A composite energy storage system power distribution method and device and electronic equipment

Abstract

The application discloses a kind of composite energy storage system power distribution method, device and electronic equipment, obtain the wind power of composite energy storage system and first filter time scale, determine target filter time scale by adaptive time scale planning method;Based on target filter time scale, the grid-connected power of composite energy storage system is carried out wavelet packet decomposition, obtain the target grid-connected power of composite energy storage system, and after polynomial fitting filter method, obtain the power distribution result of composite energy storage system.The application according to wind power fluctuation condition adaptive adjustment and determine suitable filter time scale, and according to the wavelet packet decomposition of grid-connected power of composite energy storage system is carried out in this filter time scale, so that composite energy storage system can consider each period power fluctuation in power distribution process;Further, considering the operating characteristics of composite energy storage system, the internal power of composite energy storage system is distributed using polynomial fitting filter method, and the power distribution efficiency is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of capacity configuration of hybrid energy storage systems, and in particular to a composite energy storage system power distribution method and device and electronic equipment. BACKGROUND

[0002] Composite energy storage is a combination of single energy storage with complementary characteristics and advantages, which has obvious advantages, can realize the complementary advantages of different energy storages, play their respective characteristics, and expand the space for different energy storage devices to play their own advantages; can realize the combination and complementarity of power and energy characteristics, meet the multiple needs of the power grid, and improve the power supply reliability; through regulation means, different energy storage devices operate in their optimal working intervals, optimize the charge and discharge state of each device, and prolong the service cycle and cycle life; under reasonable configuration, reduce the operation cost of energy storage devices, optimize the utilization rate, expand the industrial market, and obtain greater benefits. In energy scheduling management and microgrid integrated control, energy storage capacity optimization configuration is a key problem, and the rationality of configuration directly affects the utilization rate of distributed power and the economy and stability of the microgrid system. However, the existing research shows that the traditional filtering method cannot consider power fluctuations in different periods when distributing grid-connected power and composite energy storage system power. SUMMARY

[0003] Therefore, the embodiments of the present application provide a composite energy storage system power distribution method and device and electronic equipment to solve the technical problem that the traditional filtering method cannot consider power fluctuations in different periods when distributing grid-connected power and composite energy storage system power in the prior art.

[0004] The technical solutions of the present application are as follows:

[0005] In a first aspect, the embodiments of the present application provide a composite energy storage system power distribution method, which comprises: obtaining wind power of a composite energy storage system and a first filtering time scale; determining a target filtering time scale through an adaptive time scale planning method based on the wind power and the first filtering time scale; performing wavelet packet decomposition on grid-connected power of the composite energy storage system based on the target filtering time scale to obtain target grid-connected power of the composite energy storage system; and obtaining a power distribution result of the composite energy storage system through a polynomial fitting filtering method based on the target grid-connected power.

[0006] With reference to the first aspect, in a possible implementation manner of the first aspect, based on the wind power and the first filtering time scale, a target filtering time scale is determined through an adaptive time scale planning method, including: performing wavelet packet decomposition on the wind power of the composite energy storage system in the first filtering time scale to obtain a grid-connected component; determining whether the composite energy storage system in the first filtering time scale meets a grid-connected standard based on the grid-connected component; when the composite energy storage system in the first filtering time scale meets the grid-connected standard, performing rolling filtering on the wind power of the composite energy storage system based on a second filtering time scale to obtain a grid-connected power of the composite energy storage system, the second filtering time scale being determined according to the first filtering time scale; adjusting the first filtering time scale, and performing wavelet packet decomposition on the wind power of the composite energy storage system based on the adjusted first filtering time scale, until the adjustment of the first filtering time scale is stopped when the grid-connected component obtained by the decomposition does not meet the grid-connected standard, and the target filtering time scale is obtained.

[0007] With reference to the first aspect, in another possible implementation manner of the first aspect, the method further includes: correcting an out-of-limit power by using an interval constraint method, the out-of-limit power representing a power value that does not meet a grid-connected power allowable range corresponding to the grid-connected standard.

[0008] With reference to the first aspect, in still another possible implementation manner of the first aspect, the method further includes: obtaining a filtering parameter group and a cost-benefit data set of the composite energy storage system; establishing a full life cycle cost and benefit model of the composite energy storage system based on the cost-benefit data set; and obtaining a capacity configuration result of the composite energy storage system through the full life cycle cost and benefit model and a carnivorous plant algorithm based on the filtering parameter group.

[0009] With reference to the first aspect, in still another possible implementation manner of the first aspect, before obtaining the capacity configuration result of the composite energy storage system through the full life cycle cost and benefit model and the carnivorous plant algorithm based on the filtering parameter group, the method further includes: performing initialization processing on the filtering parameter group.

[0010] With reference to the first aspect, in still another possible implementation manner of the first aspect, obtaining the capacity configuration result of the composite energy storage system through the full life cycle cost and benefit model and the carnivorous plant algorithm based on the filtering parameter group includes: solving the full life cycle cost and benefit model by using the carnivorous plant algorithm based on the filtering parameter group to obtain a target filtering parameter group; and determining the capacity configuration result of the composite energy storage system based on the target filtering parameter group.

[0011] In a further possible implementation manner of the first aspect, based on the filter parameter group, the full life cycle cost and benefit model is solved by using the carnivorous plant algorithm to obtain a target filter parameter group, including: based on the filter parameter group, the full life cycle cost and benefit model is solved to obtain at least one net benefit value of the composite energy storage system; each net benefit value is sorted according to a preset condition, and the sorting result is classified by using the carnivorous plant algorithm to obtain a classification result; and the filter parameter group is updated based on the classification result until the preset condition is met to stop updating, and the target filter parameter group is obtained.

[0012] In the second aspect, the embodiment of the present application provides a composite energy storage system power distribution device, which comprises: an acquisition module configured to acquire wind power and a first filter time scale of a composite energy storage system; a planning module configured to determine a target filter time scale by using an adaptive time scale planning method based on the wind power and the first filter time scale; a decomposition module configured to perform wavelet packet decomposition on grid-connected power of the composite energy storage system based on the target filter time scale to obtain target grid-connected power of the composite energy storage system; and a filtering module configured to obtain a power distribution result of the composite energy storage system by using a polynomial fitting filtering method based on the target grid-connected power.

[0013] In the third aspect, the embodiment of the present application provides a computer readable storage medium storing computer instructions, which are used to make the computer execute the composite energy storage system power distribution method according to the first aspect and any one of the first aspect.

[0014] In the fourth aspect, the embodiment of the present application provides an electronic device comprising a memory and a processor, which are communicatively connected to each other, and the memory stores computer instructions, and the processor executes the composite energy storage system power distribution method according to the first aspect and any one of the first aspect by executing the computer instructions.

[0015] The technical solution provided by the present application has the following effects:

[0016] The composite energy storage system power distribution method provided by the embodiment of the present application can adaptively adjust and determine a suitable filter time scale according to wind power fluctuation, and perform wavelet packet decomposition on grid-connected power of the composite energy storage system according to the filter time scale, so that the composite energy storage system can take into account power fluctuation in each period during power distribution; further, the internal power of the composite energy storage system is distributed by using a polynomial fitting filtering method considering the operation characteristics of the composite energy storage system, thereby improving the power distribution efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to make the technical solutions in the specific embodiments or prior art of the present application clearer, the accompanying drawings needed in the specific embodiments or prior art description will be briefly introduced as follows. Obviously, the accompanying drawings in the following description are some embodiments of the present application, and other accompanying drawings can be obtained by those skilled in the art without any creative effort on the premise that the accompanying drawings are some embodiments of the present application.

[0018] Figure 1 is a flow chart of a power distribution method of a composite energy storage system according to an embodiment of the present application;

[0019] Figure 2 is a flow chart of an adaptive time scale wavelet packet decomposition according to an embodiment of the present application;

[0020] Figure 3 is a flow chart of a carnivorous plant algorithm optimization according to an embodiment of the present application;

[0021] Figure 4 is a structural block diagram of a power distribution device of a composite energy storage system according to an embodiment of the present application;

[0022] Figure 5 is a structural schematic diagram of a computer readable storage medium according to an embodiment of the present application;

[0023] Figure 6 is a structural schematic diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0024] In order to make the technical solutions in the specific embodiments or prior art of the present application clearer, the accompanying drawings needed in the specific embodiments or prior art description will be briefly introduced as follows. Obviously, the accompanying drawings in the following description are some embodiments of the present application, and other accompanying drawings can be obtained by those skilled in the art without any creative effort on the premise that the accompanying drawings are some embodiments of the present application.

[0025] The capacity configuration and operation loss of the composite energy storage system are related to the power instruction allocated by the composite energy storage system, so that the economic efficiency can be improved by reducing the power instruction of the composite energy storage system on the premise that the flattened grid-connected power meets the grid-connected requirement.

[0026] The embodiment of the present application provides a power distribution method of a composite energy storage system, as shown in the figure, the method comprises the following steps: Figure 1

[0027] Step 101: acquiring wind power and a first filtering time scale of the composite energy storage system. ​

[0028] wherein the first filtering time scale is a maximum time scale.

[0029] Step 102: determining a target filtering time scale based on the wind power and the first filtering time scale by an adaptive time scale planning method.

[0030] Specifically, according to the wind power of the composite energy storage system, the first filtering time scale is adjusted by the adaptive time scale planning method, so that the wind power fluctuation of different time periods can be considered in the adjusted filtering time scale.

[0031] Step 103: performing wavelet packet decomposition on the grid-connected power of the composite energy storage system based on the target filtering time scale to obtain a target grid-connected power of the composite energy storage system.

[0032] Specifically, according to the description in step 102, the grid-connected power of the energy storage system is decomposed by the adjusted target filtering time scale, and the wind power fluctuation of different time periods can be considered.

[0033] Step 104: obtaining a power distribution result of the composite energy storage system by a polynomial fitting filtering method based on the target grid-connected power.

[0034] Specifically, after the target grid-connected power is determined, the internal power of the composite energy storage system needs to be further distributed. In the embodiment of the present application, the polynomial fitting filtering (SG filtering) method is used for internal power decoupling, which improves the power distribution efficiency.

[0035] The composite energy storage system power distribution method provided by the embodiment of the present application adaptively adjusts and determines a suitable filtering time scale according to the wind power fluctuation, and performs wavelet packet decomposition on the grid-connected power of the composite energy storage system according to the filtering time scale, so that the power fluctuation of each time period can be considered in the power distribution process of the composite energy storage system. Further, the internal power of the composite energy storage system is distributed by the polynomial fitting filtering method considering the operating characteristics of the composite energy storage system, which improves the power distribution efficiency.

[0036] As an optional implementation of the embodiment of the application, step 102 comprises: performing wavelet packet decomposition on the wind power of the composite energy storage system in the first filtering time scale to obtain a grid-connected component; judging whether the composite energy storage system in the first filtering time scale meets a grid-connected standard based on the grid-connected component; when the composite energy storage system in the first filtering time scale meets the grid-connected standard, performing rolling filtering on the wind power of the composite energy storage system based on a second filtering time scale to obtain grid-connected power of the composite energy storage system, the second filtering time scale being determined according to the first filtering time scale; adjusting the first filtering time scale, and performing wavelet packet decomposition on the wind power of the composite energy storage system based on the adjusted first filtering time scale until the adjustment of the first filtering time scale is stopped when the grid-connected component obtained by the decomposition does not meet the grid-connected standard, and the target filtering time scale is obtained.

[0037] The grid-connected standard is the <Electrochemical Energy Storage Power Station Grid-connected Dispatching Protocol Demonstration Text (Trial)>.

[0038] Specifically, a typical day is selected, the wind power data time scale of the typical day is set as T, and the original wind power is set as P wg .

[0039] Firstly, the first filtering time scale T(n) is set as the maximum time scale, wavelet packet decomposition is performed in the T(n) period, a fundamental frequency component is obtained as shown in the following formula (1), and the fundamental frequency component is taken as a grid-connected component:

[0040]

[0041] In the formula, w Pwg [b, a, T(n)] represents energy of wind power decomposition to a base coordinate axis, that is, a fundamental frequency component; a represents a scale factor for determining a wavelet base frequency; b represents a moving factor for determining a wavelet base time domain property; d a,b (t) represents a wavelet family as shown in the following formula (2); and n represents a wavelet packet decomposition layer number.

[0042]

[0043] Secondly, whether the composite energy storage system in the first filtering time scale meets the grid-connected standard is judged based on the grid-connected component, if yes, rolling filtering is performed on the wind power of the composite energy storage system based on a second filtering time scale until the extraction of the grid-connected power is completed;

[0044] Finally, the first filtering time scale is gradually reduced by ΔT, and the target filtering time scale is determined when the grid-connected power obtained by decomposition based on the adjusted time scale does not meet the grid-connected standard.

[0045] Further, the grid-connected power of the composite energy storage system is wavelet packet decomposed by using the target filtering time scale to obtain the target grid-connected power of the composite energy storage system, that is, the grid-connected component obtained by using the fir equation (1) and the target filtering time scale are substituted into the following equation (3):

[0046]

[0047] In the equation, P' wg [t,T(n)] represents the grid-connected power.

[0048] In an embodiment, the adaptive time scale wavelet packet decomposition process is as shown in the following equation (2). Figure 2

[0049] As an optional implementation of the embodiment of the present application, the method further comprises: correcting the out-of-limit power by using an interval constraint method.

[0050] In the equation, the out-of-limit power represents a power value that does not satisfy the allowed range of the grid-connected power corresponding to the grid-connected standard.

[0051] In order to solve the optimal capacity configuration of the composite energy storage system under the condition of meeting the grid-connected requirement, according to the grid-connected standard, the minimum time scale is selected to extract the grid-connected power, and then the interval constraint method is used to correct a few out-of-limit points of the power, so that the grid-connected requirement is met, and the fluctuation of 1 / n1 of the wind power installed capacity is not more than 1 min, and the fluctuation of 1 / n2 of the wind power installed capacity is not more than 10 min. 10 The grid-connected standard is taken as an example to correct the power constraint.

[0052] In the equation, P' wg0 (t) represents the grid-connected power at t, and the wind power installed capacity is P wind , and the allowed fluctuation interval is shown in the following equation (4):

[0053] [P′ wgo,l (t),P′ wgo,u (t)]∈[P one,l (t),P one,u (t)]∩[P ten,l (t),P ten,u (t)] (4)

[0054] In the equation, [P one,l (t),P one,u (t)] represents the lower limit of the 1 min fluctuation interval; and [P ten,l (t),P ten,u (t)] represents the upper limit of the 1 min fluctuation interval, and is shown in the following equations (5) and (6):

[0055] ​P one,l (t) = max P' wg0 (i) - P wind / n1,i = t-60,...,t-1 (5)

[0056] P one,u (t) = min P' wg0 (i) - P wind / n1,i = t-60,...,t-1 (6)

[0057] Further, the lower limit and upper limit of the 10min fluctuation interval can be obtained, the grid-connected power allowable range at t time is obtained according to the grid-connected standard, and then the over-limit power is corrected, and the corrected grid-connected power is shown in the following relationship (7):

[0058] P" wgo (t) = P' wgo (t) + ΔP xz (t) (7)

[0059] Wherein, ΔP xz (t) represents the over-limit power, and is shown in the following relationship (8):

[0060]

[0061] Further, the distributed composite energy storage system primary power instruction can be obtained, and is shown in the following relationship (9):

[0062] P ess = P wg - P" wg0 (9)

[0063] Further, after the composite energy storage system power instruction is distributed, the internal power needs to be further distributed, and the embodiment of the application distributes the power based on the SG filter.

[0064] Specifically, taking the power instruction at t time as the center, 2N+1 power instruction points before and after the center are processed, a k-order polynomial fitting is adopted, and the fitting power in the interval, that is, the battery power instruction can be expressed as the following relationship (10):

[0065] P bat,(2N+1)×1 = T·(TT·T) -1 ·X (10)

[0066] Wherein:

[0067] X (2N+1)×1 = T (2N+1)×(k+1) ·A (k+1)×1 +E (2N+1)×1 (11)

[0068] In the formula, X(2N+1)×1 T represents the weight error vector; (2N+1)×(k+1) Represents the power command sampling point matrix; A (k+1)×1 E represents the vector of polynomial fitting coefficients; (2N+1)×1 This represents the least squares fitting error vector.

[0069] Among them, X needs to be satisfied. (2N+1)×1 Order less than T (2N+1)×(k+1) Only the rank of the above relation (11) can guarantee that it has a real solution.

[0070] Furthermore, after the internal power command allocation is completed, the power command of the supercapacitor can be expressed as the following relationship (12):

[0071] P uc =P ess -P bat (12)

[0072] As an optional implementation of this invention, the method further includes: obtaining a set of filtering parameters and a cost-benefit dataset of the composite energy storage system; establishing a life-cycle cost and benefit model of the composite energy storage system based on the cost-benefit dataset; and obtaining the capacity configuration result of the composite energy storage system based on the set of filtering parameters, the life-cycle cost and benefit model, and the carnivorous plant algorithm.

[0073] The filter parameter set includes the filter window length and the set of polynomial fitting orders (population), which can be obtained according to step 104.

[0074] The cost-benefit dataset can include the investment cost, replacement cost, auxiliary cost, operation and maintenance cost, recovery cost, and operating benefits of the composite energy storage system. Its net benefit over its life cycle can be expressed as the following equation (13):

[0075] C NPV =C hj +C by +C hs -(C tz +C gx +C fz +C yw +C cl (13)

[0076] In the formula: C hj Indicates the benefits of delayed grid connection construction; C by This indicates a reduction in the benefits of wind farm reserve capacity; C hs The recycling benefits are represented by the following equations (14), (15), and (16):

[0077] C hj= k max{0, max[P wg (t)] - max[P wg (t) - P ess (t)]} (14)

[0078]

[0079] C hs = β (C tz + C gx + C fz ) F(i, t) (16)

[0080] wherein P wg (t) represents the original wind power at time t; P ess (t) represents the output of the composite energy storage system at time t; P rate represents the power configuration of the energy storage system; k represents the unit power cost of the slow build grid connection channel, taking 1500 yuan / kW; b r represents the standby capacity price, taking 0.9 yuan / kW; a represents the wind power output prediction reliability, taking 0.85; b represents the residual value recovery rate, taking 0.05; N d represents the number of wind power prediction sampling points; LC represents the life cycle; F(i, t) = (1 + i) -T represents the one-time payment discount rate; A(i, T) = [(1 + i) T - 1] / [i (1 + i) T ] represents the equivalent payment present value coefficient, wherein i represents the discount rate, taking 10%.

[0081] Further, in the above formula (13), C tz represents the investment cost; C gx represents the replacement cost; C fz represents the auxiliary cost; C yw represents the operation and maintenance cost; and C cl represents the scrap disposal cost, i.e. the recovery cost, which are respectively shown in the following formulas (17), (18), (19), (20) and (21):

[0082] C tz = C tz_p · C tz_e · E rate (17)

[0083]

[0084] C fz = C fz_p · P rate + C fz_e · E rate (19)

[0085]

[0086] C cl =(C cl_p ·P rate +C cl_e ·E rate )·(n+1)·F(i,T) (21)

[0087] In the formula: E rate Indicates the capacity configuration of the energy storage system; C tz_p C fz_p C cl_p C cl_p Both represent unit power; C tz_e Indicates the cost of capacity investment; C gx_p Indicates the unit power replacement cost; C fz_e Indicates capacity auxiliary cost; C yw_p Indicates unit power; C yw_w Indicates charge / discharge capacity and operating / maintenance costs; C cl_e This indicates the cost of disposing of obsolete capacity.

[0088] Specifically, after power allocation using SG filtering, energy-type energy storage undertakes the main power, while power-type energy storage assists in handling the high-frequency portion. Then, to make the economic analysis more closely resemble engineering practice, a full life-cycle cost-benefit model of the composite energy storage system is constructed. With the goal of maximizing the net benefit over the life cycle, the SG filtering window length and filtering order are used as decision variables, and the CPA algorithm is employed for optimization to obtain the final configuration scheme.

[0089] Furthermore, before obtaining the capacity configuration result of the composite energy storage system based on the filter parameter set, the method further includes: initializing the filter parameter set.

[0090] Specifically, initialize the filter window length and the set of polynomial fitting orders (population) and calculate the corresponding net present value (fitness):

[0091] The population is randomly initialized within the range of filter parameter variation, and then the net benefit value of each individual's life cycle, i.e., fitness value, is obtained, as shown in the following relationships (22) and (23):

[0092]

[0093]

[0094] In the formula, Pop(·) represents a population; Fit(·) represents population fitness; l represents a filter window scale; k represents a polynomial fitting order; and n represents a population size.

[0095] It should be noted that l and k are positive integers; l is an odd number; and further, to make the relationship (11) have a real number solution, k < l should be satisfied.

[0096] Further, based on the filter parameter group, the capacity configuration result of the composite energy storage system is obtained through the life cycle cost and benefit model and the carnivorous plant algorithm, including: based on the filter parameter group, the target filter parameter group is obtained by solving the life cycle cost and benefit model using the carnivorous plant algorithm; and based on the target filter parameter group, the capacity configuration result of the composite energy storage system is determined.

[0097] In the formula, Pop(·) represents a population; Fit(·) represents population fitness; l represents a filter window scale; k represents a polynomial fitting order; and n represents a population size.

[0098] First, the population is sorted according to the net present value, and the population is grouped based on the carnivorous plant algorithm.

[0099] Specifically, the net present values are arranged in descending order from small to large, the first nCP individuals with the largest net present values are taken as carnivorous plants, and the remaining individuals are taken as prey, denoted as nPrey. Next, grouping is performed, the first-level prey is allocated to the first-level carnivorous plant according to the fitness arrangement, the second-level prey is allocated to the second-level carnivorous plant, and so on. When the carnivorous plants finish allocating the prey, the nCP+1-level prey is allocated to the first-level carnivorous plant, the nCP+2-level prey is allocated to the second-level carnivorous plant, and so on, until all the prey are allocated. The classification process is shown in the following relationship (24) and (25), and the grouping process is shown in Table 1, where m = nPrey / nCP depends on the population size of the prey and the carnivorous plant.

[0100]

[0101]

[0102] Table 1, CPA grouping process

[0103]

[0104] Secondly, the filter parameter set is optimized.

[0105] Specifically, since the initialization of the parameters is a random process, in order to make the filter parameter combination change towards the trend of increasing the life cycle net benefit, in the updating process, the process of the predation of the carnivorous plant, the growth of the prey and the escape of the prey is imitated, and the update of the first nCP filter parameter combinations with the maximum life cycle net benefit value is shown in the following relation (26):

[0106] NCP i,j = grow i,j ×CP i,j + (1-grow i,j )Prey i,j (26)

[0107] In the formula, CP i,j represents the i-th filter parameter combination; Prey i,j represents the prey allocated to the i-th filter parameter combination selected randomly, that is, the filter parameter combination with relatively poor fitness.

[0108] Wherein:

[0109] grow i,j = grow_rate × r i,j (27)

[0110] In the formula, grow_rate represents the growth rate, which is 0.8 in the embodiment of the present application; r i,j represents a random number in [0, 1].

[0111] Further, the update process of the nPrey filter parameter combinations is shown in the following relation (28):

[0112] NPrey i,j = grow i,j ×Prey u,j + (1-grow i,j )Prey v,j ,u≠v (28)

[0113] Wherein:

[0114]

[0115] In the formula, Prey u,j represents the prey allocated to the u-th filter parameter combination selected randomly, that is, the filter parameter combination with relatively poor fitness; Prey v,j represents the prey allocated to the v-th filter parameter combination selected randomly, that is, the filter parameter combination with relatively poor fitness.

[0116] Then, the replication and expansion of the filter parameter set is performed.

[0117] Specifically, the carnivorous plant is imitated to absorb nutrients from the prey and grow and reproduce with the nutrients, wherein in order to ensure that the CPA optimization is focused on the optimal filter parameter set, thereby reducing the calculation cost, only the filter parameter set with the largest net present value is allowed to be replicated. The replication process is shown in the following formula (30):

[0118] NewCP i,j = CP 1,j + Rep_rate x rand i,j x mate i,j (30)

[0119] Wherein:

[0120]

[0121] In the formula: CP 1,j represents the optimal filter parameter set; CP v,j represents a randomly selected filter parameter set; Rep_rate represents the reproduction rate, which is 1.8. The above process is repeated nCP times.

[0122] Further, considering the window length and fitting order constraints, the related constraint processing process of the above optimization process, replication and expansion process is shown in the following formulas (32), (33), (34):

[0123] NCP i,j or NewCP i,j = ceilodd(NCP i,j ) (32)

[0124] In the formula: ceilodd(·) represents taking an odd number up;

[0125] Wherein:

[0126] NPrey i,j = ceil(NPrey i,j ) (33)

[0127] In the formula: ceil(·) represents rounding up;

[0128]

[0129] In the formula: randi(n) represents taking a positive integer between [1, n].

[0130] Finally, the net present value is rearranged and the filter parameter set is updated.

[0131] Specifically, the above process of updating and expanding the set produces a new set of filter parameter groups of dimension [n+nCP(g_iter)+nCP]x2, n, nCP(g_iter), nCP are the filter parameter groups in the original, optimization and expansion process, then according to the descending order of net benefit size, select the first n parameter groups as the new parameter set, and select the first nCP parameter groups as the carnivorous plant, and the remaining nPrey parameter groups as the prey. This process ensures that the update of the filter parameter group always makes the net benefit better.

[0132] Repeat the above process until the maximum number of iterations or the convergence condition is reached, i.e. the final filter parameter group, i.e. the target filter parameter group, is determined

[0133] Further, the capacity configuration result of the composite energy storage system is determined.

[0134] Specifically, the power configuration rule of the energy storage system is to meet the maximum demand of the power instruction in the period, as shown in the following relationship (35):

[0135] P rate =max{max(P ess (t i )·n ch ),|min(P ess (t j ) / n dic )|},t i ,t j ∈[t0,t0+T] (35)

[0136] In the formula: P ess (·) represents the power instruction of the energy storage system; n ch represents the charging efficiency of the energy storage; n dic represents the discharging efficiency of the energy storage; t0 represents the initial time; T represents the considered energy storage operation period.

[0137] Further, the capacity configuration rule of the energy storage system is to make it within a reasonable range, and according to the cumulative energy of the energy storage in the period, the configuration is as shown in the following relationship (36):

[0138]

[0139] In the formula: E(t) represents the real-time cumulative energy, as shown in the following relationship (37); SOC max represents the maximum value of the allowable state of charge of the energy storage; SOC min represents the minimum value of the allowable state of charge of the energy storage.

[0140]

[0141] In the formula, E0 represents initial energy storage.

[0142] The embodiment of the present application considers that the SG filtering effect is related to the filtering parameters and the parameter set is large while performing internal power allocation by using the SG filtering method, and an optimization model is constructed to optimize the filtering parameters with the maximum net benefit in the life cycle as the target, and considering that the model is a multi-peak model, the algorithm performance requirement is high, and a new emerging swarm intelligence algorithm CPA algorithm is applied to optimize and solve to obtain the final configuration scheme.

[0143] In an embodiment, the economic optimal capacity configuration process of the composite energy storage system based on the SG filtering and the carnivorous plant algorithm is as shown in Figure 3

[0144] Further, the above method provided by the embodiment of the present application is simulated and analyzed, including:

[0145] (1) Simulation analysis of the composite energy storage system primary power distribution based on the optimal capacity configuration and the minimum operation loss.

[0146] Taking two typical daily wind power curves of a certain wind power base as an example for simulation analysis, the sampling time is 1s, and the method is verified, wherein the typical day 1 fluctuates greatly, and the 1, 10 maximum fluctuation rates are 19.6119%, 48.1322% respectively, and the typical day 2 power fluctuation is relatively gentle, and the 1, 10 maximum fluctuation rates are 17.2806%, 36.9322% respectively. In order to analyze more comprehensively, the evaluation indexes such as fluctuation rate and close power difference are defined, and the traditional filtering method is compared and analyzed from the aspects of scene applicability. First, from the perspective of the composite energy storage system primary power distribution, the effectiveness of the self-adaptive time scale wavelet packet decomposition method and the improvement effect of the minimum time scale wavelet packet decomposition-interval constraint correction method (hereinafter referred to as the method) derived therefrom are verified.

[0147] (1) The fluctuation rate index is used to analyze the grid-connected power, and the cumulative positive and negative fluctuation rate difference (CFD) index is used to evaluate the capacity configuration rationality, and further, the cumulative redundant fluctuation rate is defined to measure the daily charging and discharging capacity of the composite energy storage system, that is, the operation and maintenance cost, and several evaluation indexes include;

[0148] The N-second time scale fluctuation rate λ is as shown in the following relationship (38):

[0149]

[0150] In the formula, P * (i), P * (j) represents the power in the N time scale; * represents different types, which can include original wind power and grid-connected power; P wg,rate ​The wind power installed capacity is represented.

[0151] The cumulative redundancy fluctuation rate CRV is shown in the following relation (39):

[0152]

[0153] Wherein:

[0154]

[0155] In the formula, λ pwg (i) represents the original wind power; λ bw (i) represents the fluctuation rate of the grid-connected power at time i; λ up represents the upper limit of the fluctuation rate.

[0156] In addition, in order to facilitate the analysis of the degree to which the grid-connected power approaches the original wind power under the satisfaction of the grid-connected standard, the approach power difference and the average approach power difference are also defined.

[0157] Wherein, the approach power difference is shown in the following relation (41):

[0158] P ce (t) = |P wg (t) - P" wg0 (t) |, t = 1, 2,..., n (41)

[0159] The average approach power difference is shown in the following relation (42):

[0160]

[0161] In the formula, n * represents the number of power sampling points in which the approach power difference of the method is greater than or less than that of other methods.

[0162] (2) Adaptive time scale wavelet packet decomposition and minimum time scale wavelet packet decomposition-interval constraint correction simulation analysis.

[0163] Specifically, first, the adaptive time scale wavelet packet decomposition is simulated and analyzed, and the planning idea is to gradually reduce from the maximum time scale (24h scale) to 1h scale at an interval of 1h scale, and then gradually reduce to the minimum time scale (10min scale) at an interval of 10min scale. Further, several time scales are selected for intuitive display and analysis in the embodiments of the present application to verify the effectiveness of the proposed strategy. At the same time, in order to optimize the capacity configuration and operation of the energy storage system, the minimum time scale wavelet packet decomposition is used for filtering, and a small number of grid-connected power out-of-limit points are corrected by the interval power constraint method.

[0164] Under the premise of meeting the grid-connection standard, the adaptive time scale wavelet packet decomposition method can reduce the filtering time scale at a given time scale interval. According to the grid-connection power comparison results of the adaptive time scale wavelet packet decomposition, the grid-connection power of several filtering time scales in the adaptive planning process can be obtained, and it is found that the grid-connection power extracted at the maximum time scale is very smooth. The reason is that the determination of the wavelet packet decomposition layer, that is, the decomposition degree, is limited to the maximum power fluctuation period, thereby leading to excessive decomposition of the power in other periods, and the grid-connection power is smooth. With the reduction of the time scale, the extracted grid-connection power can gradually approach the original wind power, and the power instruction of the composite energy storage system is reduced, thereby reducing the energy storage capacity configuration and operation cost. However, this process is accompanied by an increase in the grid-connection power fluctuation. In the simulation process, it is found that the time scale interval planned according to the embodiment of the present application, 30min scale, is the minimum time scale to meet the grid-connection standard. The grid-connection power at this time scale can better approach the original wind power.

[0165] From the perspective of cumulative close-to-power difference, the cumulative close-to-power differences of 24h, 8h, 6h, 4h, 2h, 1h and 30min in the typical day are 51728MW, 31692MW, 31021MW, 27511MW, 22956MW, 19668MW, 13769MW and 7369MW, respectively. Compared with 24h filtering, the cumulative close-to-power differences corresponding to 8h, 6h, 4h, 1h and 30min scales decrease by 38.73%, 39.31%, 40.03%, 46.82% and 55.62%, respectively. Analysis shows that as the time scale decreases, the close-to-degree of the grid-connection power under the requirement of meeting the grid-connection increases, which indicates the effectiveness of the method. On this basis, the cumulative close-to-power difference obtained by using the method is 7369MW, which decreases by 85.75% compared with the 24h scale and decreases by 46.48% compared with the 30min scale, which indicates the effectiveness of the method and the improvement of the close-to-effect compared with the adaptive time scale planning wavelet packet method.

[0166] From the perspective of fluctuation rate, during the process of continuously reducing to the minimum time scale, a small number of power sudden change moments may exceed the limit. After the grid-connection power is extracted by the 10min scale wavelet packet, most of the power sampling points in the typical day are constrained within the grid-connection standard, and only a small number of sudden change power sampling points exceed the limit. The 1min and 10min exceeding power sampling points are 34 and 145, respectively, accounting for 0.0394% and 0.17% of the total sampling points, respectively, which indicates that the exceeding probability is extremely small.

[0167] On the basis of the minimum time scale wavelet packet, the interval constraint power correction method is used to correct the out-of-limit power points again (this method), which can constrain all power sampling points within the grid connection standard, and the effectiveness of this method is verified.

[0168] From the perspective of grid-connected power, the comparison between this method and the adaptive time scale wavelet packet method shows that the 30min time scale wavelet packet decomposition can well approach the original wind power under the premise of meeting the grid connection standard, but when the sampling points containing sudden wind power changes are included, the time period considered by the 30min scale is large, and the wavelet packet decomposition level is high. The effect of approaching the original wind power under this scale is not as good as this method.

[0169] From the perspective of approaching power, in order to make the simulation results more intuitive, 1min sampling is used for demonstration, but the data analyzed is still 1s sampling. The analysis of the difference in approaching power is the same as above. Compared with the adaptive time scale wavelet packet method, the power sampling points with smaller approaching power difference (the upper part of the horizontal axis) of this method are 45479, accounting for 52.64%, and the maximum approaching power difference and the average approaching power difference are reduced by 1.6987MW and 0.1694MW respectively. Although there are 40921 power sampling points with larger approaching power difference, the maximum approaching power difference and the average approaching power difference are only increased by 0.6389MW and 0.0322MW respectively, which shows that although this method has slightly lower approaching degree at some time points compared with the adaptive time scale wavelet packet decomposition method, the overall improvement effect and improvement degree are greatly improved.

[0170] (3) Simulation analysis of different filtering methods for extracting grid-connected power.

[0171] Specifically, the advantages of the proposed method are further verified. From the perspective of approaching power, compared with the moving average method, the power sampling points with smaller approaching power difference of this method are 79370, accounting for 91.86%, and the maximum approaching power difference and the average approaching power difference are reduced by 2.3085MW and 0.4053MW respectively. The power sampling points with larger approaching power difference are 7030, and the maximum approaching power difference and the average approaching power difference are only increased by 0.7973MW and 0.1486MW respectively, which shows that this method has improvement effect at most time points compared with the moving average method, and the maximum improvement degree and the overall improvement degree are improved.

[0172] From the perspective of volatility, compared with the moving average method, the 1 min cumulative redundant volatility of the grid-connected power extracted by the method is reduced from 249290 to 59837, a decrease of 76%, and the 10 min cumulative redundant volatility is reduced from 611960 to 114490, a decrease of 81.29%. This means that the energy storage as an energy exchange medium can meet the requirements under smaller power exchange, which shows that the method has an advantage over the moving average method when applied to energy storage configuration.

[0173] In order to further illustrate the scene applicability of the method, a typical day 2 with relatively smooth wind power is simulated. Since the power of the typical day is relatively smooth, the window length of the moving average method for extracting grid-connected power is smaller than that of the typical day 1. After averaging the power in the window, it can better approach the original power, but in the window containing the sudden power sampling point, its approaching effect is not as good as the method.

[0174] From the perspective of approaching power, compared with the moving average method, the power sampling points with smaller power difference are 69538, accounting for 80.48%, and the maximum approaching power difference and the average approaching power difference are reduced by 0.1968 MW and 0.1858 MW, respectively. The power sampling points with larger approaching power difference are 16862, and the maximum approaching power difference and the average approaching power difference are only increased by 0.2771 MW and 0.0828 MW, respectively. Although the maximum approaching power difference of the method is slightly smaller than that of the moving average method, further analysis shows that this result is caused by a few sampling points at around 9:00. The reason is that the power sampling points at the end of the rolling process of the minimum time scale filter fluctuate greatly, resulting in a higher number of decomposition layers. However, the probability of this situation is very small, and the overall improvement effect of the method is good and the overall improvement effect is good compared with the moving average method.

[0175] From the perspective of volatility, compared with the moving average method, the 1 min cumulative redundant volatility of the grid-connected power extracted by the method is reduced from 249290 to 59837, a decrease of 76%, and the 10 min cumulative redundant volatility is reduced from 611960 to 114490, a decrease of 81.29%. This means that the energy storage as an energy exchange medium can meet the requirements under smaller power exchange, which shows that the method has an advantage over the moving average method when applied to energy storage configuration.

[0176] Through the above analysis, compared with typical day 1, the grid-connected power extracted by the moving average method of typical day 2 is closer to the original power and the method. The reason for this is that the power fluctuation of typical day 2 is smaller, the window length of the moving average method is less restricted, and the overall power extraction smoothness constraint is reduced, which shows that the moving average method is less adaptable than the method in different scenarios.

[0177] (II) The simulation analysis of the secondary power distribution and capacity configuration of the composite energy storage system based on the maximum net benefit.

[0178] Specifically, an optimization model is established with the maximum net benefit value of the composite energy storage system in a 15-year cycle as the target, the CPA algorithm is applied to solve the model, and the effectiveness of the algorithm is verified by comparing with the GA and PSO algorithms, and then the feasibility of the method in the economic aspect is verified from the economic point of view.

[0179] (1) Simulation analysis of optimization solution by different algorithms.

[0180] Specifically, the optimization solution is performed by using the GA algorithm, the PSO algorithm and the CPA algorithm respectively with the maximum net benefit value of the composite energy storage system in the life cycle as the target and the filter window length and the polynomial fitting order in the window as the decision variables, and the related simulation parameters are shown in Table 2:

[0181] Table 2: Simulation parameters of optimization algorithms

[0182]

[0183] The model is optimized and solved by using the three optimization algorithms, and according to the solution results, among the three optimization algorithms, the CPA algorithm converges the fastest, and the optimal solution is obtained in the 10th generation, and is less affected by the initial value of the population, while the PSO algorithm has a slower convergence speed, and the optimal solution is obtained in the 26th generation, and is slightly smaller than that of the CPA algorithm, the initial value of the GA algorithm is similar to that of the CPA algorithm, but the convergence process is slower, and the optimal solution is smaller than those of the CPA algorithm and the PSO algorithm. From the data point of view, the optimization results of the GA, PSO and CPA algorithms are 1.1236×10 8 , 1.1342×10 8 , 1.1353×10 8 , respectively, and compared with the PSO and GA algorithms, the economic benefits of the CPA optimization are improved by 0.1% and 1.04%, respectively. From the above analysis, it can be seen that the algorithm described in the embodiment has high efficiency and effectiveness in model solving.

[0184] From the output characteristics of the composite energy storage system, based on the power distribution results obtained by the optimized filter parameter set, the power distribution based on the method can maintain the power instruction characteristics of the composite energy storage system, and the lower frequency and high power instructions are distributed to the lithium ion battery storage, and the higher frequency and small power instructions are distributed to the super capacitor to reduce the charging and discharging times of the lithium ion battery, prolong the life of the lithium ion battery and improve the economy of the composite energy storage system.

[0185] According to the internal power instruction obtained based on the optimized filter parameter set, the power and capacity of the composite energy storage system are configured, and the results are shown in Table 3:

[0186] Table 3, optimization results of different algorithms

[0187] Parameter GA PSO CPA Lithium battery power / MW 2.7722 2.7705 2.7867 Lithium battery capacity / MWh 0.2207 0.2279 0.2327 Super capacitor power kW 185.9 177.5 144.1 Super capacitor capacity kWh 11.4629 10.3442 9.858 Net benefit / ten thousand yuan 11236 11342 11353

[0188] (2) Economic analysis of different method optimization configurations.

[0189] Specifically, in order to verify the economic feasibility of the method, the net benefits of the composite energy storage system in a 15-year period are compared by the method, and the results are shown in Table 4 as follows:

[0190] Table 4, comparison of different method configurations and economics

[0191] Parameter Result Lithium battery power / MW 2.7867 Lithium battery capacity / MWh 0.2327 Super capacitor power / MW 0.1441 Super capacitor capacity / MWh 0.09858 Net benefit / ten thousand yuan 11353

[0192] The embodiment of the present application proposes an optimization configuration method for the capacity of the composite energy storage system for the wind power fluctuation suppression scene from the perspective of twice power distribution, which has the following effects:

[0193] (1) The adaptive wavelet packet decomposition method based on adaptive time scale is proposed, which can determine the appropriate filtering time scale according to the wind power fluctuation, and take into account the wind power fluctuation in different periods. At the same time, the interval constraint method is proposed for secondary correction to solve the influence of a small number of power over-limit points on further reducing the filtering time scale, so as to further reduce the composite energy storage system power instruction while meeting the grid connection requirements.

[0194] (2) Considering the operation characteristics of the composite energy storage system, the SG filtering method is used for internal power distribution. At the same time, considering that the SG filtering effect is related to the filtering parameters and the parameter set is large, an optimization model is constructed to optimize the filtering parameters with the goal of maximizing the net benefit in the life cycle. Considering that the model is a multi-peak model, the algorithm performance requirement is high, the emerging swarm intelligence algorithm CPA algorithm is used for optimization and solution, and compared with GA, PSO and other algorithms, the efficiency of the algorithm in solving the model is verified, which provides a reference for subsequent complex model solving;

[0195] (3) The cumulative redundant fluctuation rate is defined, which is close to the power difference and other indicators. The feasibility and advantages of the method described in the embodiment of the present application are analyzed from the aspects of grid-connected power, grid-connected power redundant fluctuation rate and close power difference. At the same time, two typical days with different fluctuation characteristics are used to compare and analyze the method described in the embodiment of the present application and other methods from the aspects of grid-connected power and energy storage system capacity configuration, verify the scene applicability and rationality of the method described in the embodiment of the present application. The optimization results of different optimization algorithms are compared and analyzed to verify the efficiency of the algorithm in solving the model, and the different configuration methods are compared to verify the economic feasibility of the method described in the embodiment of the present application.

[0196] The embodiment of the present application also provides a composite energy storage system power distribution device, as shown in the figure, the device comprises: Figure 4

[0197] The acquisition module 401 is used for acquiring wind power and a first filtering time scale of a composite energy storage system; for details, refer to the related description of step 101 in the above method embodiment.

[0198] The planning module 402 is used for determining a target filtering time scale through an adaptive time scale planning method based on the wind power and the first filtering time scale; for details, refer to the related description of step 102 in the above method embodiment.

[0199] The decomposition module 403 is used for performing wavelet packet decomposition on grid-connected power of the composite energy storage system based on the target filtering time scale to obtain target grid-connected power of the composite energy storage system; for details, refer to the related description of step 103 in the above method embodiment.

[0200] The filtering module 404 is used for obtaining a power distribution result of the composite energy storage system through a polynomial fitting filtering method based on the target grid-connected power; for details, refer to the related description of step 104 in the above method embodiment.

[0201] The composite energy storage system power distribution device provided by the embodiment of the present application can adaptively adjust and determine a suitable filtering time scale according to wind power fluctuation, and perform wavelet packet decomposition on grid-connected power of the composite energy storage system according to the filtering time scale, so that the composite energy storage system can take into account power fluctuation in each period during power distribution; further, the internal power of the composite energy storage system is distributed by using a polynomial fitting filtering method considering the operating characteristics of the composite energy storage system, and the power distribution efficiency is improved.

[0202] ​As an optional implementation of the embodiment of the present application, the planning module comprises: a decomposition submodule, configured to perform wavelet packet decomposition on the wind power of the composite energy storage system in the first filtering time scale to obtain a grid-connected component; a judgment submodule, configured to judge whether the composite energy storage system meets a grid-connected standard in the first filtering time scale based on the grid-connected component; a rolling filtering submodule, configured to perform rolling filtering on the wind power of the composite energy storage system based on a second filtering time scale when the composite energy storage system meets the grid-connected standard in the first filtering time scale, to obtain grid-connected power of the composite energy storage system, the second filtering time scale being determined according to the first filtering time scale; and an adjustment submodule, configured to adjust the first filtering time scale and perform wavelet packet decomposition on the wind power of the composite energy storage system based on the adjusted first filtering time scale until the adjustment of the first filtering time scale is stopped when the grid-connected component obtained by the decomposition does not meet the grid-connected standard, and the target filtering time scale is obtained.

[0203] As an optional implementation of the embodiment of the present application, the device further comprises a correction module, configured to correct the out-of-limit power by using an interval constraint method, the out-of-limit power representing a power value that does not meet a grid-connected power allowable range corresponding to the grid-connected standard.

[0204] As an optional implementation of the embodiment of the present application, the device further comprises: a first acquisition module, configured to acquire a filtering parameter group and a cost-benefit data set of the composite energy storage system; an establishment module, configured to establish a full-life-cycle cost and benefit model of the composite energy storage system based on the cost-benefit data set; and a configuration module, configured to obtain a capacity configuration result of the composite energy storage system by passing the full-life-cycle cost and benefit model and a carnivorous plant algorithm based on the filtering parameter group.

[0205] As an optional implementation of the embodiment of the present application, the device further comprises an initialization module, configured to perform initialization processing on the filtering parameter group.

[0206] As an optional implementation of the embodiment of the present application, the configuration module comprises: a first solving submodule, configured to solve the full-life-cycle cost and benefit model by using the carnivorous plant algorithm based on the filtering parameter group to obtain a target filtering parameter group; and a determination submodule, configured to determine the capacity configuration result of the composite energy storage system based on the target filtering parameter group.

[0207] As an optional implementation of the embodiment of the present application, the first solving sub-module comprises: a second solving sub-module, configured to solve the full life cycle cost and benefit model based on the filter parameter set, to obtain at least one net benefit value of the composite energy storage system; a classification sub-module, configured to sort each net benefit value according to a preset condition, and classify the sorting result by using the carnivorous plant algorithm to obtain a classification result; and an updating sub-module, configured to update the filter parameter set based on the classification result until the preset condition is met to stop updating, to obtain the target filter parameter set.

[0208] The function description of the composite energy storage system power distribution device provided by the embodiment of the present application is described in detail in the above-mentioned embodiment of the composite energy storage system power distribution method.

[0209] The embodiment of the present application further provides a storage medium, as shown in the figure, which stores a computer program 501, and the instructions are executed by a processor to realize the steps of the composite energy storage system power distribution method in the above-mentioned embodiment. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk drive (HDD) or a solid-state drive (SSD), etc. The storage medium can also include a combination of the above-mentioned types of memories. Figure 5 The skilled in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by a computer program instructing related hardware. The program can be stored in a computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiment of each method. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk drive (HDD) or a solid-state drive (SSD), etc. The storage medium can also include a combination of the above-mentioned types of memories.

[0210] The embodiment of the present application further provides an electronic device, as shown in the figure, which can include a processor 61 and a memory 62, wherein the processor 61 and the memory 62 can be connected by a bus or other means,

[0211] The embodiment of the present application further provides an electronic device, as shown in the figure, which can include a processor 61 and a memory 62, wherein the processor 61 and the memory 62 can be connected by a bus or other means, Figure 6 Figure 6 The embodiment of the present application further provides an electronic device, as shown in the figure, which can include a processor 61 and a memory 62, wherein the processor 61 and the memory 62 can be connected by a bus or other means, ​

[0212] The processor 61 can be a central processing unit (CPU). The processor 61 can also be other general-purpose processors, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware component, or a combination thereof.

[0213] The memory 62, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs and modules, such as corresponding program instructions / modules in the embodiments of the present application. The processor 61 performs various functional applications and data processing of the processor by running the non-transitory software programs, instructions and modules stored in the memory 62, that is, implements the composite energy storage system power distribution method in the above method embodiments.

[0214] The memory 62 can include a program storage area and a data storage area, wherein the program storage area can store application programs required by the operation device and at least one function; and the data storage area can store data created by the processor 61 and the like. In addition, the memory 62 can include a high-speed random access memory, and can also include a non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state memory device. In some embodiments, the memory 62 can optionally include a memory disposed remotely with respect to the processor 61, and these remote memories can be connected to the processor 61 through a network. Examples of the above network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0215] The one or more modules are stored in the memory 62, and when executed by the processor 61, perform the composite energy storage system power distribution method in the embodiments as shown. Figures 1-3 The composite energy storage system power distribution method in the embodiments as shown.

[0216] The above electronic device specific details can be understood by referring to the corresponding related descriptions and effects in the embodiments as shown, which will not be described here again. Figures 1 to 3 The above electronic device specific details can be understood by referring to the corresponding related descriptions and effects in the embodiments as shown, which will not be described here again.

[0217] Although the embodiments of the present application are described in conjunction with the accompanying drawings, various modifications and changes can be made by those skilled in the art without departing from the spirit and scope of the present application, and such modifications and changes fall within the scope defined by the appended claims.

Claims

1. A power distribution method for a composite energy storage system, characterized in that, The method includes: Obtain the wind power and first filtering time scale of the composite energy storage system; Based on the wind power and the first filtering time scale, the target filtering time scale is determined using an adaptive time scale planning method. Based on the target filtering time scale, the grid-connected power of the composite energy storage system is decomposed by wavelet packet to obtain the target grid-connected power of the composite energy storage system. Based on the target grid-connected power, the power allocation result of the composite energy storage system is obtained through a polynomial fitting and filtering method. Specifically, based on the wind power and the first filtering time scale, the target filtering time scale is determined using an adaptive time scale planning method, including: Wavelet packet decomposition is performed on the wind power of the composite energy storage system within the first filtering time scale to obtain the grid-connected component; Based on the grid connection component, determine whether the composite energy storage system meets the grid connection standard within the first filtering time scale; When the composite energy storage system meets the grid connection standard within the first filtering time scale, the wind power of the composite energy storage system is rolled filtered based on the second filtering time scale to obtain the grid connection power of the composite energy storage system. The second filtering time scale is determined according to the first filtering time scale. The first filtering time scale is adjusted, and wavelet packet decomposition is performed on the wind power of the composite energy storage system based on the adjusted first filtering time scale until the decomposition results in the grid-connected component not meeting the grid-connected standard. Then the adjustment of the first filtering time scale is stopped, and the target filtering time scale is obtained. The method further includes: The over-limit power is corrected using the interval constraint method. The over-limit power represents the power value that does not meet the grid connection power allowable range corresponding to the grid connection standard. The method further includes: Obtain the filter parameter set and cost-benefit dataset of the composite energy storage system; Based on the cost-benefit dataset, a full life-cycle cost and benefit model of the composite energy storage system is established. Based on the filter parameter set, the full life cycle cost and benefit model is solved to obtain at least one net benefit value of the composite energy storage system. Each net benefit value is sorted according to preset conditions, and the sorting results are classified using the carnivorous plant algorithm to obtain the classification results; The filter parameter set is updated based on the classification result until a preset condition is met, at which point the update stops, and the target filter parameter set is obtained. The capacity configuration result of the composite energy storage system is determined based on the target filter parameter set.

2. The method according to claim 1, characterized in that, Before obtaining the capacity configuration result of the composite energy storage system based on the filter parameter set, the method further includes: The filter parameter set is initialized.

3. A power distribution device for a composite energy storage system, characterized in that, The device includes: The acquisition module is used to acquire the wind power and the first filtering time scale of the composite energy storage system; The planning module is used to determine the target filtering time scale based on the wind power and the first filtering time scale using an adaptive time scale planning method. The decomposition module is used to perform wavelet packet decomposition on the grid-connected power of the composite energy storage system based on the target filtering time scale to obtain the target grid-connected power of the composite energy storage system. The filtering module is used to obtain the power allocation result of the composite energy storage system based on the target grid-connected power through a polynomial fitting filtering method. The planning module includes: a decomposition submodule, used to perform wavelet packet decomposition on the wind power of the composite energy storage system within the first filtering time scale to obtain a grid-connected component; a judgment submodule, used to judge whether the composite energy storage system meets the grid connection standard within the first filtering time scale based on the grid connection component; a rolling filtering submodule, used to perform rolling filtering on the wind power of the composite energy storage system based on a second filtering time scale when the composite energy storage system meets the grid connection standard within the first filtering time scale to obtain the grid-connected power of the composite energy storage system, wherein the second filtering time scale is determined according to the first filtering time scale; and an adjustment submodule, used to adjust the first filtering time scale and perform wavelet packet decomposition on the wind power of the composite energy storage system based on the adjusted first filtering time scale until the decomposed grid connection component does not meet the grid connection standard, at which point the adjustment of the first filtering time scale is stopped, and the target filtering time scale is obtained. The device further includes: a correction module, used to correct the over-limit power using an interval constraint method, wherein the over-limit power represents the power value that does not meet the grid connection power allowable range corresponding to the grid connection standard; The device further includes: The first acquisition module is used to acquire the filter parameter set and cost-benefit dataset of the composite energy storage system; A module is established to build a full life-cycle cost and benefit model of the composite energy storage system based on the cost-benefit dataset. The second solution submodule is used to solve the full life cycle cost and benefit model based on the filter parameter set to obtain at least one net benefit value of the composite energy storage system. The classification submodule is used to sort each net benefit value according to preset conditions, and to classify the sorting results using the carnivorous plant algorithm to obtain the classification result; An update submodule is used to update the filter parameter set based on the classification result until a preset condition is met, and then stop updating to obtain the target filter parameter set. The determination submodule is used to determine the capacity configuration result of the composite energy storage system based on the target filter parameter set.

4. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the power distribution method of the composite energy storage system as described in claim 1 or 2.

5. An electronic device, characterized in that, include: The system includes a memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to perform the power distribution method of the composite energy storage system as described in claim 1 or 2.

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