CN113792911B

CN113792911B - Double-layer collaborative optimization configuration method and system for energy storage capacity of optical storage system

Info

Publication number: CN113792911B
Application number: CN202110942544.XA
Authority: CN
Inventors: 陆超; 刘杰
Original assignee: Tsinghua University
Current assignee: Tsinghua University
Priority date: 2021-08-17
Filing date: 2021-08-17
Publication date: 2024-09-06
Anticipated expiration: 2041-08-17
Also published as: CN113792911A

Abstract

The invention provides a double-layer collaborative optimization configuration method and system for energy storage capacity of an optical storage system, wherein the method comprises the following steps: acquiring photovoltaic power data of a photovoltaic power station to be configured, and screening sample data with optimized energy storage capacity based on fluctuation amount analysis; constructing an energy storage capacity double-layer multi-objective collaborative optimization configuration model comprising an economic optimization layer model and an operation optimization layer model; and inputting the sample data of energy storage capacity optimization into the energy storage capacity double-layer multi-target collaborative optimization configuration model, and solving the economic optimization layer model and the operation optimization layer model based on a multi-target particle swarm algorithm and an optimization solver to obtain an optimal configuration result of the energy storage capacity. The invention realizes reasonable and effective configuration of the energy storage capacity of the optical storage system under the influence of double uncertainties.

Description

Double-layer collaborative optimization configuration method and system for energy storage capacity of optical storage system

Technical Field

The invention relates to the technical field of energy storage capacity configuration of an optical storage system, in particular to a double-layer collaborative optimization configuration method and system for energy storage capacity of the optical storage system.

Background

In the existing researches on the problem of optimizing the energy storage capacity of the new energy power generation, most of the existing researches are based on a certain operation strategy, and the single angle which aims at meeting the system operation performance requirement or optimizing the system economy is selected for consideration, so that the coupling relation between the energy storage capacity planning and the system optimizing operation is not considered. In addition, the existing energy storage capacity optimization method does not fully consider the influence of uncertainty factors on the energy storage capacity requirement, is not applicable to complex scenes with various uncertainty coupling, is inconvenient to analyze the coping capability of the configuration scheme on the influence of uncertainty factors, and is difficult to coordinate the contradiction between robustness and economy.

For optical storage systems, the problem of optimizing the capacity of the stored energy is more complex. On one hand, the application functions of the energy storage are diversified, and a series of new energy grid-connected standards formulated by China provide clear requirements for power fluctuation level, power prediction error, electric energy quality and the like of new energy power generation, so that the energy storage can stabilize the power fluctuation and compensate the prediction error. On the other hand, the uncertainty factors in the optical storage system are more, besides the random uncertainty existing objectively in the photovoltaic output under the influence of weather, the uncertainty existing in the photovoltaic power prediction needs to be considered, and the characteristics of double uncertainty coupling lead the capacity optimization of energy storage to have great challenges. Therefore, the method for optimizing the energy storage capacity can fully consider the influence of uncertainty factors and simultaneously consider the capacity planning and the system optimization operation coupling relation.

Disclosure of Invention

The embodiment of the invention provides a double-layer collaborative optimization method and a double-layer collaborative optimization system for energy storage capacity of an optical storage system, which are used for solving the problem that the energy storage capacity of the optical storage system cannot be reasonably and effectively configured under the influence of double uncertainties at present.

In a first aspect, an embodiment of the present invention provides a method for dual-layer collaborative optimization configuration of an energy storage capacity of an optical storage system, including:

Acquiring photovoltaic power data of a photovoltaic power station to be configured, and screening sample data with optimized energy storage capacity based on fluctuation amount analysis;

Constructing an energy storage capacity double-layer multi-objective collaborative optimization configuration model comprising an economic optimization layer model and an operation optimization layer model;

And inputting the sample data with optimized energy storage capacity into the energy storage capacity double-layer multi-target collaborative optimization configuration model, and solving the economic optimization layer model and the operation optimization layer model based on a multi-target particle swarm algorithm and an optimization solver to obtain an optimal configuration result of the energy storage capacity.

Further, the sample data with optimized energy storage capacity is screened out based on fluctuation amount analysis, and the method comprises the following steps:

Forming a photovoltaic power sequence P _d＝(P_d,1,P_d,2,...,P_d,m by photovoltaic power data of the d-th operation day of the photovoltaic power station to be configured according to time sequence; wherein m is the number of data of the photovoltaic power sequence;

Performing fluctuation amount analysis according to the sample entropy E _SE of the photovoltaic power sequence and a preset sequence complexity judgment reference value E ^* _SE to screen sample data with optimized energy storage capacity: if E _SE＞E^* _SE is carried out, the photovoltaic power sequence is a complex meteorological power sequence, and the fluctuation amount is analyzed and ended;

Otherwise, counting out-of-limit times N _D of elements in a first-order differential sequence of the photovoltaic power sequence: if N _D＞N^* _D is reached, the photovoltaic power sequence is a complex meteorological power sequence, otherwise the photovoltaic power sequence is a simple meteorological power sequence; n ^* _D is a preset judgment threshold value of the out-of-limit times of the photovoltaic power fluctuation of the complex weather.

Further, the counting the number of times of out-of-limit N _D of the elements in the first order differential sequence of the photovoltaic power sequence includes: the number of Δp _d,j is counted such that inequality Δp _d,j＞β_D, j=1, 2,..m-1 holds; wherein Δp _d,j＝|p_i,j+1-p_i,j|,j＝1,2,...,m-1,β_D is a reference value for measuring the power fluctuation amplitude at adjacent time.

Further, the construction of the energy storage capacity double-layer multi-objective collaborative optimization configuration model comprising an economic optimization layer model and an operation optimization layer model comprises the following steps:

Based on the daily net gain of the optical storage system and the energy balance capacity index of the energy storage system as optimization targets and the rated power and rated capacity of the energy storage as decision variables, an economic optimization layer model comprising an objective function and constraint conditions is constructed; the objective function of the economic optimization layer model comprises an assessment penalty cost generated when the active power change value of the photovoltaic power station exceeds an assessment limit value;

And constructing an operation optimizing layer model comprising an objective function and constraint conditions based on taking an assessment penalty cost generated when the active power change value of the photovoltaic power station exceeds an assessment limit value in the operation process of the optical storage system as an optimization target and taking photovoltaic output at each time of energy storage as a decision variable.

Further, the objective function of the economic optimization layer model is expressed as follows:

Wherein, C _NI is the equivalent daily net gain of the optical storage system in the running process; c _sal is the electricity selling income obtained by the system on-line electricity quantity; c' _LCC is the corresponding conversion cost of the energy storage full life cycle cost in the operation process; c _pen is the assessment penalty cost generated when the active power change value of the photovoltaic power station exceeds the assessment limit value; b _o is an energy balance capacity index of the energy storage system; p _o ^down (t) is the lower limit value of the probability that the energy storage system is in the energy balance state at the moment t; t is the number of times contained in each operation day;

the objective function of the run optimization layer model is expressed as follows:

Wherein, beta _pen is the check unit price corresponding to the check electric quantity; Δp _U ^d,i is the output power change value at time i in the d-th operation day; p _b ^d,i is the output power of the stored energy at the moment i in the d-th operation day; p _lim is the allowable limit of power variation; p _pv ^d,i is the output power of the photovoltaic single unit at the time i of the d operation day; t _d is the number of days of operation contained in the energy storage capacity optimized sample data; n _T is the number of times each of the operation days contains during the illumination period.

Further, the constraint conditions of the economic optimization layer model comprise limit constraints of rated power and rated capacity of the energy storage system and limit constraints of investment funds of the energy storage system, and the constraint conditions are expressed as follows:

0≤C_LCC≤C_max；

0≤P_rate≤k_p,max·G_pv；

0≤Q_rate≤k_q,max·P_rate；

Wherein, C _LCC is the corresponding cost of the energy storage full life cycle cost in the operation process; c _max is an allowable limit value of the investment planning cost of the energy storage system; g _pv is the power specification of the photovoltaic power station; k _p,max is the limit value of the rated power of the energy storage system and the installed scale ratio of the photovoltaic power station; k _q,max is the sustainable charge-discharge time length under the rated working condition of the energy storage system.

Further, the constraint conditions of the operation optimization layer model include operation constraint of the energy storage system and opportunity constraint for guaranteeing prediction error compensation performance, and the constraint conditions are expressed as follows:

S_min≤S^d,i≤S_max；

0.5-Δδ≤S^d,end≤0.5+Δδ；

Wherein, P { gamma ^d,j is more than or equal to L% } is more than or equal to alpha, which is the opportunity constraint of the prediction error, and is expressed as that the probability that the accuracy of the prediction error of the photovoltaic output meets the assessment requirement is not lower than a certain confidence coefficient alpha; The photovoltaic power predicted value at the j moment of the d operation day; gamma ^d,j is the ultra-short period prediction accuracy of the time j of the d-th operation day; l% is the allowable lower limit of prediction accuracy; n is the number of samples selected during calculation of the ultra-short-term prediction accuracy, and n=16 is usually taken; s ^d,i is the charge state of energy storage at the time of the d-th operation day i; s _min、S_max is the upper limit value and the lower limit value allowed by the charge state in the process of energy storage charging and discharging respectively; η ^d,i is the charge and discharge efficiency of energy storage at the time of the d-th operation day i; ηc and η _d are respectively the charging efficiency and the discharging efficiency of the energy storage; The output power of the energy storage at the moment i in the d-th operation day; p _rate、Q_rate is the rated power and rated capacity of the energy storage system respectively; Δt is the acquisition interval of the sample data with optimized energy storage capacity; s ^d,end is the state of charge value of energy storage at the end time of the d-th operation day; delta is the allowable fluctuation range of the state of charge.

In a second aspect, an embodiment of the present invention provides a dual-layer collaborative optimization configuration system for energy storage capacity of an optical storage system, including:

The data screening unit is used for acquiring photovoltaic power data of the photovoltaic power station to be configured and screening sample data with optimized energy storage capacity based on fluctuation amount analysis;

The model construction unit is used for constructing an energy storage capacity double-layer multi-target collaborative optimization configuration model comprising an economic optimization layer model and an operation optimization layer model;

The configuration solving unit is used for inputting the sample data with optimized energy storage capacity into the energy storage capacity double-layer multi-target collaborative optimization configuration model, and solving the economic optimization layer model and the operation optimization layer model based on a multi-target particle swarm algorithm and an optimization solver to obtain an optimal configuration result of the energy storage capacity.

In a third aspect, an embodiment of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the processor executes the program to implement the steps of the method for dual-layer collaborative optimization configuration of energy storage capacity of an optical storage system according to any one of the first aspect.

In a fourth aspect, an embodiment of the present invention provides a non-transitory computer readable storage medium, on which a computer program is stored, which when executed by a processor, implements the steps of the method for dual-layer collaborative optimization configuration of energy storage capacity of an optical storage system according to any one of the first aspect above.

According to the double-layer collaborative optimization configuration method and system for the energy storage capacity of the optical storage system, provided by the embodiment of the invention, the running day data of complex weather with larger fluctuation is screened out through fluctuation quantity analysis to be used as sample data for optimizing the energy storage capacity, so that the effectiveness of sample data selection is ensured; and constructing an optical storage system energy storage capacity planning-operation double-layer multi-target collaborative optimization model through the coupling relation of energy storage capacity planning and system optimization operation, wherein the outer-layer economic optimization layer model ensures the economical efficiency of an energy storage configuration scheme, and the inner-layer operation optimization layer model ensures the power stabilizing performance and the prediction error compensation performance of the optical storage system. The invention realizes reasonable and effective configuration of the energy storage capacity of the optical storage system under the influence of double uncertainties.

Drawings

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

FIG. 1 is a schematic flow chart of a method for double-layer collaborative optimization configuration of energy storage capacity of an optical storage system;

FIG. 2 is a schematic diagram of a strategy selection flow for comprehensive optimal configuration provided by the invention;

FIG. 3 is a schematic diagram showing a trend of energy balance capability index of the energy storage system according to the present invention;

FIG. 4 is a schematic structural diagram of a double-layer collaborative optimization configuration system for energy storage capacity of an optical storage system;

FIG. 5 is a schematic diagram of a data screening unit according to the present invention;

fig. 6 is a schematic structural diagram of an electronic device provided by the present invention.

Detailed Description

For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments. 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 technical conception of the invention: firstly, screening out operation day data of complex weather with larger fluctuation through fluctuation amount analysis, and taking the operation day data as sample data for optimizing energy storage capacity; secondly, an energy storage capacity double-layer multi-target collaborative optimization model of the optical storage system is built, an outer layer is an economic optimization layer model, daily net gain optimization of the optical storage system and energy balance capacity index optimization of the energy storage system are used as optimization targets, rated power and rated capacity of energy storage are used as decision variables, and economical efficiency of an energy storage configuration scheme and coping capacity of random uncertainty of photovoltaic output are guaranteed; the inner layer is an operation optimization layer model, the lowest assessment penalty cost of the active power change rate in the system operation process is taken as an optimization target, the output condition at each time of energy storage is taken as a decision variable, the power stabilizing performance and the prediction error compensation performance of the optical storage system are ensured, and meanwhile, the influence of the uncertainty of the photovoltaic power prediction is considered through opportunity constraint; and finally, respectively solving the inner and outer layer optimization models by adopting a multi-target particle swarm algorithm and a CPLEX optimization solver to obtain an optimal configuration scheme of energy storage. The invention can consider the influence of random uncertainty of the photovoltaic output and uncertainty of prediction of the photovoltaic power, can consider the coupling relation between energy storage capacity planning and system optimization operation, and can provide theoretical basis and guidance opinion for investment construction of the optical storage system.

The invention provides a double-layer collaborative optimization configuration method and system for energy storage capacity of an optical storage system, which are described below with reference to fig. 1-6.

The embodiment of the invention provides a double-layer collaborative optimization configuration method for energy storage capacity of an optical storage system. Fig. 1 is a schematic flow chart of a method for configuring energy storage capacity double-layer collaborative optimization of an optical storage system according to an embodiment of the present invention, as shown in fig. 1, the method includes:

step 110, obtaining photovoltaic power data of a photovoltaic power station to be configured, and screening sample data with optimized energy storage capacity based on fluctuation analysis;

Step 120, constructing a storage capacity double-layer multi-objective collaborative optimization configuration model comprising an economic optimization layer model and an operation optimization layer model;

And 130, inputting the sample data with optimized energy storage capacity into the energy storage capacity double-layer multi-objective collaborative optimization configuration model, and solving the economic optimization layer model and the operation optimization layer model based on a multi-objective particle swarm algorithm and an optimization solver to obtain an optimal configuration result of the energy storage capacity.

According to the method provided by the embodiment of the invention, the running day data of complex weather with larger volatility is screened out through fluctuation amount analysis to be used as the sample data of energy storage capacity optimization, the energy storage capacity planning-running double-layer multi-target collaborative optimization model of the optical storage system is constructed through the coupling relation between the energy storage capacity planning and the system optimization running, and the double-layer multi-target collaborative optimization model is solved to obtain an optimal configuration result. The invention realizes that the optical storage system reasonably and effectively configures the energy storage capacity under the influence of double uncertainties.

For the above step 130, it should be specifically described that, a Multi-objective particle swarm (MOPSO) Optimization algorithm and a CPLEX Optimization solver are respectively adopted to solve the inner and outer layer Optimization models corresponding to the operation Optimization layer model and the economic Optimization layer model constructed in the step 120, where the problem that the inner layer Optimization model is difficult to solve due to the oversized variable scale is solved, a method of piecewise Optimization with the day as the Optimization period is adopted to solve, and the optimizing processes of the inner and outer layer Optimization models are mutually nested, and the specific solving flow is as follows:

1) Economic layer (outer layer) parameter initialization: particle x= { X ₁＝P_rate,x₂＝Q_rate }, determining parameters of a group particle size N _PSO, iteration times I _max, an inertia factor w and an acceleration factor (c ₁,c₂), randomly initializing positions and speeds of the particles, and determining coefficients of an economic layer objective function and constraint conditions;

2) Run layer (inner layer) optimization was performed for each particle:

2-1) run layer parameter settings: acquiring photovoltaic output and corresponding power prediction data, setting energy storage rated power and rated capacity according to current particle parameters, and simultaneously determining each coefficient in a target function and constraint conditions of an operation layer;

2-2) based on a CPLEX solver, taking the illumination time period in each running day as an optimization period, and solving the optimal running scheme of each running day under the current particle parameters one by one;

2-3) combining the optimization results of all the operation days, and outputting an energy storage output process P _b ^d,i and an assessment penalty cost C _pen;

3) Calculating fitness function values C _NI and B _o of each particle in the economic layer model population according to the output result of the step 2), and screening pareto optimal solutions according to the dominance relation of the fitness values of each particle to generate a non-inferior solution set;

4) Updating individual historical optimal and global optimal positions of each particle based on an optimal solution selection strategy; the specific steps of the optimal compromise solution selection strategy are as follows:

4-1) carrying out normalization processing on the objective functions, and eliminating the influence of different objective functions due to dimension and order of magnitude differences:

Wherein, C _NI(X)、B_o (X) and C' _NI(X)、B'_o (X) are respectively the original value and the normalized value of the objective function corresponding to each decision point in the non-inferior solution set; c _NI,max、B_o,max、C_NI,min、B_o,min is the extremum of the corresponding objective function;

4-2) calculating the closeness of each decision point in the non-inferior solution set to the ideal optimal decision point X ⁺ and the ideal worst decision point X ^-:

wherein X ⁺ is (1, 1), and X ^- is (0, 0); c _j is the corresponding degree of closeness of the decision point X _j; For the geometric distance of decision points X _j and X ⁺, let d _j ⁺＝|X_jX⁺|;d_j ^- be the geometric distance of decision points X _j and X ^-, let d _j ^-＝|X_jX^-|;d⁺ be the minimum value of all d _j ⁺, let d ⁺＝min{d_j ⁺};d^- be the maximum value of all d _j ^-, let d+=max { d _j ⁺ };

4-3) selecting a decision point with the minimum intensive and tangential degree in the non-inferior solution set as an optimal compromise solution;

5) Updating the position and speed of each particle, and resetting the position of the particle beyond the search space;

6) Judging whether the maximum iteration times of the outer layer are reached, executing the step 7) if the maximum iteration times of the outer layer are reached, otherwise, turning to the step 2);

7) And selecting the optimal particles in the final pareto front based on an optimal compromise solution selection strategy, wherein the corresponding parameters are the configuration scheme of comprehensive energy storage optimization. In the embodiment of the invention, a schematic diagram of determining a comprehensive optimal configuration scheme through an optimal compromise solution selection strategy is shown in fig. 2.

Through the steps, the double-layer multi-objective optimization model in the embodiment is solved, and the comprehensive optimal scheme of the photovoltaic site configuration energy storage can be obtained, as shown in table 1.

Table 1 comprehensive optimal configuration scheme

Based on any of the above embodiments, the screening sample data with optimized energy storage capacity based on fluctuation amount analysis includes the following steps:

Based on any of the foregoing embodiments, the counting the number of out-of-limit times N _D of the element in the first-order differential sequence of the photovoltaic power sequence includes: the number of Δp _d,j is counted such that inequality Δp _d,j＞β_D, j=1, 2,..m-1 holds; wherein Δp _d,j＝|p_i,j+1-p_i,j|,j＝1,2,...,m-1, β_D is a reference value for measuring the power fluctuation amplitude at adjacent time.

Specifically, 1) acquiring photovoltaic power data of a photovoltaic power station to be configured, forming a power sequence of the photovoltaic power data of each operation day according to time sequence, and screening out operation day data of complex weather with larger fluctuation as sample data of capacity optimization through fluctuation amount analysis; for the photovoltaic power sequence P _d＝(P_d,1,P_d,2,...,P_d,m of the d-th operation day, where m is the number of data contained in the power sequence, the fluctuation amount analysis specifically includes the following steps:

1) Calculating a sample entropy E _SE of the power sequence;

1-2) judging the result of the step 1), if E _SE＞E^* _SE is the complex meteorological power sequence, ending the fluctuation amount analysis; otherwise, enter step 1-3); wherein E ^* _SE is a reference value for determining the complexity of the sequence, and in the embodiment of the present invention, E ^* _SE =0.3 is set;

1-3) calculating a first order differential sequence (Δp _i,1,Δp_i,2,…,Δp_i,m-1) of the power sequence, wherein Δp _i,j＝ |p_i,j+1-pi,j |, j=1, 2, …, m-1;

1-4) counting the number N _D of out-of-limit times of elements in the first-order differential sequence of the power sequence obtained in the step 1-3), namely counting the number of deltap _i,j for which the inequality deltap _i,j＞β_D, j=1, 2, …, m-1 is established; then, the following is determined:

if N _D＞N^* _D is reached, the power sequence is a complex meteorological power sequence; otherwise, the power sequence is a simple meteorological power sequence.

Wherein, beta _D is a reference value for measuring the power fluctuation amplitude of adjacent moments, and when beta _D＝1.5kW;Δp_i,j is larger than beta _D in the embodiment of the invention, the moment corresponding to p _i,j is considered to have larger fluctuation; n ^* _D is a decision threshold for the number of times of out-of-limit photovoltaic power fluctuation of the complex weather, and N ^* _D =4 in the embodiment of the invention.

Based on any one of the above embodiments, the constructing the energy storage capacity double-layer multi-objective collaborative optimization configuration model including the economic optimization layer model and the operation optimization layer model includes:

it should be noted that, with the daily net gain of the optical storage system and the energy balance capacity index of the energy storage system being optimal as optimization targets, the rated power and rated capacity of the energy storage being decision variables, a multi-target optimization model is constructed as an outer model of the energy storage capacity double-layer collaborative optimization model, namely an economic optimization layer model, to ensure the economy of the energy storage configuration scheme. The influence of random uncertainty existing objectively in the photovoltaic output is considered through the energy balance capacity index of the energy storage system.

It should be noted that, with the lowest assessment penalty cost C _pen generated when the active power variation value exceeds the assessment limit value in the operation process of the optical storage system as an optimization target, the output condition at each time of energy storage is a decision variable, an operation optimization layer model is constructed as an inner layer model of the energy storage capacity double-layer collaborative optimization model, so as to ensure the power stabilizing performance and the prediction error compensation performance of the optical storage system. Wherein the influence of photovoltaic power prediction uncertainty is taken into account by opportunistic constraints,

Based on any of the above embodiments, the objective function of the economic optimization layer model is expressed as follows:

Specifically, C _pen is related to the stabilizing performance of the photovoltaic power, is directly influenced by the system optimizing operation result, and is used as an objective function of an operation optimizing layer model; the calculated expressions for C _sal and C' _LCC are as follows:

C′_LCC＝L_d·C_LCC (5)

Wherein, T _d is the number of operation days contained in the selected sample data; n _T is the number of times (time resolution 1 minute) included in the illumination period in each operation day; beta _pv is the photovoltaic internet electricity price considering policy subsidy; p _U ^d,i is the light-storage combined output force at the ith moment of the d operation day; c _LCC is the total cost corresponding to the whole life cycle of the energy storage system; l _d(0≤L_d is less than or equal to 1) is a corresponding daily equivalent life loss coefficient in the operation process of the energy storage system, and the equivalent life loss is converted by adopting a method based on a rain flow counting method; n _c is the energy storage charge-discharge cycle number calculated by adopting a rain flow counting method; d _DOD,j is the charge-discharge depth corresponding to the j-th charge-discharge cycle period; n _ctf(D_DOD,j) is the cycle life when the charge and discharge depth is D _DOD,j, and the corresponding functional relation can be obtained according to the fitting of the performance parameters of the energy storage battery; n _ctf(D_DOD,st) is the cycle life corresponding to 100% of the charge-discharge depth; when L _d =1, then the energy storage battery life is considered to be exhausted;

The computational expression of C _LCC is as follows:

C_LCC＝C_I+C_oM+C_s (7)

C_I＝(1+λ_bop)·[k_qQ_rate+(k_p+k_pcs)P_rate] (8)

C_oM＝λ_omC_I (9)

C_S＝(k_spP_rate+k_sqQ_rate)·γ_PF (10)

N_T＝1/365/L_d (12)

Wherein, C _I is the fixed investment cost of the energy storage system which is input at one time in the initial construction stage; c _OM is the operation maintenance cost; c _S is the cost generated in the process of removing and destroying the energy storage battery in the scrapping treatment stage; p _rate、Q_rate is divided into rated power and rated capacity of the energy storage system; k _p、 k_q is the power unit price and the capacity unit price of the energy storage system respectively; k _pcs is the unit price of the power conversion device; lambda _bop is the ratio of the civil engineering cost to the energy storage system body cost, which is about 5% -15%; lambda _om is the ratio coefficient of the operation maintenance cost and the system fixed investment cost; k _sp、k_sq is the unit power processing unit price and unit capacity processing unit price of the energy storage system; gamma _PF is the final value to present value conversion coefficient; r is the depreciation rate; n _T is the number of operational periods (years) of the energy storage system. In the embodiment of the invention, k _p = 2000 yuan/kW, k _q = 1500 yuan/kWh, k _pcs = 800 yuan/kW, lambda _bop＝10％,λ_om＝5％,k_sp = 120 yuan/kW, k _sq = 80 yuan/kWh, and r = 0.08.

The method is characterized in that photovoltaic power fluctuation of sample data is stabilized based on a Kalman filtering algorithm, charge and discharge requirements of energy storage when the photovoltaic power fluctuation is stabilized are obtained, an envelope curve model for representing uncertainty of the charge and discharge energy requirements of the energy storage is further constructed, and energy balance capacity indexes of the energy storage system capable of describing the capacity of the energy storage to cope with the uncertainty are defined based on a random network algorithm, and the specific steps are as follows:

1) Stabilizing photovoltaic power fluctuation of sample data by adopting a conventional Kalman filtering algorithm to obtain target output data meeting the power fluctuation requirement, wherein the deviation between the actual output and the target output of the photovoltaic power station is the charge and discharge requirement of energy storage;

2) An envelope curve model for representing the uncertainty of the energy demand of energy storage charging and discharging is constructed, and the specific model is as follows:

2-1) energy storage charging energy demand envelope characterization model

E _c (s, t) is a cumulative charging energy demand process of energy storage in a period [ s, t ], E _c(s,t)：E_c(t)-E_c(s);α_c ^up(·)、α_c ^down (DEG) is an upper limit function and a lower limit function of the cumulative charging energy demand process respectively, and a multi-order linear function is adopted to fit the cumulative charging energy demand process; epsilon _c ^up(x)、ε_c ^down (x) is a corresponding probability boundary function, and an exponential decay function is adopted to fit the probability boundary function; p is a probability symbol; sup is the supreme operator.

2-2) Energy storage and discharge energy demand envelope characterization model

E _d (s, t) is an accumulated discharge energy demand process of energy storage in a time period [ s, t ], E _d(s,t)＝E_d(t)-E_d(s);α_d ^up(·)、α_d ^down (DEG) is an upper limit function and a lower limit function of the accumulated discharge energy demand process respectively, and a multi-order linear function is adopted to fit the accumulated discharge energy demand process; epsilon _d ^up(x)、ε_d ^down (x) is the corresponding probability boundary function, which is fitted with an exponential decay function.

3) Characterization of energy storage system energy change process

3-1) Description of charging and discharging Process of energy storage System

If the charging energy requirement of the energy storage in a certain period is greater than the discharging energy requirement, the energy storage is in a charging state in the period as a whole and the charging electric quantity is deviated from the charging state in the period, otherwise, if the discharging energy requirement of the energy storage in a certain period is greater than the charging energy requirement, the energy storage is in a discharging state in the period as a whole, and the corresponding mathematical description is as follows:

b(t)＝min[Q_rate,[b(t-1)+E_c(t-1,t)-E_d(t-1,t)]⁺] (15)

Wherein b (t) represents the electric quantity stored by energy storage at the moment t; q _rate is the rated capacity of the energy storage system; e _c(t-1,t)＝E_c(t)-E_c(t-1)、E_d(t-1,t)＝E_d(t)-E_d (t-1) is the charging energy requirement and the discharging energy requirement of the stored energy in the time period [ t-1, t ] respectively.

Further, a non-recursive version of b (t) can be derived as:

Wherein sup is the upper-bounding operator; inf is the infinit operator.

3-2) Energy storage system energy deficit State description

If the energy storage system is continuously in a discharge state within a certain period and the corresponding deviation of the charge and discharge energy requirements is larger than the stored electric quantity in the energy storage, the discharge energy requirements of the system still cannot be met after all the energy storage system is released, and the energy storage system is considered to be in an energy shortage state, and the corresponding shortage energy l (t) can be expressed as:

l(t)＝[E_d(t-1,t)-E_c(t-1,t)-b(t-1)]⁺ (17)

combining the non-recursive form of b (t) in step 3-1), the corresponding non-recursive expression for l (t) can be obtained as:

Further based on the random network algorithm theory and the envelope characterization model of E _c(t)、E_d (t) constructed in the above steps, the probability P _L (t) that the energy storage system is in the energy deficit state at any time t can be deduced to satisfy:

it can be seen that this equation determines the upper limit of the probability that the energy storage system is in an energy deficit state.

Wherein, Is the minimum addition convolution operator, defined as:

3-3) description of energy storage System energy surplus State

If the energy storage is continuously in a charging state within a certain period and the corresponding deviation of the charging and discharging energy demands is greater than the rated capacity of the energy storage, the charging demands cannot be continuously met after the energy storage is charged to the rated capacity, and the energy storage system is considered to be in an energy surplus state, and the corresponding surplus energy m (t) can be expressed as:

m(t)＝[E_c(t-1,t)-E_d(t-1,t)+b(t-1)-Q_rate]⁺ (21)

Similar to step 3-2), it can be deduced that the probability P _M (t) that the energy storage system is in the energy surplus state at any time t satisfies:

similarly, the equation determines an upper limit for the probability of being in an energy surplus state during operation of the energy storage system.

4) Energy balance capability evaluation index construction of energy storage system

When the energy storage system does not belong to the two states described in the step 3-2) and the step 3-3), the energy storage is considered to be in an energy balance state, that is, the configured energy storage capacity can meet the charge and discharge requirements of the system on the energy storage, and the sum of probabilities that the energy storage system is in various energy states at any time is 1, so that the probability P _o (t) that the energy storage system is in the energy balance state at the time t can be obtained to meet the following conditions:

Wherein P _M ^up(t)、P_L ^up (t) is the probability upper limit value of the energy storage system in the energy surplus state and the energy shortage state at the moment t respectively; p _o ^down (t) is the lower limit value of the probability that the energy storage system is in the energy balance state at the moment t.

Further, an energy balance capacity index B _o of the energy storage system is constructed based on P _o ^down (t), the comprehensive coping capacity of the energy storage capacity configured in the running process of the system to uncertain charge and discharge requirements is measured, and the expression of B _o is as follows:

Wherein T is the number of times contained in each operation day; the physical meaning of B _o is the average probability lower limit value of energy storage in an energy balance state in the system operation period under a certain capacity configuration scale. The trend of the energy balance capacity index of the energy storage system obtained by the embodiment of the invention along with the change trend of the rated capacity of the energy storage is shown in fig. 3.

wherein, beta _pen is the check unit price corresponding to the check electric quantity; Δp _U ^d,i is the output power change value at time i in the d-th operation day; p _b ^d,i is the output power of the stored energy at the moment i in the d-th operation day; p _lim is the allowable limit of power variation; p _pv ^d,i is the output power of the photovoltaic single unit at the time i of the d operation day; t _d is the number of days of operation contained in the energy storage capacity optimized sample data; n _T is the number of times each of the operation days contains during the illumination period. Beta _pen＝1.9 yuan/kWh,P_lim＝0.1·G_pv in the embodiment of the invention.

Based on any of the above embodiments, the constraint conditions of the economic optimization layer model include limit constraints of rated power and rated capacity of the energy storage system and limit constraints of investment funds of the energy storage system, and the constraint conditions are expressed as follows:

0≤C_LCC≤C_max； (28)

0≤P_rate≤k_p,max·G_pv； (29)

0≤≤Q_rate≤k_q,max·P_rate； (30)

In the embodiment of the invention, G _pv＝15kW,C_max =2 ten thousand yuan, and k _p,max＝60％,k_q,max =6 hours.

Based on any of the above embodiments, the constraint conditions of the operation optimization layer model include an operation constraint of the energy storage system and an opportunity constraint for guaranteeing the prediction error compensation performance, where the constraint conditions are expressed as follows:

S_min≤S^d,i≤S_max； (35)

0.5-Δδ≤S^d,end≤0.5+Δδ； (36)

Wherein, P { gamma ^d,j is more than or equal to L% } is more than or equal to alpha, which is the opportunity constraint of the prediction error, and is expressed as that the probability that the accuracy of the prediction error of the photovoltaic output meets the assessment requirement is not lower than a certain confidence coefficient alpha; The photovoltaic power predicted value at the j moment of the d operation day; gamma ^d,j is the ultra-short period prediction accuracy of the time j of the d-th operation day; l% is the allowable lower limit of prediction accuracy; n is the number of samples selected during calculation of the ultra-short-term prediction accuracy, and n=16 is usually taken; s ^d,i is the charge state of energy storage at the time of the d-th operation day i; s _min、S_max is the upper limit value and the lower limit value allowed by the charge state in the process of energy storage charging and discharging respectively; η ^d,i is the charge and discharge efficiency of energy storage at the time of the d-th operation day i; ηc and η _d are respectively the charging efficiency and the discharging efficiency of the energy storage; p _b ^d,i is the output power of the stored energy at the moment i in the d-th operation day; p _rate、Q_rate is the rated power and rated capacity of the energy storage system respectively; Δt is the acquisition interval of the sample data with optimized energy storage capacity; s ^d,end is the state of charge value of the stored energy at the end of the d-th operation day; delta is the allowable fluctuation range of the state of charge. In the embodiment of the present invention, α=0.95, l% =90%, S _min＝0.9,S_max＝0.1,η_c＝η_d =95%, and Δδ=0.05.

It should be noted that, introducing a relaxation variable converts the machine constraint P { gamma ^d,j ≡l% } ≡α of the running optimization layer model in the above steps into a general constraint which is convenient for model solution, and the following constraints are corresponded after conversion:

Wherein, β= (1-L%) is the allowable range of prediction error; a _n ^d,j、A_p ^d,j is an introduced positive and negative relaxation variable (both larger than 0) and represents the out-of-limit quantity of the prediction error exceeding the allowable range; a ^d,j is the sum of positive and negative relaxation variables, namely the total out-of-limit quantity of the prediction error; a _o is the number of all elements equal to 0 in A ^d,j; t _d is the number of days involved in the selected run period; m is the time number of the predicted power to be reported in the illumination period of each operation day; a _o/T_d/M can represent the confidence that the power prediction error meets the assessment requirement, and when A _o is equal to 0, all time points are met the assessment requirement. In the embodiment of the invention, T _d =40 and m=56.

The energy storage capacity double-layer collaborative optimal configuration system of the optical storage system is described below, and the energy storage capacity double-layer collaborative optimal configuration method of the optical storage system described below and the energy storage capacity double-layer collaborative optimal configuration method of the optical storage system described above can be referred to correspondingly.

Fig. 4 is a schematic structural diagram of a dual-layer collaborative optimization configuration system for energy storage capacity of an optical storage system according to an embodiment of the present invention, where, as shown in fig. 4, the system includes a data screening unit 410, a model building unit 420, and a configuration solving unit 430;

the data screening unit 410 is configured to obtain photovoltaic power data of a photovoltaic power station to be configured, and screen sample data with optimized energy storage capacity based on fluctuation amount analysis;

The model construction unit 420 is configured to construct an energy storage capacity double-layer multi-objective collaborative optimization configuration model including an economic optimization layer model and an operation optimization layer model;

The configuration solving unit 430 is configured to input the sample data of energy storage capacity optimization into the energy storage capacity double-layer multi-objective collaborative optimization configuration model, and solve the economic optimization layer model and the operation optimization layer model based on a multi-objective particle swarm algorithm and an optimization solver to obtain an optimal configuration result of energy storage capacity.

According to the system provided by the embodiment of the invention, the running day data of complex weather with larger fluctuation is screened out through fluctuation amount analysis to serve as sample data for energy storage capacity optimization, the energy storage capacity planning-running double-layer multi-target collaborative optimization model of the optical storage system is constructed through the coupling relation between the energy storage capacity planning and the system optimization running, and the double-layer multi-target collaborative optimization model is solved to obtain an optimal configuration result. The invention realizes that the optical storage system reasonably and effectively configures the energy storage capacity under the influence of double uncertainties.

Based on any of the above embodiments, as shown in fig. 5, the data screening unit includes a sequence building block 510 and a sample screening block 520;

the sequence building module 510 is configured to build a photovoltaic power sequence P _d＝(P_d,1,P_d,2,...,P_d,m from photovoltaic power data of the d-th operation day of the light Fu Gong rate data of the photovoltaic power station to be configured according to the time sequence; wherein m is the data of the photovoltaic power sequence;

The sample screening module 520 is configured to perform a fluctuation amount analysis according to the sample entropy E _SE of the photovoltaic power sequence and a preset sequence complexity determination reference value E ^* _SE to screen sample data with optimized energy storage capacity: if E _SE＞E^* _SE, the photovoltaic power sequence is a complex meteorological power sequence, and the fluctuation amount analysis is finished;

Based on any one of the above embodiments, the model building unit includes an economic optimization layer model building module and an operational optimization layer model building module;

The economic optimization layer model construction module is used for constructing an economic optimization layer model comprising an objective function and constraint conditions based on taking daily average net benefit of the optical storage system and energy balance capacity index of the energy storage system as optimization targets and rated power and rated capacity of energy storage as decision variables; the objective function of the economic optimization layer model comprises an assessment penalty cost generated when the active power change value of the photovoltaic power station exceeds an assessment limit value;

the operation optimization layer model construction module is used for constructing an operation optimization layer model comprising an objective function and constraint conditions based on an assessment penalty cost generated when the active power change value of the photovoltaic power station in the operation process of the optical storage system exceeds an assessment limit value as an optimization target and the photovoltaic output at each time of energy storage as a decision variable.

0≤C_LCC≤C_max； (42)

0≤P_rate≤k_p,max·G_pv； (43)

0≤Q_rate≤k_q,max·P_rate； (44)

S_min≤S^d,i≤S_max； (49)

0.5-Δδ≤S^d,end≤0.5+Δδ； (50)

Wherein, P { gamma ^d,i is more than or equal to L% } is more than or equal to alpha, which is the opportunity constraint of the prediction error, and is expressed as that the probability that the accuracy of the prediction error of the photovoltaic output meets the assessment requirement is not lower than a certain confidence coefficient alpha; The photovoltaic power predicted value at the j moment of the d operation day; gamma ^d,j is the ultra-short period prediction accuracy of the time j of the d-th operation day; l% is the allowable lower limit of prediction accuracy; n is the number of samples selected during calculation of the ultra-short-term prediction accuracy, and n=16 is usually taken; s ^d,i is the charge state of energy storage at the time of the d-th operation day i; s _min、S_max is the upper limit value and the lower limit value allowed by the charge state in the process of energy storage charging and discharging respectively; η ^d,i is the charge and discharge efficiency of energy storage at the time of the d-th operation day i; ηc and η _d are respectively the charging efficiency and the discharging efficiency of the energy storage; p _b ^d,i is the output power of the stored energy at the moment i in the d-th operation day; p _rate、Q_rate is the rated power and rated capacity of the energy storage system respectively; Δt is the acquisition interval of the sample data with optimized energy storage capacity; s ^d,end is the state of charge value of the stored energy at the end of the d-th operation day; delta is the allowable fluctuation range of the state of charge.

Fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, as shown in fig. 6, the electronic device may include: processor (processor) 610, communication interface (Communications Interface) 620, memory (memory) 630, and communication bus 640, wherein processor 610, communication interface 620, memory 630 communicate with each other over communication bus 640. The processor 610 may invoke logic instructions in the memory 630 to perform a method of dual-layer collaborative optimization configuration of the energy storage capacity of an optical storage system, the method comprising: acquiring photovoltaic power data of a photovoltaic power station to be configured, and screening sample data with optimized energy storage capacity based on fluctuation amount analysis; constructing an energy storage capacity double-layer multi-objective collaborative optimization configuration model comprising an economic optimization layer model and an operation optimization layer model; and inputting the sample data of energy storage capacity optimization into the energy storage capacity double-layer multi-target collaborative optimization configuration model, and solving the economic optimization layer model and the operation optimization layer model based on a multi-target particle swarm algorithm and an optimization solver to obtain an optimal configuration result of the energy storage capacity.

Further, the logic instructions in the memory 630 may be implemented in the form of software functional units and stored in a computer-readable storage medium when sold or used as a stand-alone product. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.

In another aspect, an embodiment of the present invention further provides a computer program product, where the computer program product includes a computer program stored on a non-transitory computer readable storage medium, where the computer program includes program instructions, when the program instructions are executed by a computer, the computer is capable of executing the method for dual-layer collaborative optimization configuration of energy storage capacity of an optical storage system provided by the above methods, where the method includes: acquiring photovoltaic power data of a photovoltaic power station to be configured, and screening sample data with optimized energy storage capacity based on fluctuation amount analysis; constructing an energy storage capacity double-layer multi-objective collaborative optimization configuration model comprising an economic optimization layer model and an operation optimization layer model; and inputting the sample data with optimized energy storage capacity into the energy storage capacity double-layer multi-target collaborative optimization configuration model, and solving the economic optimization layer model and the operation optimization layer model based on a multi-target particle swarm algorithm and an optimization solver to obtain an optimal configuration result of the energy storage capacity.

In still another aspect, an embodiment of the present invention further provides a non-transitory computer readable storage medium, on which a computer program is stored, where the computer program is implemented when executed by a processor to perform the above-provided method for collaborative optimization configuration of energy storage capacity of an optical storage system, where the method includes: acquiring photovoltaic power data of a photovoltaic power station to be configured, and screening sample data with optimized energy storage capacity based on fluctuation amount analysis; constructing an energy storage capacity double-layer multi-objective collaborative optimization configuration model comprising an economic optimization layer model and an operation optimization layer model; and inputting the sample data of energy storage capacity optimization into the energy storage capacity double-layer multi-target collaborative optimization configuration model, and solving the economic optimization layer model and the operation optimization layer model based on a multi-target particle swarm algorithm and an optimization solver to obtain an optimal configuration result of the energy storage capacity.

The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate components may or may not be physically separate, and the components shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of the embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.

From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform the method described in the various embodiments or some parts of the embodiments.

Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced equivalently; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. The double-layer collaborative optimization configuration method for the energy storage capacity of the optical storage system is characterized by comprising the following steps of:

Inputting the sample data of energy storage capacity optimization into the energy storage capacity double-layer multi-target collaborative optimization configuration model, and solving the economic optimization layer model and the operation optimization layer model based on a multi-target particle swarm algorithm and an optimization solver to obtain an optimal configuration result of energy storage capacity;

The construction of the energy storage capacity double-layer multi-objective collaborative optimization configuration model comprising an economic optimization layer model and an operation optimization layer model comprises the following steps:

Based on the assessment penalty cost generated when the active power change value of the photovoltaic power station exceeds the assessment limit value in the operation process of the optical storage system as an optimization target and the photovoltaic output at each time of energy storage as decision variables, an operation optimization layer model comprising an objective function and constraint conditions is constructed;

the objective function of the economic optimization layer model is expressed as follows:

wherein, beta _pen is the check unit price corresponding to the check electric quantity; Δp _U ^d,i is the output power change value at time i in the d-th operation day; p _b ^d,i is the output power of the stored energy at the moment i in the d-th operation day; p _lim is the allowable limit of power variation; p _pv ^d,i is the photovoltaic independent output power at the time of the d operation day i; t _d is the number of days of operation contained in the energy storage capacity optimized sample data; n _T is the time number contained in the illumination period in each operation day; p _U ^d,i is the light-storage combined output force at the ith moment of the d operation day;

the constraint conditions of the economic optimization layer model comprise limit constraints of rated power and rated capacity of the energy storage system and limit constraints of investment funds of the energy storage system, and the constraint conditions are expressed as follows:

0≤C_LCC≤C_max；

0≤P_rate≤k_p,max·G_pv；

0≤Q_rate≤k_q,max·P_rate；

Wherein, C _LCC is the total cost corresponding to the whole life cycle of the energy storage system; c _max is an allowable limit value of the investment planning cost of the energy storage system; g _pv is the power scale of the photovoltaic power plant; k _p,max is the limit value of the rated power of the energy storage system and the installed scale ratio of the photovoltaic power station; k _q,max is the sustainable charge-discharge time length under the rated working condition of the energy storage system;

the constraint conditions of the operation optimization layer model comprise the operation constraint of the energy storage system and the opportunity constraint for guaranteeing the prediction error compensation performance, and the constraint conditions are expressed as follows:

S_min≤S^d,i≤S_max；

0.5-Δδ≤S^d,end≤0.5+Δδ；

Wherein, P { gamma ^d,j is more than or equal to L% } is more than or equal to alpha, which is the opportunity constraint of the prediction error, and is expressed as that the probability that the accuracy of the prediction error of the photovoltaic output meets the assessment requirement is not lower than a certain confidence coefficient alpha; The photovoltaic power predicted value at the j moment of the d operation day; gamma ^d,j is the ultra-short-term prediction accuracy of the time j of the d-th operation day; l% is the allowable lower limit of prediction accuracy; n is the number of samples selected during calculation of the ultra-short-term prediction accuracy, and n=16 is usually taken; s ^d,i is the state of charge of energy storage at the time of the d-th operation day i; s _min、S_max is the upper limit value and the lower limit value allowed by the charge state in the process of energy storage charging and discharging respectively; η ^d,i is the charge and discharge efficiency of energy storage at the time of the d-th operation day i; ηc and η _d are respectively the charging efficiency and the discharging efficiency of the energy storage; The output power of the energy storage at the moment i in the d-th operation day; p _rate、Q_rate is the rated power and rated capacity of the energy storage system respectively; Δt is the acquisition interval of the sample data with optimized energy storage capacity; s ^d,end is the state of charge value of energy storage at the end time of the d-th operation day; delta is the allowable fluctuation range of the state of charge; p _U ^d,i is the light-storage combined output force at the ith moment of the d operation day; g _pv is the power scale of the photovoltaic power plant.

2. The method for double-layer collaborative optimization configuration of energy storage capacity of an optical storage system according to claim 1, wherein the method for screening sample data with optimized energy storage capacity based on fluctuation amount analysis comprises the following steps:

Carrying out fluctuation amount analysis according to the sample entropy E _SE of the photovoltaic power sequence and a preset sequence complexity judgment reference value E ^* _SE to screen sample data with optimized energy storage capacity: if E _SE>E^* _SE, the photovoltaic power sequence is a complex meteorological power sequence, and the fluctuation amount analysis is finished;

Otherwise, counting out-of-limit times N _D of elements in a first-order differential sequence of the photovoltaic power sequence: if N _D>N^* _D is reached, the photovoltaic power sequence is a complex meteorological power sequence, otherwise the photovoltaic power sequence is a simple meteorological power sequence; n ^* _D is a preset judgment threshold value of the out-of-limit times of the photovoltaic power fluctuation of the complex weather.

3. The method for double-layer collaborative optimization configuration of energy storage capacity of an optical storage system according to claim 2, wherein the counting of the number of out-of-limit N _D of elements in a first-order differential sequence of the photovoltaic power sequence comprises: the number of Δp _d,j is counted such that inequality Δp _d,j＞β_D, j=1, 2,..m-1 holds; wherein Δp _d,j＝│p_i,j+1-p_i,j│,j＝1,2,...,m-1,β_D is a reference value for measuring the power fluctuation amplitude at adjacent time.

4. The energy storage capacity double-layer collaborative optimization configuration system of the optical storage system is characterized by comprising:

The configuration solving unit is used for inputting the sample data of energy storage capacity optimization into the energy storage capacity double-layer multi-objective collaborative optimization configuration model, and solving the economic optimization layer model and the operation optimization layer model based on a multi-objective particle swarm algorithm and an optimization solver to obtain an optimal configuration result of the energy storage capacity;

0≤C_LCC≤C_max；

0≤P_rate≤k_p,max·G_pv；

0≤Q_rate≤k_q,max·P_rate；

S_min≤S^d,i≤S_max；

0.5-Δδ≤S^d,end≤0.5+Δδ；

5. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor performs the steps of the method for dual-layer collaborative optimization configuration of energy storage capacity of an optical storage system according to any one of claims 1-3 when the program is executed by the processor.

6. A non-transitory computer readable storage medium having stored thereon a computer program, which when executed by a processor, implements the steps of the optical storage system energy storage capacity bilayer collaborative optimization configuration method according to any of claims 1 to 3.