A multi-stage decomposition method for optimizing hybrid energy storage capacity

Abstract

The multi-stage decomposition hybrid energy storage capacity optimization method, device and storage medium provided by the application comprise the following steps: decomposing an obtained power signal into a target high-frequency component and a target low-frequency component; determining the rated power of a hybrid energy storage system based on the target high-frequency component and the target low-frequency component; establishing a hybrid energy storage system capacity optimization configuration model based on the rated power of the hybrid energy storage system; solving the hybrid energy storage system capacity optimization configuration model by using multi-stage decomposition combined with a multi-dimensional improved particle swarm optimization algorithm to obtain a hybrid energy storage system capacity optimization configuration result. Thus, the optimization ability and speed of the particle swarm algorithm are improved, the individual extreme point and the global optimal point of the particle are updated through a mutation factor, the search ability of the particle is further enhanced, and the target function of the annual comprehensive cost is optimized by using the method.

Description

Technical Field

[0001] This application relates to the field of energy storage capacity optimization technology, and in particular to a multi-level decomposition hybrid energy storage capacity optimization method, apparatus and storage medium. Background Technology

[0002] Introducing a hybrid energy storage system (HESS) into the power output of wind and solar power generation utilizes power-type supercapacitors and energy-type batteries to smooth out fluctuations in wind and solar power output, thereby improving the output power capacity of energy storage and optimizing the charging and discharging process of batteries. Optimizing the capacity configuration of the hybrid energy storage system is crucial.

[0003] In existing technologies, the standard PSO (Particle Swarm Optimization) algorithm is used to solve the capacity optimization configuration problem. However, the iteration of the particle optimization algorithm is prone to local optima, thus becoming constrained by them. Furthermore, in related technologies, the optimal position in the PSO algorithm is related to the particle velocity; the limitation of velocity means that the search space for each iteration step is a finite region, preventing the search range from expanding to the entire feasible solution space and failing to guarantee finding the globally optimal solution. Summary of the Invention

[0004] This application provides a multi-stage decomposition method for optimizing hybrid energy storage capacity to solve the technical problems mentioned above in the related technologies.

[0005] The first aspect of this application proposes a multi-level decomposition method for optimizing hybrid energy storage capacity, the method comprising:

[0006] The acquired power signal is decomposed into the target high-frequency component and the target low-frequency component;

[0007] The rated power of the hybrid energy storage system is determined based on the target high-frequency component and the target low-frequency component.

[0008] Based on the rated power of the hybrid energy storage system, a capacity optimization configuration model for the hybrid energy storage system is established.

[0009] The capacity optimization configuration model of the hybrid energy storage system is solved by using a multi-level decomposition combined with a multi-dimensional improved particle swarm optimization algorithm, and the capacity optimization configuration result of the hybrid energy storage system is obtained.

[0010] Optionally, the step of decomposing the acquired power signal into a target high-frequency component and a target low-frequency component includes:

[0011] The acquired power signal is decomposed into multiple intrinsic mode functions using the ensemble empirical mode decomposition method. Among the multiple intrinsic mode functions, there are a first high-frequency intrinsic mode function, a second low-frequency intrinsic mode function, and an intrinsic mode function at the high-low frequency boundary.

[0012] Wavelet packet decomposition is used to decompose the intrinsic mode function of the high-low frequency boundary point into a second high frequency and a second low frequency.

[0013] The target high-frequency component and the target low-frequency component are determined based on the intrinsic mode function of the first high frequency, the intrinsic mode function of the first low frequency, the second high frequency, and the second low frequency.

[0014] Optionally, the rated power of the hybrid energy storage system includes the rated power of the supercapacitor and the rated power of the battery.

[0015] Optionally, establishing a capacity optimization configuration model for the hybrid energy storage system based on the rated power of the hybrid energy storage includes:

[0016] Establish an objective function that minimizes the annual comprehensive cost, where the objective function is:

[0017] min C j =min{C B +C C +C kp +C sp},

[0018] Among them, C B For battery investment costs; C C The investment cost of supercapacitors; C kp To maintain costs; C sp Cost of handling hybrid energy storage;

[0019] Based on the rated power of the supercapacitor and the rated power of the battery, several constraints are established for the capacity optimization configuration model of the hybrid energy storage system.

[0020] Optionally, the multiple constraints include hybrid energy storage system SOC constraints and power constraints;

[0021] The SOC constraint of the hybrid energy storage system is:

[0022]

[0023] Among them, SOC bat-min SOC bat-max SOC cap-min SOC cap-max These are the upper and lower limits of the state of charge of the battery and the upper and lower limits of the state of charge of the supercapacitor, respectively, and the power constraints.

[0024] The power constraint is:

[0025]

[0026] Among them, P b For storage batteries and P sc This refers to the rated power of the supercapacitor.

[0027] Optionally, the multi-level decomposition combined with multi-dimensional improved particle swarm optimization algorithm includes: improving the inertia weight function of the standard particle swarm optimization algorithm, optimizing the acceleration factor based on the inertia weight value, and introducing a mutation factor to obtain the multi-level decomposition combined with multi-dimensional improved particle swarm optimization algorithm.

[0028] Optionally, the inertia weight function of the standard particle swarm optimization algorithm is improved, and the improved inertia weight function is as follows:

[0029]

[0030] Where: ω is the weight, c represents the scaling factor, and i is the current iteration number of the particle, ω start ω is the initial weight value for the start of the iteration. end Here, T represents the weight value in the later stages of the iteration, and T represents the maximum number of iterations.

[0031] Optionally, the optimization of the acceleration factor based on the inertia weight value includes: at the beginning of the iteration, the particles search in a larger space to ensure that the acceleration factor C1 is large and the acceleration factor C2 is small, thereby accelerating the particle swarm search speed; in the later stage of the iteration, the centroid of the particle swarm is at the global optimal solution to ensure that the acceleration factor C1 is small and the acceleration factor C2 is large, thereby maintaining the ability of the particles to search precisely; the acceleration factor formula is as follows:

[0032]

[0033]

[0034] Where: C 1s and C 1e Let C1 represent the initial and final values, respectively. 2s and C 2e Let C1 and C2 represent the initial and final values, respectively.

[0035] Optionally, the variation factor includes: the individuals in the population are represented by a perturbation vector σ = (σ1, σ2, ..., σ...). n ) and a solution vector x = (x1, x2, ..., x n The perturbation vector is the control vector for the mutated solution vector, and the perturbation vector itself also needs to be mutated. If an individual (x,σ) is selected, the mutated new individual will be (x',σ').

[0036] σ i ′=σ i ×exp(C i-1 (0,0.5))

[0037] x i ′=x i +5×C i (0,σ i ′)

[0038] Where, x i Indicates the current position of the particle, x i '' represents the position after mutation, C is a random variable following a Gaussian or Cauchy distribution for categorical selection, i is the current iteration number of the particle, T represents the maximum iteration number, and σ i Perturbation vector.

[0039] A second aspect of this application provides a multi-stage decomposition hybrid energy storage capacity optimization device, the device comprising:

[0040] The decomposition module is used to decompose the acquired power signal into target high-frequency components and target low-frequency components;

[0041] The determination module is used to determine the rated power of the hybrid energy storage system based on the target high-frequency component and the target low-frequency component;

[0042] A module is established to create a capacity optimization configuration model for the hybrid energy storage system based on its rated power.

[0043] The solution module is used to solve the capacity optimization configuration model of the hybrid energy storage system using a multi-level decomposition combined with a multi-dimensional improved particle swarm optimization algorithm, and to obtain the capacity optimization configuration result of the hybrid energy storage system.

[0044] The technical solutions provided by the embodiments of this application bring at least the following beneficial effects:

[0045] The multi-level decomposition hybrid energy storage capacity optimization method, apparatus, and storage medium proposed in this application include decomposing the acquired power signal into target high-frequency components and target low-frequency components; determining the rated power of the hybrid energy storage system based on the target high-frequency components and target low-frequency components; establishing a capacity optimization configuration model for the hybrid energy storage system based on the rated power of the hybrid energy storage system; and solving the capacity optimization configuration model of the hybrid energy storage system using multi-level decomposition combined with a multi-dimensional improved particle swarm optimization algorithm to obtain the capacity optimization configuration result of the hybrid energy storage system. Therefore, this application can improve the inertia weight function of the particle swarm optimization algorithm and optimize the acceleration factor based on the inertia weight value, thereby improving the optimization capability and speed of the particle swarm algorithm. Simultaneously, a mutation factor is introduced to update the individual extreme points and global optimal points of particles, further enhancing the particle search capability, thus making the objective function for obtaining the annual comprehensive cost optimal through this method. Furthermore, in the embodiments of this disclosure, by performing multi-level decomposition on the acquired power signal, the problem of insufficient signal detail information capture and inadequate capture of information at critical frequencies in single-level decomposition is effectively solved.

[0046] Additional aspects and advantages of this application 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 application. Attached Figure Description

[0047] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0048] Figure 1 This is a flowchart illustrating a multi-level decomposition hybrid energy storage capacity optimization method according to an embodiment of this application;

[0049] Figure 2 A schematic diagram illustrating a dynamic change in inertia weight provided in an embodiment of this disclosure;

[0050] Figure 3 This is a schematic diagram of a multi-stage decomposition hybrid energy storage capacity optimization device provided according to an embodiment of this application. Detailed Implementation

[0051] The embodiments of this application are described in detail below. Examples of these embodiments are shown 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 application, and should not be construed as limiting this application.

[0052] The following describes, with reference to the accompanying drawings, a method and apparatus for optimizing the capacity of multi-stage hybrid energy storage based on the embodiments of this application.

[0053] Example 1

[0054] Figure 1 This is a flowchart illustrating a multi-stage decomposition method for optimizing hybrid energy storage capacity according to an embodiment of this application. Figure 1 As shown, the method may include:

[0055] Step 101: Decompose the acquired power signal into the target high-frequency component and the target low-frequency component.

[0056] In the embodiments of this disclosure, the method for decomposing the acquired power signal into a target high-frequency component and a target low-frequency component may include the following steps:

[0057] Step 1011: Decompose the obtained power signal into multiple intrinsic mode functions using the ensemble empirical mode decomposition method.

[0058] In the embodiments of this disclosure, the aforementioned plurality of intrinsic mode functions may include a first high-frequency intrinsic mode function, a first low-frequency intrinsic mode function, and an intrinsic mode function at the high-low frequency boundary.

[0059] Specifically, in the embodiments of this disclosure, the ensemble empirical mode decomposition method is used for decomposition, and the decomposition result is as follows:

[0060]

[0061] Where X(t) is the original power signal sequence at time t, h i (t) represents the i-th intrinsic mode function, i.e., the IMF component, and n is the number of decomposed modes. h is then used to construct... i Based on the graph of (t), the intrinsic mode function h located at the high-low frequency boundary is selected according to the macroscopic selection method. z (t).

[0062]

[0063] in, It is the sum of the high-frequency components of the first high-frequency intrinsic mode function after set empirical mode decomposition. It is the sum of the low-frequency components of the first low-frequency intrinsic mode function after set empirical mode decomposition.

[0064] Step 1012: Wavelet packet decomposition is used to decompose the intrinsic mode function of the high-low frequency boundary into the second high frequency and the second low frequency.

[0065] In the embodiments of this disclosure, the inherent mode function h at the high-low frequency boundary point is... z(t) represents whether the frequency cannot be decomposed into high frequency or low frequency using the ensemble empirical mode decomposition method. Based on this, wavelet packet decomposition can be used to decompose the intrinsic mode function of the high-low frequency boundary point into the second high frequency and the second low frequency.

[0066] Specifically, in the embodiments of this disclosure, for h z (t) Perform VMD (Variational Mode Decomposition) to obtain K subsequences u k (t), where each subsequence is a finite bandwidth with a different center frequency. Based on this, the objective of decomposing the K subsequences is to find the minimum value of the estimated bandwidth sum of each subsequence, with the constraint that the sum of the subsequences equals the input h. z (t), then the above variational problem is constructed as follows:

[0067]

[0068] Among them, {u k} = {u1, u2, ..., u K} represents the K modulo components after decomposition, {ω k}={ω1,ω2,…,ω K} represents the center frequency of each modal component. This indicates a constraint condition, namely that all modal components are the original input signal. δ(t) is the partial derivative of the function with respect to time t, and is the unit impulse function. j is the imaginary unit. The above K subsequences can be 8 subsequences.

[0069] Furthermore, in the embodiments of this disclosure, the VMD algorithm described above needs to comprehensively utilize the advantages of the quadratic penalty term and the Lagrange multiplier method to solve the aforementioned constrained optimization problem. Based on this, an augmented Lagrange function is introduced to transform the constrained variational problem into an unconstrained variational problem, the expression of which is as follows:

[0070]

[0071] Where α is the penalty factor and λ(t) is the Lagrange multiplier; the alternating direction method of multipliers is used for alternating updates. and λ n+1 Find the minimum point of the first formula; where, The expression is

[0072]

[0073] in, ∑ i u i (t) is equivalent to Σ i ui (t) n+1 .

[0074] Furthermore, in the embodiments of this disclosure, based on the Parseval / Plancherel Fourier isometric transform, the above equation is transformed to the frequency domain to obtain the frequency domain update of each subsequence. Specifically, in the embodiments of this disclosure, the problem of determining the value of the center frequency is transformed to the frequency domain to obtain the update of the center frequency, and λ is updated. The transformed expression is as follows:

[0075]

[0076]

[0077]

[0078] For a given decision precision e>0, if the iteration stopping condition is met, the iteration stops.

[0079] Furthermore, in embodiments of this disclosure, the intrinsic mode function h located at the high-low frequency boundary point... z (t) Through VMD decomposition, different subsequences with frequencies ranging from low to high can be obtained. Based on this, the subsequences can be divided into high-frequency and low-frequency parts.

[0080]

[0081] P 2 H (t) is the sum of the high-frequency components of the second high frequency, P 2 L (t) is the sum of the low-frequency components of the second high frequency.

[0082] Step 1013: Determine the target high-frequency component and the target low-frequency component based on the intrinsic mode function of the first high frequency, the intrinsic mode function of the first low frequency, the second high frequency, and the second low frequency.

[0083] Specifically, in the embodiments of this disclosure, the target high-frequency component and the target low-frequency component are determined based on the intrinsic mode function of the first high frequency, the intrinsic mode function of the first low frequency, and the second high frequency and the second low frequency as follows:

[0084]

[0085] In the embodiments of this disclosure, the above-mentioned P sc0 For the target high-frequency component, P b The target is the low-frequency component.

[0086] Furthermore, in the embodiments of this disclosure, the target high-frequency component and the target low-frequency component are obtained by performing multi-level decomposition on the acquired power signal as described above. Multi-level decomposition can solve the problem that traditional single-level decomposition methods cannot decompose signals with a large amount of detailed information well and cannot capture information at critical frequencies.

[0087] Step 102: Determine the rated power of the hybrid energy storage system based on the target high-frequency component and the target low-frequency component.

[0088] In some embodiments of this disclosure, the rated power of the hybrid energy storage system may include the rated power of the supercapacitor and the rated power of the battery.

[0089] Furthermore, in the embodiments of this disclosure, based on the sustainable operation of the hybrid energy storage system, the rated power of the energy storage element should not be less than the absolute value of its charging and discharging power at time t. And due to losses during the energy storage and release process of the hybrid energy storage system, and considering the energy storage efficiency issue, the rated power of the supercapacitor is:

[0090]

[0091] In the embodiments of this disclosure, P sc This refers to the rated power of the supercapacitor; P sc0 (t) represents the initial reference power signal of the supercapacitor at time t, η c η d These represent the charging and discharging efficiencies of the supercapacitor.

[0092] Furthermore, in the embodiments of this disclosure, based on the obtained power parameters of the supercapacitor's real-time charging and discharging, the cumulative capacity value relative to the initial capacity within one cycle t0 is calculated as follows:

[0093]

[0094]

[0095] Among them, E sc0 (t) represents the cumulative capacitance of the supercapacitor during this period, P sc1 (t) represents the energy storage and release power of the battery during this cycle. Considering the overcharging and over-discharging issues of the energy storage components, the maximum cumulative capacity, max{E}, is taken. sc0 (t)} and minimum value min{E sc0 (t)}, the rated capacitance E of the supercapacitor is calculated. sc for:

[0096]

[0097] Among them, SOC up SOC downThese are the upper and lower limits of the state of charge of the supercapacitor.

[0098] Furthermore, in embodiments of this disclosure, the rated power P of the battery is calculated using the HESS initial power signal. b Specifically, the rated power of the battery is:

[0099]

[0100] Among them, P b P is the rated power of the battery. b0 (t) represents the initial reference power signal of the battery at time t, η e η f These represent the charging and discharging efficiencies of the supercapacitor.

[0101] Furthermore, in embodiments of this disclosure, based on the obtained real-time charging and discharging power parameters of the battery, the cumulative capacity value relative to the initial capacity within one cycle t0 is calculated as follows:

[0102]

[0103]

[0104] Among them, E b0 (t) represents the cumulative capacitance of the supercapacitor during this period, P b1 (t) represents the energy storage and release power of the battery during this cycle. Considering the overcharging and over-discharging issues of the energy storage components, the maximum cumulative capacity, max{E}, is taken. b0 (t)} and minimum value min{E b0 (t)}, the rated capacitance E of the supercapacitor is calculated. b for:

[0105]

[0106] Among them, SOC b up SOC b down These are the upper and lower limits of the state of charge of the supercapacitor.

[0107] Step 103: Based on the rated power of the hybrid energy storage system, establish a capacity optimization configuration model for the hybrid energy storage system.

[0108] In one embodiment of this disclosure, the method for establishing a capacity optimization configuration model for a hybrid energy storage system based on the rated power of the hybrid energy storage may include: establishing an objective function with the goal of minimizing the annual comprehensive cost, and establishing multiple constraints applicable to the capacity optimization configuration model of the hybrid energy storage system based on the rated power of the supercapacitor and the rated power of the battery.

[0109] Specifically, in the embodiments of this disclosure, the objective function is:

[0110] min C j =min{C B +C C +C kp +C sp},

[0111] Among them, C B For battery investment costs; C C The investment cost of supercapacitors; C kp To maintain costs; C sp Cost of handling hybrid energy storage.

[0112] Furthermore, in the embodiments of this disclosure, the investment cost of the battery and the supercapacitor is related to the unit power cost and the capacity cost, and the investment costs of the battery and the supercapacitor are respectively:

[0113]

[0114]

[0115] Where, k BP k B,E k represents the unit power cost and unit capacity cost of the battery. C,P k C,E Z represents the unit power cost and unit capacity cost of the supercapacitor; z represents the discount rate; T represents... B T C These refer to the lifespan of the storage battery and the supercapacitor, respectively.

[0116] Furthermore, in the embodiments of this disclosure, the maintenance cost C kp With hybrid energy storage disposal cost C sp The ones are respectively:

[0117]

[0118] C k =N.(m b,t ·c b,t +m c,p ·c c,p )

[0119] C s =l bat,p ·P Bt +l bat,c ·C Bt +l cap,p ·P Bp +l cap,c ·C Cp

[0120] Where x is the interest rate, m b,t The unit price for battery maintenance is m. c，p The unit price for maintenance of supercapacitors, l bat，p l bat，c These are the unit price for power disposal and the unit price for capacity disposal of the battery, respectively. cap，p l cap，c These are the unit prices for power handling and capacity handling of supercapacitors, respectively.

[0121] Furthermore, in embodiments of this disclosure, the aforementioned constraints may include hybrid energy storage system SOC constraints and power constraints.

[0122] In the embodiments of this disclosure, the SOC constraint of the hybrid energy storage system is as follows:

[0123]

[0124] Among them, SOC bat-min SOC bat-max SOC cap-min SOC cap-max These are the upper and lower limits of the state of charge (SOC) for batteries and the upper and lower limits of the SOC for supercapacitors, respectively, representing power constraints.

[0125] Furthermore, in the embodiments of this disclosure, the power constraint described above is:

[0126]

[0127] Among them, P b For storage batteries and P sc This refers to the rated power of the supercapacitor.

[0128] Furthermore, in the embodiments of this disclosure, the SOC constraint of the hybrid energy storage system described above can prevent overcharging and over-discharging of the supercapacitor and the battery.

[0129] Step 104: Solve the capacity optimization configuration model of the hybrid energy storage system using a multi-level decomposition combined with a multi-dimensional improved particle swarm optimization algorithm to obtain the capacity optimization configuration result of the hybrid energy storage system.

[0130] In the embodiments of this disclosure, the particle swarm optimization algorithm is an iterative optimization tool with advantages such as simplicity, ease of implementation, robustness, high accuracy, and fast convergence, making it suitable for application in engineering practice. Furthermore, to improve the convergence performance of the basic particle swarm optimization algorithm, an inertia weight ω is introduced into the velocity evolution equation, resulting in the standard particle swarm optimization algorithm, whose velocity update formula is modified as follows:

[0131]

[0132] in: ω represents the weight; c1 and c2 are acceleration factors; r1 and r2 represent random numbers distributed in the interval [0,1]; i and T are the current iteration number and the maximum iteration number, respectively. The particle velocity; This represents the particle's current position. ω represents the individual extreme value and the group extreme value, respectively. When ω is set to decrease linearly, the approximate location of the optimal solution can be determined relatively quickly at the beginning of the search. As ω gradually decreases, the particle speed also slows down, which is beneficial for fine-grained local search and improves the performance of the algorithm.

[0133] Furthermore, in the embodiments of this disclosure, the above-mentioned multi-level decomposition combined with multi-dimensional improved particle swarm optimization algorithm may include: improving the inertia weight function of the particle swarm optimization algorithm, optimizing the acceleration factor based on the inertia weight value, and introducing a mutation factor to obtain the multi-level decomposition combined with multi-dimensional improved particle swarm optimization algorithm.

[0134] In the embodiments of this disclosure, in the linearly decreasing PSO algorithm, the inertia weight is prone to premature convergence to local optima in the later stages of the search, resulting in a lack of diversity in the positions of particles in the population. Therefore, this paper considers allowing particles to traverse the entire search space as much as possible in the early stages of the search to achieve particle diversity and thus avoid premature convergence to local optima.

[0135] Specifically, in the embodiments of this disclosure, the inertia weight function of the particle swarm optimization algorithm is improved, and the improved inertia weight function is as follows:

[0136]

[0137] Where: ω is the weight, s represents the scaling factor, which can be set to 0.45 in this invention, and i is the current iteration number of the particle. start ω is the initial weight value for the start of the iteration. end Here, T represents the weight value in the later stages of the iteration, and T represents the maximum number of iterations.

[0138] For example, assuming ω end ω is 0.4 start The value of the dynamic inertia weight is 1.1, and the value is obtained by iterating 100 times. Figure 2 This is a schematic diagram illustrating a dynamic inertia weight change as provided in an embodiment of this disclosure. Figure 2 As shown, in the early stage of the overall search, ω is more likely to take a large value, indicating that the global search capability is enhanced. In the later stage, ω is more likely to take a small value, indicating that the local development capability is enhanced, thereby improving the diversity of the population and the local search capability.

[0139] Furthermore, in the embodiments of this disclosure, optimizing the acceleration factor based on the inertia weight value may include: at the beginning of the iteration, the particles search in a large space to ensure that the acceleration factor C1 is large and the acceleration factor C2 is small, thereby accelerating the particle swarm search speed; in the later stages of the iteration, the centroid of the particle swarm is at the global optimal solution to ensure that the acceleration factor C1 is small and the acceleration factor C2 is large, thereby maintaining the ability of the particles to search precisely. The formula for the above acceleration factor is as follows:

[0140]

[0141]

[0142] Among them, C 1s and C 1e Let C1 represent the initial and final values, respectively. 2s and C 2e Let C1 and C2 represent the initial and final values, respectively.

[0143] Furthermore, in the embodiments of this disclosure, the mutation operator, also known as the mutation operation, modifies certain genes in the encoding string of an individual according to a certain mutation rate, forming a new individual different from the previous one, which can help the algorithm improve the diversity of the population. Common mutation operators include boundary mutation, non-uniform mutation, Gaussian mutation, and Cauchy mutation, etc., and mutation operators have a significant impact on the convergence, stability, and optimization performance of the algorithm. Since the particle swarm optimization algorithm performs local search in the later stages, when an individual approaches the optimal position, the individual will wander around the optimal position. The phenomenon of surrounding assimilation causes the particle to mistakenly believe that it has found the global optimal position and stagnate, making it difficult to escape. Based on this, the present invention introduces a mutation factor, which may include: the individuals in the population are represented by a perturbation vector σ = (σ1, σ2, ..., σ...). n ) and a solution vector x = (x1, x2, ..., x n The composition (particle positions in the particle swarm optimization algorithm) is as follows: the perturbation vector is the control vector for the mutated solution vector, and the perturbation vector itself also needs to be mutated. If an individual (x, σ) is selected, the mutated new individual will be (x', σ').

[0144] σ i ′=σ i ×exp(C i-1 (0,0.5))

[0145] x i ′=x i +5×C i (0,σ i ′)

[0146] Where, x ix represents the current position of a particle in the particle swarm optimization algorithm. i '' represents the position after mutation, C is a random variable following a Gaussian or Cauchy distribution for categorical selection, i is the current iteration number of the particle, T represents the maximum iteration number, and σ i The perturbation vector. And, the above C can be further written as...

[0147]

[0148] in, Δ is the probability density function of a Gaussian distribution, μ is the expected value, which determines the location; Δ is the standard deviation, which determines the dispersion of the distribution. 2 Let the variance be N(μ, Δ). 2 ).

[0149] in, γ is the probability density function of the Cauchy distribution, where x0 is the location parameter that determines the location of the peak; γ is the scale parameter, which is half the width of the half-peak location of the function.

[0150] Furthermore, in the embodiments of this disclosure, the above-mentioned multi-level decomposition combined with multi-dimensional improved particle swarm optimization algorithm may specifically include the following steps:

[0151] Step 1: Initialize the position and velocity of the particles;

[0152] Step 2: Calculate the fitness of each particle;

[0153] Step 3: If the termination condition is met (the specified convergence is met or the iteration ends), proceed to step 8; otherwise, proceed to step 4.

[0154] Step 4: Compare the fitness value of each particle with its individual extreme value P. best If compared, it is better than P best The particle's current position is taken as the best current position.

[0155] Step 5: Compare the fitness value of each particle with the global optimum g. best If compared, it is better than g best The particle's current position is taken as the best position for the current group.

[0156] Step Six: Perform a mutation operation on each particle, with a mutation probability of P. m Furthermore, the mutation result of the particle is compared with the particle's current individual extreme value P. best If compared, it is better than g best The particle's current position is taken as its best position; the particle's extreme value P best With global extremum g best Comparison, if the particle's extreme value pbest Superior to g best If the particle's current position is taken as the best position for the current group, then the particle's current position will be considered as the best position for the group.

[0157] Step 7: Update the particle's velocity and position, then proceed to Step 2;

[0158] Step 8: Output g best The algorithm ends.

[0159] Furthermore, in the embodiments of this disclosure, the improvement effect of the multi-level decomposition combined with the multi-dimensional improved particle swarm optimization algorithm can be verified through the above description of the multi-level decomposition combined with the multi-dimensional improved particle swarm optimization algorithm.

[0160] For example, in an embodiment of this disclosure, it is assumed that Table 1 contains basic parameters of the battery and the supercapacitor.

[0161] Table 1

[0162]

[0163]

[0164] Furthermore, in the embodiments of this disclosure, to verify the improvement effect, 10 runs of analysis were performed for each model: PSO, multidimensional improved PSO, single-level decomposition combined with multidimensional improved PSO, and multi-level decomposition combined with multidimensional improved PSO, and the mean values ​​of the objective function were compared. Tables 2-5 show the data results for PSO, multidimensional improved PSO, single-level decomposition combined with multidimensional improved PSO, and multi-level decomposition combined with multidimensional improved PSO, respectively, and Table 6 shows the average cost data.

[0165] Table 2

[0166]

[0167] Table 3

[0168]

[0169]

[0170] Table 4

[0171]

[0172] Table 5

[0173]

[0174]

[0175] Table 6

[0176]

[0177] As can be seen from the data in Tables 2-5, the load shortage rate obtained by the above four methods all meet the requirement of being less than 0.5%; the number of batteries / supercapacitors configured, the objective function (annual comprehensive cost), and the number of iterations that reach the optimal value are shown in each table.

[0178] Furthermore, data from Tables 2-5 show that, during the ten iterations, the annual comprehensive cost of the multi-level decomposition combined with multi-dimensional improvement method was consistently lower than that of the other three methods. Table 6 shows that the average annual comprehensive cost of the hybrid energy storage system using the four methods was RMB 155,000, RMB 158,000, RMB 159,000, and RMB 164,000, respectively. Therefore, it can be concluded that the multi-level decomposition combined with multi-dimensional improvement method has the best ability to reduce the annual comprehensive cost of the hybrid energy storage system.

[0179] In summary, the multi-level decomposition hybrid energy storage capacity optimization method proposed in this application includes decomposing the acquired power signal into target high-frequency components and target low-frequency components; determining the rated power of the hybrid energy storage system based on the target high-frequency components and target low-frequency components; establishing a capacity optimization configuration model for the hybrid energy storage system based on the rated power of the hybrid energy storage system; and solving the capacity optimization configuration model of the hybrid energy storage system using multi-level decomposition combined with a multi-dimensional improved particle swarm optimization algorithm to obtain the capacity optimization configuration result of the hybrid energy storage system. Therefore, this application can improve the inertia weight function of the particle swarm optimization algorithm and optimize the acceleration factor based on the inertia weight value, thereby improving the optimization capability and speed of the particle swarm algorithm. Simultaneously, a mutation factor is introduced to update the individual extreme points and global optimal points of particles, further enhancing the particle search capability, thus making the objective function for obtaining the annual comprehensive cost optimal through this method. Furthermore, in the embodiments of this disclosure, by performing multi-level decomposition on the acquired power signal, the problem of insufficient signal detail information decomposition and inadequate capture of information at critical frequencies in single-level decomposition is effectively solved.

[0180] Figure 3 This is a schematic diagram of a multi-stage decomposition hybrid energy storage capacity optimization device according to an embodiment of this application, as shown below. Figure 1 As shown, the device may include:

[0181] The decomposition module 301 is used to decompose the acquired power signal into a target high-frequency component and a target low-frequency component;

[0182] The determination module 302 is used to determine the rated power of the hybrid energy storage system based on the target high-frequency component and the target low-frequency component;

[0183] Module 303 is established to create a capacity optimization configuration model for the hybrid energy storage system based on its rated power.

[0184] The solver module 304 is used to solve the capacity optimization configuration model of the hybrid energy storage system by using multi-level decomposition combined with multi-dimensional improved particle swarm optimization algorithm, and obtain the capacity optimization configuration result of the hybrid energy storage system.

[0185] In summary, the multi-level decomposition hybrid energy storage capacity optimization device proposed in this application includes decomposing the acquired power signal into target high-frequency components and target low-frequency components; determining the rated power of the hybrid energy storage system based on the target high-frequency components and target low-frequency components; establishing a capacity optimization configuration model for the hybrid energy storage system based on the rated power of the hybrid energy storage system; and solving the capacity optimization configuration model of the hybrid energy storage system using multi-level decomposition combined with a multi-dimensional improved particle swarm optimization algorithm to obtain the capacity optimization configuration result of the hybrid energy storage system. Therefore, this application can improve the inertia weight function of the particle swarm optimization algorithm and optimize the acceleration factor based on the inertia weight value, thereby improving the optimization capability and speed of the particle swarm algorithm. Simultaneously, a mutation factor is introduced to update the individual extreme points and global optimal points of particles, further enhancing the particle search capability, thus making the objective function for obtaining the annual comprehensive cost optimal through this method. Furthermore, in the embodiments of this disclosure, by performing multi-level decomposition on the acquired power signal, the problem of insufficient signal detail information capture and inadequate capture of information at critical frequencies in single-level decomposition is effectively solved.

[0186] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. 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.

[0187] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0188] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method for optimizing the capacity of hybrid energy storage through multi-stage decomposition, characterized in that, The method includes: The acquired power signal is decomposed into multiple intrinsic mode functions (IMFs) using ensemble empirical mode decomposition (EMD). These IMFs include a first high-frequency IMF, a first low-frequency IMF, and an IMF at the high-low frequency boundary. Wavelet packet decomposition is then used to further decompose the IMF at the high-low frequency boundary into a second high-frequency and a second low-frequency component. Based on the first high-frequency IMF, the first low-frequency IMF, and the second high-frequency and second low-frequency components, the target high-frequency component and the target low-frequency component are determined. The rated power of the hybrid energy storage system is determined based on the target high-frequency component and the target low-frequency component. Based on the rated power of the hybrid energy storage system, a capacity optimization configuration model for the hybrid energy storage system is established. The capacity optimization configuration model of the hybrid energy storage system is solved by using a multi-level decomposition combined with a multi-dimensional improved particle swarm optimization algorithm, and the capacity optimization configuration result of the hybrid energy storage system is obtained.

2. The method according to claim 1, characterized in that, The rated power of the hybrid energy storage system includes the rated power of the supercapacitor and the rated power of the battery.

3. The method according to claim 2, characterized in that, The process of establishing a capacity optimization configuration model for the hybrid energy storage system based on its rated power includes: Establish an objective function that minimizes the annual comprehensive cost, where the objective function is: ， Among them, C B For battery investment costs; C C The investment cost of supercapacitors; C kp To maintain costs; C sp Cost of handling hybrid energy storage; Based on the rated power of the supercapacitor and the rated power of the battery, several constraints are established for the capacity optimization configuration model of the hybrid energy storage system.

4. The method according to claim 3, characterized in that, The multiple constraints include hybrid energy storage system SOC constraints and power constraints; The SOC constraint of the hybrid energy storage system is: ， Among them, SOC bat-min SOC bat-max SOC cap-min SOC cap-max These are the upper and lower limits of the state of charge of the battery and the upper and lower limits of the state of charge of the supercapacitor, respectively, and the power constraints. The power constraint is: Among them, P b For storage batteries and P sc This refers to the rated power of the supercapacitor.

5. The method according to claim 1, characterized in that, The multi-level decomposition combined with multi-dimensional improved particle swarm optimization algorithm includes: improving the inertia weight function of the particle swarm optimization algorithm, optimizing the acceleration factor based on the inertia weight value, and introducing a mutation factor to obtain the multi-level decomposition combined with multi-dimensional improved particle swarm optimization algorithm.

6. The method according to claim 5, characterized in that, The inertia weight function of the particle swarm optimization algorithm is improved, and the improved inertia weight function is as follows: Where: ω is the weight, c represents the scaling factor, and i is the current iteration number of the particle, ω start ω is the initial weight value for the start of the iteration. end Here, T represents the weight value in the later stages of the iteration, and T represents the maximum number of iterations.

7. The method according to claim 5, characterized in that, The acceleration factor optimization based on inertia weight values ​​includes: at the beginning of the iteration, particles search in a large space to ensure that the acceleration factor C1 is large and the acceleration factor C2 is small, thereby accelerating the particle swarm search speed; in the later stages of the iteration, the center of gravity of the particle swarm is at the global optimal solution to ensure that the acceleration factor C1 is small and the acceleration factor C2 is large, thereby maintaining the ability of particles to search precisely. The acceleration factor formula is as follows: Among them, C 1s and C 1e Let C1 represent the initial and final values, respectively. 2s and C 2e Let C1 and C2 represent the initial and final values, respectively.

8. The method according to claim 5, characterized in that, The variation factors include: individuals in the population are represented by a perturbation vector. and a solution vector The perturbation vector is the control vector for the mutated solution vector, and the perturbation vector itself also needs to be mutated. Select an individual (x, Then the new individual after the mutation is (x', ') in, Indicates the current position of the particle. Let C represent the position after mutation, be a random variable following a Gaussian or Cauchy distribution for categorical selection, be i (the current iteration number of the particle), and be T (the maximum iteration number). Perturbation vector.

9. A multi-stage decomposition hybrid energy storage capacity optimization device, characterized in that, The device includes: The decomposition module is used to decompose the acquired power signal into multiple intrinsic mode functions (IMFs) using ensemble empirical mode decomposition (EMD). These IMFs include a first high-frequency IMF, a first low-frequency IMF, and an IMF at the high-low frequency boundary. Wavelet packet decomposition is then used to decompose the IMF at the high-low frequency boundary into a second high-frequency and a second low-frequency component. Based on the first high-frequency IMF, the first low-frequency IMF, and the second high-frequency and second low-frequency components, a target high-frequency component and a target low-frequency component are determined. The determination module is used to determine the rated power of the hybrid energy storage system based on the target high-frequency component and the target low-frequency component; A module is established to create a capacity optimization configuration model for the hybrid energy storage system based on its rated power. The solution module is used to solve the capacity optimization configuration model of the hybrid energy storage system using a multi-level decomposition combined with a multi-dimensional improved particle swarm optimization algorithm, and to obtain the capacity optimization configuration result of the hybrid energy storage system.

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