Composite energy storage adaptive power dynamic allocation method, system, equipment and medium

Abstract

The invention relates to the technical field of energy storage, and discloses a composite energy storage adaptive power dynamic allocation method, system and device and a medium, and the method comprises the steps: determining composite energy storage reference power based on obtained load power and network side output power; performing modal decomposition on the composite energy storage reference power to obtain a plurality of IMF components and residual errors from high frequency to low frequency, and determining initial power type reference power and initial energy type reference power based on permutation entropies corresponding to the adjacent IMF components and the residual errors; generating power type adjustment power corresponding to the initial power type reference power and energy type adjustment power corresponding to the initial energy type reference power; a target power-type reference power is determined based on the preliminary power-type reference power, the power-type adjustment power, and the energy-type adjustment power, and a target energy-type reference power is determined based on the preliminary energy-type reference power, the energy-type adjustment power, and the power-type adjustment power. The method has the beneficial effects that the charging and discharging process is optimized, and the cycle life of energy-type energy storage is prolonged.

Description

Technical Field

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

[0002] With the widespread application of wind and solar energy, energy storage technology has become the key to solving their volatility and instability problems. Composite energy storage technology combines the advantages of energy-type and power-type energy storage, which can effectively improve the grid-connected capacity of wind and 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 instabilities. Although the CEEMD algorithm can effectively solve the problem of modal aliasing, most methods do not consider the current SOC state of the energy storage system, resulting in overcharging and over-discharging, which affects the life of the equipment.

[0003] The current problem that needs 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 realize adaptive dynamic power allocation of composite energy storage, and achieves the technical effect of optimizing the charging and discharging process, improving the utilization rate of power-type energy storage, reducing the high-frequency and high-power charging and discharging of energy-type energy storage, and extending the cycle life of energy-type energy storage.

[0005] In order to achieve the above objectives, the main technical solutions adopted in this application include: In a first aspect, an embodiment of the present application provides a composite energy storage adaptive power dynamic allocation method, the method comprising: 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; Based on the preliminary power-type reference power, the power-type adjustment power and the energy-type adjustment power, a target power-type reference power is determined; and based on the preliminary energy-type reference power, the energy-type adjustment power and the power-type adjustment power, a target energy-type reference power is determined.

[0006] The present embodiment provides a composite energy storage adaptive power dynamic allocation method, which accurately determines the composite energy storage reference power by combining the load power and the grid-side output power in real time, and provides a reliable data basis for subsequent dynamic adjustment. Then, the power signal is decomposed into multiple IMF components and residuals through modal decomposition, covering the power characteristics of different frequency bands, and the permutation entropy analysis is applied to identify the complexity and dynamic changes of the power signal. This efficient frequency domain analysis method enables the multi-level characteristics of power fluctuations to be accurately grasped, providing a scientific basis for generating preliminary reference power. On this basis, by dynamically adjusting the power type and energy type reference power, it is possible to adapt to load and grid-side fluctuations in real time to avoid excessive response or slow response. 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 refined, thereby realizing dynamic power allocation and recovery in a short period of time, optimizing the charging and discharging process, and reducing the high-frequency and high-power charging and discharging of energy type energy storage, so as to extend the cycle life of energy type energy storage.

[0007] Optionally, modal decomposition is performed on the composite energy storage reference power 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.

[0008] Optionally, the determining 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.

[0009] This embodiment provides basic data for subsequent frequency analysis by calculating the permutation entropy of the IMF components and their residuals. The permutation entropy difference between adjacent IMF components is calculated based on the permutation entropy, reflecting the difference in frequency between these components. By selecting the adjacent IMF components with the largest permutation entropy difference, the dividing point between the high-frequency and low-frequency components is accurately determined. Then, the IMF components before the dividing point are determined as high-frequency components, and these high-frequency components are reconstructed to obtain preliminary power-type reference powers. The IMF components and residuals after the dividing point are regarded as low-frequency components, and preliminary energy-type reference powers are obtained by reconstructing these low-frequency components. This embodiment can effectively divide the signal into high-frequency and low-frequency components, thereby providing a more accurate basis for the calculation of power-type and energy-type reference powers, and improving overall performance and accuracy.

[0010] Optionally, 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.

[0011] This embodiment can effectively quantify the complexity and information content of the time series by using Shannon entropy to calculate the time series permutation entropy of the IMF component. As a tool for measuring information uncertainty, Shannon entropy can reveal the degree of chaos and regularity of the time series, thereby providing accurate data features for further analysis. Then, by standardizing the permutation entropy, it can be adjusted to a unified range or standard to ensure the comparability and consistency of different time series, which helps to improve the accuracy and stability of data processing.

[0012] Optionally, 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 includes: 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.

[0013] This embodiment uses a fuzzy control algorithm to obtain the charge state of power-type and energy-type energy storage and the change in the composite energy storage reference power in real time, providing an accurate data basis for subsequent power regulation. The fuzzy control algorithm is used to process these inputs to generate a power-type adjustment coefficient and an energy-type adjustment coefficient, thereby finely adjusting the preliminary power-type and energy-type reference powers to 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 stable operation of the system when the load fluctuates. Overall, this embodiment achieves coordination between energy storage devices through intelligent and dynamic adjustment, significantly improves the stability and operating efficiency of the system, ensures efficient and balanced energy supply under different working conditions, and extends the service life of the system.

[0014] Optionally, 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 includes: The target power type reference power=the preliminary power type reference power-the power type adjustment power+the energy type adjustment power.

[0015] Optionally, 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 includes: The target energy-type reference power=the preliminary energy-type reference power-the energy-type adjustment power+the power-type adjustment power.

[0016] In a second aspect, an embodiment of the present application provides a composite energy storage adaptive power dynamic distribution system, the system comprising: 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.

[0017] In a third aspect, an embodiment of the present application provides a computer device, including: 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 above-mentioned composite energy storage adaptive power dynamic allocation method by executing the computer instructions.

[0018] 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 enable a computer to execute the above-mentioned composite energy storage adaptive power dynamic allocation method. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the specific implementation methods of the present application or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0020] Figure 1 A flow chart of a composite energy storage adaptive power dynamic allocation method provided in an embodiment of the present application; Figure 2 A flowchart for determining a preliminary power-type reference power and a preliminary energy-type reference power provided in an embodiment of the present application; Figure 3 A flowchart for determining the permutation entropy of IMF components provided in an embodiment of the present application; Figure 4 A flowchart of step S5 provided in an embodiment of the present application; Figure 5 A framework diagram of a composite energy storage adaptive power dynamic allocation method provided in an embodiment of the present application; Figure 6 A schematic diagram of the energy storage SOC membership function provided in an embodiment of the present application; Figure 7 A schematic diagram of a power-type energy storage SOC membership function provided in an embodiment of the present application; Figure 8 A schematic diagram of a membership function of a change amount corresponding to a composite energy storage reference power provided in an embodiment of the present application; Fig. 9 A schematic diagram of the power type adjustment coefficient membership provided in an embodiment of the present application; Fig.10 A schematic diagram of the energy type adjustment coefficient membership provided in an embodiment of the present application; Fig.11A signal diagram of a composite energy storage reference power provided in an embodiment of the present application; Fig.12 A schematic diagram for comparing power distribution of power-type energy storage provided in an embodiment of the present application; Fig.13 A schematic diagram for comparing power distribution of energy storage provided in an embodiment of the present application; Fig.14 A schematic diagram comparing the changes in the power-type energy storage SOC provided in the embodiment of the present application; Fig.15 A schematic diagram comparing the changes in the energy storage SOC provided in the embodiment of the present application; Fig.16 A block diagram of a composite energy storage adaptive power dynamic distribution system provided in an embodiment of the present application; Fig.17 A schematic diagram of the structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0022] In modern society, with the rapid development of economy and high-tech, the demand for electricity is increasing, and the ensuing energy crisis and environmental problems are gradually emerging. In response to these problems, the large-scale development of renewable energy such as wind and solar energy has become the main goal of current energy development and an inevitable trend of energy transformation. However, wind and photovoltaic power generation have inherent randomness, intermittency and uncertainty. These characteristics make it possible for directly integrating wind power into the power grid to cause power fluctuations and deterioration of power quality, and even cause instability in the power system. Therefore, in order to solve this problem, the development of energy storage technology is particularly important.

[0023] With the continuous advancement of energy storage technology, especially the application of composite energy storage technology, an effective solution has been provided. Composite energy storage technology combines the advantages of energy storage and power storage. It has both high energy density and long support time, high power density and fast response speed, and can meet the multi-time scale energy storage needs from milliseconds to hours. This technology can not only improve the grid connection capacity of wind and solar power generation, alleviate the fluctuation of power generation, but also reduce the economic cost of energy storage system.

[0024] However, despite the progress in the development of energy storage technology, the power fluctuation problem of wind and solar power generation is still prominent, so how to smooth these fluctuations and optimize power distribution is still an urgent problem to be solved. At present, the power distribution methods of composite energy storage mainly include low-pass filtering (LPF), wavelet transform (WT) and empirical mode decomposition (EMD). 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 circulation 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 a suitable wavelet basis; although EMD has strong adaptability, there is a modal aliasing problem in the signal decomposition process, which affects the accuracy of power distribution. In contrast, the CEEMD algorithm effectively solves the modal aliasing problem by adding white noise to the original signal and decomposing and averaging it multiple times.

[0025] Despite this, most of the current composite energy storage power allocation technologies only consider the high- and low-frequency decomposition of the power reference signal, and do not combine the current SOC (State of Charge) of the energy storage system for dynamic power allocation, which can easily lead to overcharging and over-discharging of the energy storage system, thereby reducing the service life of the energy storage equipment.

[0026] In order to overcome these problems, this application proposes a composite energy storage adaptive power dynamic allocation method. First, the CEEMD algorithm is used in combination with permutation entropy to allocate the initial power of the composite energy storage, and then the SOC adaptive recovery mechanism is considered, and fuzzy logic control (FLC) is used for secondary optimization to achieve dynamic power allocation of the composite energy storage system. This method can more accurately adjust power allocation and improve the reliability and efficiency of the energy storage system.

[0027] According to an embodiment of the present application, an embodiment of a composite energy storage adaptive power dynamic allocation method 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 an order different from that shown here.

[0028] In this embodiment, a composite energy storage adaptive power dynamic allocation method is provided. Figure 1 A flow chart of a composite energy storage adaptive power dynamic allocation method provided in an embodiment of the present application, such as Figure 1 As shown, the process includes the following steps: Step S1, determining the composite energy storage reference power based on the acquired load power and grid-side output power.

[0029] Specifically, the load power P is obtained by measuring the power demand of the load connected to the grid at time t.load (t), the grid-side output power P at time t is obtained through the grid monitoring system grid (t). Using the formula To calculate the composite energy storage reference power that the composite energy storage system needs to provide at time t. If it is a positive value, the composite energy storage system needs to discharge to meet the load demand; if it is a negative value, the composite energy storage needs to be charged to store excess energy.

[0030] Step S3, 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 the preliminary power type reference power and the preliminary energy type reference power based on the permutation entropy corresponding to adjacent IMF components and residuals.

[0031] Specifically, the composite energy storage reference power is modally decomposed to obtain multiple intrinsic mode function (IMF) components and residuals from high frequency to low frequency. Modal decomposition can use empirical mode decomposition (EMD) or its variants, such as variational mode decomposition (VMD). The permutation entropy is calculated for each IMF component and residual to determine its complexity. The permutation entropy can be calculated using the method of phase space reconstruction and permutation conversion. Based on the permutation entropy corresponding to adjacent IMF components and residuals, the preliminary power-type reference power and the preliminary energy-type reference power are determined. Generally, high-frequency IMF components correspond to power-type energy storage, while low-frequency IMF components correspond to energy-type energy storage.

[0032] In some preferred embodiments, 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.

[0033] The CEEMD algorithm is used to perform modal decomposition on the composite energy storage reference power. The CEEMD algorithm adds white noise to the signal and decomposes it multiple times, then takes the average, and can obtain multiple intrinsic mode function IMF components from high frequency to low frequency and a residual.

[0034] Specifically, the composite energy storage reference power signal Perform necessary preprocessing, such as noise removal and smoothing. Use the CEEMD algorithm to perform modal decomposition on the preprocessed signal. The CEEMD algorithm can obtain multiple IMF components and residuals by adding white noise and performing multiple decompositions, as shown in the following formula: Among them, IMF i is the i-th intrinsic mode function, i.e., IMF component, and RES is the composite energy storage reference power signal The residual after decomposition.

[0035] Step S5, 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.

[0036] Specifically, through the fuzzy control algorithm, the power-type energy storage charge state and energy-type energy storage charge state acquired in real time and the change in the composite energy storage reference power are used to generate a power-type adjustment coefficient and an energy-type adjustment coefficient, 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.

[0037] Step S7, 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.

[0038] Specifically, the target power-type reference power is determined based on the preliminary power-type reference power, the power-type adjustment power and the energy-type adjustment power, and the target energy-type reference power is determined based on the preliminary energy-type reference power, the energy-type adjustment power and the power-type adjustment power. This ensures that the adjustments of the power-type and energy-type energy storage are balanced with each other, optimizes the use of the composite energy storage system, avoids overcharging and over-discharging, and extends the service life of the composite energy storage system.

[0039] 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.

[0040] 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 type adjustment coefficient, K lp is the energy type adjustment factor.

[0041] 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.

[0042] 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 type adjustment coefficient, K lp is the energy type adjustment factor.

[0043] For example, Php =150kW, P lp =100kW, K hp =0.5, K lp =0.5, and the energy-type adjustment power = K lp ×P lp =0.5×100kW=50kW, power type adjustment power=K hp ×P hp =0.5×150kW=75kW. Therefore, the target power type reference power = 150kW-75kW+50kW=125kW, and the target energy type reference power = 100kW-50kW+75kW=125kW.

[0044] The present embodiment provides a composite energy storage adaptive power dynamic allocation method, which accurately determines the composite energy storage reference power by combining the load power and the grid-side output power in real time, and provides a reliable data basis for subsequent dynamic adjustment. Then, the power signal is decomposed into multiple IMF components and residuals through modal decomposition, covering the power characteristics of different frequency bands, and the permutation entropy analysis is applied to identify the complexity and dynamic changes of the power signal. This efficient frequency domain analysis method enables the multi-level characteristics of power fluctuations to be accurately grasped, providing a scientific basis for generating preliminary reference power. On this basis, by dynamically adjusting the power type and energy type reference power, it is possible to adapt to load and grid-side fluctuations in real time to avoid excessive response or slow response. 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 refined, thereby realizing dynamic power allocation and recovery in a short period of time, optimizing the charging and discharging process, and reducing the high-frequency and high-power charging and discharging of energy type energy storage, so as to extend the cycle life of energy type energy storage.

[0045] Figure 2 A flowchart for determining a preliminary power-type reference power and a preliminary energy-type reference power provided in an embodiment of the present application may include the following steps: Step S331, determining the permutation entropy of the IMF components and the residual.

[0046] Specifically, for a time series x of length n, its elements are x1, x2, …, x n ; Based on the selected embedding dimension m and time delay t, where 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 a time series x of length n, where k = n-(m-1)t, and each subsequence is of the following form: Next, for each subsequence, sort the elements according to their size to get a permutation. For example, if the subsequence is , then the arrangement can be expressed as ,in is the column where each element in the sorted subsequence component is located. The expressions for the permutations (k in total, with m! sorting possibilities) are: Therefore, for the matrix obtained after sorting and reconstructing the time series x, each row can get a set of symbol sequences: , where l=1,2,…,k(k≤m!).

[0047] Then, count the number of times each arrangement occurs and divide it by the total number of subsequences k to get the probability P j .

[0048] Finally, we use the definition of Shannon entropy to calculate the permutation entropy of the time series: The permutation entropy is normalized to a specific range, usually 0 to 1, to obtain the final permutation entropy.

[0049] Step S333: determining the permutation entropy difference between adjacent IMF components according to the permutation entropy.

[0050] Specifically, .

[0051] Step S335: determine the adjacent IMF component with the largest permutation entropy difference as the dividing point between high and low frequency reconstruction.

[0052] Specifically, among all adjacent IMF component pairs, find the pair with the largest permutation entropy difference. This pair of IMF components is the dividing point between high-frequency and low-frequency reconstruction. For example, assuming there are 5 IMF components with the permutation entropy value H PE (IMF1)=0.95, H PE (IMF2)=0.85, H PE (IMF3)=0.70, H PE (IMF4)=0.55, H PE (IMF5) = 0.30. Next, calculate the permutation entropy difference of 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, we can get that the largest difference in permutation entropy is ΔH PE(4,5)=0.25, so the dividing point between IMF4 and IMF5 is the dividing point between high and low frequency reconstruction.

[0053] Step S337: determine the IMF component before the demarcation point as the high-frequency component, and reconstruct the high-frequency component to obtain a preliminary power-type reference power.

[0054] Specifically, the IMF components before the demarcation point can be determined as high-frequency components, that is, IMF1, IMF2, IMF3, and IMF4 before IMF4 are high-frequency components. The preliminary power-type reference power obtained by reconstructing the high-frequency components is .

[0055] Step S339: determine the IMF component and the residual after the demarcation point as low-frequency components, and reconstruct the low-frequency components to obtain preliminary energy-based reference power.

[0056] Specifically, the IMF component after the demarcation point plus the residual can be determined as the low-frequency component, that is, IMF5 plus the residual is the low-frequency component, and the preliminary energy-type reference power reconstructed from the low-frequency component is , where RES is the residual.

[0057] and Figure 1 Compared with the embodiment shown in the figure, this embodiment provides basic data for subsequent frequency analysis by calculating the permutation entropy of the IMF components and their residuals. The permutation entropy difference between adjacent IMF components is calculated based on the permutation entropy, which reflects the difference in frequency between these components. By selecting the adjacent IMF components with the largest permutation entropy difference, the dividing point between the high-frequency and low-frequency components is accurately determined. Then, the IMF components before the dividing point are determined as high-frequency components, and these high-frequency components are reconstructed to obtain preliminary power-type reference powers. The IMF components and residuals after the dividing point are regarded as low-frequency components, and preliminary energy-type reference powers are obtained by reconstructing these low-frequency components. This embodiment can effectively divide the signal into high-frequency and low-frequency components, thereby providing a more accurate basis for the calculation of power-type and energy-type reference powers, and improving overall performance and accuracy.

[0058] Figure 3 A flowchart for determining the permutation entropy of an IMF component provided in an embodiment of the present application may include the following steps: Step S3311, using Shannon entropy to determine the time series permutation entropy of the IMF component.

[0059] Step S3313, normalize the time series permutation entropy to obtain the permutation entropy.

[0060] Specifically, the definition of Shannon entropy is used to calculate the time series permutation entropy: Among them, k is the total number of subsequences; j is the column, P j is the probability of occurrence of the arrangement.

[0061] The time series permutation entropy is normalized to a specific range, usually from 0 to 1, to obtain the final permutation entropy.

[0062] and Figure 2 Compared with the embodiment shown, this embodiment can effectively quantify the complexity and information content of the time series by using Shannon entropy to calculate the time series permutation entropy of the IMF component. As a tool for measuring information uncertainty, Shannon entropy can reveal the degree of chaos and regularity of the time series, thereby providing accurate data features for further analysis. Then, by standardizing the permutation entropy, it can be adjusted to a unified range or standard to ensure the comparability and consistency of different time series, which helps to improve the accuracy and stability of data processing.

[0063] Figure 4 The flowchart of step S5 provided in the embodiment of the present application may include the following steps: Step S51, obtaining the change amount corresponding to the power type energy storage charge state, the energy type energy storage charge state and the composite energy storage reference power.

[0064] Specifically, 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 It is the composite energy storage reference power at the previous moment.

[0065] Step S53, 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.

[0066] Specifically, the fuzzy control rules are set in the preset fuzzy control algorithm, and the input parameters include the change ΔP 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 big", PM means "positive middle", PS means "positive small", Z means 0, NS means "negative small", NM means "negative middle", NB means "negative big"; power type energy storage state of charge SOChp and energy storage state of charge SOC lp In the fuzzy subset, L represents "low", M represents "medium", and H represents "high".

[0067] The membership function of each parameter is obtained using S-type, Z-type, II-type, triangle and Gaussian functions, among which, S-type: Z-Type: Type II: triangle: Gaussian function: Here, the boundary values ​​of the segmented values ​​of the input parameter x a, b, c, d directly affect the shape and output of the membership function, thereby affecting the result of fuzzy reasoning.

[0068] It should be noted that the fuzzy control algorithm in this embodiment needs to follow the following principles: (1) The membership function is designed to ensure that the function of each fuzzy subset is symmetrically distributed at the center point. For example, when using a Gaussian function or a triangular function, the center point should be located at the center of the subset. Ensure that the membership functions between different fuzzy subsets have a smooth transition to avoid mutations.

[0069] (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 reasoning.

[0070] (3) In the fuzzy control rules, when rapid power changes are detected, SMES is used as a response to quickly adjust the system state.

[0071] (4) In the fuzzy control rules, try to avoid frequent charging and discharging operations of the battery energy storage, especially when the power changes slightly.

[0072] (5) In the fuzzy control rules, when a rapid change in the reference power is detected, power-type energy storage is used preferentially to respond in order to quickly adjust the system state.

[0073] According to the response characteristics of power and energy storage elements, as well as the SOC (state of charge) of each energy storage element, appropriate fuzzy rules are established. The specific principles are as follows: 1. Rules when the discharge power gradually decreases When the composite energy storage reference power When the power storage device is in the discharge state and the required discharge power gradually decreases, the frequency content in the power signal will change: the larger the slope, the faster the discharge power decreases, and the richer the high-frequency components in the power signal. hp When the slope is small, the power-type energy storage can still bear more compensation for high-frequency components. Therefore, the power output of some energy-type energy storage can be appropriately reduced. The smaller the slope, the slower the discharge power decreases, and the richer the low-frequency components contained in the power signal. In this case, the energy-type energy storage can be appropriately used to share the high-frequency components that the power-type energy storage needs to compensate, that is, the power output of some power-type energy storage can be appropriately reduced.

[0074] 2. Rules when the discharge power gradually increases When the composite energy storage reference power When the power storage is in the discharge state and the required discharge power gradually increases, the change in slope has an impact on power distribution: the larger the slope, the faster the power increases. hp When the SOC of the power type energy storage is larger, the power type energy storage should bear more high frequency component compensation. Therefore, the power output of the energy type energy storage can be appropriately reduced to reduce its discharge current. hp When the slope is small, in order to avoid over-discharge of power-type energy storage, the power output of energy-type energy storage can be appropriately increased. The smaller the slope, the slower the power growth, the main power output is borne by energy-type energy storage, and the output of power-type energy storage can be appropriately reduced.

[0075] 3. Charging control logic The charging control logic is similar to the discharging logic. When the power change rate is large, the high-frequency components in the reference power signal also increase accordingly: when the power type energy storage charge state SOC hp When the power type energy storage state of charge SOC is larger, it can be charged according to normal power distribution to avoid overcharging of energy storage components. hp When it is small, the charging power of the power-type energy storage can be appropriately increased to accelerate the recovery of SOC.

[0076] Based on the above basic principles, the fuzzy inference rules are established, see Table 1, and the power type adjustment coefficient K can be inferred. hp and energy type adjustment coefficient K lp It should be noted that fuzzy membership is not a fixed value, but a representation expression. The membership function of the input parameter will determine its fuzzy value in the fuzzy domain. Then, according to the preset fuzzy inference rules, the fuzzy value of the output parameter is obtained through the inference process. Finally, the actual value of the output parameter is obtained through the defuzzification operation.

[0077] Table 1 Fuzzy reasoning rules Step S55, adjusting the preliminary power-type reference power using the power-type adjustment coefficient to obtain the power-type adjusted power.

[0078] Specifically, before using the power type adjustment coefficient to adjust the preliminary power type reference power, it is necessary to use the center of gravity method to convert the fuzzy output power type adjustment coefficient into an accurate power type adjustment coefficient. After conversion into the accurate power type adjustment coefficient, the preliminary power type reference power is multiplied by the accurate power type adjustment coefficient to obtain the power type adjustment power.

[0079] The defuzzification formula is: Among them, μ(x i ) represents the input parameter x i The membership value in the domain.

[0080] Step S57, adjusting the preliminary energy-type reference power using the energy-type adjustment coefficient to obtain the energy-type adjusted power.

[0081] Specifically, before using the energy-type adjustment coefficient to adjust the preliminary energy-type reference power, it is necessary to use the centroid method to convert the fuzzy output energy-type adjustment coefficient into an accurate energy-type adjustment coefficient. After conversion into the accurate energy-type adjustment coefficient, the preliminary energy-type reference power is multiplied by the accurate energy-type adjustment coefficient to obtain the energy-type adjustment power.

[0082] The defuzzification formula is: Among them, μ(x i ) represents the input parameter x i The membership value in the domain.

[0083] and Figure 1 Compared with the embodiment shown, this embodiment can obtain the charge state of power-type and energy-type energy storage and the change in the reference power of the composite energy storage in real time through the fuzzy control algorithm, providing an accurate data basis for subsequent power regulation. The fuzzy control algorithm is used to process these inputs to generate a power-type adjustment coefficient and an energy-type adjustment coefficient, thereby finely adjusting the preliminary power-type and energy-type reference powers to 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 stable operation of the system when the load fluctuates. Overall, this embodiment achieves coordination between energy storage devices through intelligent and dynamic adjustment, significantly improves the stability and operating efficiency of the system, ensures efficient and balanced energy supply under different working conditions, and extends the service life of the system.

[0084] The following describes the specific implementation of the present invention by using simulation software to simulate the power fluctuation of new energy grid connection and compensate it through composite energy storage. Figure 5 .

[0085] Step S1001, based on the collected data, the composite energy storage reference power that needs to be compensated by the composite energy storage system is calculated, which is recorded as .

[0086] Step S1002: The calculated composite energy storage reference power As input, the complementary ensemble empirical mode decomposition (CEEMD) algorithm is used for decomposition, and a series of intrinsic mode functions (IMFs) from high frequency to low frequency and the remaining residuals are obtained.

[0087] Step S1003, using permutation entropy as an indicator to measure the complexity of the time series. The smaller the permutation entropy, the more regular the time series is, 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 difference between adjacent IMFs, the dividing point of high-frequency and low-frequency reconstruction can be determined, that is, the two IMF functions with the largest permutation entropy difference, and finally the preliminary energy-based reference power P is obtained. lp and the preliminary power type reference power P hp .

[0088] Step S1004, considering the SOC (state of charge) of the two types of energy storage elements and the change ΔP corresponding to the composite energy storage reference power at the current moment HESS (t), using the preset fuzzy control algorithm to output the power type adjustment coefficient and the energy type adjustment coefficient.

[0089] Step S1005: Based on the preliminary energy-based reference power P lp and the preliminary power type reference power P hp , combined with fuzzy logic control to obtain the secondary distribution of power type adjustment coefficient K hp and energy type adjustment coefficient K lp , calculate the final target power reference power and target energy reference power .

[0090] Among them, the energy storage SOC membership function is as follows: Figure 6As shown, in order to extend the cycle life of energy storage, try to avoid overcharging and over-discharging, and select an appropriate SOC range for control. In the state of low and high SOC, the S-type function and Z-type function are used to expand the value range of "L" and "H" respectively. At the same time, since the probability of over-discharging of battery storage is usually higher than overcharging when responding 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 in the range of [0.3,0.8].

[0091] The power type energy storage SOC membership function is as follows: Figure 7 As shown in the figure, the power-type energy storage has a faster response speed and a smaller energy storage capacity, so its SOC has a larger range of variation. In order to avoid overcharging and over-discharging, Z-type function and S-type function are used as the membership functions of the "L" and "H" language variables. Compared with energy-type energy storage, the value range of power-type energy storage is relatively small. In the membership design of the "M" language variable, the symmetry principle is adopted, and in order 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" language variable, thereby improving the charging and discharging response range of the superconducting magnet.

[0092] The change ∆P corresponding to the composite energy storage reference power HESS (t) Membership function is as follows Figure 8 As shown in the figure, since the value range of the variation corresponding to the composite energy storage reference power is large, multiple linguistic variables are set to divide the value interval. According to the symmetry principle and overlap rate requirements in the design of the membership function, the membership function of the "NM" to "PM" linguistic variables is designed as a trigonometric function, while the membership functions of the "NB" and "PB" variables are Z-type functions and S-type functions, respectively. In order to reduce the frequent response of energy-type energy storage, the value range of the "NB", "NM", "PM" and "PB" variables is appropriately expanded, thereby improving the response performance of power-type energy storage.

[0093] Power type adjustment factor K hp and energy type adjustment coefficient K lp The membership degree of Fig. 9 and Fig.10 As shown in the figure, the design ideas of the two parameters are basically the same, but the range of values ​​is different. Since energy storage is usually used for long-term, large-capacity power support, its compensation reference power is relatively large. In order to reduce the overcharge and over-discharge of power storage caused by secondary power adjustment, the energy adjustment coefficient K lp The membership function design of the two parameters follows the symmetry principle and the overlap requirement, while ensuring that the output parameters change smoothly. Therefore, the Gaussian function, Z function and S function are designed as the membership functions of the linguistic variables "Z", "S", "M" and "H" respectively.

[0094] The simulation software was used to simulate the fluctuation of the grid-connected power of renewable energy, and the power was compensated by the composite energy storage system. The initial parameters of the composite energy storage system were set as follows: the energy storage SOC was 0.7 and the power storage SOC was 1.01. The signal of the composite energy storage reference power is as follows: Fig.11 In order to verify the effectiveness of the embodiment of the present application, a comparative analysis between the traditional EMD solution and the embodiment of the present application was conducted, focusing on the comparison of power distribution and the SOC state changes of each energy storage element. The results show that the embodiment of the present application can effectively reduce the overcharge and overdischarge of the energy storage element and extend its service life.

[0095] It can be seen that the power distribution comparison of power-type energy storage is Fig.12 As shown, the traditional EMD power distribution will cause the reference power of the power-type energy storage to fluctuate violently when compensating for the rapid change of the reference power. Since the capacity of the power-type energy storage is usually small, it is easy to cause it to be overcharged or over-discharged. The power distribution strategy of the embodiment of the present application can effectively reduce the power fluctuation of the power-type energy storage and avoid overcharging and over-discharging 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 embodiment of the present application. The icons in the subsequent figures are all explained for this purpose and will not be repeated.

[0096] Power distribution comparison of energy storage Fig.13 As shown, the embodiment of the present application can effectively smooth the change of the supplementary power of the energy storage and reduce the rapid change of the power of the energy storage.

[0097] Comparison of changes in power-type energy storage SOC Fig.14 As shown in the figure, the change of energy storage SOC is as follows: Fig.15 As shown, the power allocation strategy of the embodiment of the present application can dynamically adjust the power allocation 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.

[0098] Accordingly, please refer to Fig.16 A block diagram of a composite energy storage adaptive power dynamic distribution system provided in an embodiment of the present application, the system comprising: The reference power determination unit S101 is used to determine the composite energy storage reference power based on the acquired load power and grid-side output power; The initial power determination unit S103 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; The adjustment power generation unit S105 is used 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; The target power determination unit S107 is used to 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 to 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.

[0099] In some optional implementations, 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.

[0100] In some optional implementations, determining a preliminary power-type reference power and a preliminary energy-type reference power based on adjacent IMF components and permutation entropies corresponding to residuals includes: Determine the permutation entropy of the IMF components and residuals; According to the permutation entropy, the permutation entropy difference of adjacent IMF components is determined; The adjacent IMF components with the largest permutation entropy difference are determined as the dividing points of high-frequency and low-frequency reconstruction; The IMF components before the demarcation point are determined as high-frequency components, and the high-frequency components are reconstructed to obtain preliminary power-type reference power; The IMF components and residuals after the demarcation point are determined as low-frequency components, and the low-frequency components are reconstructed to obtain preliminary energy-based reference power.

[0101] In some optional implementations, determining the permutation entropy of the IMF component includes: Use Shannon entropy to determine the IMF components and the time series permutation entropy of the residual; The time series permutation entropy is standardized to obtain the permutation entropy.

[0102] In some optional implementations, generating a power-type adjustment power corresponding to a preliminary power-type reference power and an energy-type adjustment power corresponding to a preliminary energy-type reference power includes: Obtain the change corresponding to the power type energy storage charge state, energy type energy storage charge state and composite energy storage reference power; Input the power-type energy storage charge state, energy-type energy storage charge state, composite energy storage reference power and variation into a preset fuzzy control algorithm to obtain a power-type adjustment coefficient and an energy-type adjustment coefficient; The preliminary power type reference power is adjusted using the power type adjustment coefficient 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.

[0103] In some optional implementations, 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: Target power type reference power = preliminary power type reference power - power type adjustment power + energy type adjustment power.

[0104] In some optional implementations, 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: Target energy-type reference power = preliminary energy-type reference power - energy-type adjustment power + power-type adjustment power.

[0105] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0106] In this embodiment, a composite energy storage adaptive power dynamic distribution system is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0107] See also Fig.17 , Fig.17 A schematic diagram of the structure of a computer device provided in an embodiment of the present application is shown in FIG. Fig.17 As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional 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 group of blade servers, or a multi-processor system). Fig.17 A processor 10 is taken as an example.

[0108] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0109] 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.

[0110] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via 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.

[0111] 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 also include a combination of the above types of memory.

[0112] The computer device further comprises a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0113] The embodiment of the present application also provides a computer-readable storage medium. The above method according to the embodiment of the present application can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above 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. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0114] The systems and units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. 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 a combination of any of these devices.

[0115] For the convenience of description, the above device is described in various units 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.

[0116] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods and systems. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may 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 codes.

[0117] The present application is described with reference to the flowcharts and / or block diagrams of the 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 the 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 a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0118] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0119] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0120] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0121] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. 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 partial description of the method embodiment.

[0122] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

[0123] Although the embodiments of the present application have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present application, and such modifications and variations are all 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: Determine the composite energy storage reference power based on the acquired load power and grid-side output 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; Based on the preliminary power-type reference power, the power-type adjustment power and the energy-type adjustment power, a target power-type reference power is determined; and based on the preliminary energy-type reference power, the energy-type adjustment power and the power-type adjustment power, a target energy-type reference power is determined.

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; According to the permutation entropy, determining the permutation entropy difference between adjacent IMF components; 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, 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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