CN116436037A - Power distribution method and device of composite energy storage system and electronic equipment - Google Patents

Abstract

The invention discloses a power distribution method, a device and electronic equipment of a composite energy storage system, which are used for acquiring wind power and a first filtering time scale of the composite energy storage system, and determining a target filtering time scale through a self-adaptive time scale planning method; and carrying out 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, and obtaining a power distribution result of the composite energy storage system through a polynomial fitting filtering method. According to the method, a proper filtering time scale is adaptively adjusted and determined according to wind power fluctuation conditions, and wavelet packet decomposition is carried out on grid-connected power of the composite energy storage system according to the filtering time scale, so that the composite energy storage system can give consideration to power fluctuation in each period in the power distribution process; further, the operation characteristics of the composite energy storage system are considered, the internal power of the composite energy storage system is distributed by using a polynomial fitting filtering method, and the power distribution efficiency is improved.

Description

Power distribution method and device of composite energy storage system and electronic equipment

Technical Field

The invention relates to the technical field of capacity configuration of hybrid energy storage systems, in particular to a power distribution method and device of a hybrid energy storage system and electronic equipment.

Background

The composite energy storage is formed by polymerizing single energy storage with complementary advantages of different characteristics, has obvious advantages, can realize complementary advantages of different energy storage, exerts respective characteristics, and expands the space of different energy storage devices for exerting own advantages; the combination complementation of the power and energy characteristics can be realized, so that the multiple requirements of the power grid are met, and the power supply reliability is improved; different energy storage devices are enabled to operate in the self optimization working interval through a regulation and control means, the charge and discharge states of the devices are optimized, and the service cycle and the cycle life are prolonged; under reasonable configuration, the operation cost of the energy storage device is reduced, the utilization rate is optimized, the industrial market is enlarged, and larger benefits are obtained. In energy scheduling management and micro-grid integrated control, energy storage capacity optimal configuration is a key problem, and the rationality of configuration directly influences the utilization rate of a distributed power supply and the economy and stability of a micro-grid system. However, the conventional filtering method in the existing research cannot give consideration to power fluctuation in different periods when distributing grid-connected power and composite energy storage system power.

Disclosure of Invention

In view of the above, the embodiment of the invention provides a method, a device and an electronic device for distributing power of a composite energy storage system, so as to solve the technical problem that the power fluctuation of different periods cannot be considered when the grid-connected power and the power of the composite energy storage system are distributed by the traditional filtering method in the prior art.

The technical scheme provided by the invention is as follows:

in a first aspect, an embodiment of the present invention provides a method for distributing power of a composite energy storage system, where the method for distributing power of the composite energy storage system includes: acquiring wind power and a first filtering time scale of a composite energy storage system; determining a target filtering time scale based on the wind power and the first filtering time scale through a self-adaptive time scale planning method; performing wavelet packet decomposition on the 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.

With reference to the first aspect, in a possible implementation manner of the first aspect, determining, based on the wind power and the first filtering time scale, a target filtering time scale through an adaptive time scale planning method includes: carrying out 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 meets grid-connected standards in the first filtering time scale based on the grid-connected components; when the composite energy storage system meets the grid-connected standard in the first filtering time scale, rolling filtering 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, wherein the second filtering time scale is determined according to the first filtering time scale; and adjusting the first filtering time scale, carrying out wavelet packet decomposition on the wind power of the composite energy storage system based on the adjusted first filtering time scale until the grid-connected component is decomposed to obtain the grid-connected component which does not meet the grid-connected standard, stopping adjusting the first filtering time scale, and obtaining the target filtering time scale.

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

With reference to the first aspect, in a further possible implementation manner of the first aspect, the method further includes: acquiring a filtering parameter set and a cost-effective 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 the carnivorous plant algorithm based on the filter parameter set.

With reference to the first aspect, in a further possible implementation manner of the first aspect, before the obtaining, based on the set of filter parameters, a capacity configuration result of the composite energy storage system through the full life cycle cost and benefit model and the carnivorous plant algorithm, the method further includes: and initializing the filter parameter set.

With reference to the first aspect, in a further possible implementation manner of the first aspect, based on the set of filter parameters, obtaining a capacity configuration result of the composite energy storage system through the full life cycle cost and benefit model and the carnivorous plant algorithm includes: solving the full life cycle cost and benefit model by utilizing the carnivorous plant algorithm based on the filter parameter set to obtain a target filter parameter set; and determining the capacity configuration result of the composite energy storage system based on the target filtering parameter set.

With reference to the first aspect, in a further possible implementation manner of the first aspect, based on the set of filter parameters, solving the full life cycle cost and benefit model by using the carnivorous plant algorithm to obtain a set of target filter parameters includes: solving 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; sorting each net benefit value according to preset conditions, and sorting the sorting results by utilizing the carnivorous plant algorithm to obtain sorting results; and updating the filter parameter set based on the classification result until a preset condition is met, and stopping updating to obtain the target filter parameter set.

In a second aspect, an embodiment of the present invention provides a composite energy storage system power distribution device, including: the acquisition module is used for acquiring wind power and a first filtering time scale of the composite energy storage system; the planning module is used for determining a target filtering time scale based on the wind power and the first filtering time scale through a self-adaptive time scale planning method; the decomposition module is used for carrying out 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; and the filtering module 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.

In a third aspect, an embodiment of the present invention provides a computer readable storage medium, where computer instructions are stored, where the computer instructions are configured to cause the computer to perform the method for allocating power to a composite energy storage system according to the first aspect of the embodiment of the present invention and any one of the first aspect.

In a fourth aspect, an embodiment of the present invention provides an electronic device, including: the system comprises a memory and a processor, wherein the memory and the processor are in communication connection, the memory stores computer instructions, and the processor executes the computer instructions so as to execute the power distribution method of the composite energy storage system according to any one of the first aspect and the first aspect of the embodiment of the invention.

The technical scheme provided by the invention has the following effects:

according to the power distribution method of the composite energy storage system, provided by the embodiment of the invention, the proper filtering time scale is self-adaptively adjusted and determined according to the wind power fluctuation condition, and the wavelet packet decomposition is carried out on the grid-connected power of the composite energy storage system according to the filtering time scale, so that the composite energy storage system can give consideration to the power fluctuation of each period in the power distribution process; further, the operation characteristics of the composite energy storage system are considered, the internal power of the composite energy storage system is distributed by using a polynomial fitting filtering method, and the power distribution efficiency is improved.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.

FIG. 1 is a flow chart of a method for power distribution of a composite energy storage system according to an embodiment of the present invention;

FIG. 2 is an exploded flow chart of an adaptive time scale wavelet packet provided in accordance with an embodiment of the present invention;

FIG. 3 is a flowchart of a carnivorous plant algorithm optimization provided according to an embodiment of the present invention;

FIG. 4 is a block diagram of a composite energy storage system power distribution device according to an embodiment of the present invention;

FIG. 5 is a schematic diagram of a computer-readable storage medium provided according to an embodiment of the present invention;

fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.

Detailed Description

For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

The capacity configuration and the running loss of the composite energy storage system are related to the power instruction allocated by the composite energy storage system, so that the economical efficiency can be improved by reducing the power instruction of the composite energy storage system on the premise that the stabilized grid-connected power meets the grid-connected requirement.

The embodiment of the invention provides a power distribution method of a composite energy storage system, as shown in fig. 1, comprising the following steps:

step 101: and acquiring wind power and a first filtering time scale of the composite energy storage system.

Wherein the first filtering time scale is a maximum time scale.

Step 102: and determining a target filtering time scale based on the wind power and the first filtering time scale through a self-adaptive time scale planning method.

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

Step 103: and carrying out 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.

Specifically, according to the description in step 102, the adjusted target filtering time scale is utilized to perform wavelet packet decomposition on the grid-connected power of the energy storage system, so that wind power fluctuation in different periods can be considered.

Step 104: 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.

Specifically, after determining the target grid-connected power, further distribution of the internal power of the composite energy storage system is required. In the embodiment of the invention, the internal power decoupling is carried out by adopting a polynomial fitting filtering (SG filtering) method, so that the power distribution efficiency is improved.

According to the power distribution method of the composite energy storage system, provided by the embodiment of the invention, the proper filtering time scale is self-adaptively adjusted and determined according to the wind power fluctuation condition, and the wavelet packet decomposition is carried out on the grid-connected power of the composite energy storage system according to the filtering time scale, so that the composite energy storage system can give consideration to the power fluctuation of each period in the power distribution process; further, the operation characteristics of the composite energy storage system are considered, the internal power of the composite energy storage system is distributed by using a polynomial fitting filtering method, and the power distribution efficiency is improved.

As an alternative implementation of the embodiment of the present invention, step 102 includes: carrying out 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 meets grid-connected standards in the first filtering time scale based on the grid-connected components; when the composite energy storage system meets the grid-connected standard in the first filtering time scale, rolling filtering 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, wherein the second filtering time scale is determined according to the first filtering time scale; and adjusting the first filtering time scale, carrying out wavelet packet decomposition on the wind power of the composite energy storage system based on the adjusted first filtering time scale until the grid-connected component is decomposed to obtain the grid-connected component which does not meet the grid-connected standard, stopping adjusting the first filtering time scale, and obtaining the target filtering time scale.

The grid-connected standard is an electrochemical energy storage power station grid-connected scheduling protocol demonstration text (trial run).

Specifically, a typical day is selected, the time scale of wind power data of the typical day is set to be T, and the original wind power is set to be P _wg 。

Firstly, taking the maximum time scale from the first filtering time scale T (n), carrying out wavelet packet decomposition in the period of T (n), obtaining a fundamental frequency component as shown in the following relation (1), and taking the fundamental frequency component as a grid-connected component:

wherein: w (w) _Pwg [b,a,T(n)]The energy of wind power decomposition to a base coordinate axis is represented, namely a fundamental frequency component; a represents a scale factor for determining a wavelet base frequency; b represents a shifting factor for determining wavelet base time domain properties; d, d _a,b (t) represents a wavelet family, and the following relation (2); n represents the number of wavelet packet decomposition layers.

Secondly, judging whether the composite energy storage system in the first filtering time scale meets the grid-connected standard or not based on the grid-connected component, and if so, using a second filtering time scale

Rolling filtering is carried out until extraction of grid-connected power is completed;

and finally, gradually reducing the first filtering time scale by using the delta T scale until the grid-connected power decomposed by using the adjusted time scale does not meet the grid-connected standard, and determining the target filtering time scale.

Further, wavelet packet decomposition is carried out on the grid-connected power of the composite energy storage system by utilizing the target filtering time scale, so that the target grid-connected power of the composite energy storage system is obtained, namely, the grid-connected component obtained by the fir relational expression (1) and the target filtering time scale are substituted into the following relational expression (3):

wherein: p'. _wg [t,T(n)]Representing grid-tied power.

In one embodiment, the adaptive time scale wavelet packet decomposition process is shown in FIG. 2.

As an optional implementation manner of the embodiment of the present invention, the method further includes: and correcting the out-of-limit power by using a section constraint method.

The out-of-limit power representation does not meet the power value of the grid-connected power allowable range corresponding to the grid-connected standard.

In order to solve the optimal capacity configuration of the composite energy storage system under the condition of meeting the grid-connected requirement, the embodiment of the invention selects the minimum time scale for grid-connected power extraction according to the grid-connected standard, and then corrects the minimum power out-of-limit point by adopting an interval constraint method so as to meet the grid-connected requirement, and the fluctuation of 1min does not exceed the wind1/n of electric installation capacity ₁ The fluctuation of 10min is not more than 1/n of the wind power installation capacity ₁₀ And carrying out power constraint correction by taking grid-connected standards as examples.

Let P' _wg0 (t) is grid-connected power at time t, and the wind power installed capacity is P _wind The allowable fluctuation interval is represented by the following relation (4):

[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)

wherein: [ P ] _one,l (t),P _one,u (t)]Representing the lower limit of the fluctuation interval of 1 min; [ P ] _ten,l (t),P _ten,u (t)]The upper limit of the 1min fluctuation interval is represented by the following relations (5) and (6), respectively:

P _one,l (t)＝maxP′ _wg0 (i)-P _wind /n ₁ ,i＝t-60,...,t-1 (5)

P _one,u (t)＝minP′ _wg0 (i)-P _wind /n ₁ ,i＝t-60,...,t-1 (6)

further, the lower limit and the upper limit of the fluctuation interval of 10min are obtained in the same way, the allowable range of the grid-connected power at the moment t is obtained according to the grid-connected standard, further, the out-of-limit power is corrected, and the corrected grid-connected power is shown in the following relation (7):

P″ _wgo (t)＝P′ _wgo (t)+ΔP _xz (t) (7)

wherein DeltaP _xz (t) represents an out-of-limit power, and the following relation (8) shows:

further, an allocated primary power command of the composite energy storage system can be obtained, as shown in the following relation (9):

P _ess ＝P _wg -P″ _wg0 (9)

further, after the power instruction of the composite energy storage system is distributed, the internal power is further distributed, and the power is distributed based on SG filtering in the embodiment of the invention.

Specifically, the power command at time t is taken as the center, the front and back 2n+1 power command points are processed, and k-order polynomial fitting is adopted, so that the fitting power in the interval, namely the power command of the storage battery, can be expressed as the following relational expression (10):

P _{bat,(2N+1)×1} ＝T·(TT·T) ^-1 ·X (10)

wherein:

X _(2N+1)×1 ＝T _{(2N+1)×(k+1)} ·A _(k+1)×1 +E _(2N+1)×1 (11)

wherein: x is X _(2N+1)×1 Representing a weight error vector; t (T) _{(2N+1)×(k+1)} Representing a power instruction sampling point matrix; a is that _(k+1)×1 Representing a polynomial fit coefficient vector; e (E) _(2N+1)×1 Representing a least squares fit error vector.

Wherein X is required to be satisfied _(2N+1)×1 The order is less than T _{(2N+1)×(k+1)} The rank of (2) can ensure that the above relation (11) has a real solution.

Further, after the internal power command distribution is completed, the power command of the supercapacitor can be expressed as the following relation (12):

P _uc ＝P _ess -P _bat (12)

as an optional implementation manner of the embodiment of the present invention, the method further includes: acquiring a filtering parameter set and a cost-effective 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 the carnivorous plant algorithm based on the filter parameter set.

Wherein the set of filter parameters comprises a filter window length and a polynomial fit order set (population), may be obtained according to step 104.

The cost-benefit data set may include investment costs, update replacement costs, auxiliary costs, operational maintenance costs, recovery costs, and operational benefits of the composite energy storage system, the net benefits over its life cycle may be expressed as the following relationship (13):

C _NPV ＝C _hj +C _by +C _hs -(C _tz +C _gx +C _fz +C _yw +C _cl ) (13)

wherein: c (C) _hj The benefits of the slow-building grid-connected channel are represented; c (C) _by Representing the benefits of reducing the standby capacity of the wind farm; c (C) _hs The recovery benefits are represented by the following relations (14), (15) and (16):

C _hj ＝k·max{0,max[P _wg (t)]-max[P _wg (t)-P _ess (t)]} (14)

C _hs ＝β·(C _tz +C _gx +C _fz )·F(i,t) (16)

wherein: p (P) _wg (t) represents the original wind power at the moment t; p (P) _ess (t) represents the output of the composite energy storage system at the moment t; p (P) _rate Representing an energy storage system power configuration; k represents the unit power cost of the slow-building grid-connected channel, and 1500 yuan/kW is taken; b _r Representing the spare capacity price, taking 0.9 yuan/kW; alpha represents the wind power output prediction reliability, and 0.85 is taken; beta represents recovery residual value rate, and 0.05 is taken; n (N) _d The number of wind power prediction sampling points is represented; LC represents a life cycle; f (i, t) = (1+i) ^-T Representing a one-time payment discount rate; a (i, T) = [ (1+i) ^T -1]/[i(1+i) ^T ]The equal share coefficient is represented, where i represents the discount rate and 10% is taken.

Further, in the above-mentioned relational expression (13): c (C) _tz Representing investment costs; c (C) _gx Representing update replacement costs; c (C) _fz Representing auxiliary costs; c (C) _yw Representing operational maintenance costs; c (C) _cl The disposal cost, that is, the recovery cost, is represented by the following relations (17), (18), (19), (20), (21):

C _tz ＝C _{tz_p} ·C _{tz_e} ·E _rate (17)

C _fz ＝C _{fz_p} ·P _rate +C _{fz_e} ·E _rate (19)

C _cl ＝(C _{cl_p} ·P _rate +C _{cl_e} ·E _rate )·(n+1)·F(i,T) (21)

wherein: e (E) _rate Representing a capacity configuration of the energy storage system; c (C) _{tz_p} 、C _{fz_p} 、C _{cl_p} 、C _{cl_p} All represent unit power; c (C) _{tz_e} Representing capacity investment costs; c (C) _{gx_p} Representing a unit power update cost; c (C) _{fz_e} Representing capacity assistance costs; c (C) _{yw_p} Representing a unit power; c (C) _{yw_w} Representing the charge and discharge amount operation maintenance cost; c (C) _{cl_e} Representing the cost of capacity discard processing.

Specifically, after power is distributed by SG filtering, energy storage is enabled to bear main power, power storage is enabled to bear a high-frequency part in an auxiliary mode, then in order to enable economic analysis to be closer to engineering practice, a composite energy storage system full life cycle cost benefit model is constructed, the maximum life cycle net benefit is taken as a target, SG filtering window length and filtering order are taken as decision variables, and CPA algorithm is adopted for optimization solving to obtain a final configuration scheme.

Further, based on the set of filter parameters, 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, the method further comprises: and initializing the filter parameter set.

Specifically, the filter window length and polynomial fit order set (population) are initialized and the corresponding net present value (fitness) is calculated:

randomly initializing a population in the variation range of the filtering parameters, and then obtaining the net benefit value of each life cycle in the population, namely the fitness value, as shown in the following relational expressions (22) and (23):

Wherein: pop (·) represents population; fit (·) represents population fitness; l represents the filter window scale; k represents a polynomial fitting order; n represents the population size.

Wherein, it should be noted that l and k should be positive integers; and l is an odd number, and further, k < l needs to be satisfied in order for the relation (11) to have a real solution.

Further, based on the set of filter parameters, obtaining a capacity configuration result of the composite energy storage system through the full life cycle cost and benefit model and the carnivorous plant algorithm, including: solving the full life cycle cost and benefit model by utilizing the carnivorous plant algorithm based on the filter parameter set to obtain a target filter parameter set; and determining the capacity configuration result of the composite energy storage system based on the target filtering parameter set.

Based on the filtering parameter set, solving the full life cycle cost and benefit model by utilizing the carnivorous plant algorithm to obtain a target filtering parameter set, wherein the method comprises the following steps: solving 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; sorting each net benefit value according to preset conditions, and sorting the sorting results by utilizing the carnivorous plant algorithm to obtain sorting results; and updating the filter parameter set based on the classification result until a preset condition is met, and stopping updating to obtain the target filter parameter set.

First, the populations are ordered according to net present value size and grouped based on carnivorous plant algorithms.

Specifically, the first nCP individuals with the largest net present value are taken as carnivorous plants, and the other individuals are taken as prey, which is marked as npry, in descending order of net present value. Next, the first level prey is assigned to the first level carnivorous plant, the second level prey is assigned to the second level carnivorous plant, and so on, when the carnivorous plant has completed a round of prey, the nCP +1 level prey is assigned to the first level carnivorous plant, the nCP +2 level prey is assigned to the second level carnivorous plant, and so on, until all preys are assigned. The classification procedure is shown in the following relations (24) and (25), the grouping procedure is shown in the following table 1, where m=nprey/nCP depends on the population size of the prey and carnivorous plants.

Table 1, CPA grouping procedure

Next, the set of filtering parameters is optimized.

Specifically, since the initialization of the parameters is a random process, in order to enable the filter parameter combinations to change towards a trend of increasing the net benefit of the life cycle, during the update process, the predation, growth of carnivorous plants and the escape and growth process of prey are simulated, and the update of the first nCP filter parameter combinations with the largest net benefit value of the life cycle is represented by the following relation (26):

NCP _i,j ＝grow _i,j ×CP _i,j +(1-grow _i,j )Prey _i,j (26)

Wherein: CP (control program) _i,j Representing an i-th level of filter parameter combinations; prey _i,j Representing a randomly selected i-th level of filter parameter combination assignment, i.e., a corresponding relatively poorly adapted filter parameter combination.

Wherein:

grow _i,j ＝grow_rate×r _i,j (27)

wherein: the growth_rate represents the growth rate, which is 0.8 in the embodiment of the invention; r is (r) _i,j Represents [0,1 ]]Random numbers within.

Further, the updating process of npry filter parameter combinations is shown in the following relation (28):

NPrey _i,j ＝grow _i,j ×Prey _u,j +(1-grow _i,j )Prey _v,j ,u≠v (28)

wherein:

wherein: prey _u,j Representing a randomly selected prey assigned to the u-th level of filter parameter combination, i.e., a corresponding filter parameter combination with relatively poor fitness; prey _v,j Representing a randomly selected v-th level of filtering parameter combination assignment, i.e., a corresponding relatively poorly adapted filtering parameter combination.

The set of filtering parameters is then replicated and expanded.

In particular, the simulation carnivorous plants absorb nutrients from the prey and utilize nutrient growth and reproduction, wherein, to ensure that CPA optimization is focused on the optimal set of filter parameters, thus reducing computational costs, only the set of filter parameters with the largest net present value is allowed to be replicated. The replication process is shown in the following relation (30):

NewCP _i,j ＝CP _1,j +Rep_rate×rand _i,j ×mate _i,j (30)

wherein:

wherein: CP (control program) _1,j Representing an optimal set of filtering parameters; CP (control program) _v,j Representing a randomly selected set of filter parameters; rep_rate represents the reproduction rate and has a value of 1.8. Wherein the above process is repeated nCP times.

Further, in consideration of window length and fitting order constraints, the above-described optimization process, copy and expansion process related constraint processing processes are shown in the following relational expressions (32), (33) and (34):

NCP _i,j orNewCP _i,j ＝ceilodd(NCP _i,j ) (32)

wherein: ceilodid (·) represents taking an odd number up;

wherein:

NPrey _i,j ＝ceil(NPrey _i,j ) (33)

wherein: ceil (·) represents rounding up;

wherein: randi (n) represents taking a positive integer between [1, n ].

Finally, the net present values are rearranged and the set of filtering parameters is updated.

Specifically, through the updating and expanding of the set in the above process, a new filter parameter set of [ n+ nCP (g_iter) + nCP ] ×2 dimension is generated, n, nCP (g_iter), nCP are filter parameter sets in the original, optimizing and expanding process respectively, then the first n parameter sets are selected as the new parameter set according to descending order of net benefit, the first nCP parameter sets are selected as carnivorous plants, and the rest npry parameter sets are selected as prey, so that the process ensures that the updating of the filter parameter sets always makes the net benefit better.

Repeating the above processes until reaching maximum iteration times or convergence condition, and determining final filter parameter group, i.e. target filter parameter group

Further, a capacity configuration result of the composite energy storage system is determined.

Specifically, the power configuration rule of the energy storage system is set to meet the maximum demand of the power command in the period, as shown in the following relation (35):

P _rate ＝max{max(P _ess (t _i )·n _ch ),|min(P _ess (t _j )/n _dic )|},t _i ,t _j ∈[t ₀ ,t ₀ +T] (35)

wherein: p (P) _ess (-) represents an energy storage system power command; n is n _ch Representing energy storage charging efficiency; n is n _dic Representing energy storage discharge efficiency; t is t ₀ Representing an initial time; t represents the considered period of energy storage operation.

Further, the capacity configuration rule of the energy storage system is to restrict the capacity configuration rule to a reasonable range, and the configuration is performed according to the accumulated energy of the stored energy in the period, as shown in the following relation (36):

wherein: e (t) represents real-time accumulated energy, as shown in the following relational expression (37); SOC (State of Charge) _max Representing a maximum value of the energy storage allowable state of charge; SOC (State of Charge) _min Representing the minimum value of the energy storage allowable state of charge.

Wherein: e (E) ₀ Representing the stored initial energy.

According to the embodiment of the invention, while the SG filtering method is adopted for distributing internal power, an optimization model aiming at the maximum net benefit in the life cycle is constructed for optimizing the filtering parameters in consideration of the fact that the SG filtering effect is related to the filtering parameters and the parameter set is large, and the final configuration scheme is obtained by optimizing and solving the CPA algorithm of the emerging group intelligent algorithm in consideration of the fact that the model is a multimodal model with high performance requirements.

In one embodiment, the flow of the economic optimal capacity configuration of the composite energy storage system based on SG filtering and carnivorous plant algorithm is shown in FIG. 3.

Further, the simulation analysis of the method provided by the embodiment of the invention comprises the following steps:

and (one) performing primary power distribution simulation analysis on the composite energy storage system with optimal capacity configuration and minimum running loss.

Taking two typical daily wind power curves of a certain wind power base as examples for simulation analysis, sampling time is 1s, and verifying the method, wherein typical daily 1 fluctuation is large, maximum fluctuation rates of 1 and 10 are 19.6119% and 48.1322%, typical daily 2 power fluctuation is relatively gentle, and maximum fluctuation rates of 1 and 10 are 17.2806% and 36.9322%. In order to analyze more comprehensively, evaluation indexes such as fluctuation rate, close power difference and the like are defined, and the method is compared and analyzed with the traditional filtering method in aspects such as scene applicability and the like. Firstly, discussing from the aspect of primary power distribution of a composite energy storage system, verifying the effectiveness of a self-adaptive time scale planning wavelet packet decomposition method and the improvement effect of a minimum time scale wavelet packet decomposition-interval constraint correction method (hereinafter referred to as the method) derived on the basis of the effectiveness of the self-adaptive time scale planning wavelet packet decomposition method.

(1) The evaluation index adopts a fluctuation rate index to analyze grid-connected power, adopts a cumulative positive and negative fluctuation rate difference (CFD) index to evaluate capacity allocation rationality, and further defines a cumulative redundancy fluctuation rate to measure daily charge and discharge capacity, namely operation and maintenance cost, of the composite energy storage system, wherein several evaluation indexes comprise;

the N second time scale fluctuation rate λ is shown in the following relation (38):

wherein: p (P) _* (i)、P _* (j) Representing power within an N time scale; * Representing different types, which can include two types of original wind power and grid-connected power; p (P) _wg,rate Representing the installed capacity of the wind power.

The cumulative redundancy fluctuation ratio CRV is represented by the following relational expression (39):

wherein:

wherein: lambda (lambda) _pwg (i) Representing the original wind power; lambda (lambda) _bw (i) The fluctuation rate of grid-connected power i moment is represented; lambda (lambda) _up Indicating the upper limit of the ripple rate.

In addition, in order to facilitate analysis of the degree of the grid-connected power approaching the original wind power under the condition that the grid-connected standard is met, approaching power difference and average approaching power difference are defined.

Wherein the proximate power difference is represented by the following relationship (41):

P _ce (t)＝|P _wg (t)-P″ _wg0 (t)|,t＝1,2,...,n (41)

the average proximate power difference is shown in the following relationship (42):

wherein: n is n _* Indicating that the method is close to the power sampling points with the power difference larger or smaller than that of other methods.

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

Specifically, simulation analysis is performed on the self-adaptive time scale wavelet packet decomposition, and the planning thought is to gradually reduce from a maximum time scale (24 h scale) to a 1h scale at 1h scale intervals, and then gradually reduce from a minimum time scale (10 min scale) at 10min scale intervals. Furthermore, in the embodiment of the invention, a plurality of time scales are selected for visual display and analysis, the validity of the proposed strategy is verified, meanwhile, 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, a minimum number of grid-connected power out-of-limit points are corrected by an interval power constraint method.

On the premise of meeting grid-connected standards, the adaptive time scale wavelet packet decomposition method can reduce the filtering time scale at given time scale intervals, grid-connected power of a plurality of filtering time scales in the adaptive planning process can be obtained according to comparison results of the adaptive time scale wavelet packet decomposition grid-connected power, and grid-connected power extracted under the maximum time scale is very smooth, because the determination of the wavelet packet decomposition layer number, namely the decomposition degree, is limited by the maximum power fluctuation period, so that power in other periods is excessively decomposed, grid-connected power is smooth, the extracted grid-connected power can gradually approach to original wind power along with the reduction of the time scale, and the power instruction of the composite energy storage system is reduced, so that energy storage capacity configuration and running cost are reduced. However, the process is accompanied by the increase of grid-connected power fluctuation, and the time scale interval planned according to the embodiment of the invention is found in the simulation process, wherein the 30min scale is the minimum time scale meeting the grid-connected standard, and the time scale can be better close to the original wind power.

From the perspective of accumulated close power differences, the accumulated close 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, and compared with 24h filtering, the accumulated close power differences corresponding to the scales of 8h, 6h, 4h, 1h and 30min are respectively reduced by 38.73%, 39.31%, 40.03%, 46.82%, 55.62% and 61.98%. Analysis shows that as the time scale is reduced, the close degree of the grid-connected power meeting the grid-connected requirement is increased, so that the effectiveness of the method is improved, the accumulated close power difference obtained by further using the method is 7369MW, the accumulated close power difference is reduced by 85.75% compared with the 24h scale, the accumulated close power difference is reduced by 46.48% compared with the 30min scale, and the effectiveness of the method is improved and the close effect of the method is improved compared with the self-adaptive time scale planning wavelet packet method.

And analyzing the time scale from the angle of the fluctuation rate, continuously reducing the time scale to the minimum time scale, wherein the out-of-limit condition occurs at the moment of extremely few power abrupt changes, and after the grid-connected power is extracted by a wavelet packet with the 10min scale, most of power sampling points in the typical day are constrained in the grid-connected standard, and only the out-of-limit condition occurs at a few abrupt power sampling points, the out-of-limit power sampling points for 1min and 10min are respectively 34 and 145, and respectively account for 0.0394% and 0.17% of the total sampling points, so that the out-of-limit probability is extremely small.

On the basis of a minimum time scale wavelet packet, the interval constraint power correction method is adopted to carry out secondary correction on out-of-limit power points (the method), all power sampling points can be constrained in a grid-connected standard, and the effectiveness of the method is verified.

Comparing the method with the self-adaptive time scale wavelet packet method from the angle of grid-connected power, the 30min time scale wavelet packet decomposition can be well close to the original wind power on the premise of meeting the grid-connected standard, but when the sampling point of wind power suddenly changes is included, the 30min scale time period is larger, the number of layers of the wavelet packet decomposition is higher, and the effect of the scale close to the original wind power is not as good as that of the method.

From the point of view of the proximate power, in order to be more visual in illustration, simulation results are shown by sampling for 1min, but the analyzed data is still 1s sampling point, and the following analysis of the proximate power difference is the same. Compared with the self-adaptive time scale wavelet packet method, the method has 45479 power sampling points (the part above the horizontal axis) with smaller power difference, the ratio is 52.64 percent, the maximum proximate power difference and the average proximate power difference are respectively reduced by 1.6987MW and 0.1694MW, and the maximum proximate power difference and the average proximate power difference are only respectively increased by 0.6389MW and 0.0322MW although the sampling points with larger proximate power difference are 40921, so that the method has the advantages that the overall improvement effect and the improvement degree are greatly improved although the proximate degree is slightly lower when compared with the self-adaptive time scale wavelet packet decomposition method.

(3) And extracting grid-connected power simulation analysis by different filtering methods.

In particular, the advantages of the method are further verified, compared with a sliding average method, from the perspective of approaching power, the method has the advantages that the number of power sampling points with smaller approaching power difference is 79370, the ratio of the power sampling points is 91.86%, the maximum approaching power difference and the average approaching power difference are respectively reduced by 2.3085MW and 0.4053MW, the sampling point with larger approaching power difference is 7030, the maximum approaching power difference and the average approaching power difference are respectively increased by 0.7973MW and 0.1486MW, and the method has an improvement effect on most moments and improves the maximum improvement degree and the overall improvement degree compared with the sliding average method.

Compared with a moving average method, the method has the advantages that compared with the moving average method, the method has the advantages that the integrated redundancy fluctuation rate of the grid-connected power extracted by the method for 1min is reduced from 249290 to 59837, the integrated redundancy fluctuation rate of the grid-connected power for 10min is reduced from 611960 to 114490, and the integrated redundancy fluctuation rate of the grid-connected power for 10min is reduced by 81.29 percent, so that the energy storage can be better close to the original wind power on the premise of meeting the grid-connected standard, the requirement can be met under the condition that the energy storage is used as an energy exchange medium for smaller power exchange, and the method has the advantage when being applied to the energy storage configuration.

In order to further explain the scene applicability of the method, a typical day 2 with relatively smooth wind power is simulated, because the typical day power is relatively smooth, the window length is smaller than that of the typical day 1 when grid-connected power is extracted by adopting a moving average method, the power in the window can be better close to the original power after being averaged, but the close effect is inferior to that of the method in the window containing the suddenly-changed power sampling points.

From the perspective of close power, compared with a moving average method, the method has 69538 power sampling points with smaller close power difference, which account for 80.48 percent, and the maximum close power difference and the average close power difference are respectively reduced by 0.1968MW and 0.1858MW, the method has 168662 as the sampling point with larger close power difference, and the maximum close power difference and the average close power difference are respectively increased by 0.2771MW and 0.0828MW, although the method has smaller maximum close power difference than the moving average method, further analysis shows that the result is about 9:00 and fewer sampling points, the analysis is due to the fact that the fluctuation of the endpoint power sampling points in the process of filtering and rolling in the minimum time scale is larger, the probability of the condition is extremely low, and compared with the moving average method, the method has good improvement effect on most moments and considerable overall improvement effect.

Compared with a moving average method, the method has the advantages that the cumulative redundancy fluctuation rate of the grid-connected power extracted by the method for 1min is reduced from 257780 to 87200, the cumulative redundancy fluctuation rate for 10min is reduced from 400860 to 149150, and the cumulative redundancy fluctuation rate for 10min is reduced by 62.79%, so that the grid-connected power can be better close to the original wind power on the premise of meeting the grid-connected standard.

According to the analysis, compared with typical day 1, grid-connected power extracted by adopting a moving average method on typical day 2 is closer to the method and original power, and the analysis is because power fluctuation of the typical day 2 is smaller, the limitation on the window length of the moving average method is smaller, the smoothness constraint of the whole power extraction is reduced, and the adaptability of the moving average method in different scenes is not better than that of the method.

And secondly, performing secondary power distribution and capacity configuration simulation analysis based on the composite energy storage system with the largest net benefit.

Specifically, with the maximum net benefit value of the composite energy storage system within 15 years, an optimization model is established, CPA algorithm is applied to solve, meanwhile, the effectiveness of the algorithm of the embodiment of the invention is verified by comparing with GA and PSO algorithms, and then the feasibility of the method in the aspect of economy is verified from the aspect of economy.

(1) Different algorithms optimize solution simulation analysis.

Specifically, with the maximum net benefit value of the life cycle of the composite energy storage system as a target, the filtering window length and the polynomial fitting order in the window are used as decision variables, and the GA algorithm, the PSO algorithm and the CPA algorithm are adopted to carry out optimization solution respectively, wherein the relevant simulation parameters are shown in the table 2:

TABLE 2 optimization algorithm simulation parameters

And the model is optimized and solved by adopting three optimization algorithms, according to the solving result, the CPA algorithm is converged to the optimal solution in the 10 th generation, the influence of the initial value of the population is small, the PSO algorithm is lower in convergence speed, the initial value is converged to the optimal solution in the 26 th generation, the optimal solution is slightly smaller than the CPA algorithm, the initial value of the GA algorithm is similar to the CPA algorithm, the convergence process is lower, and the optimal solution is smaller than the CPA algorithm and the PSO algorithm. From the data point of view, GA, PSO,The optimization results of CPA algorithm are 1.1236 ×10 respectively ⁸ 、1.1342×10 ⁸ 、1.1353×10 ⁸ Compared with PSO and GA algorithms, the economic benefits of CPA optimization are respectively improved by 0.1% and 1.04%. From the above analysis, the algorithm of the embodiment of the invention has high efficiency and effectiveness in model solving.

From the aspect of the output characteristic of the composite energy storage system, according to the power distribution result obtained by the optimized filter parameter set, the power distribution based on the method can keep the power instruction characteristic of the composite energy storage system, lower-frequency and high-power instructions are distributed to the lithium ion battery for energy storage, higher-frequency and low-power instructions are distributed to the super capacitor to reduce the charge and discharge times of the lithium ion battery, prolong the service life of the lithium ion battery and improve the economical efficiency of the composite energy storage system.

The power and capacity configuration of the composite energy storage system is carried out according to the internal power instruction obtained based on the optimized filtering parameter set, and the result is shown in table 3:

TABLE 3 optimization results for different algorithms

Parameters (parameters)	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

(2) Different methods optimize the configuration economy analysis.

Specifically, to verify the economic viability of the present method, the net benefit of the composite energy storage system over a 15 year period was compared by the present method and the results are shown in table 4 below:

table 4, different method configurations and economic comparison

Parameters (parameters)	Results
		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

From the perspective of twice power distribution, the embodiment of the invention provides an optimal configuration method for the capacity of the composite energy storage system for stabilizing the wind power fluctuation scene, which has the following effects:

(1) The self-adaptive wavelet packet decomposition method based on the self-adaptive time scale is provided, the proper filtering time scale can be determined according to the wind power fluctuation condition, the wind power fluctuation of different time periods is considered, meanwhile, the interval constraint method is provided for secondary correction, the influence of a few power out-of-limit points on the further reduction of the filtering time scale is solved, and the power instruction of the composite energy storage system is further reduced under the condition that the grid-connected requirement is met.

(2) Taking the running characteristics of the composite energy storage system into consideration, adopting an SG filtering method to distribute internal power, simultaneously taking the correlation of SG filtering effect and filtering parameters and the larger parameter set into consideration, constructing an optimization model with the maximum net benefit in the life cycle as a target to optimize the filtering parameters, taking the modeling as a multimodal model, taking the modeling into consideration, carrying out optimization solution on the algorithm performance requirement, applying an emerging group intelligent algorithm CPA algorithm to carry out optimization solution, comparing the algorithm with algorithms such as GA, PSO and the like, verifying the high efficiency of the algorithm in the aspect of solving the model, and providing reference for the subsequent complex model solution;

(3) The method has the advantages that the indexes such as the accumulated redundancy fluctuation rate, the proximate power difference and the like are defined, the feasibility and the advantages of the method are analyzed from the angles of grid-connected power, the grid-connected power redundancy fluctuation rate and the proximate power difference, meanwhile, two typical days with different fluctuation characteristics are adopted, the method and the other methods are compared and analyzed from the two aspects of grid-connected power and energy storage system capacity configuration, and the scene applicability and the rationality of the method are verified. The method has the advantages that the optimization results of different optimization algorithms are compared and analyzed, the algorithm provided by the embodiment of the invention is verified to have high efficiency in solving the built model, and meanwhile, different configuration methods are compared, so that the feasibility of the method in the aspect of economy is verified.

The embodiment of the invention also provides a power distribution device of the composite energy storage system, as shown in fig. 4, which comprises:

an obtaining module 401, configured to obtain wind power and a first filtering time scale of the composite energy storage system; for details, see the description of step 101 in the above method embodiment.

A planning module 402, configured to determine a target filtering time scale based on the wind power and the first filtering time scale through an adaptive time scale planning method; for details, see the description of step 102 in the method embodiment described above.

The decomposition module 403 is configured to perform wavelet packet decomposition on the grid-connected power of the composite energy storage system based on the target filtering time scale, so as to obtain a target grid-connected power of the composite energy storage system; for details, see the description of step 103 in the method embodiment described above.

The filtering module 404 is configured to obtain 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, see the description of step 104 in the method embodiment described above.

According to the power distribution device of the composite energy storage system, provided by the embodiment of the invention, the proper filtering time scale is self-adaptively adjusted and determined according to the wind power fluctuation condition, and the wavelet packet decomposition is carried out on the grid-connected power of the composite energy storage system according to the filtering time scale, so that the composite energy storage system can give consideration to the power fluctuation of each period in the power distribution process; further, the operation characteristics of the composite energy storage system are considered, the internal power of the composite energy storage system is distributed by using a polynomial fitting filtering method, and the power distribution efficiency is improved.

As an optional implementation manner of the embodiment of the present invention, the planning module includes: the decomposition sub-module is used for carrying out 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; the judging submodule is used for judging whether the composite energy storage system meets grid-connected standards in the first filtering time scale based on the grid-connected components; the rolling filtering sub-module is used for rolling filtering 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, and the second filtering time scale is determined according to the first filtering time scale; and the adjustment sub-module is used for adjusting the first filtering time scale, carrying out wavelet packet decomposition on the wind power of the composite energy storage system based on the adjusted first filtering time scale until the decomposition results in stopping the adjustment of the first filtering time scale when the grid-connected component does not meet the grid-connected standard, and obtaining the target filtering time scale.

As an alternative implementation manner of the embodiment of the present invention, the apparatus further includes: and the correction module is used for correcting the out-of-limit power by using an interval constraint method, wherein the out-of-limit power represents a power value which does not meet the grid-connected power allowable range corresponding to the grid-connected standard.

As an alternative implementation manner of the embodiment of the present invention, the apparatus further includes: a first acquisition module for acquiring a set of filter parameters and a set of cost-effective data for the composite energy storage system; the building module is used for building a full life cycle cost and benefit model of the composite energy storage system based on the cost benefit data set; and the configuration module is used for obtaining a 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 filter parameter set.

As an alternative implementation manner of the embodiment of the present invention, the apparatus further includes: and the initialization module is used for initializing the filter parameter set.

As an optional implementation manner of the embodiment of the present invention, the configuration module includes: the first solving sub-module is used for solving the full life cycle cost and benefit model by utilizing the carnivorous plant algorithm based on the filtering parameter set to obtain a target filtering parameter set; and the determining submodule is used for determining the capacity configuration result of the composite energy storage system based on the target filtering parameter set.

As an optional implementation manner of the embodiment of the present invention, the first solving sub-module includes: the second solving sub-module is used for solving the full life cycle cost and benefit model based on the filtering parameter set to obtain at least one net benefit value of the composite energy storage system; the classification sub-module is used for sequencing each net benefit value according to preset conditions, classifying the sequencing results by utilizing the carnivorous plant algorithm, and obtaining classification results; and the updating sub-module is used for updating the filter parameter set based on the classification result until the updating is stopped when a preset condition is met, so as to obtain the target filter parameter set.

The functional description of the power distribution device of the composite energy storage system provided by the embodiment of the invention is detailed with reference to the description of the power distribution method of the composite energy storage system in the embodiment.

An embodiment of the present invention further provides a storage medium, as shown in fig. 5, on which a computer program 501 is stored, where the instructions, when executed by a processor, implement the steps of the power allocation method of the composite energy storage system in the foregoing embodiment. The storage medium may be a magnetic Disk, an optical disc, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a Flash Memory (Flash Memory), a Hard Disk (HDD), a Solid State Drive (SSD), or the like; the storage medium may also comprise a combination of memories of the kind described above.

It will be appreciated by those skilled in the art that implementing all or part of the above-described embodiment method may be implemented by a computer program to instruct related hardware, where the program may be stored in a computer readable storage medium, and the program may include the above-described embodiment method when executed. The storage medium may be a magnetic Disk, an optical disc, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a Flash Memory (Flash Memory), a Hard Disk (HDD), a Solid State Drive (SSD), or the like; the storage medium may also comprise a combination of memories of the kind described above.

The embodiment of the present invention further provides an electronic device, as shown in fig. 6, which may include a processor 61 and a memory 62, where the processor 61 and the memory 62 may be connected by a bus or other means, and in fig. 6, the connection is exemplified by a bus.

The processor 61 may be a central processing unit (Central Processing Unit, CPU). Processor 61 may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or a combination of the above.

The memory 62 serves as a non-transitory computer readable storage medium that may be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as corresponding program instructions/modules in embodiments of the present invention. The processor 61 executes various functional applications of the processor and data processing, i.e., implements the composite energy storage system power distribution method in the method embodiments described above, by running non-transitory software programs, instructions, and modules stored in the memory 62.

The memory 62 may include a memory program area that may store an operating device, an application program required for at least one function, and a memory data area; the storage data area may store data created by the processor 61, etc. In addition, the memory 62 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, memory 62 may optionally include memory located remotely from processor 61, which may be connected to processor 61 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

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 of the embodiments shown in fig. 1-3.

The specific details of the electronic device may be understood in reference to the corresponding related descriptions and effects in the embodiments shown in fig. 1 to 3, which are not repeated herein.

Although embodiments of the present invention have been described in connection with the accompanying drawings, various modifications and variations may be made by those skilled in the art without departing from the spirit and scope of the invention, and such modifications and variations fall within the scope of the invention as defined by the appended claims.

Claims (10)

1. A method of power distribution for a composite energy storage system, the method comprising:

acquiring wind power and a first filtering time scale of a composite energy storage system;

determining a target filtering time scale based on the wind power and the first filtering time scale through a self-adaptive time scale planning method;

performing wavelet packet decomposition on the 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.

2. The method of claim 1, wherein determining a target filtered time scale based on the wind power and the first filtered time scale via an adaptive time scale planning method comprises:

carrying out 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 meets grid-connected standards in the first filtering time scale based on the grid-connected components;

when the composite energy storage system meets the grid-connected standard in the first filtering time scale, rolling filtering 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, wherein the second filtering time scale is determined according to the first filtering time scale;

and adjusting the first filtering time scale, carrying out wavelet packet decomposition on the wind power of the composite energy storage system based on the adjusted first filtering time scale until the grid-connected component is decomposed to obtain the grid-connected component which does not meet the grid-connected standard, stopping adjusting the first filtering time scale, and obtaining the target filtering time scale.

3. The method according to claim 2, wherein the method further comprises:

and correcting the out-of-limit power by using an interval constraint method, wherein the out-of-limit power represents a power value which does not meet the grid-connected power allowable range corresponding to the grid-connected standard.

4. The method according to claim 1, wherein the method further comprises:

acquiring a filtering parameter set and a cost-effective 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 the carnivorous plant algorithm based on the filter parameter set.

5. The method of claim 4, wherein prior to obtaining the capacity configuration result of the composite energy storage system via the full life cycle cost and benefit model and carnivorous plant algorithm based on the set of filter parameters, the method further comprises:

and initializing the filter parameter set.

6. The method of claim 5, wherein obtaining a capacity configuration result of the composite energy storage system based on the set of filter parameters through the full life cycle cost and benefit model and carnivorous plant algorithm comprises:

Solving the full life cycle cost and benefit model by utilizing the carnivorous plant algorithm based on the filter parameter set to obtain a target filter parameter set;

and determining the capacity configuration result of the composite energy storage system based on the target filtering parameter set.

7. The method of claim 6, wherein solving the full life cycle cost and benefit model using the carnivorous plant algorithm based on the set of filter parameters results in a set of target filter parameters comprising:

solving 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;

sorting each net benefit value according to preset conditions, and sorting the sorting results by utilizing the carnivorous plant algorithm to obtain sorting results;

and updating the filter parameter set based on the classification result until a preset condition is met, and stopping updating to obtain the target filter parameter set.

8. A composite energy storage system power distribution apparatus, the apparatus comprising:

the acquisition module is used for acquiring wind power and a first filtering time scale of the composite energy storage system;

The planning module is used for determining a target filtering time scale based on the wind power and the first filtering time scale through a self-adaptive time scale planning method;

the decomposition module is used for carrying out 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;

and the filtering module 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.

9. A computer readable storage medium storing computer instructions for causing the computer to perform the composite energy storage system power distribution method of any one of claims 1 to 7.

10. An electronic device, comprising: a memory and a processor, the memory and the processor being communicatively coupled to each other, the memory storing computer instructions, the processor executing the computer instructions to perform the composite energy storage system power distribution method of any of claims 1 to 7.

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