CN115459310A - Hybrid energy storage capacity optimization method and system based on improved variational modal decomposition - Google Patents
Hybrid energy storage capacity optimization method and system based on improved variational modal decomposition Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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Abstract
The method comprises the steps of decomposing the obtained power of the hybrid energy storage system configured on the side of the power plant by adopting an improved variational modal decomposition algorithm to obtain a preset number of subsequences, wherein the improved variational modal decomposition algorithm is obtained by utilizing a correlation coefficient and an average Pearson correlation coefficient to optimize the variational modal decomposition algorithm; obtaining the power of a target hybrid energy storage system aiming at a preset number of subsequences; constructing an objective function based on the power of a target hybrid energy storage system, the hybrid energy storage capacity and the cost coefficient, and constructing a constraint condition based on the load power shortage rate and the energy storage of the hybrid energy storage; when the constraint condition is met, the objective function is solved by using the improved particle swarm optimization algorithm to obtain the optimal hybrid energy storage capacity, and the hybrid energy storage capacity is controlled based on the optimal hybrid energy storage capacity. According to the method disclosed by the invention, the hybrid energy storage cost can be reduced better, and the capacity configuration can be carried out better.
Description
Technical Field
The disclosure relates to the technical field of hybrid energy storage capacity optimization, and in particular relates to a hybrid energy storage capacity optimization method and system based on improved variational modal decomposition.
Background
At present, in an electric power system, an energy storage technology plays a great role in the fields of sources, networks, loads, uses and the like, and most of the traditional energy storage technology adopts a lithium ion battery for energy storage, but the lithium ion battery has the problems of short cycle life, poor safety performance, low power density and the like, so that the quality and the economy of an energy storage project are seriously influenced. Compared with a lithium ion battery, the super capacitor has the advantages of high charging and discharging speed, high power density, long cycle life, high safety performance and the like, so that the super capacitor is a new choice of a power frequency modulation technology. Particularly, a hybrid energy storage system combining a super capacitor and a storage battery is concerned by the power industry. However, the hybrid energy storage system of the super capacitor and the storage battery has the problem of capacity optimization. Most of the existing hybrid energy storage capacity optimization methods adopt the existing particle swarm optimization to research the capacity optimization configuration problem, and although the capacity optimization configuration can be carried out to a certain extent, the optimization capacity needs to be improved.
Disclosure of Invention
The disclosure provides a hybrid energy storage capacity optimization method and system based on improved variational modal decomposition, and mainly aims to better reduce the hybrid energy storage cost and better configure the capacity.
According to an embodiment of a first aspect of the present disclosure, a hybrid energy storage capacity optimization method based on improved variational modal decomposition is provided, including:
acquiring power of a hybrid energy storage system configured at a power plant side, decomposing the power of the hybrid energy storage system by adopting an improved variational modal decomposition algorithm to obtain a preset number of subsequences, wherein the improved variational modal decomposition algorithm is obtained by optimizing the variational modal decomposition algorithm by utilizing a correlation coefficient and an average Pearson correlation coefficient;
reconstructing the subsequences of the preset number according to different frequency intervals to obtain the power of the target hybrid energy storage system;
obtaining inherent cost, operation cost and punishment cost based on the power of the target hybrid energy storage system, the hybrid energy storage capacity and the cost coefficient, further constructing an objective function, and constructing a constraint condition based on the load shortage rate and the energy storage of the hybrid energy storage;
and when the constraint condition is met, solving the objective function by using an improved particle swarm algorithm to obtain the optimal hybrid energy storage capacity, and controlling the capacity of the hybrid energy storage based on the optimal hybrid energy storage capacity.
In an embodiment of the present disclosure, the optimizing the variational modal decomposition algorithm by using the correlation coefficient and the average pearson correlation coefficient to obtain an improved variational modal decomposition algorithm includes: obtaining a first quantity parameter by adopting a correlation coefficient method; acquiring a second numerical parameter by adopting a Pearson correlation coefficient method; and calculating the average value of the first quantity parameter and the second quantity parameter, and taking the average value as the subsequence quantity of the variation modal decomposition algorithm, thereby obtaining the improved variation modal decomposition algorithm.
In an embodiment of the present disclosure, the obtaining the first quantity parameter by using a correlation coefficient method includes: carrying out variation modal decomposition on the power of the hybrid energy storage system based on a preset value, and calculating a correlation coefficient between a modal component obtained by decomposition and the power of the hybrid energy storage system before decomposition; and obtaining the correlation coefficient smaller than a preset threshold value as a target quantity parameter based on the correlation coefficient and the preset threshold value, and taking the minimum value in the target quantity parameter as a first quantity parameter.
In an embodiment of the disclosure, the obtaining the second numerical parameter by using a pearson correlation coefficient method includes: and iterating the number of subsequences of the variational modal decomposition algorithm by adopting an average Pearson correlation coefficient minimum principle, thereby obtaining a second numerical parameter.
In one embodiment of the disclosure, the acceleration factor in the particle swarm optimization is optimized based on the inertia weight, the random value and the iteration number to obtain the improved particle swarm optimization.
In an embodiment of the present disclosure, the obtaining the intrinsic cost, the operating cost, and the penalty cost based on the target hybrid energy storage system power, the hybrid energy storage capacity, and the cost coefficient, and then constructing the objective function includes: the hybrid energy storage comprises storage battery energy storage and super capacitor energy storage; obtaining an intrinsic cost based on the battery capacity, the supercapacitor capacity, and corresponding cost coefficients of the battery and the supercapacitor; obtaining an operating cost based on the operating cost of deep charging and discharging of the storage battery and the operating cost of excessive charging and discharging of the storage battery; acquiring the corresponding shortage cost and the wind abandoning cost of the storage battery and the super capacitor based on the power of the target hybrid energy storage system, and further acquiring the punishment cost; and constructing an objective function by using the minimum value of the sum of the inherent cost, the operation cost and the penalty cost.
In one embodiment of the present disclosure, the constructing constraints based on the load deficit rate and the stored energy of the hybrid energy storage includes: the constraint conditions comprise a load power shortage constraint condition and an energy constraint condition; acquiring a load power shortage rate based on the electric quantity of the power plant, the load electric quantity and the power conversion efficiency, and further acquiring a load power shortage rate constraint condition; and obtaining an energy constraint condition based on the corresponding residual stored energy and rated stored energy of the storage battery and the super capacitor.
According to an embodiment of the second aspect of the present disclosure, there is also provided a hybrid energy storage capacity optimization system based on improved variational modal decomposition, including:
the decomposition module is used for obtaining the power of a hybrid energy storage system configured on the side of a power plant, decomposing the power of the hybrid energy storage system by adopting an improved variational modal decomposition algorithm to obtain a preset number of subsequences, wherein the improved variational modal decomposition algorithm is obtained by optimizing the variational modal decomposition algorithm by utilizing a correlation coefficient and an average Pearson correlation coefficient;
the reconstruction module is used for reconstructing the subsequences with the preset number according to different frequency intervals to obtain the power of the target hybrid energy storage system;
the processing module is used for obtaining inherent cost, operation cost and punishment cost based on the power of the target hybrid energy storage system, the hybrid energy storage capacity and the cost coefficient, further constructing an objective function, and constructing constraint conditions based on the load shortage rate and the energy storage of the hybrid energy storage;
and the control module is used for solving the objective function by utilizing an improved particle swarm algorithm when the constraint condition is met to obtain the optimal hybrid energy storage capacity, and controlling the capacity of the hybrid energy storage based on the optimal hybrid energy storage capacity.
In an embodiment of the present disclosure, the decomposition module is specifically configured to: obtaining a first quantity parameter by adopting a correlation coefficient method; acquiring a second numerical parameter by adopting a Pearson correlation coefficient method; and calculating the average value of the first quantity parameter and the second quantity parameter, and taking the average value as the subsequence quantity of the variation modal decomposition algorithm, thereby obtaining the improved variation modal decomposition algorithm.
According to a third aspect of the present disclosure, there is also provided an electronic device, including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are 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 modal decomposition as set forth in the embodiments of the first aspect of the present disclosure.
In one or more embodiments of the disclosure, the power of a hybrid energy storage system configured at a power plant side is obtained, the power of the hybrid energy storage system is decomposed by adopting an improved variational modal decomposition algorithm to obtain a preset number of subsequences, wherein the improved variational modal decomposition algorithm is obtained by optimizing the variational modal decomposition algorithm by utilizing a correlation coefficient and an average pearson correlation coefficient; obtaining the power of a target hybrid energy storage system aiming at a preset number of subsequences; constructing an objective function based on the power of a target hybrid energy storage system, the hybrid energy storage capacity and the cost coefficient, and constructing a constraint condition based on the load power shortage rate and the energy storage of the hybrid energy storage; when the constraint condition is met, the objective function is solved by using the improved particle swarm 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. Under the condition, the improved variational modal decomposition algorithm is obtained by optimizing the variational modal decomposition algorithm by utilizing the correlation coefficient and the average Pearson correlation coefficient, the power of the hybrid energy storage system is decomposed by adopting the improved variational modal decomposition algorithm to obtain a preset number of subsequences, and then the subsequences are reconstructed to obtain the power of the target hybrid energy storage system, so that the constructed target function can be more accurate, and the optimal hybrid energy storage capacity obtained when the target function is solved by utilizing the improved particle swarm algorithm is better, therefore, the hybrid energy storage cost can be better reduced, and the capacity configuration can be better carried out.
Additional aspects and advantages of the 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 the disclosure.
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The above and/or additional aspects and advantages of the present disclosure will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
fig. 1 illustrates a flow chart of a hybrid energy storage capacity optimization method based on improved variational modal decomposition according to an embodiment of the present disclosure;
FIG. 2 illustrates a schematic diagram of a wind-storage combined power generation system architecture provided by an embodiment of the present disclosure;
FIG. 3 is a diagram illustrating an optimization result provided by an embodiment of the present disclosure using an improved particle swarm algorithm;
fig. 4 is a block diagram illustrating a hybrid energy storage capacity optimization system based on improved variational modal decomposition according to an embodiment of the present disclosure;
fig. 5 is a block diagram of an electronic device for implementing a hybrid energy storage capacity optimization method based on improved variational modal decomposition according to an embodiment of the present disclosure.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with embodiments of the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the disclosed embodiments, as detailed in the appended claims.
In the description of the present specification, reference to the description of "one embodiment," "some embodiments," "an example," "a specific example," or "some examples" or the like means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present disclosure. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present disclosure, "a plurality" means at least two, e.g., two, three, etc., unless explicitly specifically limited otherwise. It should also be understood that the term "and/or" as used in this disclosure refers to and encompasses any and all possible combinations of one or more of the associated listed items.
Reference will now be made in detail to the embodiments of the present disclosure, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the drawings are exemplary and intended to be illustrative of the present disclosure, and should not be construed as limiting the present disclosure.
The disclosure provides a hybrid energy storage capacity optimization method and system based on improved variational modal decomposition, and mainly aims to better reduce the hybrid energy storage cost and better configure the capacity.
In a first embodiment, fig. 1 shows a flow diagram of a hybrid energy storage capacity optimization method based on improved variational modal decomposition according to an embodiment of the present disclosure. Specifically, as shown in fig. 1, the hybrid energy storage capacity optimization method based on improved variational modal decomposition includes:
and S11, acquiring the power of a hybrid energy storage system configured on the side of the power plant, decomposing the power of the hybrid energy storage system by adopting an improved variational modal decomposition algorithm to obtain a preset number of subsequences, wherein the improved variational modal decomposition algorithm is obtained by optimizing the variational modal decomposition algorithm by utilizing the correlation coefficient and the average Pearson correlation coefficient.
In step S11, the power plant is a wind-storage combined power generation system. The wind-storage combined power generation system is characterized in that a hybrid energy storage system is configured on the side of a wind power plant, and the wind power fluctuation of the wind-storage combined power generation system meets the national grid-connection standard.
Fig. 2 shows a schematic diagram of a wind-storage combined power generation system structure provided by the embodiment of the disclosure. As shown in fig. 2, the wind-storage combined power generation system includes a wind turbine set for generating power and a hybrid energy storage system. The hybrid energy storage system comprises a super capacitor and a storage battery. When the hybrid energy storage system stores energy, electric energy generated by the wind turbine generator set can enter the hybrid energy storage system through a rectifier (AC/DC) and a bidirectional direct current converter (bidirectional DC/DC), when the hybrid energy storage system discharges, the electric energy of the hybrid energy storage system enters a power grid through the bidirectional DC/DC, then the electric energy supplies power to an alternating current load through an inverter (DC/AC), and supplies power to a direct current load through the direct current converter (DC/DC). As shown in fig. 2, the wind-storage combined power generation system further includes a photovoltaic array for generating power, and the electric energy generated by the photovoltaic array can enter the hybrid energy storage system via a rectifier (AC/DC) and a bidirectional direct current converter (bidirectional DC/DC).
In some embodiments, in order to avoid insufficient or excessive stabilization of the wind power, the wind power is subjected to moving average filtering, so that the fluctuation of the wind power grid-connected power is controlled within a certain range, and thus smoother wind power grid-connected power and charge-discharge power of the hybrid energy storage system are obtained. And the charging and discharging power of the hybrid energy storage system is the power of the hybrid energy storage system. The calculation formula of the moving average filtering satisfies:
in the formula (1), x (t) is the power of the hybrid energy storage system at the moment t, P HISS (t) is the charging and discharging power of the hybrid energy storage system at the moment t, P HISS When (t) is positive, the charging of the hybrid energy storage system is indicated, and P HISS (t) indicates that the hybrid energy storage system is discharging when the value is negative; p W (t) the output of the wind power generation system at the moment t; p out And (t) is the wind power grid-connected power at the moment t. P E (t) is the energy type energy storage charging and discharging power at the time t; p P And (t) is power type energy storage charging and discharging power at the time t.
In step S11, the hybrid energy storage system power is decomposed by using an improved variational modal decomposition algorithm to obtain a preset number of subsequences, wherein the improved variational modal decomposition algorithm is obtained by optimizing the variational modal decomposition algorithm using the correlation coefficient and the average pearson correlation coefficient.
First, a process of decomposing the power of the hybrid energy storage system by using a Variational Mode Decomposition (VMD) is described, where the process is as follows:
performing VMD decomposition on the power of the hybrid energy storage system to obtain a plurality of subsequences (namely a plurality of modes), wherein the kth subsequence can use a symbol u k Expressing that the number of subsequences is K, and each subsequence is a limited bandwidth with different center frequency; and with the minimum value of the estimated bandwidth sum of each subsequence as a target and the sum of the subsequences equal to the input power of the hybrid energy storage system as a constraint condition, the construction formula of the variation problem is as follows:
in the formula (2), ω is k Representing the center frequency of the k-th sub-sequence, t represents u k (t) partial derivatives of t, δ (t) unit impulse function, j imaginary number, t time, u k (t) denotes the kth subsequence (i.e. modal component) at time t, e denotes a mathematical constant, f denotes the hybrid energy storage system power, s.t. tableThe display is limited. Wherein { u k Is the set of all subsequences, { u } k }＝{u 1 ,u 2 ,…u K }，{ω k Is the set of center frequencies of all subsequences, { ω k }＝{ω 1 ,ω 2 ,…,ω K }。
Introducing an augmented Lagrange function, and converting the constrained variable problem into an unconstrained variable problem, wherein the expression is as follows:
in the formula (3), L represents a Lagrangian function, lambda (t) is a Lagrangian multiplier at the t moment, alpha is a penalty factor, | | · | torry 2 Represents the square of the l1 norm; i | · | live through 2 2 Represents the square of the l2 norm; alternately updating omega by adopting multiplier alternate direction method k n+1 、u k n+1 And λ n+1 Searching a 'saddle point' of the formula (2); wherein u is k n+1 The expression of (a) is:
in the formula (4), ω k ＝ω k n+1 ,∑ i u i (t) is equivalent to ∑ i≠k u i (t) n+1 。u i (t) denotes the modal component u k (t) a single-sided spectrum corresponding to the analytic signal obtained by Hilbert transform, f (t) representing K u k (t) the sum of the values.
Converting the formula (3) into a frequency domain based on Parseval/Plancherel Fourier equidistant transformation to obtain frequency domain update of each subsequence; and then, converting the value problem of the central frequency into a frequency domain to obtain the update of the central frequency, and updating a Lagrange multiplier lambda, wherein the specific expression is as follows:
in the formulas (5), (6) and (7), n represents the number of updates, ω represents the frequency,
the kth subsequence representing the (n + 1) th update ω frequency, f (ω) represents the hybrid energy storage system power in the frequency domain,
denotes u i (t) the corresponding mode in the frequency domain,
lagrange multiplier, ω, representing ω frequency k n+1 Indicating the center frequency of the (n + 1) th update,
the k-th sub-sequence representing the omega frequency,
representing the Lagrange multiplier of the (n + 1) th updated omega frequency, tau representing a coefficient to be determined, the coefficient to be determined tau generally taking 1.2, and setting an updated (i.e. iterative) judgment precision value, wherein the judgment precision value is represented by a symbol c, and c>0, judging whether the iteration stop condition is satisfied, namely judging
And if the iteration stop condition is met, stopping iteration. Thus, each subsequence and its center frequency and bandwidth are determined based on the optimal solution after iteration stops.
In this embodiment, considering that the VMD method continuously updates each modal component (i.e., subsequence) and its corresponding center frequency by using a multiplicative operator alternating direction method, and after decomposing the power of the hybrid energy storage system, each variable mode component and its center frequency are obtained. The number K of the sub-sequences needing modal decomposition is preset before the signal decomposition, and since the VMD method decomposes the signal into a plurality of modal components with limited bandwidth through adaptive decomposition, under-decomposition of the signal is caused by an excessively small value of K, and over-decomposition of the signal is caused by an excessively large value of K, the number K of the sub-sequences is optimized by using the correlation coefficient and the average Pearson correlation coefficient in the embodiment, so that the accuracy of the VMD decomposition is improved.
In this embodiment, optimizing the variational modal decomposition algorithm by using the correlation coefficient and the average pearson correlation coefficient is to optimize the number K of subsequences of the variational modal decomposition algorithm by using the correlation coefficient and the average pearson correlation coefficient. And the variation modal decomposition algorithm after optimizing the number K of the subsequences is an improved variation modal decomposition algorithm (CPVMD).
In some embodiments, optimizing the variational modal decomposition algorithm using the correlation coefficient and the average pearson correlation coefficient results in an improved variational modal decomposition algorithm comprising: obtaining a first quantity parameter by adopting a correlation coefficient method; acquiring a second numerical parameter by adopting a Pearson correlation coefficient method; and calculating the average value of the first quantity parameter and the second quantity parameter, and taking the average value as the subsequence quantity of the variation modal decomposition algorithm, thereby obtaining the improved variation modal decomposition algorithm.
In some embodiments, obtaining the first quantity parameter using a correlation coefficient method includes: carrying out variation modal decomposition on the power of the hybrid energy storage system based on a preset value, and calculating a correlation coefficient between a modal component obtained by decomposition and the power of the hybrid energy storage system before decomposition; and obtaining the correlation coefficient smaller than the preset threshold value as a target quantity parameter based on the correlation coefficient and the preset threshold value, and taking the minimum value in the target quantity parameter as a first quantity parameter. Wherein the first quantity parameter may be represented by the symbol K 1 And (4) showing.
Specifically, the variable modal decomposition is performed on the power of the hybrid energy storage system based on a preset value, and a correlation coefficient between a modal component obtained by decomposition and the power of the hybrid energy storage system before decomposition is calculated, wherein the correlation coefficient comprises:
determining a limit value for the first quantity parameter: according to experience, a plurality of first quantity parameters K are randomly selected 1 Analyzing 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 determining the boundary value of the first quantity parameter, for example, empirically, randomly selecting K 1 By analyzing the correlation coefficient between each modal component and the original signal, when K is equal to K, it can be seen that K is an initial value of 10, 11, 20, 24, or the like 1 When the correlation coefficient is more than or equal to 11, the correlation coefficient is very small, and the over-decomposition phenomenon of the signal is serious, so 10 is selected as a boundary value of the first quantity parameter;
calculating a correlation coefficient: performing VMD decomposition on the power of the hybrid energy storage system by using the boundary value as a preset value, performing correlation coefficient analysis on the obtained modal component and the original signal, and solving a correlation coefficient, such as K 1 And =10 VMD decomposition is performed on the power of the hybrid energy storage system, correlation coefficient analysis is performed on the obtained modal component and the original signal, and the correlation coefficient is solved.
Specifically, based on the correlation coefficient and a preset threshold, obtaining a correlation coefficient smaller than the preset threshold as a target quantity parameter, and using a minimum value in the target quantity parameter as a first quantity parameter, includes:
setting a preset threshold, wherein when the correlation coefficient of the modal component is greater than or equal to the preset threshold, the modal component is an effective modal component, and when the correlation coefficient of the modal component is less than the preset threshold, the modal component is an ineffective modal component;
selecting the minimum value from the correlation coefficients of the invalid modal components as the optimal K 1 Value based on optimal K 1 The values are VMD decomposed.
In some embodiments, obtaining the second quantity parameter using a pearson correlation coefficient method comprises: and iterating the number of subsequences of the variational modal decomposition algorithm by adopting an average Pearson correlation coefficient minimum principle, thereby obtaining a second number parameter. Wherein the second quantity parameterCan use the symbol K 2 And (4) showing.
Specifically, first, the pearson correlation coefficient calculation formula is:
in the formula (8), ρ (X, Y) is Pearson correlation coefficient, X and Y are two variables for calculating correlation, wherein X is each modal component, Y is original signal,
and
and the standard deviation is corresponding to X and Y.
The average pearson correlation coefficient for the decomposed signal using the adjacent bands is calculated as follows according to the number of modes:
in the formula (9), the reaction mixture is,
i is the respective modal component (IMF) after signal decomposition, which is the average pearson correlation coefficient. I is i Is the ith modal component, I i+1 Is the (i + 1) th modal component. Iteration is performed by using the equation (9), and when the average pearson correlation coefficient is minimum, a second quantity parameter is obtained.
In some embodiments, the average value of the first quantity parameter and the second quantity parameter is calculated in step S11, and the average value is used as the number of subsequences of the variational modal decomposition algorithm, so as to obtain the improved variational modal decomposition algorithm. The average value is the value of the optimized subsequence quantity, namely the subsequence quantity meets the following requirements: k = AVG (K) 1 ，K 2 ). Presetting the optimized subsequence number as an average value, namely, taking the average value as a preset number, and adopting an improved variable division mode for the power of the hybrid energy storage systemAnd decomposing by a state decomposition algorithm to obtain a preset number of subsequences.
And S12, reconstructing the subsequences with the preset number according to different frequency intervals to obtain the power of the target hybrid energy storage system.
Specifically, in step S12, the hybrid energy storage system power is decomposed into a plurality of subsequences with frequencies from low to high through the CPVMD, and the subsequences are divided into two frequency intervals, i.e. a high frequency portion and a low frequency portion, where 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, that is:
in equation (10), z represents the subsequence index at the low-frequency cut point, and s represents the number of subsequences in the total decomposition.
The process of dividing the high-frequency part and the low-frequency part is as follows:
for each subsequence, its entropy satisfies
Wherein H (u) k ) Entropy of information for the kth sub-sequence, P (u) k ) Is the kth sub-sequence energy.
The mutual entropy between two contiguous subsequence 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 ) Is the mutual information entropy between the kth sub-sequence and the (k + 1) th sub-sequence, H (u) k+1 ) Is the information entropy of the (k + 1) th subsequence, H (u) k ,u k+1 ) Is the joint information entropy of the kth sub-sequence and the (k + 1) th sub-sequence. Normalizing the mutual information entropy, selecting a minimum value point of the mutual information entropy as a boundary point of a high-frequency part and a low-frequency part based on the condition that the mutual information entropy between adjacent subsequences appears from large to small to large from low frequency to high frequency after the mutual information entropy is normalized, reconstructing to obtain power type energy storage charging and discharging power and energy type energy storage charging and discharging power, and further obtaining the energy storage charging and discharging powerAnd obtaining the reconstructed hybrid energy storage system power (namely the target hybrid energy storage system power).
And S13, obtaining inherent cost, running cost and punishment cost based on the power of the target hybrid energy storage system, the hybrid energy storage capacity and the cost coefficient, further constructing an objective function, and constructing constraint conditions based on the load shortage rate and the energy storage of the hybrid energy storage.
In some embodiments, the obtaining of the intrinsic cost, the operation cost, and the penalty cost in step S13 based on the target hybrid energy storage system power, the hybrid energy storage capacity, and the cost coefficient further constructs an objective function, including: the hybrid energy storage comprises storage battery energy storage and super capacitor energy storage; obtaining an intrinsic cost based on the battery capacity, the supercapacitor capacity, and corresponding cost coefficients of the battery and the supercapacitor; obtaining an operating cost based on the operating cost of deep charging and discharging of the storage battery and the operating cost of excessive charging and discharging of the storage battery; acquiring the corresponding shortage cost and the corresponding wind abandoning cost of a storage battery and a super capacitor based on the power of the target hybrid energy storage system, and further acquiring a punishment cost; and constructing an objective function by using the minimum value of the sum of the inherent cost, the operation cost and the penalty cost.
Specifically, the inherent cost refers to the initial investment and installation and construction cost of the hybrid energy storage system, and is in direct proportion to the capacity of the hybrid energy storage system, and the calculation formula of the inherent cost is as follows:
C i ＝a A S A+ a F S F
wherein, C i For inherent cost, a A And a F The intrinsic cost coefficients are respectively the initial investment and construction of the storage battery and the super capacitor, and can be set according to actual conditions; s A To the capacity of the battery, S F Is the supercapacitor capacity. Wherein the storage battery capacity S A And super capacitor capacity S F Is the optimization objective parameter.
The operation cost refers to the cost caused by the service life loss of the device due to deep charge and discharge and excessive charge and discharge of the hybrid energy storage system in the operation process. For supercapacitors, deep charge-discharge adds to the operating cost of the supercapacitor itself andthe influence of the service life is small, and the super capacitor can be charged and discharged according to rated power for a long time under an ideal condition, so that the operation cost of the super capacitor is not considered. The operation cost Cd of deep charging and discharging of the storage battery is as follows: cd = μ A m A In which μ A The deep charge and discharge running cost of the storage battery can be set according to the actual situation; m is A The number of deep charge and discharge times of the storage battery. The excessive charge-discharge operation cost Co of the storage battery is as follows: co = σ A n A Where σ is A The excessive charging and discharging operation cost of the storage battery is set according to the actual situation; n is A The number of overcharging and overcharging of the storage battery is set according to actual conditions. The running cost Cr is: cr = Cd + Co.
The penalty cost mainly comprises two parts: respectively the cost of the shortage and the cost of the abandoned wind. The shortage cost is the loss caused by the fact that the hybrid energy storage system cannot meet the expected target of continuously supplementing the wind power when discharging to the minimum charge state level; the wind abandon cost is the cost of wind abandon caused by the fact that wind power cannot be continuously absorbed when the hybrid energy storage system is charged to the maximum charge state level. Obtaining a punishment cost C based on the shortage cost and the abandoned wind cost p 。
C p ＝C A1 +C F1 +C A2 +C F2
Wherein, C A1 And C A2 Respectively the shortage cost and the wind abandoning cost of the storage battery; c F1 And C F2 Respectively the shortage cost and the wind abandoning cost of the super capacitor. τ 1 and β are the deficit cost coefficient and the curtailment cost coefficient, respectively. The g (·) function is a positive function, and when the variable is larger than zero, the function value is a variable value; and when the variable is less than or equal to zero, the function value is zero. Delta P L And Δ P H Respectively a low-frequency offset component and a high-frequency offset component of wind power output power (namely the output of a wind power generation system). Δ t is the sampling time interval. S A V and S F And (upsilon) is the capacity of the storage battery and the super capacitor respectively under upsilon. S. the A (. Nu-1) and S F And (upsilon-1) are the capacities of the storage battery and the super capacitor under upsilon-1 respectively. The target hybrid energy storage system power comprises the maximum discharge power and the maximum charge power of the storage battery and the maximum discharge power and the maximum charge power of the super capacitor, wherein P is Admax And P Acmax Respectively the maximum discharge power and the maximum charge power of the storage battery; p Fdmax And P Fcmax The maximum discharge power and the maximum charge power of the supercapacitor respectively. N is the total number of samples (i.e. total duration), penalty cost C p The parameters can be set according to actual conditions.
In step S13, an objective function is constructed using the minimum of the sum of the intrinsic cost, the operating cost and the penalty cost. Namely, the objective function satisfies: minf L ＝C i +Cr+C p . Therefore, when the optimal solution of the objective function is obtained, namely the optimal hybrid energy storage capacity is obtained, the sum of economic cost converted by various influence factors during the operation of the system can be minimized, and the economy of the hybrid energy storage system is improved.
In some embodiments, the constructing the constraint condition based on the load power shortage and the energy storage of the hybrid energy storage in step S13 includes: the constraint conditions comprise a load power shortage constraint condition and an energy constraint condition; acquiring a load power shortage rate based on the electric quantity of the power plant, the load electric quantity and the power conversion efficiency, and further acquiring a load power shortage rate constraint condition; and obtaining an energy constraint condition based on the corresponding residual stored energy and rated stored energy of the storage battery and the super capacitor.
Specifically, the Load of Power Supply Probability (LPSP) is an important operation index of the wind-solar hybrid Power generation system, and the load shortage is defined as the Power shortage of the load and the total demand E of the load L The ratio of (a) to (b). Namely, the constraint conditions of the load power shortage rate are as follows:
wherein f is LPSP Q represents the time, Q represents the number of times, E represents the power shortage of the load LPS (q) is the load power shortage at time q, E L (q) is the load demand at time q.
Rate of lack of power f under load LPSP The specific calculation of (2) includes: let Δ E = (E) w (q)+E pv (q))η c -E L (q) wherein E w (q) is the wind energy electric quantity at the moment q, E pv (q) is the solar electric quantity at the moment q, the electric quantity of the power plant comprises the wind electric quantity and the solar electric quantity, E L And (q) is the load electric quantity at the moment q. Eta c Is the power conversion efficiency of the inverter. When the wind-solar hybrid power generation quantity meets the load requirement, namely delta E is larger than 0, the load is lack of power E LPS =0, hybrid energy storage system charging; when the wind-solar hybrid power generation amount is insufficient, namely delta E<When the power is 0, the hybrid energy storage system discharges to supplement the shortage of the power supply power, and at the time, let Δ E = - Δ E, that is: e LPS ＝E L (q)-(E w (q)+E pv (q))η c And calculating the load power shortage under different conditions based on the comparison of the delta E, the rated energy storage and the minimum residual energy storage of the storage battery and the maximum energy storage and the minimum residual energy storage of the super capacitor bank, and further calculating to obtain the load power shortage rate.
Wherein the storage battery has a rated energy storage capacity of E bn (unit MWh), minimum remaining energy storage E bmin (units MWh), i.e.:
E bn ＝N B C B U B /10 6
E bmin ＝N B C B U B ·(1-DOD)/10 6
wherein U is B Represents the rated voltage (in V) of the battery; c B Represents the rated capacitance (in Ah); DOD represents the maximum depth of discharge. N is a radical of hydrogen B The number of the storage batteries is shown. Under actual operation, the super capacitor needs to work in a proper voltage range, which is marked as U cmin ～U cmax Maximum energy storage E of the supercapacitor bank cmax And minimum residual energy storage E cmin Respectively as follows:
wherein U is cmax Is the maximum terminal voltage of the supercapacitor; u shape cmin Is the minimum terminal voltage of the supercapacitor; cc denotes a capacitance value. Nc represents the number of supercapacitors.
Specifically, in step S13, the energy constraint condition is satisfied:
wherein E cn For the rated stored energy of the supercapacitor, E c (s) is the remaining stored energy of the supercapacitor; e b (s) residual energy of battery, E b And(s) is less than or equal to mu delta E, wherein mu is the proportion of the stored energy born by the storage battery in delta E.
And S14, when the constraint conditions are met, solving the objective function by using an improved particle swarm optimization algorithm to obtain the optimal hybrid energy storage capacity, and controlling the capacity of the hybrid energy storage based on the optimal hybrid energy storage capacity.
In the embodiment, in consideration of the problem of capacity optimization configuration in the existing particle swarm optimization, although the convergence rate is high, local extreme points are easy to appear in iteration, and the constraint of the local extreme points is difficult to escape. The optimal position in the particle swarm algorithm is related to the particle velocity, and the velocity limitation causes that the search space of each iteration step is a limited area, so that the search range cannot be expanded to the whole feasible solution space, and the global optimal solution cannot be searched. Therefore, the present disclosure solves the objective function with an improved particle swarm algorithm to obtain a globally optimal solution.
It is easy to understand that the Particle Swarm Optimization (PSO) is a Swarm optimization algorithm, in which the Particle inside the search space transforms the position of the Particle itself according to the experience of the Particle itself and the experience of other particles, the PSO finally finds the best point by updating its position continuously and adopts a 'speed-displacement' search model, the PSO algorithm is a method for simulating bird predation, the individual of the PSO is called 'Particle' (Particle), distributed in the multi-dimensional search space, the change of the Particle in the search space is based on the successful experience of the individual in social psychology to imitate others. x is a radical of a fluorine atom i (t a ) Representing the position of the particle, the change of position of the particle being obtained by adding the velocity v of the particle to the current position i (t a ) To change the convergence performance of the basic PSO algorithm, an inertial weight ω is introduced into the velocity equation p (inert weight), then the standard PSO algorithm, the velocity formula is:
v i (t a )＝ω p v u (t a -1)+C 1 r 1 (x p,best -x i (t a )+C 2 r 2 (x p,best -x i (t a )
in the formula v i (t a ) Is at the t a Velocity of the particle, ω, at the number of sub-iterations p In order to be the inertial weight,C 1 is a first acceleration factor, C 2 Is a second acceleration factor, r 1 ，r 2 Represents a random number between (0, 1), x p,best Is at the t a Optimum position of particles, omega, at times of sub-iterations pmax ＝0.8，ω pmin =0.3, T is the current iteration number, and the inertia weight omega is continuously iterated p Becomes smaller, the velocity v of the particle i Becomes smaller, allowing more careful searching in the solution space, thereby improving the optimization performance.
Due to the particle swarm optimization, the first acceleration factor C 1 And a second acceleration factor C 2 The search result is also influenced greatly, and the acceleration factor is usually set to a fixed value in the prior art, such as C 1 ＝C 2 =0.647283, has certain limitations and is prone to local convergence, so in this embodiment, the acceleration factor in the particle swarm optimization is optimized based on the inertial weight, the random value and the iteration number, thereby obtaining an Improved Particle Swarm Optimization (IPSO). Optimized first acceleration factor C 1 And a second acceleration factor C 2 Satisfies the following conditions:
C 1 ＝ω p *rand(1)+T(C 1e -C 1s )/T max
wherein rand (1) represents [0,1 ]]A random value therebetween, tmax is the maximum number of iterations, C 1e Is a first acceleration factor C 1 End value of, C 2e Is a second acceleration factor C 2 The final value of (a); c 1s Is a first acceleration factor C 1 Initial value of (C) 2s Is the second acceleration factor C 2 The initial value of (a). In this case, a larger first acceleration factor C is used in the initial search 1 And a smaller second acceleration factor C 2 So that the particles can be freely dispersed into the search space, thereby increasing the diversity of the particles; as the number of iterations increases, a first acceleration factor C 1 Gradually smaller, second acceleration factor C 2 Gradually increase to accelerate its harvestingThe convergence speed is high, the cost of the whole life cycle of the hybrid energy storage system is reduced, and the convergence speed of the system reaching the optimal value is increased.
In step S14, the objective function is solved by using the improved particle swarm optimization algorithm to obtain the optimal hybrid energy storage capacity, and the hybrid energy storage capacity is controlled based on the optimal hybrid energy storage capacity. And the optimal hybrid energy storage capacity comprises the optimal storage battery energy storage capacity and the optimal super capacitor energy storage capacity.
In some embodiments, when the objective function is solved in step S14, the optimal economic cost can also be obtained.
In order to verify the hybrid energy storage capacity optimization method based on the improved variational modal decomposition, simulation verification is carried out. Wherein matlab is used for example analysis; the basic parameters of the storage battery and the super capacitor are shown in table 1.
TABLE 1 basic parameters of accumulators and supercapacitors
| Parameter name | Storage battery | Super capacitor |
| Rated voltage/V | 12 | 2.7 |
| Rated |
100/Ah | 3500/F |
| Efficiency of charging | 0.75 | 0.98 |
| Efficiency of discharge | 0.85 | 0.98 |
| Depth of discharge | 0.4 | / |
| Coefficient of performance | 0.1 | 0.01 |
| Maintenance factor | 0.02 | / |
| Cycle life/time | 1500 | 500000 |
| Monovalent per unit | 400 | 350 |
| Coefficient of treatment | 0.08 | 0.04 |
Determining the number of subsequences (namely K value) by using fusion correlation coefficient-average Pearson correlation coefficient, and determining the power P of the hybrid energy storage system HISS (t) performing CPVMD decomposition to calculate the mutual entropy between adjacent subsequences, as shown in Table 2. As can be seen from Table 2, a minimum point occurs between the subsequences U3 and U4, and therefore 3 is selected as a boundary point to reconstruct the primary power of the hybrid energy storage.
TABLE 2 mutual entropy between adjacent subsequences
| Adjacent subsequences | Normalized mutual information entropy beta |
| 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 |
And reconstructing the subsequences U1 and U2 into the primary charge and discharge power of the storage battery, and reconstructing the subsequent subsequences into the primary charge and discharge power of the super capacitor.
And solving by combining an improved particle swarm algorithm according to the objective function, the constraint condition and each parameter, and simulating in the matlab. When ω =0.8, the population size is 100 and the maximum number of iterations is 300. The simulation results are shown in fig. 3 and table 3. Fig. 3 shows a graph of an optimization result provided by an embodiment of the present disclosure using an improved particle swarm optimization algorithm. The abscissa of fig. 3 is the evolution times (i.e., iteration times), and the ordinate is the fitness of the particle swarm algorithm. And obtaining the optimal individual fitness along with the increase of the times of the evolution times.
TABLE 3 simulation results
| Optimizing parameters | Improved particle swarm optimization | Particle swarm algorithm |
| Storage battery/ | 40021 | 45622 |
| Super capacitor/ | 5591342 | 5533300 |
| LPSP | 0.0234 | 0.0227 |
| Minimum cost/dollar | 157642 | 172617 |
Comparing the particle swarm algorithm with the improved particle swarm algorithm, it can be seen that 45622 storage batteries and 5533300 super capacitors are needed by adopting the traditional particle swarm algorithm, the minimum life cycle cost is 172617 yuan at the moment, and the load power shortage rate is 0.0227. When the improved particle swarm algorithm is adopted, 40021 storage batteries and 5591342 supercapacitors need to be configured, the minimum life cycle cost is 157642 yuan at the moment, and the load power shortage rate is 0.0234. Therefore, when the improved particle swarm algorithm is adopted, the full life cycle cost is reduced by 8.68%, the number of the storage batteries needing to be configured is reduced by 12.28%, and the improved particle swarm algorithm has better optimizing capacity.
According to the simulation verification, the improved function inertia weight and the optimized acceleration factor in the method can be well applied to capacity optimization configuration of the wind-solar hybrid energy storage system; compared with most of the existing improved inertia weight particle swarm optimization algorithms, the improved function inertia weight and the optimized acceleration factor provided by the disclosure can further reduce the full life cycle cost of the hybrid energy storage system, and simultaneously improve the convergence rate of the system to the optimal value; the CPVMD decomposition well realizes the frequency band separation, effectively avoids mode aliasing and reduces the influence of adjacent frequency bands on the distribution of the primary power of the hybrid energy storage.
In the hybrid energy storage capacity optimization method based on improved variational modal decomposition of the embodiment of the disclosure, the power of a hybrid energy storage system configured at a power plant side is obtained, the power of the hybrid energy storage system is decomposed by adopting an improved variational modal decomposition algorithm to obtain a preset number of subsequences, wherein the improved variational modal decomposition algorithm is obtained by utilizing a correlation coefficient and an average Pearson correlation coefficient to optimize the variational modal decomposition algorithm; acquiring the power of a target hybrid energy storage system aiming at a preset number of subsequences; constructing an objective function based on the power of a target hybrid energy storage system, the hybrid energy storage capacity and the cost coefficient, and constructing a constraint condition based on the load power shortage rate and the energy storage of the hybrid energy storage; when the constraint condition is met, the objective function is solved by using the improved particle swarm 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. Under the condition, the improved variational modal decomposition algorithm is obtained by optimizing the variational modal decomposition algorithm by utilizing the correlation coefficient and the average Pearson correlation coefficient, the power of the hybrid energy storage system is decomposed by adopting the improved variational modal decomposition algorithm to obtain a preset number of subsequences, and then the subsequences are reconstructed to obtain the power of the target hybrid energy storage system, so that the constructed target function can be more accurate, the optimal hybrid energy storage capacity obtained when the target function is solved by utilizing the improved particle swarm optimization algorithm is better, and therefore, the hybrid energy storage cost can be better reduced and the capacity configuration can be better carried out. The method can improve the electric energy quality of the wind power generation and storage combined power generation system, reduce the fluctuation of the wind power generation grid-connected power, and improve the stability and the economy of the system operation, and is a CPVMD-IPSO-based hybrid energy storage capacity optimal configuration method for stabilizing the wind power fluctuation.
The following are embodiments of the disclosed system that may be used to perform embodiments of the disclosed method. For details not disclosed in the embodiments of the system of the present disclosure, refer to the embodiments of the method of the present disclosure.
Referring to fig. 4, fig. 4 shows a block diagram of a hybrid energy storage capacity optimization system based on improved variational modal decomposition according to an embodiment of the present disclosure. The hybrid energy storage capacity optimization system 10 based on improved variational modal decomposition comprises a decomposition module 11, a reconstruction module 12, a processing module 13 and a control module 14, wherein:
the decomposition module 11 is configured to obtain power of a hybrid energy storage system configured on a power plant side, decompose the power of the hybrid energy storage system by using an improved variational modal decomposition algorithm to obtain a preset number of subsequences, wherein the improved variational modal decomposition algorithm is obtained by optimizing the variational modal decomposition algorithm by using a correlation coefficient and an average pearson correlation coefficient;
the reconstruction module 12 is configured to reconstruct the preset number of subsequences according to different frequency intervals to obtain a target hybrid energy storage system power;
the processing module 13 is configured to obtain an inherent cost, an operation cost and a penalty cost based on the target hybrid energy storage system power, the hybrid energy storage capacity and the cost coefficient, further construct an objective function, and construct a constraint condition based on the load shortage rate and the energy storage of the hybrid energy storage;
and the control module 14 is configured to solve the objective function by using an improved particle swarm algorithm when the constraint condition is met, obtain an optimal hybrid energy storage capacity, and control the capacity of the hybrid energy storage based on the optimal hybrid energy storage capacity.
Optionally, the decomposition module 11 is specifically configured to: obtaining a first quantity parameter by adopting a correlation coefficient method; acquiring a second numerical parameter by adopting a Pearson correlation coefficient method; and calculating the average value of the first quantity parameter and the second quantity parameter, and taking the average value as the subsequence quantity of the variation modal decomposition algorithm, thereby obtaining the improved variation modal decomposition algorithm.
Optionally, the decomposition module 11 is specifically configured to: carrying out variation modal decomposition on the power of the hybrid energy storage system based on a preset value, and calculating a correlation coefficient between modal components obtained by decomposition and the power of the hybrid energy storage system before decomposition; and obtaining the correlation coefficient smaller than the preset threshold value as a target quantity parameter based on the correlation coefficient and the preset threshold value, and taking the minimum value in the target quantity parameter as a first quantity parameter.
Optionally, the decomposition module 11 is specifically configured to: and iterating the number of subsequences of the variational modal decomposition algorithm by adopting an average Pearson correlation coefficient minimum principle, thereby obtaining a second numerical parameter.
Optionally, the processing module 13 is specifically configured to: the hybrid energy storage comprises storage battery energy storage and super capacitor energy storage; obtaining an intrinsic cost based on the battery capacity, the supercapacitor capacity, and corresponding cost coefficients for the battery and the supercapacitor; obtaining an operating cost based on the operating cost of deep charging and discharging of the storage battery and the operating cost of excessive charging and discharging of the storage battery; acquiring the corresponding shortage cost and the wind abandoning cost of the storage battery and the super capacitor based on the power of the target hybrid energy storage system, and further acquiring the punishment cost; and constructing an objective function by using the minimum value of the sum of the inherent cost, the operation cost and the penalty cost.
Optionally, the processing module 13 is specifically configured to: the constraint conditions comprise a load power shortage constraint condition and an energy constraint condition; acquiring a load power shortage rate based on the electric quantity of the power plant, the load electric quantity and the power conversion efficiency, and further acquiring a load power shortage rate constraint condition; and obtaining an energy constraint condition based on the corresponding residual stored energy and rated stored energy of the storage battery and the super capacitor.
Optionally, the acceleration factor in the particle swarm algorithm is optimized based on the inertial weight, the random value and the iteration number, so as to obtain an improved particle swarm algorithm.
It should be noted that the foregoing explanation of the embodiment of the hybrid energy storage capacity optimization method based on improved variational modal decomposition is also applicable to the hybrid energy storage capacity optimization system based on improved variational modal decomposition of this embodiment, and is not repeated here.
In the hybrid energy storage capacity optimization system based on improved variational modal decomposition of the embodiment of the disclosure, a decomposition module obtains the power of a hybrid energy storage system configured at the side of a power plant, and decomposes the power of the hybrid energy storage system by adopting an improved variational modal decomposition algorithm to obtain a preset number of subsequences, wherein the improved variational modal decomposition algorithm is obtained by utilizing a correlation coefficient and an average Pearson correlation coefficient to optimize the variational modal decomposition algorithm; the reconstruction module reconstructs the subsequences with preset number according to different frequency intervals to obtain the power of the target hybrid energy storage system; the processing module obtains inherent cost, running cost and punishment cost based on the power of the target hybrid energy storage system, the hybrid energy storage capacity and the cost coefficient, further constructs a target function, and constructs constraint conditions based on the load power shortage rate and the energy storage capacity of the hybrid energy storage; and when the constraint conditions are met, the control module solves the objective function by using the improved particle swarm 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. Under the condition, the improved variational modal decomposition algorithm is obtained by optimizing the variational modal decomposition algorithm by utilizing the correlation coefficient and the average Pearson correlation coefficient, the power of the hybrid energy storage system is decomposed by adopting the improved variational modal decomposition algorithm to obtain a preset number of subsequences, and then the subsequences are reconstructed to obtain the power of the target hybrid energy storage system, so that the constructed target function can be more accurate, and the optimal hybrid energy storage capacity obtained when the target function is solved by utilizing the improved particle swarm algorithm is better, therefore, the hybrid energy storage cost can be better reduced, and the capacity configuration can be better carried out. The system disclosed by the invention can improve the electric energy quality of the wind power generation and storage combined power generation system, reduce the fluctuation of the wind power generation grid-connected power, and improve the stability and economy of the system operation, and is a CPVMD-IPSO-based hybrid energy storage capacity optimal configuration system for stabilizing the wind power fluctuation.
The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.
Fig. 5 is a block diagram of an electronic device for implementing a hybrid energy storage capacity optimization method based on improved variational modal decomposition according to an embodiment of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable electronic devices, and other similar computing devices. The components shown in this disclosure, the connections and relationships of the components, and the functions of the components, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed in this disclosure.
As shown in fig. 5, the electronic device 20 includes a computing unit 21 that can perform various appropriate actions and processes according to 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. In the RAM 23, various programs and data necessary for the operation of the electronic apparatus 20 can also be stored. The calculation unit 21, the ROM 22, and the RAM 23 are connected to each other via a bus 24. An input/output (I/O) interface 25 is also connected to bus 24.
A number of components in the electronic device 20 are connected to the I/O interface 25, including: an input unit 26 such as a keyboard, a mouse, or the like; an output unit 27 such as various types of displays, speakers, and the like; a storage unit 28, such as a magnetic disk, an optical disk, etc., the storage unit 28 being communicatively connected to the computing unit 21; and a communication unit 29 such as a network card, modem, wireless communication transceiver, etc. The communication unit 29 allows the electronic device 20 to exchange information/data with other electronic devices via a computer network such as the internet and/or various telecommunication networks.
The computing unit 21 may be a variety of general and/or special purpose processing components having 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 dedicated Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. The calculation unit 21 performs the various methods and processes described above, for example, performs a hybrid energy storage capacity optimization method based on an improved variational modal decomposition. For example, in some embodiments, the hybrid energy storage capacity optimization method based on improved variational modal decomposition may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 28. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 20 via the ROM 22 and/or the communication unit 29. When the computer program is loaded into the RAM 23 and executed by the computing unit 21, one or more steps of the above described method of hybrid energy storage capacity optimization based on improved variational modal decomposition may be performed. Alternatively, in other embodiments, the 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 modal decomposition.
Various implementations of the systems and techniques described above in this disclosure may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic electronic (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the present disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or electronic device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or electronic device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a compact disc read-only memory (CD-ROM), an optical storage electronic device, a magnetic storage electronic device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a 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 acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), the internet, and blockchain networks.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The Server can be a cloud Server, also called a cloud computing Server or a cloud host, and is a host product in a cloud computing service system, so as to solve the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service ("Virtual Private Server", or simply "VPS"). The server may also be a server of a distributed system, or a server incorporating a blockchain.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel or sequentially or in different orders, and the present disclosure is not limited thereto as long as the desired results of the technical solutions of the present disclosure can be achieved.
The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.
Claims (10)
1. A hybrid energy storage capacity optimization method based on improved variational modal decomposition is characterized by comprising the following steps:
acquiring power of a hybrid energy storage system configured at a power plant side, decomposing the power of the hybrid energy storage system by adopting an improved variational modal decomposition algorithm to obtain a preset number of subsequences, wherein the improved variational modal decomposition algorithm is obtained by optimizing the variational modal decomposition algorithm by utilizing a correlation coefficient and an average Pearson correlation coefficient;
reconstructing the subsequences of the preset number according to different frequency intervals to obtain the power of the target hybrid energy storage system;
obtaining inherent cost, running cost and punishment cost based on the power of the target hybrid energy storage system, the hybrid energy storage capacity and the cost coefficient, further constructing a target function, and constructing constraint conditions based on the load power shortage rate and the energy storage capacity of the hybrid energy storage;
and when the constraint condition is met, solving the objective function by using an improved particle swarm algorithm to obtain the optimal hybrid energy storage capacity, and controlling the capacity of the hybrid energy storage based on the optimal hybrid energy storage capacity.
2. The method for optimizing hybrid energy storage capacity based on improved variational modal decomposition according to claim 1, wherein the optimizing the variational modal decomposition algorithm using correlation coefficients and average pearson correlation coefficients to obtain the improved variational modal decomposition algorithm comprises:
obtaining a first quantity parameter by adopting a correlation coefficient method;
acquiring a second numerical parameter by adopting a Pearson correlation coefficient method;
and calculating the average value of the first quantity parameter and the second quantity parameter, and taking the average value as the subsequence number of the variation modal decomposition algorithm, thereby obtaining the improved variation modal decomposition algorithm.
3. The method for optimizing the hybrid energy storage capacity based on the improved variational modal decomposition according to claim 2, wherein the obtaining the first quantity parameter by using a correlation coefficient method comprises:
carrying out variation modal decomposition on the power of the hybrid energy storage system based on a preset value, and calculating a correlation coefficient between modal components obtained by decomposition and the power of the hybrid energy storage system before decomposition;
and obtaining the correlation coefficient smaller than a preset threshold value as a target quantity parameter based on the correlation coefficient and the preset threshold value, and taking the minimum value in the target quantity parameter as a first quantity parameter.
4. The method for optimizing the hybrid energy storage capacity based on the improved variational modal decomposition according to claim 2 or 3, wherein the obtaining the second numerical parameter by using the Pearson's correlation coefficient method comprises:
and iterating the number of subsequences of the variational modal decomposition algorithm by adopting an average Pearson correlation coefficient minimum principle, thereby obtaining a second numerical parameter.
5. The method of claim 4, wherein the acceleration factors in the particle swarm algorithm are optimized based on the inertial weight, the random value and the iteration number to obtain the improved particle swarm algorithm.
6. The hybrid energy storage capacity optimization method based on the improved variational modal decomposition according to claim 1 or 5, wherein the inherent cost, the operation cost and the penalty cost are obtained based on the target hybrid energy storage system power, the hybrid energy storage capacity and the cost coefficient, and an objective function is further constructed, and the method comprises the following steps:
the hybrid energy storage comprises storage battery energy storage and super capacitor energy storage;
obtaining an intrinsic cost based on the battery capacity, the supercapacitor capacity, and corresponding cost coefficients of the battery and the supercapacitor;
obtaining an operating cost based on the operating cost of deep charging and discharging of the storage battery and the operating cost of excessive charging and discharging of the storage battery;
acquiring the corresponding shortage cost and the corresponding wind abandoning cost of a storage battery and a super capacitor based on the power of the target hybrid energy storage system, and further acquiring a punishment cost;
and constructing an objective function by using the minimum value of the sum of the inherent cost, the operation cost and the penalty cost.
7. The method for optimizing the hybrid energy storage capacity based on the improved variational modal decomposition according to claim 6, wherein the constructing constraints based on the load deficit rate and the energy storage of the hybrid energy storage comprise:
the constraint conditions comprise a load power shortage constraint condition and an energy constraint condition;
acquiring a load power shortage rate based on the electric quantity of the power plant, the load electric quantity and the power conversion efficiency, and further acquiring a load power shortage rate constraint condition;
and obtaining an energy constraint condition based on the corresponding residual stored energy and rated stored energy of the storage battery and the super capacitor.
8. A hybrid energy storage capacity optimization system based on improved variational modal decomposition, comprising:
the decomposition module is used for obtaining the power of a hybrid energy storage system configured on the side of a power plant, decomposing the power of the hybrid energy storage system by adopting an improved variational modal decomposition algorithm to obtain a preset number of subsequences, wherein the improved variational modal decomposition algorithm is obtained by optimizing the variational modal decomposition algorithm by utilizing a correlation coefficient and an average Pearson correlation coefficient;
the reconstruction module is used for reconstructing the subsequences with the preset number according to different frequency intervals to obtain the power of the target hybrid energy storage system;
the processing module is used for obtaining inherent cost, operation cost and punishment cost based on the power of the target hybrid energy storage system, the hybrid energy storage capacity and the cost coefficient, further constructing an objective function, and constructing constraint conditions based on the load shortage rate and the energy storage of the hybrid energy storage;
and the control module is used for solving the objective function by utilizing the improved particle swarm optimization when the constraint condition is met to obtain the optimal hybrid energy storage capacity, and controlling the capacity of the hybrid energy storage based on the optimal hybrid energy storage capacity.
9. The system of claim 7, wherein the decomposition module is specifically configured to:
obtaining a first quantity parameter by adopting a correlation coefficient method;
acquiring a second numerical parameter by adopting a Pearson correlation coefficient method;
and calculating the average value of the first quantity parameter and the second quantity parameter, and taking the average value as the subsequence quantity of the variation modal decomposition algorithm, thereby obtaining the improved variation modal decomposition algorithm.
10. An electronic device, comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of improving hybrid energy storage capacity optimization based on variational modal decomposition of claims 1-7.
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