A multi-modal hybrid energy storage power distribution method in a wind-solar-storage system

Abstract

The application provides a multi-modal hybrid energy storage power distribution method in a wind-solar-storage system, which is a hybrid energy storage power distribution method based on an improved Gao-Hao porcupine algorithm-adaptive noise complete set empirical mode decomposition. First, Savitzky-Golay filtering is performed on wind-solar composite power to obtain multi-modal HES reference power P HES , which lays a foundation for subsequent power distribution; then, an ICPO algorithm is established, which improves the convergence speed and search quality of the algorithm while avoiding falling into a local minimum value; and an ICPO-ICEEMDAN method is proposed, which improves the decomposition effect of P HES ; finally, the reconstruction of the intrinsic modal component of the multi-modal HES is realized based on relative entropy, which reduces the pseudo-mode while performing power distribution on the multi-modal HES. The application optimizes the parameters of ICEEMDAN by introducing Cubic chaotic mapping and population reduction technology to form the ICPO algorithm, realizes the IMF reconstruction of the multi-modal HES based on relative entropy, and examples show that the proposed power distribution method can reduce the pseudo-mode, realize power distribution in line with the characteristics of energy storage, solve the problem of power distribution when the number of HES modes is more, and verify the advantages of the ICPO algorithm through comparison with other heuristic algorithms.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy storage power distribution, in particular to a multi-modal hybrid energy storage power distribution method in a wind-solar-storage system. BACKGROUND

[0002] With the continuous growth of global economy and the increasing population, the demand for energy is showing a rapid growth trend. Traditional fossil energy such as coal, oil and natural gas, not only limited resources and use process will produce a large amount of greenhouse gases, causing serious impact on the environment. Therefore, the demand for renewable energy in the world is rising sharply, and the proportion of new energy installed capacity dominated by wind and solar is increasing. However, wind and solar energy both have intermittent and random problems, resulting in large fluctuations in output power, which causes certain interference to the stable operation of the system, and the configuration of energy storage and the formation of wind-solar-storage combined operation system become increasingly important. Energy storage has multiple modalities, such as power flywheel and energy lithium battery. Hybrid energy storage (HES) can fully play the complementary characteristics of different energy storage technologies, balance the volatility of wind and solar power generation, improve the overall efficiency and stability of the system, and achieve more efficient and flexible energy storage and management.

[0003] Due to the advantages of hybrid energy storage system in suppressing power fluctuations, enhancing the resilience of power grid, and improving the recovery ability of power grid, its position in the power system is increasingly important, and its development trend also presents the characteristics of diversification and rapidity. Reasonable power distribution of hybrid energy storage system can optimize the charging and discharging process of different energy storage units, reduce energy loss and waste, improve the utilization efficiency of energy storage resources, and help reduce the construction and operation cost of hybrid energy storage system, and improve the redundancy of power system, and ensure the continuous power supply of power system. At present, most of the power distribution strategies are based on the given "1 energy type + 1 power type" two-modal HES scheme to study the power distribution, while in actual application, HES is often affected by the type of energy storage technology, the complexity of system design, and the specific needs of application scenarios, etc. The number of modalities is not limited to two modalities and is not fixed, so the research on the power distribution method of multi-modal HES has become a new hotspot. SUMMARY

[0004] In view of the problems in the prior art, the present application aims to provide a multi-modal hybrid energy storage power distribution method in a wind-solar-storage system, solve the power distribution problem when the number of HES modes is more, and is a hybrid energy storage power distribution method based on an Improved Crested Porcupine Optimizer (ICPO)-Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN). HES , which lays a foundation for subsequent power distribution; then an ICPO algorithm is established to improve the convergence speed and search quality of the algorithm while avoiding falling into a local minimum value; an ICPO-ICEEMDAN method is proposed to improve the decomposition effect on P HES ; finally, the relative entropy is used to realize the reconstruction of Intrinsic Mode Function (IMF) of the multi-modal HES, reduce the pseudo-mode, and achieve the purpose of power distribution of the multi-modal HES.

[0005] To solve the above technical problems, the technical scheme provided by the present application is as follows:

[0006] A multi-modal hybrid energy storage power distribution method in a wind-solar-storage system, comprising the following steps,

[0007] S1. Savitzky-Golay filtering is performed on wind-solar composite power to obtain multi-modal HES reference power P HES , which lays a foundation for subsequent power distribution;

[0008] S2. The maximum number of iterations t, the population size N, the noise addition number Nstd, and the amplitude weight Ne optimization range are set, the Cubic chaotic mapping is introduced as a random source, the cycle number is introduced for population reduction, the ICPO algorithm is established, and the ICPO population Cubic chaotic initialization is performed.

[0009] S3. Under the ICPO algorithm established in S2, the noise addition process and the iteration mechanism are improved, the noise level added to the signal is adaptively adjusted, the ICPO-ICEEMDAN method is obtained, and the P HES, and the optimal parameter combination is obtained when the iteration number is greater than or equal to t, and the best IMF component is output according to the parameter combination;

[0010] S4. Based on the optimal IMF component obtained in S3, the high and low frequency regions are divided according to the relative entropy, and the high and low frequency regions are divided according to the rated cycle number, and the p power types and q energy types of energy storage are used to bear the corresponding IMF components in the high and low frequency regions, so as to realize the power distribution of n modal HES.

[0011] Preferably, the wind power, photovoltaic power, composite output, HES output power and direct grid-connected power at time t are respectively represented as P wind (t), P pv (t), P fuhe (t), P HES (t), P grid (t); the S1 comprises the following steps,

[0012] S1.1. Establishing the relationship between the composite output and the output power

[0013] P fuhe (t) is calculated according to the following formula:

[0014] P fuhe (t) = P wind (t) + P pv (t) (1)

[0015] S1.2. Calculating the direct grid-connected power P grid (t)

[0016] The SG filter adopts a sliding window mode, and a high-order polynomial fitting is performed in each window to smooth the signal, and the calculation formula of the direct grid-connected power P grid (t) is as follows, and the fluctuation degree meets the pre-set grid-connected standard;

[0017]

[0018] In the formula, c j is a weighted coefficient related to the least square fitting degree of the polynomial; w is the window size; P ref is the fluctuation limit value within Δt time;

[0019] S1.3. Calculating the HES output power

[0020] P HES (t) is expressed as follows:

[0021] P HES (t) = P fuhe(t)-P grid (t) (3).

[0022] Preferably, said S2 comprises the following steps:

[0023] S2.1 Classification of CP defense strategies

[0024] As the predator gets closer to the center of the crested porcupine, the CP's defense strategy gradually escalates, from the least aggressive to the most aggressive: Zone A - flapping back spines, Zone B - making noise, Zone C - secreting odors, and Zone D - backstab attacks;

[0025] S2.2 Improve CPO algorithm and establish ICPO algorithm

[0026] Cubic chaotic map is introduced as a random source, and cyclic population reduction technology is introduced to establish ICPO to improve the convergence speed and search quality of the algorithm while avoiding falling into local minima. Specifically:

[0027] S2.2.1 Chaotic Map Initialization

[0028] CPO starts the search process from the initial set of individuals, and the calculation formula is as follows:

[0029] x i =L+r×(UL),i=1,2...,N (4)

[0030] x i+1 =ρx i ×(1-x i 2 ) (5)

[0031] Where N represents the population size; x i is the i-th candidate solution in the search space; L and U are the lower and upper limits of the search range respectively; r is a random value in [0,1]; ρ is the Cubic mapping control parameter;

[0032] The cyclic population reduction technique means that not all CPs activate their defense mechanisms. Only those CPs that are threatened will defend themselves. The calculation formula is as follows:

[0033]

[0034] Where T is an integer variable that determines the number of cycles, T max is the maximum value; t is the number of iterations; % represents the remainder operation; N min is the minimum number of individuals in the population. As t increases, the size of the new population N' gradually decreases;

[0035] S2.2.2 Exploration stage

[0036] A region: CP fans its spines to deter the predator, if the predator still moves towards CP, the distance between them is reduced, select to encourage exploration A region; if select to leave, the distance between them is maximized to explore whether there is a solution in the unknown region, the calculation formula is as follows:

[0037]

[0038] In the formula, x t CP is the solution at iteration t, y t i is the vector between the current CP and a randomly selected CP from the population, used to represent the position of the predator at time t, τ1 is a normally distributed random number, τ2 is a random value in [0, 1], r0 is a random value in [1, N];

[0039] B region: CP makes noise to deter, when the predator approaches it, the sound will change larger, otherwise smaller, the calculation formula is as follows:

[0040]

[0041] In the formula, τ3 is a random value in [0, 1], r1 and r2 are random numbers in [1, N];

[0042] When |U1| = 0, the predator stops moving, the distance remains unchanged, and the sound is weakened; when |U1| = 1, the predator is still nearby, and the sound is enhanced; otherwise, the general sound is emitted;

[0043] S2.2.3 development stage

[0044] C region: CP secretes odor to prevent the predator from approaching, the calculation formula is as follows:

[0045]

[0046] In the formula, r3 is a random number in [1, N], δ is a search direction control parameter, γ t is a defense factor, S t i is an odor diffusion factor;

[0047] When |U1| = 0, the predator stops moving, the distance remains unchanged, and the odor stops emitting; when |U1| = 1, the predator is still nearby, and the odor is emitted stronger; otherwise, the predator keeps a safe distance from the CP, and the general odor is emitted;

[0048] D region: CP attacks the predator with its dorsal spine, which is analogous to one-dimensional inelastic collision, the calculation formula is as follows:

[0049]

[0050] Where x t CP is the optimal solution, namely CP; x t i represents the position of the i-th individual at iteration t, i.e., the predator; β is the convergence rate factor, τ4 is a random value in [0,1]; F i t To affect the attack power of the CP of the i-th predator, the relevant calculation formula is as follows:

[0051]

[0052] Where, is the mass of the i-th individual at iteration t, f is the objective function, is the final velocity of the i-th individual at time t+1, and is allocated based on the random solution selected from the current population, is the initial velocity of the i-th individual at iteration t, τ6 is a random vector in [0,1], ε is a minimum value to avoid being divided by 0, and r4 is a random number in [1,N].

[0053] Preferably, the S3 is specifically:

[0054] S3.1 Decomposition of P using ICEEMDAN HES

[0055] S3.1.1 Use empirical mode decomposition to decompose Gaussian white noise and obtain the IMF component of the noise l i(i＝1,2,.,N) , add the original signal x(t) to each component after processing, and get the extended signal x i (t):

[0056] x i (t) = x(t) + a0l i (15)

[0057] Where α0 = βstd(x(t)) / std(l1) is the standard deviation of white noise, β is a constant, and std represents the standard deviation operation;

[0058] S3.1.2 Define the first residual component res1:

[0059]

[0060] In the formula, M[x i (t)] represents x i (t) minus the first component IMF1 obtained by EMD decomposition. At this step, the first component IMF1 of the original signal x(t) is as follows:

[0061] IMF1=x(t)-res1 (17)

[0062] S3.1.3 Calculate the second residual component res2 and derive IMF2:

[0063]

[0064] IMF2 = resl - res2 (19)

[0065] where αl = βstd(rl);

[0066] S3.1.4 Calculate the kth residual and IMF by the same method:

[0067]

[0068] IMF k = res k-1 -res k (21)

[0069] where α k-1 = βstd(r k-1 ), k > 1;

[0070] S3.1.5 Implement iteration until the maximum iteration number is satisfied;

[0071] S3.2 Construct the composite entropy as the evaluation index, which consists of envelope entropy E p and mutual information entropy MI;

[0072] S3.2.1 Calculate the envelope entropy E p

[0073] The envelope entropy can reflect whether the IMF frequency characteristics are obvious. When the IMF characteristic information is less, the envelope entropy value is larger, and vice versa. The envelope entropy E p can be expressed as:

[0074]

[0075] where a(j) is the envelope signal of the signal x(j) after Hilbert modulation; p j is the normalized form of a(j);

[0076] S3.2.2 Calculate the mutual information entropy MI

[0077] The mutual information represents the correlation degree of two events in information theory, and is not easily disturbed by external factors. The greater the mutual information entropy value MI is, the stronger the correlation of the two events is. In terms of IMF, the more abundant the original signal characteristic information contained is. The calculation formula is as follows:

[0078] MI(X, Y) = H(Y) - H(Y IX) (24)

[0079] where X and Y are different events, H(Y) is the entropy of Y, X is P HES , Y is IMF, and H(Y|X) is the conditional entropy of Y given X;

[0080] S3.2.3 Establishing a composite fitness function

[0081] The expression of the composite fitness function is as follows:

[0082]

[0083] ICEEMDAN improves the stability and robustness of the results by repeatedly adding noise and decomposing;

[0084] S3.2.4 Selecting Nstd and Ne as optimization objects, taking the composite entropy as the fitness function, and using ICPO to optimize the parameter combination [Nstd, Ne] to improve the decomposition effect of ICEEMDAN on P HES .

[0085] Preferably, the S4 is specifically:

[0086] P HES After ICPO-ICEEMDAN decomposition, the frequency spectrum is obtained through fast Fourier transform;

[0087] S4.1 Calculate the relative entropy D

[0088] Considering the relative entropy size between the spectra of each IMF, the relative entropy between the spectra of each IMF is calculated to represent the degree of differentiation of the spectrum, and the IMFs are divided into n combinations. The relative entropy D is calculated as follows:

[0089]

[0090] where x is the amplitude, and N is the number of time points;

[0091] S4.2 Set the power division point

[0092] Take the maximum point of the relative entropy D as the boundary point K of the high-frequency component P H and the low-frequency component P L . After obtaining the high-frequency region and the low-frequency region from the boundary point K, further distribution is performed on the p kinds of power types and q kinds of energy types in the HES selection result;

[0093] For the high frequency area, arrange the relative entropy in descending order, take the first p-1 points with larger relative entropy values as power division points, divide the high frequency area into p segments, and the low frequency area is the same; Select the energy storage with more rated cycle times as the bearer of the higher frequency segment power; The power division points of the M IMF are set as follows: take the maximum relative entropy as the division point, the left side contains K IMF power, which is the high frequency area, and is borne by the power type energy storage; The right side contains M-K IMF power, which is the low frequency area, and is borne by the energy type energy storage;

[0094] Place the decomposed residual res in the low frequency area, and the energy type is borne by the cycle with more times;

[0095] S4.3 Reconstruct the power of each modal energy storage

[0096] The calculation formula of the power of each modal energy storage is as follows:

[0097]

[0098] In the formula, x, y, x', y' are relative entropy division points.

[0099] The beneficial effects of the present application are:

[0100] The present application studies the multi-modal power distribution of HES in a wind-solar-storage system, establishes a hybrid energy storage power distribution strategy based on the ICPO-ICEEMDAN method, optimizes the parameters of ICEEMDAN by introducing the Cubic chaotic mapping and population reduction technology to form the ICPO algorithm, and realizes the IMF reconstruction of multi-modal HES based on relative entropy. The example shows that the proposed power distribution method can reduce pseudo modes, realize power distribution in accordance with the characteristics of energy storage, and the advantages of the ICPO algorithm are verified by comparison with other heuristic algorithms. BRIEF DESCRIPTION OF DRAWINGS

[0101] The accompanying drawings are included to provide a further understanding of the application, and constitute a part of the specification, together with the embodiments of the application, to explain the application, and do not constitute a limitation on the application. In the drawings:

[0102] Figure 1 Flow chart of the multi-modal hybrid energy storage power distribution method in the wind-solar-storage system of the present application

[0103] Figure 2 CP defense mechanism diagram

[0104] Figure 3 Power division point setting diagram

[0105] Figure 4 Wind-solar composite output diagram of the embodiment

[0106] Figure 5HES reference power diagram for example

[0107] Figure 6 This is the power decomposition result diagram of the embodiment ICPO-ICEEMDAN

[0108] Figure 7 This is the power output result of the embodiment "lead-carbon battery + flow battery, supercapacitor"

[0109] Figure 8 Convergence curve comparison diagram of the embodiment

[0110] Figure 9a The power decomposition result diagram of ICEEMDAN is used in the embodiment. Figure 9b The power decomposition result diagram using VMD

[0111] Figure 10 The VMD power output results of the embodiment are shown in FIG. DETAILED DESCRIPTION

[0112] The following describes preferred embodiments of the present invention in conjunction with the accompanying drawings. It should be understood that the following embodiments are provided for illustrative purposes only and are not intended to limit the scope of the present invention. Those skilled in the art may make various modifications and substitutions to the present invention without departing from the purpose and spirit of the present invention.

[0113] The present invention provides a multi-modal hybrid energy storage power allocation method in a wind-solar-storage system to solve the power allocation problem when the number of HES modes is larger. It is a hybrid energy storage power allocation method based on the Improved Crested Porcupine Optimizer (ICPO)-Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN). First, the wind-solar composite power is filtered by Savitzky-Golay (SG) to obtain the multi-modal HES reference power P HES , laying the foundation for subsequent power allocation; then establishing the ICPO algorithm, while improving the algorithm's convergence speed and search quality while avoiding falling into local minima; and proposing the ICPO-ICEEMDAN method to improve P HES Finally, the intrinsic mode function (IMF) of the multimodal HES is reconstructed based on relative entropy, achieving the purpose of power allocation for the multimodal HES while reducing pseudo-modes. The specific steps are as follows:

[0114] 1. Calculate the multi-modal HES reference power

[0115] (1) The wind power, photovoltaic power, composite output power, HES output power and grid-connected point power at time t are respectively denoted as P wind (t), P pv (t), P fuhe (t), P HES (t), P grid (t), P fuhe (t). The calculation formula of P fuhe (t) is as follows:

[0116] P wind (t) = P pv (t) (1)

[0117] (2) Calculate the direct grid-connected power P grid (t)

[0118] The SG filter adopts a sliding window manner, and high-order polynomial fitting is performed in each window to realize the smoothing processing of the signal. The calculation formula of the direct grid-connected power is as follows, which is the direct grid-connected power obtained by SG filtering, and the fluctuation degree meets the pre-set grid-connected standard.

[0119]

[0120] In the formula, c j is a weighting coefficient, which is related to the polynomial least square fitting degree; w is the window size; P ref is the fluctuation limit value within Δt time.

[0121] (3) Calculate the HES output power P HES (t) as follows:

[0122] P HES (t) = P fuhe (t) - P grid (t) (3)

[0123] 2. Establish an improved Crested Porcupine optimization algorithm (ICPO)

[0124] (1) Divide the CP defense strategy level

[0125] The Crested Porcupine optimization algorithm (Crested Porcupine Optimizer, CPO) is a new meta-heuristic algorithm. As the predator is closer to the center where the Crested Porcupine (CP) is located, the defense strategy of the CP gradually upgrades. From the least aggressive to the most aggressive, they are A area-fanning back spines, B area-making noise, C area-secreting smell, and D area-back spine attack, as shown in Figure 2CPO uses random generation method to initialize population, which leads to uneven distribution and easily falls into local extreme value. Chaos mapping has randomness, pervasiveness and other characteristics.

[0126] (2) Improved CPO algorithm, establish ICPO algorithm

[0127] 1) Improved method of CPO

[0128] The application introduces Cubic chaos mapping as a random source, and introduces cyclic population reduction technology to establish ICPO, so as to improve the convergence speed and search quality of the algorithm and avoid falling into local minimum.

[0129] 2) ICPO algorithm flow

[0130] ① Chaos mapping initialization

[0131] CPO starts the search process from the initial individual set, and the calculation formula is as follows:

[0132] x i =L+r×(U-L),i=1,2...,N (4)

[0133] x i+1 =ρx i ×(1-x i 2 ) (5)

[0134] In the formula, N represents the population size; x i is the ith candidate solution in the search space; L and U are the lower limit and upper limit of the search range respectively; r is a random value in [0,1]; and p is the Cubic mapping control parameter.

[0135] Cyclic population reduction technology means that not all CPs activate defense mechanism, and the threatened CPs will defend, and the calculation formula is as follows:

[0136]

[0137] In the formula, T is an integer variable for determining the number of cycles, T max is the maximum value; t is the iteration number; % represents the remainder operation; N min is the minimum number of individuals in the population, and the new population size N' gradually decreases as t increases.

[0138] ② Exploration stage

[0139] A area: CP finds the predator and fans the spines to deter, if the predator still moves towards the CP, the distance between them is reduced, this selection encourages to explore A area; if the selection leaves, the distance between them is maximized to explore whether there is a solution in the unknown area, and the calculation formula is as follows:

[0140]

[0141] where x t CP is the solution at iteration t, y t i is the vector between the current CP and a randomly selected CP from the population, representing the position of the predator at time t, τ1 is a random number from a normal distribution, τ2 is a random value in [0, 1], and r0 is a random value in [1, N].

[0142] B region: CP makes noise to deter, when the predator is close to it, the sound will change larger, otherwise smaller, the calculation formula is as follows:

[0143]

[0144] where τ3 is a random value in [0, 1], r1 and r2 are random numbers in [1, N]. When |U1| = 0, the predator stops moving, the distance remains unchanged, and the sound is weakened; when |U1| = 1, the predator is still nearby, and the sound is enhanced; otherwise, the general sound is emitted.

[0145] ③ Development stage

[0146] C region: CP secretes odor to prevent the predator from approaching, the calculation formula is as follows:

[0147]

[0148] where r3 is a random number in [1, N], δ is a search direction control parameter, γ t is a defense factor, S i t is an odor diffusion factor. When |U1| = 0, the predator stops moving, the distance remains unchanged, and the odor emission stops; when |U1| = 1, the predator is still nearby, and the odor is emitted more strongly; otherwise, the predator maintains a safe distance from the CP, and the general odor is emitted.

[0149] D region: CP attacks the predator with a dorsal spine, analogous to one-dimensional inelastic collision, the calculation formula is as follows:

[0150]

[0151] where x t CP is the optimal solution, i.e., the CP; x t i represents the position of the i-th individual at iteration t, i.e., the predator; β is a convergence speed factor, τ4 is a random value in [0, 1]; F i tTo affect the attack power of the CP of the i-th predator, the relevant calculation formula is as follows:

[0152]

[0153] Where, is the mass of the i-th individual at iteration t, f is the objective function, is the final velocity of the i-th individual at time t+1, and is allocated based on the random solution selected from the current population, is the initial velocity of the i-th individual at iteration t, τ6 is a random vector in [0,1], ε is a minimum value to avoid being divided by 0, and r4 is a random number in [1,N].

[0154] 3. Propose ICPO-ICEEMDAN method

[0155] (1) Principle of ICEEMDAN

[0156] ICEEMDAN ensures that all information in the original signal is retained after decomposition, improving decomposition accuracy and gaining widespread application in signal processing. Unlike traditional methods that add ordinary white Gaussian noise, ICEEMDAN improves the noise addition process and iterative mechanism. By adaptively adjusting the noise level added to the signal, it improves decomposition quality and reduces modal aliasing.

[0157] Decompose P using ICEEMDAN HES , the specific decomposition process is as follows:

[0158] 1) Use Empirical Mode Decomposition (EMD) to decompose Gaussian white noise and obtain the IMF component of the noise l i(i＝1,2,.,N) , add the original signal x(t) to each component after processing, and get the extended signal x i (t):

[0159] x i (t) = x(t) + a0l i (15)

[0160] Where: α0 = βstd(x(t)) / std(l1) is the standard deviation of white noise, β is a constant, and std represents the standard deviation operation.

[0161] 2) Define the first residual component res1:

[0162]

[0163] Where: M[x i (t)] represents x i(t) Subtract the first component IMF1 from the EMD decomposition. In this step, the first component IMF1 of the original signal x(t) is given by:

[0164] IMF1 = x(t) - resl (17)

[0165] 3) Calculate the second residual component res2 and derive IMF2.

[0166]

[0167] IMF2 = resl - res2 (19)

[0168] where: a1 = βstd(r1).

[0169] 4) In the same way, the kth residual and IMF are calculated:

[0170]

[0171] IMF k = res k-1 -res k (21)

[0172] where: a k-1 = βstd(r k-1 ), k > 1.

[0173] 5) The iteration is implemented until the maximum iteration number is satisfied.

[0174] (2) Construct the composite entropy as an evaluation index

[0175] The composite entropy is composed of the envelope entropy E p and the mutual information entropy MI.

[0176] 1) Envelope entropy E p

[0177] The envelope entropy can reflect whether the frequency characteristics of each IMF are obvious. When the IMF characteristic information is less, the envelope entropy value is larger, and vice versa. The envelope entropy E p can be expressed as:

[0178]

[0179] where a(j) is the envelope signal obtained after Hilbert modulation of the signal x(j); p j is the normalized form of a(j).

[0180] 2) Mutual information entropy MI

[0181] Mutual information is used in information theory to represent the degree of correlation between two events, and is not easily affected by external factors. The greater the mutual information entropy value MI, the stronger the correlation between the two events, and the richer the original signal characteristic information contained in the IMF. The calculation formula is as follows:

[0182] MI(X,Y)=H(Y)-H(Y|X) (24)

[0183] In the formula, X and Y are different events, H(Y) is the entropy of Y, X is P HES , Y is IMF, and H(Y|X) is the conditional entropy of Y given X.

[0184] (3) Establish a composite fitness function

[0185] The expression of the composite fitness function is as follows:

[0186]

[0187] ICEEMDAN improves the stability and robustness of the results by repeatedly adding noise and decomposing. Among them, the adding number Nstd and the amplitude weight Ne of the noise have obvious influence on the decomposition effect. Larger Nstd can enhance the processing ability of complex and IMF modal in the signal, but also increases the calculation cost, affects the accuracy and stability of decomposition; Ne is larger, which will reduce the signal decomposition precision, and smaller will cause the decomposition result to appear aliasing.

[0188] The present application selects Nstd and Ne as optimization objects, takes composite entropy as fitness function, and uses ICPO to optimize the parameter combination [Nstd, Ne], so as to realize the improvement of ICEEMDAN decomposition effect on P HES .

[0189] 4. Multi-modal HES power distribution

[0190] P HES After ICPO-ICEEMDAN decomposition, the frequency spectrum diagram can be obtained through Fast Fourier Transform (FFT).

[0191] (1) Relative entropy D

[0192] Considering the relative entropy size between the frequency spectrums of each IMF, the relative entropy between the frequency spectrums of each IMF is calculated to represent the differentiation degree of the frequency spectrum, and the IMF is divided into n combinations. The calculation formula of relative entropy D is as follows:

[0193]

[0194] In the formula, x is the amplitude, and N is the number of time points.

[0195] (2) Setting power split points

[0196] In the calculation result, the relative entropy D(IMF i , IMF i+1 ) must have a process from large to small, so that the maximum point is taken as the demarcation point K of the high-frequency component P H and the low-frequency component P L . After obtaining the high-frequency region and the low-frequency region from the demarcation point K, the p power types and q energy types in the HES selection result are further distributed. For the high-frequency region, the relative entropy is arranged in descending order, and the first p-1 points with larger relative entropy values are taken as the power split points, so that the high-frequency region is divided into p segments, and the low-frequency region is the same. Considering the life loss of different types of energy storage, the energy storage with more rated cycle times is selected as the bearer of the higher frequency segment power, so as to overcome the small capacity of the power type and reduce the cycle times of the energy type and prolong its service life. The power split point setting of the M IMF is shown in Figure 3 , wherein x, y, x', y' are relative entropy division points.

[0197] Considering that the residual res mainly contains lower frequency power after decomposition, it is placed in the low-frequency region and is borne by the energy type with more cycle times.

[0198] (3) Reconstructing the power of each modal energy storage

[0199] The calculation formula of the power of each modal energy storage is as follows:

[0200]

[0201] Embodiment

[0202] In this embodiment, the value of the HES modal number n is 2 or 3, and p, q≥1. There are 6 kinds of commercial energy storage available for selection in the HES scheme, including lead-carbon battery, lithium ion battery, sodium-sulfur battery, and liquid flow battery, super capacitor, and flywheel. The final selection is the HES scheme composed of "lead-carbon battery + liquid flow battery, super capacitor", and the power distribution is carried out.

[0203] 1. Basic data setting

[0204] The actual sampling data of wind and light of a certain day in a certain wind and light field station in northwest China is taken as the research object, the wind power installation is 100 MW, the photovoltaic installation is 200 MW, the sampling interval is 1 min, and P fuhe is shown in Figure 4 .

[0205] The polynomial order in the SG filtering algorithm is set to 6, the w iteration size is 20, the ICPO population size is 4, the iteration number is 100 times, and the optimization range of Nstd and Ne is [50, 600] and [0.15, 0.6], respectively. According to the national standard, the maximum fluctuations of 1 min and 10 min of P grid are set to 15 MW and 50 MW, respectively.

[0206] 2. Analysis of power distribution results

[0207] Figure 4 P fuhe in the above formula. grid After the fluctuations are smoothed by SG filtering, the power P HES satisfying the grid connection standard is obtained, and the reference power P grid is further obtained, as shown in Figure 5 . Among them, the maximum fluctuations of 1 min and 10 min of P grid are 11.69 MW and 22.06 MW, respectively.

[0208] The best fitness value calculated by ICPO is 6.3138, and the best position is (0.2915, 114.563). The P HES decomposition is performed with the optimal combination as the ICEEMDAN parameters, and the results are shown in Figure 6 .

[0209] The relative entropy between each IMF of the HES scheme "lead-carbon battery + flow battery, super capacitor" is calculated, as shown in Table 1.

[0210] Table 1 Relative entropy calculation results

[0211]

[0212] From the obtained results, the relative entropy between IMF1 and IMF2 is the largest, so IMF1 is divided into the high-frequency region, and IMF2-10 is divided into the low-frequency region. The selected HES mode is 3, the energy type is lead-carbon battery and flow battery, and the power type is super capacitor. The high-frequency region IMF1 component is borne by the super capacitor. In the low-frequency region, the relative entropy between IMF5 and IMF6 is the largest, reaching 126.4. Therefore, considering the service life and cycle number of the lead-carbon battery and the flow battery, the flow battery is selected to bear IMF2-5, the lead-carbon battery is selected to bear IMF6-10 and the residual component res. In summary, the power distribution results of each mode of energy storage in the obtained HES scheme are shown in Table 2, and the power output is shown in Figure 7 .

[0213] Table 2 Power distribution results of "lead-carbon battery + flow battery, super capacitor"

[0214]

[0215] From Figure 7 It can be seen that the super capacitor has a short working cycle and frequent charging and discharging; the lead-carbon battery and the flow battery have a long working cycle and relatively gentle charging and discharging, the former charging and discharging power is basically between-4 to 4 MW, processing power in the low frequency area with a high frequency, and the latter is basically between-2 to 2 MW, processing power with a smaller frequency in the low frequency area. This reflects the characteristics of the super capacitor as a power type energy storage device that can release a large amount of electricity for a short time and the characteristics of the lead-carbon battery and the flow battery as energy type energy storage that can be charged and discharged for a long time. Since the super capacitor has the characteristics of reversible charging and discharging, the cycle number can reach hundreds of thousands of times, so it can bear frequent power fluctuations. The lead-carbon battery and the flow battery have less charging and discharging conversion, thereby reducing the cycle number of the battery and delaying the speed of its decay and aging.

[0216] 3. ICPO convergence analysis

[0217] To verify the improvement of the convergence of ICPO after introducing the Cubic chaotic mapping, the ICPO algorithm is compared with the CPO, particle swarm optimization (PSO), sparrow search algorithm (SSA) and other algorithms, and the iteration results are compared as shown in Figure 8 . Figure 8 In the comparison, the ICPO converges at the 8th iteration, which is faster than the convergence speed of the CPO and other algorithms, and the fitness function value is also the smallest among the four algorithms, further indicating that the ICPO has higher convergence accuracy.

[0218] 4. Advantage analysis of ICPO-ICEEMDAN method in optimizing parameters

[0219] To verify the advantage of the ICPO-ICEEMDAN in optimizing parameters, the traditional ICEEMDAN and variational mode decomposition (VMD) are used to decompose P HES , and the results are shown in Fig. 9.

[0220] It can be seen from Figure 9a that the number of pseudo modes in the ICEEMDAN decomposition result increases, increasing the number of signal processing and affecting the reliability of the analysis result, which is not conducive to the rapid power distribution of HES. Figure 9b In the comparison, the VMD decomposition result is transformed by FFT (sampling rate 1000), and the center frequency of IMF1 is the smallest, but as high as 125 Hz. It can be seen that when P HES has a high mutation degree, VMD is difficult to decompose P HESThe low-frequency components suitable for energy type are decomposed. Another HES solution "lithium battery, super capacitor" is selected as the VMD power allocation object, and the power reconstruction result is as follows Figure 10 As shown, the number of energy-type charge and discharge cycles reaches hundreds, seriously affecting the energy storage lifespan. This result also indirectly verifies that the use of a high-scoring three-mode HES can better utilize the complementary advantages of energy storage in terms of technology, economy, and environmental safety, and reduce energy-type lifespan loss.

[0221] The contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0222] Finally, it should be noted that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art will be able to modify the technical solutions described in the aforementioned embodiments or substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A multi-modal hybrid energy storage power distribution method in a wind-solar-storage system, characterized in that: The following steps are included: S1. Perform Savitzky-Golay filtering on the wind-solar composite power to obtain the multi-modal HES reference power P HES , laying the foundation for subsequent power allocation; S2. Set the maximum number of iterations t, the population size N, the number of noise additions Nstd, and the optimization range of the amplitude weight Ne. Introduce the Cubic chaos map as a random source and the number of cycles for population reduction. Establish the ICPO algorithm and perform Cubic chaos initialization on the ICPO population. S3. Based on the ICPO algorithm established in S2, the noise addition process and iteration mechanism are improved. By adaptively adjusting the noise level added to the signal, the ICPO-ICEEMDAN method is obtained. ICPO-ICEEMDAN is used to decompose the P obtained in S1. HES , the fitness function is iterated with the minimum composite entropy of the IMF component as the fitness function. When the number of iterations is less than t, the population position is updated and the iteration continues. When the number of iterations is greater than or equal to t, the optimal parameter combination is obtained, and the optimal IMF component is output based on the parameter combination. S4. Based on the optimal IMF components obtained in S3, the high and low frequency regions are divided according to the relative entropy, and the regions are divided according to the rated number of cycles. P types of power-type and q types of energy-type energy storage are responsible for the IMF components corresponding to the high and low frequency regions respectively, realizing the power distribution of the n-mode HES.

2. The multi-mode hybrid energy storage power distribution method in a wind-solar-storage system according to claim 1 is characterized in that: At time t, wind power, photovoltaic power, composite power, HES output power and direct grid-connected power are expressed as P wind (t), P pv (t), P fuhe (t), P HES (t), P grid (t); said S1 comprises the following steps, S1.1 Establish the relationship between composite output and output power P fuhe The calculation formula of (t) is as follows: P fuhe (t)=P wind (t)+P pv (t) (1) S1.2 Calculate the direct grid-connected power P grid (t) SG filtering uses a sliding window method to perform high-order polynomial fitting in each window to smooth the signal and directly connect the grid power P grid The calculation formula of (t) is as follows, and its fluctuation degree meets the pre-set grid connection standard; Where c j is the weighting coefficient, which is related to the number of polynomial least squares fitting; w is the window size; P ref is the limit value of the fluctuation within the time Δt; S1.3 Calculation of HES output power P HES The expression of (t) is as follows: P HES (t)=P fuhe (t)-P grid (t) (3)。 3. The multi-mode hybrid energy storage power distribution method in a wind-solar-storage system according to claim 1 is characterized in that: The S2 The following steps are included: S2.1 Classification of CP defense strategies As the predator gets closer to the center of the crested porcupine, the CP's defense strategy gradually escalates, from the least aggressive to the most aggressive: Zone A - flapping back spines, Zone B - making noise, Zone C - secreting odors, and Zone D - backstab attacks; S2.2 Improve CPO algorithm and establish ICPO algorithm Cubic chaotic map is introduced as a random source, and cyclic population reduction technology is introduced to establish ICPO to improve the convergence speed and search quality of the algorithm while avoiding falling into local minima. Specifically: S2.2.1 Chaotic Map Initialization CPO starts the search process from the initial set of individuals, and the calculation formula is as follows: x i =L+r×(U-L),i=1,2...,N (4) x i+1 =ρx i ×(1-x i 2 ) (5) Where N represents the population size; x i is the i-th candidate solution in the search space; L and U are the lower and upper limits of the search range respectively; r is a random value in [0,1]; ρ is the Cubic mapping control parameter; The cyclic population reduction technique means that not all CPs activate their defense mechanisms. Only those CPs that are threatened will defend themselves. The calculation formula is as follows: Where T is an integer variable that determines the number of cycles, T max is the maximum value; t is the number of iterations; % represents the remainder operation; N min is the minimum number of individuals in the population. As t increases, the size of the new population N' gradually decreases; S2.2.2 Exploration stage Area A: After the CP discovers the predator, it flaps its spines to deter it. If the predator still moves toward the CP, the distance between them decreases, and the predator is encouraged to explore area A. If the predator chooses to leave, the distance between them is maximized to explore whether there is a solution in the unknown area. The calculation formula is as follows: Where x t CP is the solution at iteration t, y t i is the vector between the current CP and a randomly selected CP from the population, used to represent the position of the predator at time t, τ1 is a normally distributed random number, τ2 is a random value in [0,1], and r0 is a random value in [1,N]; Zone B: CP makes noise to deter predators. When a predator approaches it, the noise becomes louder, otherwise it becomes quieter. The calculation formula is as follows: Where τ3 is a random value in [0,1], r1 and r2 are random numbers in [1,N]; When |U1|=0, the predator stops moving, the distance remains unchanged, and the sound is weakened; when |U1|=1, the predator is still nearby and the sound is strengthened; Otherwise, emit a general sound; S2.2.3 Development phase Area C: CP secretes odor to prevent predators from approaching. The calculation formula is as follows: Where r3 is a random number in [1, N], δ is the search direction control parameter, γ t is the defense factor, S t i is the odor diffusion factor; When |U1|=0, the predator stops moving, the distance remains unchanged, and the predator stops emitting scent; when |U1|=1, the predator is still nearby and emits a stronger scent; otherwise, the predator maintains a safe distance from the CP and emits a normal scent; Area D: CP uses backstab to attack the predator, analogous to a one-dimensional inelastic collision, and the calculation formula is as follows: Where x t CP is the optimal solution, namely CP; x t i represents the position of the i-th individual at iteration t, i.e., the predator; β is the convergence rate factor, τ4 is a random value in [0,1]; F i t To affect the attack power of the CP of the i-th predator, the relevant calculation formula is as follows: Where, is the mass of the i-th individual at iteration t, f is the objective function, is the final velocity of the i-th individual at time t+1, and is allocated based on the random solution selected from the current population, is the initial velocity of the i-th individual at iteration t, τ6 is a random vector in [0,1], ε is a minimum value to avoid being divided by 0, and r4 is a random number in [1,N].

4. The multi-mode hybrid energy storage power distribution method in a wind-solar-storage system according to claim 1 is characterized in that: The S3 is specifically: S3.1 Decomposition of P using ICEEMDAN HES S3.1.1 Use empirical mode decomposition to decompose Gaussian white noise and obtain the IMF component of the noise l i(i＝1,2,.,N) , add the original signal x(t) to each component after processing, and get the extended signal x i (t): x i (t)=x(t)+a0l i (15) Where α0 = βstd(x(t)) / std(l1) is the standard deviation of white noise, β is a constant, and std represents the standard deviation operation; S3.1.2 Define the first residual component res1: In the formula, M[x i (t)] represents x i (t) minus the first component IMF1 obtained by EMD decomposition. At this step, the first component IMF1 of the original signal x(t) is as follows: IMF1=x(t)-res1 (17) S3.1.3 Calculate the second residual component res2 and derive IMF2: IMF2=res1-res2 (19) Where, α1 = βstd(r1); S3.1.4 Use the same method to calculate the kth residual and IMF: IMF k =res k-1 -res k (21) Where, α k-1 =βstd(r k-1 ),k>1; S3.1.5 Iterate until the maximum number of iterations is met; S3.2 Construct composite entropy as an evaluation index. Composite entropy is composed of envelope entropy E p and mutual information entropy MI; S3.2.1 Calculation of envelope entropy E p Envelope entropy can reflect whether the frequency characteristics of each IMF are obvious. When the IMF characteristic information is less, the envelope entropy value is larger, otherwise, it is smaller. Envelope entropy E p It can be expressed as: Where a(j) is the envelope signal obtained by Hilbert modulation of signal x(j); p j is the normalized form of a(j); S3.2.2 Calculation of mutual information entropy MI Mutual information in information theory represents the degree of correlation between two events and is not easily affected by external factors. The larger the mutual information entropy value MI, the stronger the correlation between the two events. In terms of IMF, the richer the original signal feature information contained is. The calculation formula is as follows: MI(X,Y)=H(Y)-H(Y|X) (24) Where X and Y are different events, H(Y) is the entropy of Y, and X is P HES , Y is the IMF, H(Y|X) is the conditional entropy of Y when X is known; S3.2.3 Establishing a composite fitness function The expression of the composite fitness function is as follows: ICEEMDAN improves the stability and robustness of the results by repeatedly adding noise and performing decomposition. S3.2.4 Select Nstd and Ne as optimization objects, use compound entropy as fitness function, and use ICPO to optimize the parameter combination [Nstd, Ne] to improve the performance of ICEEMDAN on P HES Decomposition effect.

5. The multi-mode hybrid energy storage power distribution method in a wind-solar-storage system according to claim 1 is characterized in that: The S4 is specifically: P HES After ICPO-ICEEMDAN decomposition, the spectrum is obtained by fast Fourier transform; S4.1 Calculation of relative entropy D Considering the relative entropy between each IMF spectrum, the relative entropy between each IMF spectrum is calculated to characterize the degree of spectrum differentiation. The IMF is divided into n combinations. The relative entropy D is calculated as follows: Where x is the amplitude and N is the number of time points; S4.2 Set power distribution point The maximum value of relative entropy D is taken as the high frequency component P H and low-frequency component P L The dividing point K is obtained from the dividing point K, and after the high-frequency area and the low-frequency area are obtained, the p power types and q energy types in the HES selection results are further allocated; For the high-frequency area, arrange the relative entropies in descending order, take the first p-1 points with larger relative entropy values ​​as power points, and divide the high-frequency area into p segments. The same is true for the low-frequency area. Energy storage with a larger rated cycle number is selected as the bearer of the power in the higher frequency band; the power distribution point setting of M IMFs can be summarized as follows: with the maximum relative entropy as the dividing point, the left side contains K IMF powers, which is the high-frequency area and is borne by power-type energy storage; the right side contains MK IMF powers, which is the low-frequency area and is borne by energy-type energy storage; The decomposed residual res is placed in the low-frequency area and borne by the energy type with a larger number of cycles; S4.3 Reconstructing the power of each mode of energy storage The calculation formula for each mode of energy storage power is as follows: Where x, y, x', y' are the relative entropy division points.

Images