-type and energy-type reference powers, improving the overall performance and accuracy.

[0021] Optionally, determining the permutation entropy of the IMF components includes:

[0022] Using Shannon entropy to determine the permutation entropy of the time series of the IMF components and the residuals;

[0023] Normalizing the permutation entropy of the time series to obtain the permutation entropy.

[0024] In this embodiment, by using Shannon entropy to calculate the permutation entropy of the time series of IMF components, the complexity and information content of the time series can be effectively quantified. As a tool for measuring information uncertainty, Shannon entropy can reveal the chaotic degree and regularity of the time series, thereby providing accurate data characteristics for further analysis. Then, by normalizing the permutation entropy, it can be adjusted to a unified range or standard, ensuring the comparability and consistency of different time series, which helps to improve the accuracy and stability of data processing.

[0025] Optionally, generating the power-type adjustment power corresponding to the preliminary power-type reference power and the energy-type adjustment power corresponding to the preliminary energy-type reference power includes:

[0026] Obtaining the power-type energy storage state of charge, the energy-type energy storage state of charge, and the change amount corresponding to the composite energy storage reference power;

[0027] Inputting the power-type energy storage state of charge, the energy-type energy storage state of charge, the composite energy storage reference power, and the change amount into a preset fuzzy control algorithm to obtain a power-type adjustment coefficient and an energy-type adjustment coefficient;

[0028] Adjusting the preliminary power-type reference power by using the power-type adjustment coefficient to obtain the power-type adjustment power;

[0029] Adjust the preliminary energy-based reference power using the energy-based adjustment coefficient to obtain the energy-based adjustment power.

[0030] In this embodiment, through the fuzzy control algorithm, the state of charge of the power-type and energy-type energy storage and the change amount of the composite energy storage reference power can be obtained in real time, providing an accurate data basis for subsequent power regulation. Using the fuzzy control algorithm to process these inputs, a power-type adjustment coefficient and an energy-type adjustment coefficient are generated, so as to finely adjust the preliminary power-type and energy-type reference powers, and obtain the power-type adjustment power and the energy-type adjustment power respectively. This adjustment can optimize the response accuracy and efficiency of the energy storage system, avoid overcharging or over-discharging, and ensure the stable operation of the system under load fluctuations. Overall, this embodiment realizes the coordinated cooperation between energy storage devices through intelligent and dynamic adjustment, significantly improves the stability and operation efficiency of the system, ensures efficient and balanced energy supply under different working conditions, and extends the service life of the system.

[0031] Optionally, determining the target power-type reference power based on the preliminary power-type reference power, the power-type adjustment power, and the energy-type adjustment power includes:

[0032] The target power-type reference power = the preliminary power-type reference power - the power-type adjustment power + the energy-type adjustment power.

[0033] Optionally, determining the target energy-type reference power based on the preliminary energy-type reference power, the energy-type adjustment power, and the power-type adjustment power includes:

[0034] The target energy-type reference power = the preliminary energy-type reference power - the energy-type adjustment power + the power-type adjustment power.

[0035] In a second aspect, an embodiment of the present application provides a composite energy storage adaptive power dynamic distribution system, and the system includes:

[0036] A reference power determination unit, configured to determine a composite energy storage reference power based on the acquired load power and grid-side output power;

[0037] A primary power determination unit, configured to perform modal decomposition on the composite energy storage reference power to obtain multiple IMF components from high frequency to low frequency and a residual, and determine a preliminary power-type reference power and a preliminary energy-type reference power based on the adjacent IMF components and the permutation entropy corresponding to the residual;

[0038] An adjustment power generation unit, configured to generate a power-type adjustment power corresponding to the preliminary power-type reference power and an energy-type adjustment power corresponding to the preliminary energy-type reference power;

[0039] A target power determination unit, configured to determine a target power-type reference power based on the preliminary power-type reference power, the power-type adjustment power, and the energy-type adjustment power, and determine a target energy-type reference power based on the preliminary energy-type reference power, the energy-type adjustment power, and the power-type adjustment power.

[0040] In a third aspect, an embodiment of the present application provides a computer device, including:

[0041] A memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to execute the above-mentioned composite energy storage adaptive power dynamic allocation method.

[0042] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which computer instructions are stored, and the computer instructions are used to cause a computer to execute the above-mentioned composite energy storage adaptive power dynamic allocation method. Description of the Drawings

[0043] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0044] Figure 1 It is a flowchart of a composite energy storage adaptive power dynamic allocation method provided by an embodiment of the present application;

[0045] Figure 2 It is a flowchart of determining the preliminary power-type reference power and the preliminary energy-type reference power provided by an embodiment of the present application;

[0046] Figure 3 It is a flowchart of determining the permutation entropy of the IMF component provided by an embodiment of the present application;

[0047] Figure 4 It is a flowchart of step S5 provided by an embodiment of the present application;

[0048] Figure 5 It is a framework diagram of a composite energy storage adaptive power dynamic allocation method provided by an embodiment of the present application;

[0049] Figure 6 It is a schematic diagram of the membership function of the energy storage SOC of the energy type provided by an embodiment of the present application;

[0050] Figure 7Schematic diagram of the membership function of the power-type energy storage SOC provided by the embodiment of the present application;

[0051] Figure 8 Schematic diagram of the membership function of the change amount corresponding to the composite energy storage reference power provided by the embodiment of the present application;

[0052] Figure 9 Schematic diagram of the membership of the power-type adjustment coefficient provided by the embodiment of the present application;

[0053] Figure 10 Schematic diagram of the membership of the energy-type adjustment coefficient provided by the embodiment of the present application;

[0054] Figure 11 Signal schematic diagram of the composite energy storage reference power provided by the embodiment of the present application;

[0055] Figure 12 Schematic diagram of the power distribution comparison of the power-type energy storage provided by the embodiment of the present application;

[0056] Figure 13 Schematic diagram of the power distribution comparison of the energy-type energy storage provided by the embodiment of the present application;

[0057] Figure 14 Schematic diagram of the change comparison of the power-type energy storage SOC provided by the embodiment of the present application;

[0058] Figure 15 Schematic diagram of the change comparison of the energy-type energy storage SOC provided by the embodiment of the present application;

[0059] Figure 16 Block diagram of a composite energy storage adaptive power dynamic distribution system provided by the embodiment of the present application;

[0060] Figure 17 Schematic diagram of the structure of a computer device provided by the embodiment of the present application. Detailed implementation manners

[0061] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Apparently, the described embodiments are some, but not all, of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts shall fall within the protection scope of the present application.

[0062] In modern society, with the rapid development of the economy and high-tech, the demand for electricity is increasing day by day, and the accompanying energy crisis and environmental problems are gradually emerging. To address these issues, the large-scale development of renewable energy such as wind energy and solar energy has become the main goal of current energy development and an inevitable trend of energy transformation. However, wind power and photovoltaic power generation have inherent randomness, intermittency, and uncertainty. These characteristics make directly integrating wind power into the power grid may lead to power fluctuations and a decline in power quality, and even trigger the instability of the power system. Therefore, to solve this problem, the development of energy storage technology is particularly important.

[0063] With the continuous progress of energy storage technology, especially the application of composite energy storage technology, it provides an effective solution. Composite energy storage technology combines the advantages of energy-type energy storage and power-type energy storage. It not only has a large energy density and a long support time but also has a high power density and a fast response speed, and can meet the multi-time-scale energy storage requirements from millisecond level to hour level. This technology can not only improve the grid connection ability of wind and solar power generation, alleviate the fluctuations of power generation, but also reduce the economic cost of the energy storage system.

[0064] However, despite the progress in the development of energy storage technology, the problem of power fluctuations in wind and solar power generation remains prominent. Therefore, how to suppress these fluctuations and optimize power distribution is still an urgent problem to be solved. Currently, the power distribution methods of composite energy storage mainly include low-pass filtering (LPF), wavelet transform (WT), and empirical mode decomposition (EMD), etc. Among them, although LPF is simple in design and easy to implement, its inherent phase delay may lead to large power distribution errors and may cause unnecessary circulating currents between energy storage devices; WT is limited by the nonlinearity and non-stationarity of wind and solar power generation signals, and it is very difficult to select an appropriate wavelet basis; although EMD has strong self-adaptability, there are mode mixing problems in the signal decomposition process, which affects the accuracy of power distribution. In contrast, the CEEMD algorithm effectively solves the mode mixing problem by adding white noise to the original signal and decomposing and averaging multiple times.

[0065] Nevertheless, most of the current composite energy storage power distribution technologies only consider the high-frequency and low-frequency decomposition of the power reference signal and do not perform dynamic power distribution in combination with the current SOC (State of Charge) state of the energy storage system, which is likely to cause overcharging and over-discharging of the energy storage system, thus reducing the service life of the energy storage device.

[0066] To overcome these problems, this application proposes a method for adaptive power dynamic allocation of a composite energy storage system. First, the CEEMD algorithm combined with permutation entropy is used to allocate the initial power of the composite energy storage. Then, considering the SOC adaptive recovery mechanism, fuzzy logic control (FLC) is used for secondary optimization, thereby realizing the dynamic power allocation of the composite energy storage system. This method can more accurately adjust the power allocation and improve the reliability and efficiency of the energy storage system.

[0067] According to an embodiment of this application, an embodiment of a method for adaptive power dynamic allocation of a composite energy storage system is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. And although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0068] In this embodiment, a method for adaptive power dynamic allocation of a composite energy storage system is provided. Figure 1 The flowchart of a method for adaptive power dynamic allocation of a composite energy storage system provided by an embodiment of this application is as Figure 1 shown, and this process includes the following steps:

[0069] Step S1, based on the obtained load power and grid-side output power, determine the reference power of the composite energy storage.

[0070] Specifically, the load power P load (t) is obtained by measuring the power demand of the load connected to the power grid at time t, and the grid-side output power P grid (t) at time t is obtained through the monitoring system of the power grid. Use the formula to calculate the reference power of the composite energy storage system that needs to be provided at time t. If is positive, the composite energy storage system needs to discharge to meet the load demand; if it is negative, the composite energy storage needs to charge to store the excess electrical energy.

[0071] Step S3, perform modal decomposition on the reference power of the composite energy storage to obtain multiple IMF components from high frequency to low frequency and the residual, and determine the initial power-type reference power and the initial energy-type reference power based on the permutation entropy corresponding to adjacent IMF components and the residual.

[0072] Specifically, perform modal decomposition on the composite energy storage reference power to obtain multiple intrinsic mode function (IMF) components from high frequency to low frequency and a residual. Modal decomposition can use empirical mode decomposition (EMD) or its variants, such as variational mode decomposition (VMD). Calculate the permutation entropy for each IMF component and the residual to determine its complexity. The calculation of permutation entropy can use the methods of phase space reconstruction and permutation transformation. Based on the permutation entropy corresponding to adjacent IMF components and the residual, determine the preliminary power-type reference power and the preliminary energy-type reference power. Generally, high-frequency IMF components correspond to power-type energy storage, while low-frequency IMF components correspond to energy-type energy storage.

[0073] In some preferred embodiments, use the CEEMD algorithm to perform modal decomposition on the composite energy storage reference power to obtain multiple IMF components from high frequency to low frequency and a residual.

[0074] Use the CEEMD algorithm to perform modal decomposition on the composite energy storage reference power. The CEEMD algorithm can obtain multiple intrinsic mode function IMF components from high frequency to low frequency and a residual by adding white noise to the signal, performing multiple decompositions, and then taking the average.

[0075] Specifically, the composite energy storage reference power signal Perform necessary preprocessing, such as noise removal, smoothing, etc. Use the CEEMD algorithm to perform modal decomposition on the preprocessed signal. The CEEMD algorithm can obtain multiple IMF components and a residual by adding white noise and performing multiple decompositions, as shown in the following formula:

[0076]

[0077] where, IMF i is the i-th intrinsic mode function, i.e., the IMF component, and RES is the residual after decomposition of the composite energy storage reference power signal

[0078] Step S5, generate the power-type adjustment power corresponding to the preliminary power-type reference power and the energy-type adjustment power corresponding to the preliminary energy-type reference power.

[0079] Specifically, through the fuzzy control algorithm, generate a power-type adjustment coefficient and an energy-type adjustment coefficient based on the power-type energy storage state of charge, the energy-type energy storage state of charge, and the change amount of the composite energy storage reference power obtained in real time, so as to finely adjust the preliminary power-type and energy-type reference powers to obtain the power-type adjustment power and the energy-type adjustment power respectively.

[0080] ​Step S7: Determine the target power-type reference power based on the preliminary power-type reference power, the power-type adjustment power, and the energy-type adjustment power, and determine the target energy-type reference power based on the preliminary energy-type reference power, the energy-type adjustment power, and the power-type adjustment power.

[0081] Specifically, determining the target power-type reference power based on the preliminary power-type reference power, the power-type adjustment power, and the energy-type adjustment power, and determining the target energy-type reference power based on the preliminary energy-type reference power, the energy-type adjustment power, and the power-type adjustment power can ensure that the adjustments of the power-type and energy-type energy storages are balanced with each other, optimize the use of the composite energy storage system, avoid overcharging and over-discharging, and extend the service life of the composite energy storage system.

[0082] In some preferred embodiments, the target power-type reference power = the preliminary power-type reference power - the power-type adjustment power + the energy-type adjustment power.

[0083]

[0084] Wherein, is the target power-type reference power, P hp is the preliminary power-type reference power, P lp is the preliminary energy-type reference power, K hp is the power-type adjustment coefficient, K lp is the energy-type adjustment coefficient.

[0085] In some preferred embodiments, the target energy-type reference power = the preliminary energy-type reference power - the energy-type adjustment power + the power-type adjustment power.

[0086]

[0087] Wherein, is the target energy-type reference power, P hp is the preliminary power-type reference power, P lp is the preliminary energy-type reference power, K hp is the power-type adjustment coefficient, K lp is the energy-type adjustment coefficient.

[0088] For example, P hp = 150 kW, P lp = 100 kW, K hp = 0.5, K lp = 0.5, and the energy-type adjustment power = K lp × P lp = 0.5 × 100 kW = 50 kW, and the power-type adjustment power = K hp × P hp=0.5×150 kW = 75 kW. Therefore, the target power - type reference power = 150 kW - 75 kW + 50 kW = 125 kW, and the target energy - type reference power = 100 kW - 50 kW + 75 kW = 125 kW.

[0089] A composite energy storage adaptive power dynamic distribution method provided in this embodiment accurately determines the composite energy storage reference power by combining the load power and the grid - side output power in real - time, providing a reliable data basis for subsequent dynamic adjustment. Then, the power signal is decomposed into multiple IMF components and residuals through modal decomposition, covering power characteristics in different frequency bands. At the same time, permutation entropy analysis is applied to identify the complexity and dynamic changes of the power signal. This efficient frequency - domain analysis method enables a fine grasp of the multi - level characteristics of power fluctuations, providing a scientific basis for generating the preliminary reference power. On this basis, by dynamically adjusting the power - type and energy - type reference powers, it can adapt to load and grid - side fluctuations in real - time, avoiding over - response or sluggishness. Finally, by comprehensively considering the influence of the preliminary reference power and the adjusted power, the target power - type reference power and the target energy - type reference power are determined in a refined manner, thus achieving dynamic power distribution and recovery in the short term, optimizing the charge - discharge process, reducing high - frequency high - power charge - discharge of the energy - type energy storage, and extending the cycle life of the energy - type energy storage.

[0090] Figure 2 The flowchart for determining the preliminary power - type reference power and the preliminary energy - type reference power provided in the embodiment of this application may include the following steps:

[0091] Step S331, determine the permutation entropy of the IMF components and the residuals.

[0092] Specifically, for a time series x of length n, its elements are x 1 , x 2 , …, x n ; according to the selected embedding dimension m and time delay t, the embedding dimension m represents the length of each subsequence, and the time delay t represents the time interval between elements in the subsequence. k subsequences are generated from the time series x of length n, where k = n-(m - 1)t. The form of each subsequence is as follows:

[0093]

[0094] Then, for each subsequence, it is sorted according to the size relationship of its elements to obtain a permutation. For example, if the subsequence is , the permutation can be expressed as , where is the column where each element in the sorted subsequence component is located. The expressions of the permutations (a total of k, with m! sorting possibilities) are:

[0095]

[0096] Therefore, for the matrix obtained by sorting and reconstructing the time series x, a set of symbol sequences can be obtained for each row: , where l = 1, 2, …, k (k ≤ m!).

[0097] Then, count the number of occurrences of each permutation and divide by the total number of subsequences k to obtain the probability P j .

[0098] Finally, use the definition of Shannon entropy to calculate the permutation entropy of the time series:

[0099]

[0100] Normalize the permutation entropy to a specific range, usually 0 to 1, to obtain the final permutation entropy.

[0101] Step S333, determine the difference in permutation entropy between adjacent IMF components according to the permutation entropy.

[0102] Specifically, .

[0103] Step S335, determine the adjacent IMF components with the largest difference in permutation entropy as the boundary point for high-low frequency reconstruction.

[0104] Specifically, among all pairs of adjacent IMF components, find the pair with the largest difference in permutation entropy. This pair of IMF components is the boundary point for high-low frequency reconstruction. For example, assume there are permutation entropy values for 5 IMF components, H PE (IMF 1 ) = 0.95, H PE (IMF 2 ) = 0.85, H PE (IMF 3 ) = 0.70, H PE (IMF 4 ) = 0.55, H PE (IMF 5 ) = 0.30. Next, calculate the difference in permutation entropy for each pair of adjacent IMF components: ΔH PE (1, 2) = ∣0.95 - 0.85∣ = 0.10, ΔH PE (2, 3) = ∣0.85 - 0.70∣ = 0.15, ΔH PE (3, 4) = ∣0.70 - 0.55∣ = 0.15, ΔH PE (4, 5) = ∣0.55 - 0.30∣ = 0.25. From the above calculation results, it can be obtained that the largest difference in permutation entropy is ΔH PE(4,5) = 0.25, so the IMF 4 and the IMF 5 The demarcation point between them is the demarcation point of high - and low - frequency reconstruction.

[0105] Step S337, determine the IMF components before the demarcation point as high - frequency components, and reconstruct the high - frequency components to obtain a preliminary power - type reference power.

[0106] Specifically, the IMF components before the demarcation point can be determined as high - frequency components, that is, the IMF 4 Before includes the IMF 1 、IMF 2 、IMF 3 、IMF 4 are high - frequency components, and the preliminary power - type reference power obtained by reconstructing the high - frequency components is .

[0107] Step S339, determine the IMF components after the demarcation point and the residual as low - frequency components, and reconstruct the low - frequency components to obtain a preliminary energy - type reference power.

[0108] Specifically, the IMF components after the demarcation point plus the residual can be determined as low - frequency components, that is, the IMF 5 plus the residual is the low - frequency component, and the preliminary energy - type reference power obtained by reconstructing the low - frequency component is , where RES is the residual.

[0109] Compared with the embodiment shown in Figure 1 , in this embodiment, by calculating the permutation entropy of the IMF components and their residuals, basic data for subsequent frequency analysis is provided. By calculating the difference in permutation entropy between adjacent IMF components, the difference in frequency between these components is reflected. By selecting the adjacent IMF components with the largest difference in permutation entropy, the demarcation point between high - frequency and low - frequency components is accurately determined. Then, the IMF components before the demarcation point are determined as high - frequency components, and these high - frequency components are reconstructed to obtain a preliminary power - type reference power. The IMF components after the demarcation point and the residual are regarded as low - frequency components, and by reconstructing these low - frequency components, a preliminary energy - type reference power is obtained. This embodiment can effectively divide the signal into high - frequency and low - frequency components, thus providing a more accurate basis for the calculation of power - type and energy - type reference powers, and improving the overall performance and accuracy.

[0110] Figure 3 This is a flowchart for determining the permutation entropy of IMF components provided by an embodiment of the present application. This process may include the following steps:

[0111] Step S3311, use Shannon entropy to determine the permutation entropy of the time series of IMF components.

[0112] Step S3313: Standardize the time series permutation entropy to obtain the permutation entropy.

[0113] Specifically, use the definition of Shannon entropy to calculate the time series permutation entropy:

[0114]

[0115] where k is the total number of subsequences; j is the column, and P j is the probability of the permutation occurrence.

[0116] Standardize the time series permutation entropy to a specific range, usually from 0 to 1, to obtain the final permutation entropy.

[0117] Compared with the Figure 2 shown embodiment, in this embodiment, by using Shannon entropy to calculate the time series permutation entropy of the IMF component, the complexity and information content of the time series can be effectively quantified. As a tool for measuring information uncertainty, Shannon entropy can reveal the chaotic degree and regularity of the time series, thus providing accurate data characteristics for further analysis. Then, by standardizing this permutation entropy, it can be adjusted to a unified range or standard, ensuring the comparability and consistency of different time series, which helps to improve the accuracy and stability of data processing.

[0118] Figure 4 The flowchart of step S5 provided by the embodiment of the present application is as follows. This process may include the following steps:

[0119] Step S51: Obtain the change amounts corresponding to the power-type energy storage state of charge, the energy-type energy storage state of charge, and the composite energy storage reference power.

[0120] Specifically, the change amount ΔP HESS (t) of the composite energy storage reference power is the difference between the composite energy storage reference power at the current moment and the composite energy storage reference power at the previous moment. That is:

[0121]

[0122] where, is the composite energy storage reference power at time t is the composite energy storage reference power at the previous moment.

[0123] Step S53: Input the power-type energy storage state of charge, the energy-type energy storage state of charge, the composite energy storage reference power, and the change amount into a preset fuzzy control algorithm to obtain the power-type adjustment coefficient and the energy-type adjustment coefficient.

[0124] Specifically, set fuzzy control rules in the preset fuzzy control algorithm. The input parameters include the change amount ΔP HESS(t), the corresponding fuzzy subsets are {PB, PM, PS, ZO, NS, NM, NB}; the state of charge (SOC) of the power-type energy storage hp , the corresponding fuzzy subsets are {L, M, H}; the state of charge (SOC) of the energy-type energy storage lp , the corresponding fuzzy subsets are {L, M, H}. Among them, PB represents "Positive Big", PM represents "Positive Medium", PS represents "Positive Small", ZO represents 0, NS represents "Negative Small", NM represents "Negative Medium", and NB represents "Negative Big"; the state of charge (SOC) of the power-type energy storage hp and the state of charge (SOC) of the energy-type energy storage lp in the fuzzy subsets, L represents "Low", M represents "Medium", and H represents "High".

[0125] The membership functions of each parameter are obtained using S-type, Z-type, II-type, triangular, and Gaussian functions. Among them, the S-type:

[0126]

[0127] The Z-type:

[0128]

[0129] The II-type:

[0130]

[0131] The triangular:

[0132]

[0133] The Gaussian function:

[0134]

[0135] Here, a, b, c, and d are the boundary values of the segmented values of the input parameter x, which directly affect the shape and output of the membership function, and thus affect the result of fuzzy inference.

[0136] It should be noted that in the fuzzy control algorithm, this embodiment needs to follow the following principles:

[0137] (1) The designed membership function ensures that the functions of each fuzzy subset are symmetrically distributed around the center point. For example, when using the Gaussian function or the triangular function, the center point should be located at the center of the subset. Ensure a smooth transition between the membership functions of different fuzzy subsets and avoid sudden changes.

[0138] (2) The designed membership function ensures that the functions of adjacent subsets overlap within a certain range, usually between 0.2 and 0.6, which helps to reduce the uncertainty in fuzzy inference.

[0139] (3) In the fuzzy control rules, when a rapid power change is detected, SMES is preferentially used for response to quickly adjust the system state.

[0140] (4) In the fuzzy control rules, frequent charge and discharge operations of the battery energy storage should be avoided as much as possible, especially during small-amplitude power changes.

[0141] (5) In the fuzzy control rules, when a rapid change in the reference power is detected, power-type energy storage is preferentially used for response to quickly adjust the system state.

[0142] According to the response characteristics of power-type and energy-type energy storage elements, as well as the SOC (state of charge) status of each energy storage element, appropriate fuzzy rules are established. The specific principle is as follows:

[0143] 1. Rules when the discharge power gradually decreases

[0144] When the composite energy storage reference power is in the discharge state and the required discharge power gradually decreases, the frequency content in the power signal will change: the greater the slope, the faster the discharge power drops, and the richer the high-frequency components in the power signal. In this case, even when the state of charge SOC hp of the power-type energy storage is small, the power-type energy storage can still undertake more compensation for high-frequency components. Therefore, the power output of part of the energy-type energy storage can be appropriately reduced. The smaller the slope, the slower the discharge power drops, and the richer the low-frequency components contained in the power signal. In this case, part of the high-frequency components that the power-type energy storage needs to compensate can be appropriately shared by the energy-type energy storage, that is, the power output of part of the power-type energy storage is appropriately reduced.

[0145] 2. Rules when the discharge power gradually increases

[0146] When the composite energy storage reference power is in the discharge state and the required discharge power gradually increases, the change in the slope has an impact on power distribution: the greater the slope, the faster the power increases. When the state of charge SOC hp of the power-type energy storage is large, the power-type energy storage should undertake more compensation for high-frequency components. Therefore, the power output of the energy-type energy storage can be appropriately reduced to reduce its discharge current. When the state of charge SOC hp of the power-type energy storage is small, to avoid over-discharge of the power-type energy storage, the power output of the energy-type energy storage can be appropriately increased. The smaller the slope, the slower the power increases, and the main power output is borne by the energy-type energy storage, and the output of the power-type energy storage can be appropriately reduced.

[0147] 3. Charging control logic

[0148] The charging control is similar to the discharging logic. When the power change rate is large, the high-frequency components in the reference power signal also increase correspondingly. When the state of charge (SOC) of the power-type energy storage hp is large, charging can be carried out according to the normal power distribution to avoid overcharging of the energy storage components. When the state of charge (SOC) of the power-type energy storage hp is small, the charging power of the power-type energy storage can be appropriately increased to accelerate the recovery of the SOC.

[0149] Based on the above basic principles, fuzzy inference rules are established (see Table 1) to infer the power-type adjustment coefficient K hp and the energy-type adjustment coefficient K lp . It should be noted that the fuzzy membership degree is not a fixed value but a representation expression. The membership function of the input parameter will determine its fuzzy value in the fuzzy universe of discourse. Then, according to the preset fuzzy inference rules, the fuzzy value of the output parameter is obtained through the inference process. Finally, through the defuzzification operation, the actual value of the output parameter is obtained.

[0150] Table 1 Fuzzy Inference Rules

[0151]

[0152] Step S55: Use the power-type adjustment coefficient to adjust the preliminary power-type reference power to obtain the power-type adjusted power.

[0153] Specifically, before using the power-type adjustment coefficient to adjust the preliminary power-type reference power, the centroid method needs to be used to convert the fuzzy output power-type adjustment coefficient into an accurate power-type adjustment coefficient. After converting it into an accurate power-type adjustment coefficient, multiply the preliminary power-type reference power by the accurate power-type adjustment coefficient to obtain the power-type adjusted power.

[0154] The defuzzification formula is:

[0155]

[0156] where μ(x i ) represents the membership degree value of the input parameter x i in the universe of discourse.

[0157] Step S57: Use the energy-type adjustment coefficient to adjust the preliminary energy-type reference power to obtain the energy-type adjusted power.

[0158] Specifically, before using the energy-type adjustment coefficient to adjust the preliminary energy-type reference power, the centroid method needs to be used to convert the fuzzy output energy-type adjustment coefficient into an accurate energy-type adjustment coefficient. After converting it into an accurate energy-type adjustment coefficient, multiply the preliminary energy-type reference power by the accurate energy-type adjustment coefficient to obtain the energy-type adjusted power.

[0159] The defuzzification formula is as follows:

[0160]

[0161] where μ(x i ) represents the membership degree value of the input parameter x i in the universe of discourse.

[0162] Compared with the embodiment shown Figure 1 in the above, in this embodiment, through the fuzzy control algorithm, the state of charge of the power-type and energy-type energy storage and the change amount of the composite energy storage reference power can be obtained in real time, providing an accurate data basis for subsequent power regulation. Using the fuzzy control algorithm to process these inputs, the power-type adjustment coefficient and the energy-type adjustment coefficient are generated, so as to finely adjust the preliminary power-type and energy-type reference powers, and the power-type adjustment power and the energy-type adjustment power are obtained respectively. This adjustment can optimize the response accuracy and efficiency of the energy storage system, avoid overcharging or over-discharging, and ensure the stable operation of the system under load fluctuations. Generally speaking, in this embodiment, through intelligent and dynamic adjustment, the coordinated cooperation between energy storage devices is realized, the stability and operation efficiency of the system are significantly improved, it is ensured that energy can be supplied efficiently and balancedly under different working conditions, and the service life of the system is extended.

[0163] Next, the simulation software is used to simulate the power fluctuation of new energy grid connection, and compensation is carried out through the composite energy storage to describe the specific implementation of the present invention. See Figure 5 .

[0164] Step S1001, calculate the composite energy storage reference power that the composite energy storage system needs to compensate based on the collected data, denoted as .

[0165] Step S1002, take the calculated composite energy storage reference power as the input, and use the complementary ensemble empirical mode decomposition (CEEMD) algorithm for decomposition to obtain a series of intrinsic mode functions (IMFs) from high frequency to low frequency and the remaining residuals.

[0166] Step S1003, use permutation entropy as an index to measure the complexity of the time series. The smaller the permutation entropy, the more regular the time series, and the larger the permutation entropy, the more complex it is. By calculating the permutation entropy of all IMFs and residuals, and comparing the permutation entropy differences between adjacent IMFs, the boundary point of high and low frequency reconstruction can be determined, that is, the two IMF functions with the largest permutation entropy difference, and finally the preliminary energy-type reference power P lp and the preliminary power-type reference power P hp are obtained.

[0167] Step S1004: Consider the SOC (state of charge) of the two types of energy storage elements at the current moment and the change ΔP HESS (t) in the composite energy storage reference power, and use the pre-set fuzzy control algorithm to output the power-type adjustment coefficient and the energy-type adjustment coefficient.

[0168] Step S1005: Based on the preliminary energy-type reference power P lp and the preliminary power-type reference power P hp , combined with the power-type adjustment coefficient K hp and the energy-type adjustment coefficient K lp obtained by fuzzy logic control for secondary distribution, calculate the final target power-type reference power and the target energy-type reference power .

[0169] Among them, the membership function of the energy-type energy storage SOC is as Figure 6 shown. To extend the cycle life of the energy-type energy storage and avoid overcharging and over-discharging as much as possible, select an appropriate SOC interval for control. In the states of lower and higher SOC, the S-type function and the Z-type function are respectively used to expand the value range of "L" and "H". At the same time, since the probability of over-discharging is usually higher than that of overcharging when the battery energy storage responds to support, the value range of "L" is relatively larger. For the membership design of "M", the symmetry principle is adopted, and the Gaussian function is selected to ensure that the battery operates within the range of [0.3, 0.8].

[0170] The membership function of the power-type energy storage SOC is as Figure 7 shown. The power-type energy storage has a fast response speed and a small energy storage capacity, so the change range of its SOC is relatively large. To avoid overcharging and over-discharging, the Z-type function and the S-type function are used as the membership functions of the "L" and "H" linguistic variables. Compared with the energy-type energy storage, the value range of the power-type energy storage is relatively small. In the membership design of the "M" linguistic variable, the symmetry principle is adopted, and to improve the response speed of the magnet charging and discharging, the type-II membership function is selected to expand the value range of the "M" linguistic variable, thereby enhancing the charging and discharging response range of the superconducting magnet.

[0171] The membership function of the change amount ∆P HESS (t) in the composite energy storage reference power is as Figure 8As shown, since the value range of the change amount corresponding to the composite energy storage reference power is relatively large, multiple linguistic variables are set to divide the value range. According to the symmetry principle and overlap rate requirements in the design of the membership function, the membership functions of the linguistic variables from "NM" to "PM" are designed as trigonometric functions, while the membership functions of the variables "NB" and "PB" adopt Z-type function and S-type function respectively. In order to reduce the frequent response of the energy-type energy storage, the value ranges of the variables "NB", "NM", "PM" and "PB" are appropriately expanded, so as to improve the response performance of the power-type energy storage.

[0172] Power type adjustment coefficient K hp and energy type adjustment coefficient K lp of the membership degree, as Figure 9 and Figure 10 shown, the design ideas of the two parameters are basically the same, but the value ranges are different. Since the energy-type energy storage is usually used for long-term and large-capacity power support, its compensation reference power is relatively large. In order to reduce the overcharge and over-discharge of the power-type energy storage caused by the secondary power adjustment, the energy type adjustment coefficient K lp is limited within the range of [0, 0.4]. The design of the membership functions of the two parameters follows the symmetry principle and overlap rate requirements, and at the same time ensures the smooth change of the output parameters. Therefore, Gaussian function, Z function and S function are designed as the membership functions of the linguistic variables "Z", "S", "M" and "H" respectively.

[0173] The simulation software is used to simulate the fluctuation of the new energy grid-connected power, and the power compensation is carried out through the composite energy storage system. The initial parameters of the composite energy storage system are set as: the SOC of the energy-type energy storage is 0.7, and the SOC of the power-type energy storage is 1.01. The signal of the composite energy storage reference power is as Figure 11 shown. In order to verify the effectiveness of the embodiments of the present application, a comparative analysis between the traditional EMD scheme and the embodiments of the present application is carried out, and the power distribution and the changes of the SOC states of each energy storage element are mainly compared. The results show that the embodiments of the present application can effectively reduce the overcharge and over-discharge of the energy storage elements and extend their service life.

[0174] It can be seen that the power distribution of the power-type energy storage is as Figure 12 shown. When the traditional EMD power distribution compensates for the rapid change of the reference power, it will cause the reference power of the power-type energy storage to fluctuate violently. Since the capacity of the power-type energy storage is usually small, it is easy to cause its overcharge or over-discharge. However, the power distribution strategy of the embodiments of the present application can effectively reduce the power fluctuation of the power-type energy storage and avoid the overcharge and over-discharge of the energy storage elements. The emd in the figure is the traditional EMD power distribution scheme, and ceemd+flc is the scheme of the CEEMD algorithm combined with the fuzzy control algorithm in the embodiments of the present application. The icons in the subsequent figures are all explained in this way and will not be repeated.

[0175] Power distribution comparison of energy storage Figure 13 As shown, the embodiments of the present application can effectively smooth the supplementary power change of the energy storage and reduce the rapid change of the energy storage power.

[0176] Power distribution comparison of power storage SOC Figure 14 As shown, the power distribution comparison of energy storage SOC Figure 15 As shown, the power distribution strategy of the embodiments of the present application can dynamically adjust the power distribution of the two energy storage elements according to the SOC state of the energy storage system, thereby effectively avoiding overcharging and over-discharging of the composite energy storage system.

[0177] Correspondingly, please refer to Figure 16 which is a block diagram of a composite energy storage adaptive power dynamic distribution system provided by the embodiments of the present application. The system includes:

[0178] A reference power determination unit S101, configured to determine a composite energy storage reference power based on the obtained load power and grid-side output power;

[0179] A primary power determination unit S103, configured to perform modal decomposition on the composite energy storage reference power to obtain multiple IMF components from high frequency to low frequency and a residual, and determine a primary power storage reference power and a primary energy storage reference power based on the adjacent IMF components and the permutation entropy corresponding to the residual;

[0180] An adjustment power generation unit S105, configured to generate a power storage adjustment power corresponding to the primary power storage reference power and an energy storage adjustment power corresponding to the primary energy storage reference power;

[0181] A target power determination unit S107, configured to determine a target power storage reference power based on the primary power storage reference power, the power storage adjustment power, and the energy storage adjustment power, and determine a target energy storage reference power based on the primary energy storage reference power, the energy storage adjustment power, and the power storage adjustment power.

[0182] In some optional embodiments, performing modal decomposition on the composite energy storage reference power to obtain multiple IMF components from high frequency to low frequency includes:

[0183] Performing modal decomposition on the composite energy storage reference power by using the CEEMD algorithm to obtain multiple IMF components from high frequency to low frequency and a residual.

[0184] In some optional embodiments, determining a primary power storage reference power and a primary energy storage reference power based on the adjacent IMF components and the permutation entropy corresponding to the residual includes:

[0185] Determining the permutation entropy of the IMF components and the residual;

[0186] Determine the permutation entropy difference between adjacent IMF components according to permutation entropy;

[0187] Determine the adjacent IMF components with the largest permutation entropy difference as the demarcation point for high-frequency and low-frequency reconstruction;

[0188] Determine the IMF components before the demarcation point as high-frequency components, and reconstruct the high-frequency components to obtain a preliminary power-type reference power;

[0189] Determine the IMF components after the demarcation point and the residual as low-frequency components, and reconstruct the low-frequency components to obtain a preliminary energy-type reference power.

[0190] In some alternative embodiments, determining the permutation entropy of the IMF components includes:

[0191] Determine the permutation entropy of the time series of the IMF components and the residual using Shannon entropy;

[0192] Normalize the permutation entropy of the time series to obtain the permutation entropy.

[0193] In some alternative embodiments, generating a power-type adjustment power corresponding to the preliminary power-type reference power and an energy-type adjustment power corresponding to the preliminary energy-type reference power includes:

[0194] Obtain the change in the power-type energy storage state of charge, the energy-type energy storage state of charge, and the composite energy storage reference power;

[0195] Input the power-type energy storage state of charge, the energy-type energy storage state of charge, the composite energy storage reference power, and the change into a preset fuzzy control algorithm to obtain a power-type adjustment coefficient and an energy-type adjustment coefficient;

[0196] Adjust the preliminary power-type reference power using the power-type adjustment coefficient to obtain a power-type adjustment power;

[0197] Adjust the preliminary energy-type reference power using the energy-type adjustment coefficient to obtain an energy-type adjustment power.

[0198] In some alternative embodiments, determining the target power-type reference power based on the preliminary power-type reference power, the power-type adjustment power, and the energy-type adjustment power includes:

[0199] Target power-type reference power = preliminary power-type reference power - power-type adjustment power + energy-type adjustment power.

[0200] In some alternative embodiments, determining the target energy-type reference power based on the preliminary energy-type reference power, the energy-type adjustment power, and the power-type adjustment power includes:

[0201] Target energy-based reference power = preliminary energy-based reference power - energy-based adjustment power + power-based adjustment power.

[0202] The further function descriptions of the above-mentioned various modules and units are the same as those in the corresponding embodiments above, and will not be elaborated here.

[0203] A composite energy storage adaptive power dynamic distribution system in this embodiment is presented in the form of functional units. Here, the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and a memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0204] Please refer to Figure 17 , Figure 17 which is a schematic structural diagram of a computer device provided by an embodiment of the present application. As Figure 17 shown, the computer device includes: one or more processors 10, a memory 20, and an interface for connecting each component, including a high-speed interface and a low-speed interface. Each component communicates with each other using different buses and can be installed on a common motherboard or installed in other ways as needed. The processor can process instructions executed within the computer device, including instructions stored in the memory or on the memory to display graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some alternative embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a set of blade servers, or a multi-processor system). Figure 17 In

[0205] which, one processor 10 is taken as an example.

[0206] The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0207] The memory 20 may include a program storage area and a data storage area. The program storage area may store an operating system and application programs required for at least one function. The data storage area may store data created according to the use of the computer device and the like. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some alternative embodiments, the memory 20 may optionally include a memory remotely disposed relative to the processor 10, and these remote memories may be connected to the computer device through a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0208] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk, or a solid-state drive; the memory 20 may further include a combination of the above-mentioned types of memories.

[0209] The computer device further includes a communication interface 30 for communicating the computer device with other devices or communication networks.

[0210] The embodiments of the present application also provide a computer-readable storage medium. The methods according to the embodiments of the present application may be implemented in hardware, firmware, or be implemented as computer code that can be recorded on a storage medium, or be implemented as computer code that is originally stored in a remote storage medium or a non-transitory machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the methods described herein can be processed by such software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium may be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, or a solid-state drive, etc.; further, the storage medium may also include a combination of the above-mentioned types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code, and when the software or computer code is accessed and executed by the computer, the processor, or the hardware, the methods shown in the above embodiments are implemented.

[0211] The systems and units illustrated in the above embodiments may be specifically implemented by a computer chip or an entity, or by a product having a certain function. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or any combination of these devices.

[0212] For convenience of description, when describing the above device, various units are described separately according to their functions. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0213] Those skilled in the art should understand that the embodiments of the present application can be provided as methods and systems. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0214] The present application is described with reference to the flowcharts and / or block diagrams of methods and systems according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0215] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0216] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0217] It should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, commodity or device comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, method, commodity or device. Without further limitation, an element defined by the statement "comprising one..." does not exclude the presence of additional identical elements in the process, method, commodity or device comprising said element.

[0218] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other, and the differences between each embodiment and other embodiments are emphasized. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the description of the method embodiment.

[0219] The above are only the embodiments of the present application and are not intended to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

[0220] Although the embodiments of the present application have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present application, and such modifications and variations fall within the scope defined by the appended claims.

Claims

1. A composite energy storage adaptive power dynamic allocation method, characterized in that: The method comprises: Based on the acquired load power and grid-side output power, determine the composite energy storage reference power; Performing modal decomposition on the composite energy storage reference power to obtain multiple IMF components and residuals from high frequency to low frequency, and determining a preliminary power-type reference power and a preliminary energy-type reference power based on the permutation entropy corresponding to the adjacent IMF components and the residuals; generating a power-type adjustment power corresponding to the preliminary power-type reference power and an energy-type adjustment power corresponding to the preliminary energy-type reference power; Determining a target power-type reference power based on the preliminary power-type reference power, the power-type adjusted power and the energy-type adjusted power, and determining a target energy-type reference power based on the preliminary energy-type reference power, the energy-type adjusted power and the power-type adjusted power; The target power type reference power is determined as follows: in, is the target power type reference power, P hp is the preliminary power type reference power, P lp is the preliminary energy reference power, K hp is the power adjustment coefficient, K lp is the energy type adjustment coefficient; The target energy reference power is determined as follows: in, is the target energy reference power, P hp is the preliminary power type reference power, P lp is the preliminary energy reference power, K hp is the power adjustment coefficient, K lp is the energy type adjustment coefficient; The power type adjustment coefficient and the energy type adjustment coefficient are obtained by a preset fuzzy control algorithm, in which a fuzzy control rule is set, and the input parameters include the change amount ΔP corresponding to the composite energy storage reference power HESS (t), the corresponding fuzzy subset is {PB, PM, PS, ZO, NS, NM, NB}; power type energy storage state of charge SOC hp , the corresponding fuzzy subset is {L, M, H}; energy storage state of charge SOC lp , the corresponding fuzzy subset is {L, M, H}; among them, PB means "positive large", PM means "positive medium", PS means "positive small", ZO means 0, NS means "negative small", NM means "negative medium", and NB means "negative large"; power type energy storage state of charge SOC hp and energy storage state of charge SOC lp In the fuzzy subset, L represents "low", M represents "medium", and H represents "high". The membership function of each parameter is obtained by using S-type, Z-type, II-type, triangle and Gaussian functions to obtain the fuzzy value. The power type adjustment coefficient and the energy type adjustment coefficient are obtained by defuzzification operation. Among them, the change ΔP corresponding to the composite energy storage reference power HESS (t) is the difference between the composite energy storage reference power at the current moment and the composite energy storage reference power at the previous moment, that is: in, is the composite energy storage reference power at time t The composite energy storage reference power at the last moment; The selection method of using S-type, Z-type, II-type, triangle and Gaussian functions to obtain the membership function of each parameter is as follows: The energy storage membership function uses the S-type function as the membership function of the "L" variable, the Z-type function as the membership function of the "H" variable, and the Gaussian function as the membership function of the "M" variable; The power type energy storage membership function uses the Z-type function as the membership function of the "L" variable, the S-type function as the membership function of the "H" variable, and the II-type membership function as the membership function of the "M" variable; The composite energy storage reference power membership function uses trigonometric functions as the membership functions of the "NM" and "PM" variables, uses the Z-type function as the membership function of the "NB" variable, and uses the S-type function as the membership function of the "PB" variable; The energy-based adjustment coefficient is limited to the range of [0, 0.4].

2. The method according to claim 1, characterized in that The composite energy storage reference power is subjected to modal decomposition to obtain multiple IMF components from high frequency to low frequency, including: The CEEMD algorithm is used to perform modal decomposition on the composite energy storage reference power to obtain multiple IMF components and residuals from high frequency to low frequency.

3. The method according to claim 1, characterized in that The determining of the preliminary power-type reference power and the preliminary energy-type reference power based on the adjacent IMF components and the permutation entropy corresponding to the residual includes: Determining the permutation entropy of the IMF components and the residual; Determining the permutation entropy difference between adjacent IMF components according to the permutation entropy; Determine the adjacent IMF component with the largest permutation entropy difference as the dividing point of high-frequency and low-frequency reconstruction; Determine the IMF component before the demarcation point as a high-frequency component, and reconstruct the high-frequency component to obtain the preliminary power-type reference power; The IMF component after the demarcation point and the residual are determined as low-frequency components, and the low-frequency components are reconstructed to obtain the preliminary energy-based reference power.

4. The method according to claim 1, characterized in that: Determining the permutation entropy of the IMF component includes: Determine the time series permutation entropy of the IMF component and the residual using Shannon entropy; The time series permutation entropy is standardized to obtain the permutation entropy.

5. The method according to claim 1, characterized in that The generating the power type adjustment power corresponding to the preliminary power type reference power and the energy type adjustment power corresponding to the preliminary energy type reference power comprises: Obtaining the power type energy storage charge state, the energy type energy storage charge state and the change corresponding to the composite energy storage reference power; Inputting the power-type energy storage charge state, the energy-type energy storage charge state, the composite energy storage reference power and the variation into a preset fuzzy control algorithm to obtain a power-type adjustment coefficient and an energy-type adjustment coefficient; Using the power type adjustment coefficient to adjust the preliminary power type reference power to obtain the power type adjustment power; The preliminary energy-type reference power is adjusted using the energy-type adjustment coefficient to obtain the energy-type adjusted power.

6. The method according to claim 1, characterized in that The determining of the target power type reference power based on the preliminary power type reference power, the power type adjustment power and the energy type adjustment power comprises: The target power type reference power=the preliminary power type reference power-the power type adjustment power+the energy type adjustment power.

7. The method according to claim 1, characterized in that The determining a target energy-type reference power based on the preliminary energy-type reference power, the energy-type adjustment power and the power-type adjustment power comprises: The target energy-type reference power=the preliminary energy-type reference power-the energy-type adjustment power+the power-type adjustment power.

8. A composite energy storage adaptive power dynamic distribution system for implementing the method described in any one of claims 1 to 7, characterized in that: The system comprises: A reference power determination unit, used to determine the composite energy storage reference power based on the acquired load power and grid-side output power; A primary power determination unit is used to perform modal decomposition on the composite energy storage reference power to obtain multiple IMF components and residuals from high frequency to low frequency, and determine the preliminary power type reference power and the preliminary energy type reference power based on the permutation entropy corresponding to the adjacent IMF components and the residuals; an adjustment power generation unit, configured to generate a power-type adjustment power corresponding to the preliminary power-type reference power and an energy-type adjustment power corresponding to the preliminary energy-type reference power; A target power determination unit is used to determine a target power type reference power based on the preliminary power type reference power, the power type adjustment power and the energy type adjustment power, and to determine a target energy type reference power based on the preliminary energy type reference power, the energy type adjustment power and the power type adjustment power.

9. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the composite energy storage adaptive power dynamic allocation method according to any one of claims 1 to 7 by executing the computer instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the composite energy storage adaptive power dynamic allocation method according to any one of claims 1 to 7.

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