Improved variational mode decomposition-based hybrid energy storage capacity optimization method and system

Abstract

The disclosure provides a hybrid energy storage capacity optimization method and system based on improved variational mode decomposition, which comprises decomposing the obtained power of the hybrid energy storage system configured on the power plant side by using an improved variational mode decomposition algorithm to obtain a preset number of subsequences, wherein the improved variational mode decomposition algorithm is obtained by optimizing the variational mode decomposition algorithm by using a correlation coefficient and an average Pearson correlation coefficient; a target hybrid energy storage system power is obtained for the preset number of subsequences; a target function is constructed based on the target hybrid energy storage system power, the hybrid energy storage capacity and a cost coefficient, a constraint condition is constructed based on a load power shortage rate and an energy storage amount of the hybrid energy storage; when the constraint condition is satisfied, the improved particle swarm optimization algorithm is used to solve the target function to obtain an optimal hybrid energy storage capacity, and the capacity of the hybrid energy storage is controlled based on the optimal hybrid energy storage capacity. According to the method of the disclosure, the cost of the hybrid energy storage can be better reduced, and the capacity configuration can be better performed.

Description

Technical Field

[0001] This disclosure relates to the field of hybrid energy storage capacity optimization technology, and in particular to a hybrid energy storage capacity optimization method and system based on improved variational mode decomposition. Background Technology

[0002] Currently, energy storage technology plays a significant role in power systems across the "source, grid, load, and consumption" domains. Traditional energy storage technologies primarily utilize lithium-ion batteries; however, lithium-ion batteries suffer from short cycle life, poor safety performance, and low power density, severely impacting the quality and economic viability of energy storage projects. Compared to lithium-ion batteries, supercapacitors offer advantages such as rapid charging and discharging, high power density, long cycle life, and superior safety performance, making them a promising new option for power frequency regulation technology. Hybrid energy storage systems combining supercapacitors and batteries have garnered significant attention within the power industry. However, hybrid energy storage systems involving supercapacitors and batteries face the challenge of capacity optimization. Current methods for hybrid energy storage capacity optimization largely employ existing particle swarm optimization algorithms to study capacity optimization configuration problems. While these algorithms can achieve some degree of capacity optimization, their overall optimization capability still needs improvement. Summary of the Invention

[0003] This disclosure provides a hybrid energy storage capacity optimization method and system based on improved variational mode decomposition, with the main purpose of better reducing hybrid energy storage costs and improving capacity configuration.

[0004] According to a first aspect of this disclosure, a method for optimizing the capacity of hybrid energy storage based on improved variational mode decomposition is provided, comprising:

[0005] The power of the hybrid energy storage system configured on the power plant side is obtained, and the power of the hybrid energy storage system is decomposed into a preset number of subsequences using an improved variational mode decomposition algorithm. The improved variational mode decomposition algorithm is obtained by optimizing the variational mode decomposition algorithm using correlation coefficient and average Pearson correlation coefficient.

[0006] The target hybrid energy storage system power is obtained by reconstructing a preset number of subsequences according to different frequency ranges.

[0007] Based on the target hybrid energy storage system power, hybrid energy storage capacity and cost coefficient, the inherent cost, operating cost and penalty cost are obtained, and then the objective function is constructed. The constraints are constructed based on the load shortage rate and the energy storage capacity of the hybrid energy storage.

[0008] When the constraints are met, the objective function is solved using an improved particle swarm optimization algorithm to obtain the optimal hybrid energy storage capacity, and the capacity of the hybrid energy storage is controlled based on the optimal hybrid energy storage capacity.

[0009] In one embodiment of this disclosure, the step of optimizing the variational mode decomposition algorithm using correlation coefficient and average Pearson correlation coefficient to obtain an improved variational mode decomposition algorithm includes: obtaining a first quantitative parameter using the correlation coefficient method; obtaining a second quantitative parameter using the Pearson correlation coefficient method; calculating the average value of the first quantitative parameter and the second quantitative parameter, and using the average value as the number of subsequences in the variational mode decomposition algorithm, thereby obtaining the improved variational mode decomposition algorithm.

[0010] In one embodiment of this disclosure, obtaining the first quantity parameter using the correlation coefficient method includes: performing variational mode decomposition on the power of the hybrid energy storage system based on a preset value, calculating the correlation coefficient between the decomposed mode components and the power of the hybrid energy storage system before decomposition; obtaining a correlation coefficient less than the preset threshold as a target quantity parameter based on the correlation coefficient and a preset threshold, and taking the minimum value among the target quantity parameters as the first quantity parameter.

[0011] In one embodiment of this disclosure, obtaining the second quantitative parameter using the Pearson correlation coefficient method includes: iterating the number of subsequences of the variational mode decomposition algorithm using the principle of minimizing the average Pearson correlation coefficient, thereby obtaining the second quantitative parameter.

[0012] In one embodiment of this disclosure, the improved particle swarm algorithm is obtained by optimizing the acceleration factor in the particle swarm algorithm based on inertia weights, random values, and the number of iterations.

[0013] In one embodiment of this disclosure, the step of obtaining inherent cost, operating cost, and penalty cost based on the target hybrid energy storage system power, hybrid energy storage capacity, and cost coefficient, and then constructing an objective function, includes: the hybrid energy storage including battery energy storage and supercapacitor energy storage; obtaining inherent cost based on battery capacity, supercapacitor capacity, and cost coefficients corresponding to the battery and supercapacitor; obtaining operating cost based on the battery deep charge / discharge operating cost and the battery overcharge / discharge operating cost; obtaining the deficit cost and wind curtailment cost corresponding to the battery and supercapacitor based on the target hybrid energy storage system power, and then obtaining the penalty cost; and constructing the objective function using the minimum value of the sum of inherent cost, operating cost, and penalty cost.

[0014] In one embodiment of this disclosure, the energy storage construction constraints based on load shortage rate and hybrid energy storage include: the constraints include load shortage rate constraints and energy constraints; the load shortage rate is obtained based on the power plant's power output, the load's power output, and the power conversion efficiency, thereby obtaining the load shortage rate constraints; and the energy constraints are obtained based on the remaining energy storage and rated energy storage corresponding to the batteries and supercapacitors.

[0015] According to a second aspect of this disclosure, a hybrid energy storage capacity optimization system based on improved variational mode decomposition is also provided, comprising:

[0016] The decomposition module is used to obtain the power of the hybrid energy storage system configured on the power plant side, and to decompose the power of the hybrid energy storage system using an improved variational mode decomposition algorithm to obtain a preset number of subsequences. The improved variational mode decomposition algorithm is obtained by optimizing the variational mode decomposition algorithm using correlation coefficient and average Pearson correlation coefficient.

[0017] The reconfiguration module is used to reconfigure a preset number of subsequences according to different frequency ranges to obtain the target hybrid energy storage system power.

[0018] The processing module is used to obtain the inherent cost, operating cost and penalty cost based on the target hybrid energy storage system power, hybrid energy storage capacity and cost coefficient, and then construct the objective function and construct the constraints based on the load shortage rate and the energy storage capacity of the hybrid energy storage.

[0019] The control module is used to solve the objective function using an improved particle swarm optimization algorithm when the constraints are met, obtain the optimal hybrid energy storage capacity, and control the capacity of the hybrid energy storage based on the optimal hybrid energy storage capacity.

[0020] In one embodiment of this disclosure, the decomposition module is specifically used to: obtain a first quantitative parameter using the correlation coefficient method; obtain a second quantitative parameter using the Pearson correlation coefficient method; calculate the average value of the first quantitative parameter and the second quantitative parameter, and use the average value as the number of subsequences in the variational mode decomposition algorithm, thereby obtaining an improved variational mode decomposition algorithm.

[0021] According to a third aspect of the present disclosure, an electronic device is also provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to execute the hybrid energy storage capacity optimization method based on improved variational mode decomposition proposed in the first aspect of the present disclosure.

[0022] In one or more embodiments of this disclosure, the power of the hybrid energy storage system configured on the power plant side is obtained; the power of the hybrid energy storage system is decomposed using an improved variational mode decomposition algorithm to obtain a preset number of subsequences; wherein the variational mode decomposition algorithm is optimized using correlation coefficient and average Pearson correlation coefficient to obtain the improved variational mode decomposition algorithm; the target hybrid energy storage system power is obtained for the preset number of subsequences; an objective function is constructed based on the target hybrid energy storage system power, hybrid energy storage capacity, and cost coefficient; constraints are constructed based on the load shortage rate and the energy storage capacity of the hybrid energy storage; when the constraints are satisfied, the objective function is solved using an improved particle swarm optimization algorithm to obtain the optimal hybrid energy storage capacity; and the capacity of the hybrid energy storage is controlled based on the optimal hybrid energy storage capacity. In this context, an improved variational mode decomposition algorithm is obtained by optimizing the variational mode decomposition algorithm using correlation coefficients and average Pearson correlation coefficients. The power of the hybrid energy storage system is decomposed using the improved variational mode decomposition algorithm to obtain a preset number of subsequences. Then, the subsequences are reconstructed to obtain the target hybrid energy storage system power. This makes the constructed objective function more accurate, and thus the optimal hybrid energy storage capacity obtained when solving the objective function using the improved particle swarm optimization algorithm is better. As a result, the cost of hybrid energy storage can be reduced better, and the capacity configuration can be better optimized.

[0023] Additional aspects and advantages of this disclosure will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this disclosure. Attached Figure Description

[0024] The above and / or additional aspects and advantages of this disclosure will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, in which:

[0025] Figure 1 The diagram shows a flowchart of a hybrid energy storage capacity optimization method based on improved variational mode decomposition provided in an embodiment of this disclosure.

[0026] Figure 2 This diagram illustrates the structure of the wind-storage combined power generation system provided in the embodiments of this disclosure;

[0027] Figure 3 The diagram shows the optimization results of the improved particle swarm optimization algorithm provided in the embodiments of this disclosure;

[0028] Figure 4 This diagram illustrates a block diagram of a hybrid energy storage capacity optimization system based on improved variational mode decomposition, according to an embodiment of this disclosure.

[0029] Figure 5 This is a block diagram of an electronic device used to implement the hybrid energy storage capacity optimization method based on improved variational mode decomposition according to the embodiments of this disclosure. Detailed Implementation

[0030] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this disclosure as detailed in the appended claims.

[0031] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0032] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this disclosure, "a plurality of" means at least two, such as two, three, etc., unless otherwise expressly and specifically defined. It should also be understood that the term "and / or" as used in this disclosure refers to and includes any or all possible combinations of one or more associated listed items.

[0033] Embodiments of this disclosure are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this disclosure, and should not be construed as limiting this disclosure.

[0034] This disclosure provides a hybrid energy storage capacity optimization method and system based on improved variational mode decomposition, with the main purpose of better reducing hybrid energy storage costs and improving capacity configuration.

[0035] In the first embodiment, Figure 1 This diagram illustrates a flowchart of a hybrid energy storage capacity optimization method based on improved variational mode decomposition provided in an embodiment of this disclosure. Figure 1As shown, specifically, the hybrid energy storage capacity optimization method based on improved variational mode decomposition includes:

[0036] Step S11: Obtain the power of the hybrid energy storage system configured on the power plant side, and decompose the power of the hybrid energy storage system using an improved variational mode decomposition algorithm to obtain a preset number of subsequences. The improved variational mode decomposition algorithm is obtained by optimizing the variational mode decomposition algorithm using the correlation coefficient and the average Pearson correlation coefficient.

[0037] In step S11, the power plant is a wind-storage combined generation system. A wind-storage combined generation system refers to a hybrid energy storage system configured on the wind farm side, and the wind power fluctuations of this wind-storage combined generation system meet the national grid connection standards.

[0038] Figure 2 A schematic diagram of the structure of the wind-storage combined power generation system provided in the embodiments of this disclosure is shown. Figure 2 As shown, a wind-storage combined power generation system includes a wind turbine for power generation and a hybrid energy storage system. The hybrid energy storage system includes supercapacitors and batteries. When storing energy in the hybrid energy storage system, the electrical energy generated by the wind turbine can enter the hybrid energy storage system via a rectifier (AC / DC) and a bidirectional DC / DC converter. When the hybrid energy storage system discharges, the electrical energy from the hybrid energy storage system enters the power grid via the bidirectional DC / DC converter, and then supplies power to AC loads via an inverter (DC / AC) and to DC loads via a DC / DC converter. Figure 2 As shown, the wind-storage combined power generation system also includes a photovoltaic array for power generation. The electricity generated by the photovoltaic array can enter the hybrid energy storage system via a rectifier (AC / DC) and a bidirectional DC / DC converter.

[0039] In some embodiments, to avoid insufficient or excessive wind power smoothing, a moving average filter is applied to the wind power to control fluctuations within a certain range, thereby obtaining a smoother wind power grid-connected power and hybrid energy storage system charging / discharging power. The hybrid energy storage system charging / discharging power is the hybrid energy storage system power. The calculation formula for the moving average filter satisfies:

[0040]

[0041] In equation (1), x(t) is the power of the hybrid energy storage system at time t, P HISS (t) represents the charging and discharging power of the hybrid energy storage system at time t, P HISS When (t) is positive, it indicates that the hybrid energy storage system is being charged. HISS When (t) is negative, it indicates that the hybrid energy storage system is discharging; P W (t) represents the output of the wind power generation system at time t; P out (t) represents the grid-connected wind power at time t. PE (t) represents the charging and discharging power of the energy storage system at time t; P P (t) represents the charging and discharging power of the power-type energy storage at time t.

[0042] In step S11, the power of the hybrid energy storage system is decomposed using an improved variational mode decomposition algorithm to obtain a preset number of subsequences. The improved variational mode decomposition algorithm is obtained by optimizing the variational mode decomposition algorithm using the correlation coefficient and the average Pearson correlation coefficient.

[0043] First, the process of decomposing the power of the hybrid energy storage system using the Variational Mode Decomposition (VMD) algorithm is described as follows:

[0044] The power of the hybrid energy storage system is decomposed using VMD to obtain multiple subsequences (i.e., multiple modes). The k-th subsequence can be represented by the symbol u. k Let the number of subsequences be K, and each subsequence be a finite bandwidth with a different center frequency; with the objective of minimizing the sum of the estimated bandwidths of each subsequence, and the constraint that the sum of all subsequences equals the input power of the hybrid energy storage system, then the construction formula for the variational problem is:

[0045]

[0046] In equation (2), ω k This represents the center frequency of the k-th subsequence. t Indicate u k The partial derivative of (t) with respect to t, δ(t) denotes the unit impulse function, j denotes the imaginary number, t denotes time, and u k (t) represents the k-th subsequence (i.e., modal component) at time t, e represents a mathematical constant, f represents the power of the hybrid energy storage system, and st represents the constraint. Where {u k} represents the set of all subsequences, {u k}={u1,u2,…u K},{ω k Let {ω} be the set of center frequencies of all subsequences. k}={ω1,ω2,…,ω K}

[0047] By introducing the augmented Lagrangian function, the above constrained variational problem is transformed into an unconstrained variational problem, the expression of which is:

[0048]

[0049] In equation (3), L represents the Lagrange function, λ(t) is the Lagrange multiplier at time t, α is the penalty factor, and ||·|| 2Denotes the square of the l1 norm; ||·||2 2 Represents the square of the l2 norm; ω is updated alternately using the multiplier alternating direction method. k n+1 u k n+1 and λ n+1 Find the 'saddle point' of equation (2); where u k n+1 The expression is:

[0050]

[0051] In equation (4), ω k =ω k n+1 ,∑ i u i (t) is equivalent to ∑ i≠k u i (t) n+1 u i (t) represents the modal component u k f(t) represents the one-sided spectrum of the analytic signal obtained by performing the Hilbert transform on the signal, where f(t) represents the K u... k The sum of (t).

[0052] Based on the Parseval / Plancherel Fourier isometric transform, equation (3) is transformed to the frequency domain to obtain the frequency domain update of each subsequence; then the problem of the center frequency value is transformed to the frequency domain to obtain the center frequency update, and the Lagrange multiplier λ is updated, as shown in the following specific expression:

[0053]

[0054]

[0055]

[0056] In equations (5), (6), and (7), n represents the number of updates, and ω represents the frequency. Let f(ω) represent the k-th subsequence of the (n+1)-th update of frequency ω, and let f(ω) represent the power of the hybrid energy storage system in the frequency domain. Indicate u i (t) corresponds to the frequency domain mode. The Lagrange multiplier representing the frequency ω, ω k n+1 This represents the center frequency of the (n+1)th update. This represents the k-th subsequence of frequency ω. Let ω represent the Lagrange multiplier for the (n+1)th update frequency ω, τ represent undetermined coefficients (typically 1.2), and let c represent the precision value for the update (i.e., iteration). c > 0 is used to determine whether the iteration stopping condition is met. If the iteration stopping condition is met, the iteration stops. Then, based on the optimal solution after the iteration stops, each subsequence, its center frequency, and bandwidth are determined.

[0057] In this embodiment, considering that the VMD method continuously updates each modal component (i.e., subsequence) and its corresponding center frequency by employing a multiplicative operator alternating direction method, after decomposing the power of the hybrid energy storage system, each modal component and its center frequency are obtained. Before decomposing the signal, the number K of the subsequences for modal decomposition needs to be preset. Since the VMD method decomposes the signal into multiple modal components with finite bandwidth through adaptive decomposition, a K value that is too small will cause under-decomposition of the signal, while a K value that is too large will cause over-decomposition of the signal. Therefore, in this embodiment, the correlation coefficient and the average Pearson correlation coefficient are used to optimize the number K of the subsequences, thereby improving the accuracy of VMD decomposition.

[0058] In this embodiment, optimizing the variational mode decomposition algorithm using the correlation coefficient and the average Pearson correlation coefficient means optimizing the number of subsequences K in the variational mode decomposition algorithm using the correlation coefficient and the average Pearson correlation coefficient. The variational mode decomposition algorithm with the optimized number of subsequences K is the improved variational mode decomposition algorithm (CPVMD).

[0059] In some embodiments, optimizing the variational mode decomposition algorithm using correlation coefficients and average Pearson correlation coefficients to obtain an improved variational mode decomposition algorithm includes: obtaining a first quantitative parameter using the correlation coefficient method; obtaining a second quantitative parameter using the Pearson correlation coefficient method; calculating the average value of the first quantitative parameter and the second quantitative parameter, and using the average value as the number of subsequences in the variational mode decomposition algorithm, thereby obtaining the improved variational mode decomposition algorithm.

[0060] In some embodiments, obtaining the first quantitative parameter using the correlation coefficient method includes: performing variational mode decomposition on the power of the hybrid energy storage system based on a preset value, calculating the correlation coefficient between the decomposed mode components and the power of the hybrid energy storage system before decomposition; obtaining a correlation coefficient less than the preset threshold as a target quantitative parameter based on the correlation coefficient and a preset threshold, and taking the minimum value among the target quantitative parameters as the first quantitative parameter. The first quantitative parameter can be represented by the symbol K1.

[0061] Specifically, variational mode decomposition is performed on the power of the hybrid energy storage system based on preset values, and the correlation coefficients between the decomposed mode components and the power of the hybrid energy storage system before decomposition are calculated, including:

[0062] Determine the boundary value of the first quantitative parameter: Based on experience, randomly select multiple initial values ​​of the first quantitative parameter K1, analyze the correlation coefficient between each modal component and the original signal (i.e., the power of the hybrid energy storage system before decomposition) under each initial value, and determine the boundary value of the first quantitative parameter. For example, based on experience, randomly select initial values ​​such as K1 = 10, 11, 20, 24, etc., and analyze the correlation coefficient between each modal component and the original signal. It can be seen that when K1 ≥ 11, the correlation coefficient is very small, and the over-decomposition of the signal is serious. Therefore, 10 is selected as the boundary value of the first quantitative parameter.

[0063] Calculate the correlation coefficient: Perform VMD decomposition on the power of the hybrid energy storage system using the boundary value as a preset value, analyze the correlation coefficient between the obtained modal components and the original signal, and solve for the correlation coefficient. For example, perform VMD decomposition on the power of the hybrid energy storage system with K1=10, analyze the correlation coefficient between the obtained modal components and the original signal, and solve for the correlation coefficient.

[0064] Specifically, based on the correlation coefficient and a preset threshold, correlation coefficients less than the preset threshold are obtained as target quantity parameters, and the minimum value among the target quantity parameters is taken as the first quantity parameter, including:

[0065] A preset threshold is set. When the correlation coefficient of a modal component is greater than or equal to the preset threshold, the modal component is considered a valid modal component. When the correlation coefficient of a modal component is less than the preset threshold, the modal component is considered an invalid modal component.

[0066] The minimum value of the correlation coefficient of the invalid mode components is selected as the optimal K1 value, and VMD decomposition is performed based on the optimal K1 value.

[0067] In some embodiments, the second quantitative parameter is obtained using the Pearson correlation coefficient method, including: iterating the number of subsequences of the variational mode decomposition algorithm using the principle of minimizing the average Pearson correlation coefficient, thereby obtaining the second quantitative parameter. The second quantitative parameter can be represented by the symbol K2.

[0068] Specifically, the formula for calculating the Pearson correlation coefficient is as follows:

[0069]

[0070] In equation (8), ρ(X,Y) is the Pearson correlation coefficient, and X and Y are the two variables whose correlation needs to be calculated, where X represents each modal component and Y represents the original signal. and Let X and Y be the standard deviations.

[0071] Based on the number of modes, the average Pearson correlation coefficient of the decomposed signals from adjacent frequency bands is calculated as follows:

[0072]

[0073] In equation (9), Let I be the average Pearson correlation coefficient, and I be the individual modal components (IMFs) after signal decomposition. i For the i-th modal component, I i+1 Let i be the (i+1)th modal component. Equation (9) is used for iteration, and the second quantitative parameter is obtained when the average Pearson correlation coefficient is minimized.

[0074] In some embodiments, step S11 calculates the average value of the first quantity parameter and the second quantity parameter, and uses the average value as the number of subsequences in the variational mode decomposition algorithm to obtain an improved variational mode decomposition algorithm. This average value is the optimized number of subsequences, i.e., the number of subsequences satisfies: K = AVG(K1, K2). The optimized number of subsequences is preset as the average value, i.e., the average value is used as the preset number, and the power of the hybrid energy storage system is decomposed using the improved variational mode decomposition algorithm to obtain the preset number of subsequences.

[0075] Step S12: Reconstruct the target hybrid energy storage system power by reconstructing the preset number of subsequences according to different frequency ranges.

[0076] Specifically, in step S12, the power of the hybrid energy storage system is decomposed into multiple subsequences with frequencies ranging from low to high using CPVMD. These subsequences are divided into two frequency ranges: a high-frequency portion and a low-frequency portion. The high-frequency portion is reconstructed as the power-type energy storage charging and discharging power, and the low-frequency portion is reconstructed as the energy-type energy storage charging and discharging power.

[0077]

[0078] In equation (10), z represents the subsequence index of the mid-to-low frequency boundary point, and s represents the total number of subsequences decomposed.

[0079] The process of dividing the high-frequency part into the low-frequency part is as follows:

[0080] For each subsequence, its information entropy satisfies Where H(u) k Let P(u) be the information entropy of the k-th subsequence. k ) represents the energy of the k-th subsequence.

[0081] The mutual information entropy between two connected subsequences is: MI(u k ,u k+1 )=H(u k )+H(u k+1 )-H(u k ,u k+1), where MI(u k ,u k+1 Let H(u) be the mutual information entropy between the k-th subsequence and the (k+1)-th subsequence. k+1 Let H(u) be the information entropy of the (k+1)th subsequence. k ,u k+1 Let be the joint information entropy of the k-th subsequence and the (k+1)-th subsequence. The mutual information entropy is normalized. Based on the normalized mutual information entropy, it can be seen that the mutual information entropy between adjacent subsequences decreases and then increases again from low frequency to high frequency. The minimum point of the mutual information entropy is selected as the boundary between the high-frequency and low-frequency components. The power-type energy storage charging and discharging power and the energy-type energy storage charging and discharging power are reconstructed, and thus the reconstructed hybrid energy storage system power (i.e., the target hybrid energy storage system power) is obtained.

[0082] Step S13: Based on the target hybrid energy storage system power, hybrid energy storage capacity and cost coefficient, obtain the inherent cost, operating cost and penalty cost, and then construct the objective function, constructing constraints based on the load shortage rate and the energy storage capacity of hybrid energy storage.

[0083] In some embodiments, step S13 involves obtaining inherent costs, operating costs, and penalty costs based on the target hybrid energy storage system power, hybrid energy storage capacity, and cost coefficients, and then constructing an objective function. This includes: hybrid energy storage including battery energy storage and supercapacitor energy storage; obtaining inherent costs based on battery capacity, supercapacitor capacity, and cost coefficients corresponding to the battery and supercapacitor; obtaining operating costs based on the deep charge / discharge operating costs and overcharge / discharge operating costs of the battery; obtaining the deficit costs and wind curtailment costs corresponding to the battery and supercapacitor based on the target hybrid energy storage system power, and then obtaining penalty costs; and constructing an objective function using the minimum sum of inherent costs, operating costs, and penalty costs.

[0084] Specifically, inherent cost refers to the initial investment and installation cost of a hybrid energy storage system, which is directly proportional to the capacity of the hybrid energy storage system. The formula for calculating inherent cost is:

[0085] C i =a A S A+ a F S F

[0086] Among them, C i For inherent costs, a A and a F These are the inherent cost coefficients for the initial investment and construction of the storage battery and supercapacitor, respectively. These inherent cost coefficients can be set according to actual circumstances; S A For battery capacity, S F This refers to the supercapacitor capacity. The battery capacity S is also included.A and supercapacitor capacity S F It is about optimizing the target parameters.

[0087] Operating cost refers to the cost incurred by a hybrid energy storage system during operation due to the depletion of its lifespan caused by deep charging and discharging, as well as overcharging and discharging. For supercapacitors, deep charging and discharging has a relatively small impact on their operating cost and lifespan. Ideally, supercapacitors can be charged and discharged at their rated power for extended periods; therefore, their operating cost is not considered. The deep charging and discharging operating cost Cd for a battery is: Cd = μ A m A , where μ A The operating cost of deep charge and discharge of the battery can be set according to actual conditions; m A This refers to the number of deep charge / discharge cycles for the battery. The operating cost of overcharging / discharging the battery, Co, is: Co = σ A n A , where σ A The overcharge and over-discharge operating cost of the battery is set according to the actual situation; n A The number of overcharge / discharge cycles for the battery is set based on actual conditions. The operating cost Cr is: Cr = Cd + Co.

[0088] The penalty cost mainly consists of two parts: the shortfall cost and the curtailment cost. The shortfall cost refers to the loss incurred when the hybrid energy storage system discharges to its minimum state of charge, failing to meet the expected target for continued wind power replenishment. The curtailment cost refers to the cost incurred when the hybrid energy storage system charges to its maximum state of charge, preventing further wind power absorption and resulting in curtailment. Based on the shortfall cost and the curtailment cost, the penalty cost C is derived. p .

[0089]

[0090]

[0091]

[0092]

[0093] C p =C A1 +C F1 +C A2 +C F2

[0094] Among them, C A1 and C A2 These are the shortfall cost of batteries and the cost of wind curtailment, respectively; C F1 and C F2These represent the deficit cost of supercapacitors and the cost of wind curtailment, respectively. τ1 and β are the deficit cost coefficient and the wind curtailment cost coefficient, respectively. The g(.) function is a positive function; when the variable is greater than zero, the function value takes the value of the variable; when the variable is less than or equal to zero, the function value takes zero. ΔP L and ΔP H These represent the low-frequency and high-frequency offset components of the wind power output (i.e., the power output of the wind power generation system), respectively. Δt is the sampling time interval. S A (υ) and S F (υ) represents the capacity of the battery and the supercapacitor at υ, respectively. A (υ-1) and S F (υ-1) represents the capacity of the battery and the supercapacitor at υ-1, respectively. The target hybrid energy storage system power includes the maximum discharge power and maximum charging power of the battery, and the maximum discharge power and maximum charging power of the supercapacitor, where P... Admax and P Acmax These are the maximum discharge power and maximum charging power of the battery, respectively; P Fdmax and P Fcmax These represent the maximum discharge power and maximum charging power of the supercapacitor, respectively. N is the total number of samples (i.e., the total duration), and the penalty cost C... p All parameters can be set according to the actual situation.

[0095] In step S13, the objective function is constructed by minimizing the sum of inherent cost, operating cost, and penalty cost. That is, the objective function satisfies: minf L =C i +Cr+C p Therefore, when obtaining the optimal solution to the objective function, i.e., the optimal hybrid energy storage capacity, the total economic cost of various influencing factors during system operation can be minimized, thereby improving the economic efficiency of the hybrid energy storage system.

[0096] In some embodiments, step S13, which constructs energy storage constraints based on load shortage rate and hybrid energy storage, includes: the constraints include load shortage rate constraints and energy constraints; the load shortage rate is obtained based on the power plant's power output, the load's power output, and the power conversion efficiency, thereby obtaining the load shortage rate constraints; and the energy constraints are obtained based on the remaining energy storage and rated energy storage corresponding to the batteries and supercapacitors.

[0097] Specifically, the Loss of Power Supply Probability (LPSP) is an important operating indicator for wind-solar hybrid power generation systems. The LPSP is defined as the ratio of the power supply deficit to the total load demand E. L The ratio of the load deficit rate. That is, the load deficit rate constraint condition is:

[0098]

[0099] Where f LPSP Let E represent the load shortage rate, q represent time, Q represent the number of time points, and E represent the load shortage rate. LPS (q) represents the load deficit at time q, E L (q) represents the load demand at time q.

[0100] Load power shortage rate f LPSP The specific calculations include: Let ΔE = (E w (q)+E pv (q))η c -E L (q), where E w (q) represents the wind energy generated at time q, E pv (q) represents the solar power generation at time q. The power plant's output includes both wind and solar power. E L (q) represents the load power at time q. η c This refers to the inverter's power conversion efficiency. When the wind-solar hybrid power generation meets the load demand (ΔE > 0), the load power deficit E is... LPS =0, the hybrid energy storage system charges; when the wind-solar hybrid power generation is insufficient, i.e., ΔE<0, the hybrid energy storage system discharges to supplement the power shortage. In this case, let ΔE = -ΔE, i.e.: E LPS =E L (q)-(E w (q)+E pv (q))η c Based on the comparison of ΔE, the rated energy storage and minimum remaining energy storage of the battery, and the maximum energy storage and minimum remaining energy storage of the supercapacitor bank, the load power shortage under different conditions is calculated, and then the load power shortage rate is calculated.

[0101] The rated energy storage capacity of the battery is E bn (Unit: MWh), minimum remaining energy storage is E bmin (Unit: MWh)

[0102] E bn =N B C B U B / 10 6

[0103] E bmin =N B C B U B ·(1-DOD) / 10 6

[0104] U B This indicates the rated voltage of the battery (in V); CB The rated capacitance is indicated by the number of volts (Ah); DOD indicates the maximum depth of discharge. B This indicates the number of batteries. In actual operation, supercapacitors need to operate within a suitable voltage range, denoted as U. cmin ~U cmax The maximum energy stored in the supercapacitor bank is E cmax and minimum remaining stored energy E cmin They are respectively:

[0105]

[0106]

[0107] U cmax U is the maximum terminal voltage of the supercapacitor; cmin Cc represents the minimum terminal voltage of the supercapacitor; Cc represents the capacitance value. Nc represents the number of supercapacitors.

[0108] Specifically, in step S13, the energy constraint condition is satisfied as follows:

[0109]

[0110] Where E cn E is the rated energy storage capacity of a supercapacitor. c (s) represents the remaining stored energy of the supercapacitor; E b (s) represents the remaining energy of the battery, E b (s)≤μ△E, where μ is the proportion of energy stored by the battery to △E.

[0111] Step S14: When the constraints are met, the objective function is solved using the improved particle swarm optimization algorithm to obtain the optimal hybrid energy storage capacity, and the capacity of the hybrid energy storage is controlled based on the optimal hybrid energy storage capacity.

[0112] In this embodiment, considering that existing particle swarm optimization algorithms (PSO) are used to study capacity optimization configuration problems, although they converge quickly, they are prone to local optima during iteration, making it difficult to escape the constraints of these local optima. The optimal position in PSO is related to the particle velocity; the limitation of velocity means that the search space for each iteration step is a finite region, thus preventing the search range from expanding to the entire feasible solution space and failing to guarantee finding the globally optimal solution. Therefore, this disclosure utilizes an improved PSO algorithm to solve the objective function to obtain the globally optimal solution.

[0113] In simple terms, Particle Swarm Optimization (PSO) is a swarm optimization algorithm in which particles in the search space change their positions based on their own experience and the experience of other particles. By continuously updating their positions, they eventually find the optimal point. PSO uses a velocity-displacement search model, and the PSO algorithm is a method that simulates birds hunting. Individuals in PSO are called "particles" and are distributed in a multi-dimensional search space. The changes in the search space of a particle are based on the social psychology principle of individuals imitating the successful experiences of others. The swarm consists of a group of particles, each of which is a potential solution. The positional changes of each particle are determined by its own experience and the experience of its neighboring particles. i (t a () represents the particle's position. The particle's position changes by adding the particle's velocity v to the current position. i (t a To improve the convergence performance of the basic PSO algorithm, an inertial weight ω is introduced into the velocity equation. p (inertia weight) is the standard PSO algorithm, and its speed formula is:

[0114] v i (t a )=ω p v u (t a -1)+C1r1(x p,best -x i (t a )+C2r2(x p,best -x i (t a )

[0115]

[0116] In the formula v i (t a ) is the t-th a The particle's velocity, ω, at the nth iteration. p The inertia weights are C1 and C2, respectively. C1 is the first acceleration factor, C2 is the second acceleration factor, and r1 and r2 represent random numbers between (0,1). p,best For the tth a The optimal position of the particle at the nth iteration, ω pmax =0.8, ω pmin =0.3, where T is the current iteration number, and the inertia weight ω increases with each iteration. p As the particle shrinks, its velocity v iThe smaller size allows for a more thorough search within the solution space, thereby improving optimization performance.

[0117] Since the first acceleration factor C1 and the second acceleration factor C2 in the particle swarm optimization algorithm also have a significant impact on the search results, existing technologies typically set the acceleration factors to fixed values, such as C1 = C2 = 0.647283, which has certain limitations and is prone to getting stuck in local convergence. Therefore, in this embodiment, the acceleration factors in the particle swarm optimization algorithm are optimized based on inertia weights, random values, and the number of iterations to obtain an improved particle swarm optimization algorithm (IPSO). The optimized first acceleration factor C1 and the second acceleration factor C2 satisfy:

[0118] C1=ω p *rand(1)+T(C 1e -C 1s ) / T max

[0119]

[0120] In the formula, rand(1) represents a random value between [0,1], Tmax is the maximum number of iterations, and C 1e Let C1 be the final value of the first acceleration factor. 2e This is the final value of the second acceleration factor C2; C 1s Let C1 be the initial value of the first acceleration factor. 2s Let C1 be the initial value of the second acceleration factor C2. In this case, a larger first acceleration factor C1 and a smaller second acceleration factor C2 are used in the initial search, allowing particles to disperse freely in the search space, thereby increasing particle diversity. As the number of iterations increases, the first acceleration factor C1 gradually decreases, and the second acceleration factor C2 gradually increases, thereby accelerating the convergence speed, reducing the total lifecycle cost of the hybrid energy storage system, and speeding up the convergence speed at which the system reaches its optimal value.

[0121] In step S14, the objective function is solved using an improved particle swarm optimization algorithm to obtain the optimal hybrid energy storage capacity. Based on the optimal hybrid energy storage capacity, the capacity of the hybrid energy storage is controlled. The optimal hybrid energy storage capacity includes the optimal battery energy storage capacity and the optimal supercapacitor energy storage capacity.

[0122] In some embodiments, when solving the objective function in step S14, the optimal economic cost can also be obtained.

[0123] To verify the above-mentioned hybrid energy storage capacity optimization method based on improved variational mode decomposition, simulation verification was conducted. MATLAB was used for the numerical example analysis; the basic parameters of the battery and supercapacitor are shown in Table 1.

[0124] Table 1 Basic parameters of batteries and supercapacitors

[0125] Parameter name storage battery Supercapacitor Rated voltage / V 12 2.7 Rated capacity 100 / Ah 3500 / F Charging efficiency 0.75 0.98 Discharge efficiency 0.85 0.98 Depth of discharge 0.4 / Operating coefficient 0.1 0.01 Maintenance factor 0.02 / Cycle life / times 1500 500000 Unit price / yuan 400 350 Treatment coefficient 0.08 0.04

[0126] The number of subsequences (i.e., K value) is determined using the fusion correlation coefficient-mean Pearson correlation, and the power P of the hybrid energy storage system is then analyzed. HISS (t) Perform CPVMD decomposition to calculate the mutual information entropy between adjacent subsequences, as shown in Table 2. As can be seen from Table 2, a minimum point appears between subsequences U3 and U4. Therefore, 3 is selected as the boundary point to reconstruct the primary power of the hybrid energy storage.

[0127] Table 2 Mutual information entropy between adjacent subsequences

[0128] Adjacent subsequences Normalized mutual information entropy β U1,U2 0.1032 U2,U3 0.2219 U3, U4 0.0233 U4, U5 0.0103 U5, U6 0.3421 U6, U7 0.1583 U7, U8 0.1002

[0129] Subsequences U1 and U2 are reconstructed into the primary charge and discharge power of the battery, and subsequent subsequences are reconstructed into the primary charge and discharge power of the supercapacitor.

[0130] Based on the above objective function, constraints, and parameters, an improved particle swarm optimization algorithm is used to solve the problem, and simulations are performed in MATLAB. When ω = 0.8, the population size is 100, and the maximum number of iterations is 300. Simulation results are shown below. Figure 3 And Table 3. Figure 3 The diagram shows the optimization results of the improved particle swarm optimization algorithm provided in the embodiments of this disclosure. Figure 3 The x-axis represents the number of evolutions (i.e., the number of iterations), and the y-axis represents the fitness of the particle swarm optimization algorithm. The optimal individual fitness is obtained by increasing the number of evolutions.

[0131] Table 3 Simulation Results

[0132] Optimize parameters Improved Particle Swarm Optimization Algorithm Particle Swarm Optimization storage battery / each 40021 45622 Supercapacitor / unit 5591342 5533300 LPSP 0.0234 0.0227 Minimum cost / yuan 157642 172617

[0133] Comparing the traditional PSO algorithm and its improved version, we can see that the traditional PSO algorithm requires 45,622 batteries and 5,533,300 supercapacitors, with a minimum lifespan cost of 172,617 yuan and a load shedding rate of 0.0227. In contrast, the improved PSO algorithm requires 40,021 batteries and 5,591,342 supercapacitors, with a minimum lifespan cost of 157,642 yuan and a load shedding rate of 0.0234. Therefore, the improved PSO algorithm reduces the total lifespan cost by 8.68% and the number of batteries required by 12.28%, demonstrating better optimization capabilities.

[0134] The simulation results show that the improved functional inertia weight and optimization acceleration factor in this method can be well applied to the capacity optimization configuration of wind-solar hybrid energy storage systems. Compared with most existing improved inertia weight particle swarm optimization algorithms, the improved functional inertia weight and optimization acceleration factor proposed in this disclosure can further reduce the total life cycle cost of hybrid energy storage systems and improve the convergence speed of the system to reach the optimal value. The CPVMD decomposition effectively achieves frequency band separation, effectively avoids mode aliasing, and reduces the impact of adjacent frequency bands on the primary power allocation of hybrid energy storage.

[0135] In the hybrid energy storage capacity optimization method based on improved variational mode decomposition in this embodiment, the power of the hybrid energy storage system configured on the power plant side is obtained. The power of the hybrid energy storage system is decomposed using an improved variational mode decomposition algorithm to obtain a preset number of subsequences. The improved variational mode decomposition algorithm is obtained by optimizing the variational mode decomposition algorithm using correlation coefficient and average Pearson correlation coefficient. The target hybrid energy storage system power is obtained for the preset number of subsequences. An objective function is constructed based on the target hybrid energy storage system power, hybrid energy storage capacity, and cost coefficient. Constraints are constructed based on the load shortage rate and the energy storage capacity of the hybrid energy storage. When the constraints are satisfied, the objective function is solved using an improved particle swarm optimization algorithm to obtain the optimal hybrid energy storage capacity. The capacity of the hybrid energy storage is controlled based on the optimal hybrid energy storage capacity. In this context, an improved variational mode decomposition algorithm is obtained by optimizing the variational mode decomposition algorithm using correlation coefficients and average Pearson correlation coefficients. The power of the hybrid energy storage system is decomposed using this improved algorithm to obtain a predetermined number of subsequences. These subsequences are then reconstructed to obtain the target hybrid energy storage system power. This approach makes the constructed objective function more accurate, leading to a better optimal hybrid energy storage capacity when solving the objective function using the improved particle swarm optimization algorithm. This, in turn, can better reduce hybrid energy storage costs and improve capacity configuration. The method disclosed herein can improve the power quality of wind-storage combined power generation systems, reduce fluctuations in wind power grid connection, and improve system stability and economy. It is a hybrid energy storage capacity optimization configuration method based on CPVMD-IPSO to mitigate wind power fluctuations.

[0136] The following are system embodiments of this disclosure, which can be used to execute the method embodiments of this disclosure. For details not disclosed in the system embodiments of this disclosure, please refer to the method embodiments of this disclosure.

[0137] Please see Figure 4 , Figure 4 This diagram illustrates a block diagram of a hybrid energy storage capacity optimization system based on improved variational mode decomposition (EMD) according to an embodiment of this disclosure. The hybrid energy storage capacity optimization system 10 based on improved variational mode decomposition includes a decomposition module 11, a reconstruction module 12, a processing module 13, and a control module 14, wherein:

[0138] The decomposition module 11 is used to obtain the power of the hybrid energy storage system configured on the power plant side, and to decompose the power of the hybrid energy storage system using an improved variational mode decomposition algorithm to obtain a preset number of subsequences. The improved variational mode decomposition algorithm is obtained by optimizing the variational mode decomposition algorithm using the correlation coefficient and the average Pearson correlation coefficient.

[0139] Reconfiguration module 12 is used to reconfigure a preset number of subsequences according to different frequency ranges to obtain the target hybrid energy storage system power;

[0140] Processing module 13 is used to obtain inherent cost, operating cost and penalty cost based on target hybrid energy storage system power, hybrid energy storage capacity and cost coefficient, and then construct objective function, constructing constraints based on load shortage rate and hybrid energy storage capacity;

[0141] The control module 14 is used to solve the objective function using an improved particle swarm optimization algorithm when the constraints are met, to obtain the optimal hybrid energy storage capacity, and to control the capacity of the hybrid energy storage based on the optimal hybrid energy storage capacity.

[0142] Optionally, the decomposition module 11 is specifically used to: obtain a first quantitative parameter using the correlation coefficient method; obtain a second quantitative parameter using the Pearson correlation coefficient method; calculate the average value of the first quantitative parameter and the second quantitative parameter, and use the average value as the number of subsequences in the variational mode decomposition algorithm, thereby obtaining an improved variational mode decomposition algorithm.

[0143] Optionally, the decomposition module 11 is specifically used for: performing variational mode decomposition on the power of the hybrid energy storage system based on a preset value, calculating the correlation coefficient between the decomposed mode components and the power of the hybrid energy storage system before decomposition; obtaining a correlation coefficient less than the preset threshold as a target quantity parameter based on the correlation coefficient and a preset threshold, and taking the minimum value among the target quantity parameters as the first quantity parameter.

[0144] Optionally, the decomposition module 11 is specifically used to: iterate the number of subsequences of the variational mode decomposition algorithm using the principle of minimizing the average Pearson correlation coefficient, thereby obtaining the second quantity parameter.

[0145] Optionally, the processing module 13 is specifically used for: hybrid energy storage including battery energy storage and supercapacitor energy storage; obtaining the inherent cost based on the battery capacity, supercapacitor capacity, and cost coefficients corresponding to the battery and supercapacitor; obtaining the operating cost based on the deep charge and discharge operating cost and the overcharge and discharge operating cost of the battery; obtaining the deficit cost and wind curtailment cost corresponding to the battery and supercapacitor based on the target hybrid energy storage system power, and thus obtaining the penalty cost; and constructing an objective function using the minimum value of the sum of the inherent cost, operating cost, and penalty cost.

[0146] Optionally, the processing module 13 is specifically used for: obtaining load shortage rate constraints and energy constraints based on the constraints of the power plant power, load power and power conversion efficiency, and thus obtaining load shortage rate constraints; and obtaining energy constraints based on the remaining energy and rated energy of the batteries and supercapacitors.

[0147] Optionally, an improved particle swarm optimization algorithm can be obtained by optimizing the acceleration factor in the particle swarm algorithm based on inertia weights, random values, and the number of iterations.

[0148] It should be noted that the foregoing explanation of the embodiment of the hybrid energy storage capacity optimization method based on improved variational mode decomposition also applies to the hybrid energy storage capacity optimization system based on improved variational mode decomposition in this embodiment, and will not be repeated here.

[0149] In the hybrid energy storage capacity optimization system based on improved variational mode decomposition in this embodiment, the decomposition module obtains the power of the hybrid energy storage system configured on the power plant side, and decomposes the hybrid energy storage system power using an improved variational mode decomposition algorithm to obtain a preset number of subsequences. The improved variational mode decomposition algorithm is obtained by optimizing the variational mode decomposition algorithm using correlation coefficients and average Pearson correlation coefficients. The reconstruction module reconstructs the preset number of subsequences according to different frequency ranges to obtain the target hybrid energy storage system power. The processing module obtains the inherent cost, operating cost, and penalty cost based on the target hybrid energy storage system power, hybrid energy storage capacity, and cost coefficient, and then constructs an objective function. Constraints are constructed based on the load shortage rate and the energy storage capacity of the hybrid energy storage. When the constraints are satisfied, the control module solves the objective function using an improved particle swarm optimization algorithm to obtain the optimal hybrid energy storage capacity, and controls the capacity of the hybrid energy storage based on the optimal hybrid energy storage capacity. In this context, an improved variational mode decomposition algorithm is obtained by optimizing the variational mode decomposition algorithm using correlation coefficients and average Pearson correlation coefficients. The power of the hybrid energy storage system is decomposed using this improved algorithm to obtain a predetermined number of subsequences. These subsequences are then reconstructed to obtain the target hybrid energy storage system power. This approach makes the constructed objective function more accurate, leading to a better optimal hybrid energy storage capacity when solving the objective function using the improved particle swarm optimization algorithm. Consequently, it can better reduce hybrid energy storage costs and optimize capacity allocation. The system disclosed herein can improve the power quality of wind-storage combined power generation systems, reduce fluctuations in wind power grid connection, and improve system stability and economy. It is a hybrid energy storage capacity optimization configuration system based on CPVMD-IPSO to mitigate wind power fluctuations.

[0150] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0151] Figure 5 This is a block diagram of an electronic device used to implement the hybrid energy storage capacity optimization method based on improved variational mode decomposition according to embodiments of the present disclosure. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable electronic devices, and other similar computing devices. The components, connections and relationships between components, and functions shown in this disclosure are merely illustrative and are not intended to limit the implementation of the present disclosure as described and / or claimed herein.

[0152] like Figure 5 As shown, the electronic device 20 includes a computing unit 21, which can perform various appropriate actions and processes based on a computer program stored in a read-only memory (ROM) 22 or a computer program loaded from a storage unit 28 into a random access memory (RAM) 23. The RAM 23 may also store various programs and data required for the operation of the electronic device 20. The computing unit 21, ROM 22, and RAM 23 are interconnected via a bus 24. An input / output (I / O) interface 25 is also connected to the bus 24.

[0153] Multiple components in electronic device 20 are connected to I / O interface 25, including: input unit 26, such as keyboard, mouse, etc.; output unit 27, such as various types of monitors, speakers, etc.; storage unit 28, such as disk, optical disk, etc., which is communicatively connected to computing unit 21; and communication unit 29, such as network card, modem, wireless transceiver, etc. Communication unit 29 allows electronic device 20 to exchange information / data with other electronic devices through computer networks such as the Internet and / or various telecommunications networks.

[0154] The computing unit 21 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 21 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 21 performs the various methods and processes described above, such as performing a hybrid energy storage capacity optimization method based on improved variational mode decomposition. For example, in some embodiments, the hybrid energy storage capacity optimization method based on improved variational mode decomposition can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 28. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 20 via ROM 22 and / or communication unit 29. When the computer program is loaded into RAM 23 and executed by the computing unit 21, one or more steps of the hybrid energy storage capacity optimization method based on improved variational mode decomposition described above can be performed. Alternatively, in other embodiments, computing unit 21 may be configured by any other suitable means (e.g., by means of firmware) to perform a hybrid energy storage capacity optimization method based on improved variational mode decomposition.

[0155] Various embodiments of the systems and techniques described above in this disclosure can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic electronic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include: implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0156] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0157] In this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or electronic device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or electronic devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage electronics, magnetic storage electronics, or any suitable combination of the foregoing.

[0158] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0159] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), the Internet, and blockchain networks.

[0160] Computer systems can include clients and servers. Clients and servers are generally geographically separated and typically interact via communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. A server can be a cloud server, also known as a cloud computing server or cloud host, a hosting product within the cloud computing service ecosystem, addressing the shortcomings of traditional physical hosts and VPS (Virtual Private Server, or simply "VPS") services, such as high management difficulty and weak business scalability. Servers can also be servers for distributed systems or servers incorporating blockchain technology.

[0161] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this disclosure does not impose any limitations herein.

[0162] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for optimizing the capacity of hybrid energy storage based on improved variational mode decomposition, characterized in that, include: The power of the hybrid energy storage system configured on the power plant side is obtained, and the power of the hybrid energy storage system is decomposed into a preset number of subsequences using an improved variational mode decomposition algorithm. The improved variational mode decomposition algorithm is obtained by optimizing the variational mode decomposition algorithm using correlation coefficient and average Pearson correlation coefficient. The target hybrid energy storage system power is obtained by reconstructing a preset number of subsequences according to different frequency ranges. Based on the target hybrid energy storage system power, hybrid energy storage capacity and cost coefficient, the inherent cost, operating cost and penalty cost are obtained, and then the objective function is constructed. The constraints are constructed based on the load shortage rate and the energy storage capacity of the hybrid energy storage. When the constraints are met, the objective function is solved using an improved particle swarm optimization algorithm to obtain the optimal hybrid energy storage capacity, and the capacity of the hybrid energy storage is controlled based on the optimal hybrid energy storage capacity. The process of reconstructing a preset number of subsequences according to different frequency ranges to obtain the target hybrid energy storage system power includes: The power of the hybrid energy storage system is decomposed into multiple subsequences with frequencies ranging from low to high using CPVMD. Obtain the mutual information entropy between connected subsequences in the plurality of subsequences; The mutual information entropy is normalized, and the minimum value of the mutual information entropy is selected as the boundary between the high-frequency part and the low-frequency part. The power-type energy storage charging and discharging power and the energy-type energy storage charging and discharging power are reconstructed, and then the power of the target hybrid energy storage system is obtained. The penalty cost includes the shortfall cost and the wind curtailment cost. The shortfall cost refers to the loss caused by the hybrid energy storage system discharging to the minimum state of charge level, which prevents it from meeting the expected target of continuing to supplement wind power. The wind curtailment cost refers to the cost of wind curtailment caused by the hybrid energy storage system charging to the maximum state of charge level, which prevents it from continuing to absorb wind power.

2. The hybrid energy storage capacity optimization method based on improved variational mode decomposition as described in claim 1, characterized in that, The improved variational mode decomposition algorithm obtained by optimizing the variational mode decomposition algorithm using the correlation coefficient and the average Pearson correlation coefficient includes: The first quantitative parameter is obtained using the correlation coefficient method; The second quantitative parameter was obtained using the Pearson correlation coefficient method; Calculate the average of the first quantity parameter and the second quantity parameter, and use the average value as the number of subsequences in the variational mode decomposition algorithm to obtain an improved variational mode decomposition algorithm.

3. The hybrid energy storage capacity optimization method based on improved variational mode decomposition as described in claim 2, characterized in that, The method of obtaining the first quantitative parameter using the correlation coefficient method includes: Variational mode decomposition is performed on the power of the hybrid energy storage system based on preset values, and the correlation coefficient between the decomposed mode components and the power of the hybrid energy storage system before decomposition is calculated. Based on the correlation coefficient and the preset threshold, the correlation coefficient less than the preset threshold is obtained as the target quantity parameter, and the minimum value among the target quantity parameters is taken as the first quantity parameter.

4. The hybrid energy storage capacity optimization method based on improved variational mode decomposition as described in claim 2 or 3, characterized in that, The method of obtaining the second quantitative parameter using the Pearson correlation coefficient includes: The second quantitative parameter is obtained by iterating the number of subsequences in the variational mode decomposition algorithm using the principle of minimizing the average Pearson correlation coefficient.

5. The hybrid energy storage capacity optimization method based on improved variational mode decomposition as described in claim 4, characterized in that, The improved particle swarm optimization algorithm is obtained by optimizing the acceleration factor in the particle swarm optimization algorithm based on inertia weight, random value, and number of iterations.

6. The hybrid energy storage capacity optimization method based on improved variational mode decomposition as described in claim 1 or 5, characterized in that, The process involves obtaining inherent costs, operating costs, and penalty costs based on the target hybrid energy storage system power, hybrid energy storage capacity, and cost coefficients, and then constructing an objective function, including: The hybrid energy storage includes battery energy storage and supercapacitor energy storage; The inherent cost is obtained based on the battery capacity, the supercapacitor capacity, and the cost coefficients corresponding to the battery and the supercapacitor. The operating cost is obtained based on the operating cost of deep charge and discharge of the battery and the operating cost of overcharge and discharge of the battery. Based on the target hybrid energy storage system power, the corresponding deficit cost and wind curtailment cost of batteries and supercapacitors are obtained, and then the penalty cost is obtained; Construct the objective function by minimizing the sum of inherent cost, operating cost, and penalty cost.

7. The hybrid energy storage capacity optimization method based on improved variational mode decomposition as described in claim 6, characterized in that, The energy storage construction constraints based on load deficit rate and hybrid energy storage include: The constraints include load power shortage rate constraints and energy constraints; The load power shortage rate is obtained based on the power plant's power output, the load power output, and the power conversion efficiency, and then the load power shortage rate constraint conditions are obtained. Energy constraints are obtained based on the remaining and rated energy storage of batteries and supercapacitors.

8. A hybrid energy storage capacity optimization system based on improved variational mode decomposition, characterized in that, include: The decomposition module is used to obtain the power of the hybrid energy storage system configured on the power plant side, and to decompose the power of the hybrid energy storage system using an improved variational mode decomposition algorithm to obtain a preset number of subsequences. The improved variational mode decomposition algorithm is obtained by optimizing the variational mode decomposition algorithm using correlation coefficient and average Pearson correlation coefficient. The reconfiguration module is used to reconfigure a preset number of subsequences according to different frequency ranges to obtain the target hybrid energy storage system power. The processing module is used to obtain the inherent cost, operating cost and penalty cost based on the target hybrid energy storage system power, hybrid energy storage capacity and cost coefficient, and then construct the objective function and construct the constraints based on the load shortage rate and the energy storage capacity of the hybrid energy storage. The control module is used to solve the objective function using an improved particle swarm optimization algorithm when the constraints are met, to obtain the optimal hybrid energy storage capacity, and to control the capacity of the hybrid energy storage based on the optimal hybrid energy storage capacity. The reconstruction module is specifically used for: The power of the hybrid energy storage system is decomposed into multiple subsequences with frequencies ranging from low to high using CPVMD. Obtain the mutual information entropy between connected subsequences in the plurality of subsequences; The mutual information entropy is normalized, and the minimum value of the mutual information entropy is selected as the boundary between the high-frequency part and the low-frequency part. The power-type energy storage charging and discharging power and the energy-type energy storage charging and discharging power are reconstructed, and then the power of the target hybrid energy storage system is obtained. The penalty cost includes the shortfall cost and the wind curtailment cost. The shortfall cost refers to the loss caused by the hybrid energy storage system discharging to the minimum state of charge level, which prevents it from meeting the expected target of continuing to supplement wind power. The wind curtailment cost refers to the cost of wind curtailment caused by the hybrid energy storage system charging to the maximum state of charge level, which prevents it from continuing to absorb wind power.

9. The hybrid energy storage capacity optimization system based on improved variational mode decomposition as described in claim 8, characterized in that, The decomposition module is specifically used for: The first quantitative parameter is obtained using the correlation coefficient method; The second quantitative parameter was obtained using the Pearson correlation coefficient method; Calculate the average of the first quantity parameter and the second quantity parameter, and use the average value as the number of subsequences in the variational mode decomposition algorithm to obtain an improved variational mode decomposition algorithm.

10. An electronic device, characterized in that, include: At least one processor; And a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the hybrid energy storage capacity optimization method based on improved variational mode decomposition as described in any one of claims 1-7.

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