A method and system for optimizing energy storage capacity configuration of a multi-energy complementary power generation system

Abstract

The present invention provides a method and system for optimizing the configuration of energy storage capacity of a multi-energy complementary power generation system. The method includes clustering the original data of wind and photovoltaic output power to obtain representative data of wind and photovoltaic power; performing variable gain Kalman filtering on the representative data of wind and photovoltaic power to obtain high-frequency data and low-frequency data, and distributing the high-frequency data within the hybrid energy storage system to obtain distribution data; constructing a hierarchical model for optimizing the configuration of energy storage capacity of the multi-energy complementary power generation system; determining the constraints of the upper model and the lower model, inputting the distribution data into the upper model and the lower model, and iteratively updating and solving them according to the constraints to obtain the optimal configuration scheme of the energy storage capacity of the multi-energy complementary power generation system. The present invention makes full use of the complementary advantages between wind, solar and hybrid energy storage, and makes the power output more stable and more reliable while ensuring that the capacity optimization scheme has good economic benefits.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system planning, and specifically relates to a method and system for optimizing the configuration of energy storage capacity of a multi-energy complementary power generation system. Background Art

[0002] As the scale of renewable energy integration continues to expand, the problem of wind and solar curtailment at high renewable energy penetration rates has become increasingly prominent. Wind and photovoltaic power, due to their inherent high volatility, intermittency, and uncertainty, are less stable and controllable than traditional thermal and hydropower. This places higher demands on the grid for stability and flexibility. Energy storage systems, with their bidirectional power regulation capabilities, can help smooth renewable energy fluctuations and increase renewable energy absorption rates within the power system. Meeting the multiple requirements of smoothing photovoltaic output fluctuations while also providing uninterrupted power supply to loads requires energy storage systems with both long-term power support capabilities and short-term, rapid-response, high-power support capabilities. Currently, a single energy storage technology struggles to meet these requirements simultaneously. Using a single energy storage technology also hinders the lifespan of the storage system and presents significant limitations. Hybrid energy storage systems can leverage both high energy and high power density to address multiple needs.

[0003] The primary role of hybrid energy storage systems in renewable energy grid-connected power generation is to smooth out fluctuations in wind and solar power output and increase the absorption rate of renewable energy. The rationality of hybrid energy storage power distribution affects both the investment cost and the performance of hybrid energy storage systems. Research has been conducted domestically and internationally on the power distribution and capacity configuration of hybrid energy storage. Most studies divide renewable energy power into high-frequency and low-frequency components, obtaining the grid-connected power component that meets grid-connected standards. The remaining fluctuating power component is then distributed and smoothed within the hybrid energy storage system using energy management methods consistent with the characteristics of hybrid energy storage. However, this approach does not consider the capacity range. Capacity optimization is mostly based on power quality. Considering the low lifecycle cost of energy storage, effectively decomposing hybrid energy storage power and rationally configuring its capacity can provide theoretical guidance for energy conservation, emission reduction, and the development and construction of multi-energy power stations.

[0004] In summary, the hybrid energy storage system in the prior art has the problems of low accuracy of the hybrid energy storage power distribution method and unreasonable capacity configuration. Summary of the Invention

[0005] In order to solve the above technical problems, the present invention provides a method and system for optimizing the configuration of energy storage capacity of a multi-energy complementary power generation system, which are used to solve the technical problems in the prior art.

[0006] In one aspect, the present invention provides the following technical solution: a method for optimizing energy storage capacity configuration of a multi-energy complementary power generation system, comprising:

[0007] Obtaining original data of wind power and photovoltaic power output at the target location, and performing clustering processing on the original data of wind power and photovoltaic power output to obtain representative data of wind power and photovoltaic power;

[0008] performing variable gain Kalman filtering on the representative data of wind and photovoltaic power to obtain high-frequency data and low-frequency data, selecting a hybrid energy storage system composed of lithium-ion batteries and supercapacitors, using the low-frequency data as a reference value for grid-connected power, and distributing the high-frequency data within the hybrid energy storage system to obtain distribution data;

[0009] Constructing a hierarchical model for optimizing the energy storage capacity configuration of a multi-energy complementary power generation system based on the allocation data, wherein the hierarchical model for optimizing the energy storage capacity configuration of a multi-energy complementary power generation system includes an upper model and a lower model;

[0010] Determine the constraints of the upper model and the lower model, input the allocation data into the upper model and the lower model, and iteratively update and solve according to the constraints to obtain the optimal configuration scheme of the energy storage capacity of the multi-energy complementary power generation system.

[0011] Compared with the existing technology, the beneficial effects of the present invention are as follows: the present invention adopts an improved fuzzy clustering algorithm to divide the wind and solar output scenarios into typical scenarios, introduces the elbow rule to improve the shortcomings of the traditional method of unclear clustering number, can accurately cluster the complex changes of wind and photovoltaic output scenarios, reduce the complexity of calculation, and reduce the impact of complex changes in output scenarios on output fluctuation smoothing. The present invention adopts Kalman filtering to process the wind and photovoltaic output power under typical scenarios to obtain a low-frequency signal of the grid-connected standard. The high-frequency signal is then Kalman filtered to achieve a reasonable distribution between the supercapacitor and the lithium-ion battery to achieve power smoothing. The introduction of the adjustment factor realizes the effective decomposition of wind and photovoltaic power, which is conducive to extending the life of the hybrid energy storage and improving the economy of the system. The present invention proposes a multi-energy complementary power generation system energy storage capacity optimization configuration model, divides the operation of the multi-energy complementary power generation system into two stages, fully considers the coupling characteristics of the hierarchical model, and iteratively solves the upper and lower layers to obtain the optimal capacity optimization configuration of the hybrid energy storage, which can effectively give play to the advantages of each layer and improve the overall operation efficiency.

[0012] Preferably, the step of clustering the raw data of wind and photovoltaic output power to obtain representative data of wind and photovoltaic power includes:

[0013] The raw data of wind power and photovoltaic output power are normalized to obtain normalized data:

[0014] ;

[0015] Where, To normalize the data, is the original data, 、 Represent the maximum and minimum values in the original data respectively;

[0016] Introduce the elbow method and calculate the average sum of cluster errors :

[0017] ;

[0018] Where, For the clusters, for The data points in for The mean of all data points in , is the number of clusters;

[0019] Determine the average sum of errors under different numbers of clusters and determine the variation of the average sum of errors until the variation of the average sum of errors tends to be stable, output the number of clusters corresponding to the average sum of errors and use it as the optimal number of clusters;

[0020] Based on the optimal number of clusters, corresponding objects are selected from the normalized data as initial cluster centers, the Euclidean distance from each normalized data to the initial cluster center is determined, each normalized data is assigned to the initial cluster center with the smallest Euclidean distance, and the clustering process is repeated until the change in the initial cluster center is less than a preset threshold to obtain representative data of wind and photovoltaic power.

[0021] Preferably, the step of performing variable gain Kalman filtering on the wind and photovoltaic power representative data to obtain high-frequency data and low-frequency data includes:

[0022] Using the wind and photovoltaic power representative data as the prediction quantity of the Kalman filter to perform state estimation and establish a mathematical model of a multi-energy complementary power generation system based on a variable gain Kalman filter;

[0023] Determine the first time update equation of the mathematical model of the multi-energy complementary power generation system based on the variable gain Kalman filter:

[0024] ;

[0025] ;

[0026] Determine the first state update equation of the mathematical model of the multi-energy complementary power generation system based on the variable gain Kalman filter:

[0027] ;

[0028] ;

[0029] ;

[0030] Where, It is the output power of wind power and photovoltaic power station after adding hybrid energy storage. yes The grid-connected power smoothing value after adding the hybrid energy storage system at all times, yes Hybrid energy storage output power at all times, After grid connection The power smoothing value at the moment, The current moment of wind power and photovoltaic power station Obtained The prior estimate of the state at time t, It is the output power of wind power and photovoltaic power station before adding energy storage. yes The first covariance of the moment estimate, is the first a priori estimate of the covariance, yes The first covariance of the moment estimate, is the first process noise covariance, is the first gain value of the Kalman filter, is the first measurement noise covariance, is the regulating factor, Optimize variables for state of charge;

[0031] A first current value is predicted based on the wind and photovoltaic output power at the previous moment, the first current value is substituted into the first state update equation for correction to obtain a first current correction value, and the first current correction value is substituted into the first time update equation to predict the wind and photovoltaic output power at the next moment to obtain high-frequency data and low-frequency data.

[0032] Preferably, the hybrid energy storage system is composed of lithium-ion batteries and supercapacitors, the low-frequency data is used as a reference value for grid-connected power, and the high-frequency data is distributed within the hybrid energy storage system to obtain the distributed data, the steps comprising:

[0033] A hybrid energy storage system is composed of lithium-ion batteries and supercapacitors, and the low-frequency data is used as a reference value for grid-connected power;

[0034] The high-frequency data is used as the output power of the hybrid energy storage system, and the high-frequency data is the difference between the output power of the wind power plant and the photovoltaic power plant after adding the hybrid energy storage and the reference value of the grid-connected power. The output power of the hybrid energy storage system is used as the predicted value of the Kalman filter for state estimation, and a hybrid energy storage system power distribution model based on the Kalman filter is established;

[0035] Determine the second time update equation of the hybrid energy storage system power distribution model based on Kalman filtering:

[0036] ;

[0037] ;

[0038] Determine the second state update equation of the hybrid energy storage system power distribution model based on Kalman filtering:

[0039] ;

[0040] ;

[0041] ;

[0042] Where, yes Lithium-ion battery output power at all times, yes The supercapacitor output power at each moment, yes The output power of lithium-ion battery at any moment, Is the lithium-ion battery at the moment The current The prior estimate of the state at time t, The current moment of the hybrid energy storage system The current The prior estimate of the state at time t, yes The second covariance of the moment estimate, is the second a priori estimated covariance, yes The output power of the hybrid energy storage system at all times, yes The second covariance of the moment estimate, is the second process noise covariance, is the second gain value of the Kalman filter, is the second measurement noise covariance;

[0043] Predicting a second current value based on the high-frequency data at the previous moment, substituting the second current value into the second state update equation for correction to obtain a second current correction value, and substituting the second current correction value into the second time update equation to predict the high-frequency data at the next moment;

[0044] Calculate filter coefficients :

[0045] ;

[0046] Where, For OK Column gain matrix No. Rank Take the absolute value of the column elements and sum them continuously;

[0047] An allocation principle is determined based on the filter coefficient, and the predicted high-frequency data at the next moment is allocated according to the allocation principle to obtain allocated data, wherein the allocation principle is:

[0048] ;

[0049] ;

[0050] Where, yes Lithium-ion battery output power at all times.

[0051] Preferably, the upper model is:

[0052] ;

[0053] Where, is the wind and solar fluctuation rate, is the grid connection period, for The wind and photovoltaic output power after hybrid energy storage compensation at all times, for Wind and photovoltaic output power after hybrid energy storage compensation at all times;

[0054] The lower layer model is:

[0055] ;

[0056] ;

[0057] ;

[0058] Where, For the system grid connection income, is the life cycle cost of the hybrid energy storage unit, is the initial investment cost, For operation and maintenance costs, For scrapping costs, Penalty costs for curtailing wind and solar power, 、 are the unit capacity prices of supercapacitors and lithium-ion batteries in hybrid energy storage systems, 、 are the operation and maintenance costs per unit power of supercapacitors and lithium-ion batteries in the hybrid energy storage system, 、 are the capacities of supercapacitor and lithium-ion battery in the hybrid energy storage system, is the supercapacitor output power, is the lithium-ion battery output power, 、 are the processing coefficients of supercapacitors and lithium-ion batteries in the hybrid energy storage system, is the penalty coefficient for curtailing wind and solar power, For the The amount of wind and solar power abandoned in the year is the discount rate, For the environmental benefits of wind and solar grid integration and energy storage systems, For the on-grid electricity price, is the instantaneous value of grid-connected power, is the environmental benefit coefficient of hybrid energy storage, yes Hybrid energy storage output power at all times, is the photovoltaic environmental benefit coefficient, is the wind power environmental benefit coefficient, for The photovoltaic power station outputs power at all times. for Wind turbine output at all times, The operational benefits of the system throughout its life cycle.

[0059] Preferably, the constraints of the upper model include:

[0060] Lithium-ion battery state of charge constraints:

[0061] ;

[0062] Where, Respectively represent the lower limit and upper limit of the lithium-ion battery charge state, is the state of charge of the lithium-ion battery;

[0063] Supercapacitor terminal voltage constraint:

[0064] ;

[0065] Where, Respectively represent the lower and upper limits of the supercapacitor terminal voltage, is the supercapacitor terminal voltage;

[0066] Hybrid energy storage charging and discharging power constraints:

[0067] ;

[0068] Where, is the maximum instantaneous power loss, 、 are the lower and upper limits of supercapacitor charging and discharging power, respectively. 、 They are the lower and upper limits of the charge and discharge power of lithium-ion batteries, yes The supercapacitor output power at each moment, yes Lithium-ion battery output power at all times, is the supercapacitor output power, is the lithium-ion battery output power;

[0069] Grid-connected volatility constraints:

[0070] ; ;

[0071] Where, is the grid-connected power standard deviation, is the instantaneous value of grid-connected power, is the average grid-connected power, is the grid power change rate, 、 are the maximum and minimum power values during the grid connection period, is the upper limit of the grid-connected power standard deviation, is the maximum power change rate that the grid can withstand, is the grid connection period;

[0072] The constraints of the lower model include:

[0073] System power balance constraints:

[0074] ;

[0075] Where, for The discharge power of the hybrid energy storage system at each moment, for The load that the wind-solar hybrid power generation system meets at all times, for The charging power of the hybrid energy storage system at all times, for The photovoltaic power station outputs power at all times. for Wind turbine output at all times;

[0076] Wind and solar power output constraints:

[0077] ;

[0078] Where, For wind power, photovoltaic t Maximum output at all times.

[0079] Preferably, the step of inputting the allocation data into the upper model and the lower model and performing iterative updating and solving according to the constraint conditions to obtain the optimal configuration scheme of the energy storage capacity of the multi-energy complementary power generation system includes:

[0080] Inputting the allocation data into the upper model to perform volatility analysis to obtain a preliminary energy storage capacity configuration result;

[0081] Inputting the preliminary energy storage capacity configuration result into the lower-layer model and correcting the lower-layer model to obtain a lower-layer corrected model;

[0082] The lower-level correction model uses actual frequency data to calculate the hybrid energy storage system's participation in primary frequency regulation. The data corresponding to the preliminary energy storage capacity configuration results are input into the lithium-ion battery and supercapacitor systems respectively. With the goal of maximizing operating benefits over the entire life cycle, an iterative solution is performed under the aforementioned constraints to obtain the optimal energy storage capacity configuration scheme for the multi-energy complementary power generation system.

[0083] In a second aspect, the present invention provides the following technical solution: a system for optimizing energy storage capacity configuration of a multi-energy complementary power generation system, the system comprising:

[0084] a processing module for acquiring raw data of wind power and photovoltaic power output at a target location, and performing clustering processing on the raw data of wind power and photovoltaic power output to obtain representative data of wind power and photovoltaic power;

[0085] a decomposition module for performing variable gain Kalman filtering on the representative data of wind and photovoltaic power to obtain high-frequency data and low-frequency data, selecting a hybrid energy storage system composed of lithium-ion batteries and supercapacitors, using the low-frequency data as a reference value for grid-connected power, and distributing the high-frequency data within the hybrid energy storage system to obtain distribution data;

[0086] A configuration module, configured to construct a hierarchical model for optimizing the configuration of energy storage capacity of a multi-energy complementary power generation system based on the allocation data, wherein the hierarchical model for optimizing the configuration of energy storage capacity of a multi-energy complementary power generation system includes an upper model and a lower model;

[0087] A solution module is used to determine the constraints of the upper model and the lower model, input the allocation data into the upper model and the lower model, and iteratively update and solve according to the constraints to obtain the optimal configuration plan of the energy storage capacity of the multi-energy complementary power generation system.

[0088] In a third aspect, the present invention provides the following technical solution: a computer comprising a memory, a processor, and a computer program stored in the memory and executable on the processor; when the processor executes the computer program, the method for optimizing the configuration of energy storage capacity of a multi-energy complementary power generation system as described above is implemented.

[0089] In a fourth aspect, the present invention provides the following technical solution: a storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the above-mentioned method for optimizing the configuration of energy storage capacity of a multi-energy complementary power generation system. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0091] Figure 1 Flowchart of the method for optimizing energy storage capacity configuration of a multi-energy complementary power generation system provided in the first embodiment of the present invention;

[0092] Figure 2 A structural block diagram of a system for optimizing energy storage capacity configuration of a multi-energy complementary power generation system provided in the second embodiment of the present invention;

[0093] Figure 3 A schematic diagram of the hardware structure of a computer provided in another embodiment of the present invention.

[0094] The embodiments of the present invention will be further described below with reference to the accompanying drawings. DETAILED DESCRIPTION

[0095] The following describes embodiments of the present invention in detail, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the embodiments of the present invention, and should not be construed as limiting the present invention.

[0096] Example 1

[0097] In the first embodiment of the present invention, Figure 1 As shown, a method for optimizing energy storage capacity configuration of a multi-energy complementary power generation system includes:

[0098] S1. Obtaining original data of wind power and photovoltaic power output at a target location, and performing clustering processing on the original data of wind power and photovoltaic power output to obtain representative data of wind power and photovoltaic power;

[0099] Wherein, the step S1 includes:

[0100] S11. Normalize the original data of wind power and photovoltaic output power to obtain normalized data:

[0101] ;

[0102] Where, To normalize the data, is the original data, 、 Represent the maximum and minimum values in the original data respectively;

[0103] Specifically, the purpose of normalization is to unify the dimensions.

[0104] S12. Introduce the elbow method and calculate the average error sum of clusters :

[0105] ;

[0106] Where, For the clusters, for The data points in for The mean of all data points in , is the number of clusters.

[0107] S13, determining the average sum of errors under different numbers of clusters and determining the variation range of the average sum of errors until the variation range of the average sum of errors tends to be stable, outputting the number of clusters corresponding to the average sum of errors and using it as the optimal number of clusters;

[0108] Specifically, when the number of clusters When the number of clusters is smaller than the optimal number, the degree of clustering of each cluster will gradually increase. The reduction in will be very large; when the optimal number of clusters is reached, the rate of return of the resulting clustering degree will decrease, then The reduction of will suddenly become smaller. When it reaches a certain critical value, it means that the clustering effect is the best. This critical point corresponds to The value is the optimal number of clusters.

[0109] S14. Selecting corresponding objects in the normalized data as initial cluster centers based on the optimal number of clusters, determining the Euclidean distance of each normalized data to the initial cluster center, assigning each normalized data to the initial cluster center with the smallest Euclidean distance, and repeating the clustering process until the change in the initial cluster center is less than a preset threshold, so as to obtain representative wind and photovoltaic power data;

[0110] Specifically, the preset threshold represents the threshold corresponding to distinguishing typical day scenes of wind and solar power output.

[0111] S2. Performing variable-gain Kalman filtering on the representative wind and photovoltaic power data to obtain high-frequency data and low-frequency data, selecting a hybrid energy storage system composed of lithium-ion batteries and supercapacitors, using the low-frequency data as a reference value for grid-connected power, and distributing the high-frequency data within the hybrid energy storage system to obtain distribution data;

[0112] Specifically, lithium-ion batteries and supercapacitors are selected to form a hybrid energy storage system. The variable gain Kalman filter processing is performed on the wind and photovoltaic output power data in typical scenarios to decompose it into high-frequency and low-frequency parts. The low-frequency part is used as the reference value of the grid-connected power, and the high-frequency part is distributed within the hybrid energy storage to smooth out fluctuations. The lithium-ion batteries and supercapacitors respectively absorb the fluctuating power.

[0113] The step of performing variable gain Kalman filtering on the wind and photovoltaic power representative data to obtain high-frequency data and low-frequency data includes:

[0114] S211. Using the wind and photovoltaic power representative data as the predicted quantity of the Kalman filter to perform state estimation and establish a mathematical model of a multi-energy complementary power generation system based on a variable gain Kalman filter.

[0115] S212. Determine the first time update equation of the mathematical model of the multi-energy complementary power generation system based on the variable gain Kalman filter:

[0116] ;

[0117] .

[0118] S213, determine the first state update equation of the mathematical model of the multi-energy complementary power generation system based on the variable gain Kalman filter:

[0119] ;

[0120] ;

[0121] ;

[0122] Where, It is the output power of wind power and photovoltaic power station after adding hybrid energy storage. yes The grid-connected power smoothing value after adding the hybrid energy storage system at all times, yes Hybrid energy storage output power at all times, After grid connection The power smoothing value at the moment, The current moment of wind power and photovoltaic power station Obtained The prior estimate of the state at time t, It is the output power of wind power and photovoltaic power station before adding energy storage. yes The first covariance of the moment estimate, is the first a priori estimate of the covariance, yes The first covariance of the moment estimate, is the first process noise covariance, is the first gain value of the Kalman filter, is the first measurement noise covariance, is the regulating factor, Optimize variables for state of charge.

[0123] S214: Predicting a first current value based on the wind and photovoltaic output power at the previous moment, substituting the first current value into the first state update equation for correction to obtain a first current correction value, substituting the first current correction value into the first time update equation to predict the wind and photovoltaic output power at the next moment to obtain high-frequency data and low-frequency data;

[0124] Specifically, by combining the prediction and correction steps to solve the problem, the effective decomposition of the split power can be achieved, and high-frequency data and low-frequency data can be obtained.

[0125] The steps of selecting a lithium-ion battery and a supercapacitor to form a hybrid energy storage system, using the low-frequency data as a reference value of the grid-connected power, and distributing the high-frequency data within the hybrid energy storage system to obtain distributed data include:

[0126] S221. Select lithium-ion batteries and supercapacitors to form a hybrid energy storage system, and use the low-frequency data as a reference value for grid-connected power.

[0127] S222. Use the high-frequency data as the output power of the hybrid energy storage system. The high-frequency data is the difference between the output power of the wind power plant and the photovoltaic power plant after adding hybrid energy storage and the reference value of the grid-connected power. Use the output power of the hybrid energy storage system as the predicted value of the Kalman filter for state estimation, and establish a hybrid energy storage system power distribution model based on Kalman filtering.

[0128] S223. Determine a second time update equation of the hybrid energy storage system power distribution model based on Kalman filtering:

[0129] ;

[0130] .

[0131] S224. Determine the second state update equation of the hybrid energy storage system power distribution model based on Kalman filtering:

[0132] ;

[0133] ;

[0134] ;

[0135] Where, yes Lithium-ion battery output power at all times, yes The supercapacitor output power at each moment, yes The output power of lithium-ion battery at any moment, Is the lithium-ion battery at the moment The current The prior estimate of the state at time t, The current moment of the hybrid energy storage system The current The prior estimate of the state at time t, yes The second covariance of the moment estimate, is the second a priori estimated covariance, yes The output power of the hybrid energy storage system at all times, yes The second covariance of the moment estimate, is the second process noise covariance, is the second gain value of the Kalman filter, is the second measurement noise covariance.

[0136] S225. Predict a second current value based on the high-frequency data at the previous moment, substitute the second current value into the second state update equation for correction to obtain a second current correction value, and substitute the second current correction value into the second time update equation to predict the high-frequency data at the next moment.

[0137] S226, calculate filter coefficient :

[0138] ;

[0139] Where, For OK Column gain matrix No. Rank The absolute values of the column elements are taken and summed consecutively.

[0140] S227: Determine an allocation principle based on the filter coefficient, and allocate the predicted high-frequency data at the next moment according to the allocation principle to obtain allocated data, wherein the allocation principle is:

[0141] ;

[0142] ;

[0143] Where, yes Lithium-ion battery output power at all times.

[0144] S3. Constructing a hierarchical model for optimizing the energy storage capacity configuration of a multi-energy complementary power generation system based on the allocation data, wherein the hierarchical model for optimizing the energy storage capacity configuration of a multi-energy complementary power generation system includes an upper model and a lower model;

[0145] Wherein, the upper model is:

[0146] ;

[0147] Where, is the wind and solar fluctuation rate, is the grid connection period, for The wind and photovoltaic output power after hybrid energy storage compensation at all times, for Wind and photovoltaic output power after hybrid energy storage compensation at all times;

[0148] Specifically, the upper model takes into account the effect of suppressing the probability fluctuation of wind and solar power generation, with the goal of minimizing the fluctuation rate of wind and solar power.

[0149] The lower layer model is:

[0150] ;

[0151] ;

[0152] ;

[0153] Where, For the system grid connection income, is the life cycle cost of the hybrid energy storage unit, is the initial investment cost, For operation and maintenance costs, The scrapping cost, Penalty costs for curtailing wind and solar power, 、 are the unit capacity prices of supercapacitors and lithium-ion batteries in hybrid energy storage systems, 、 are the operation and maintenance costs per unit power of supercapacitors and lithium-ion batteries in the hybrid energy storage system, 、 are the capacities of supercapacitor and lithium-ion battery in the hybrid energy storage system, is the supercapacitor output power, is the lithium-ion battery output power, 、 are the processing coefficients of supercapacitors and lithium-ion batteries in the hybrid energy storage system, is the penalty coefficient for curtailing wind and solar power, For the The amount of wind and solar power abandoned in the year is the discount rate, For the environmental benefits of wind and solar grid integration and energy storage systems, For the on-grid electricity price, is the instantaneous value of grid-connected power, is the environmental benefit coefficient of hybrid energy storage, yes Hybrid energy storage output power at all times, is the photovoltaic environmental benefit coefficient, is the wind power environmental benefit coefficient, for The photovoltaic power station outputs power at all times. for Wind turbine output at all times, The operational benefits of the system throughout its life cycle.

[0154] Specifically, the lower-level model takes into account the charging and discharging strategy of hybrid energy storage participating in primary frequency regulation, with the goal of maximizing the operating efficiency throughout the system's life cycle.

[0155] The constraints of the upper model include:

[0156] Lithium-ion battery state of charge constraints:

[0157] ;

[0158] Where, Respectively represent the lower limit and upper limit of the lithium-ion battery charge state, is the state of charge of the lithium-ion battery;

[0159] Supercapacitor terminal voltage constraint:

[0160] ;

[0161] Where, Respectively represent the lower and upper limits of the supercapacitor terminal voltage, is the supercapacitor terminal voltage;

[0162] Hybrid energy storage charging and discharging power constraints:

[0163] ;

[0164] Where, is the maximum instantaneous power loss, 、 are the lower and upper limits of supercapacitor charging and discharging power, respectively. 、 They are the lower and upper limits of the charge and discharge power of lithium-ion batteries, yes The supercapacitor output power at each moment, yes Lithium-ion battery output power at all times, is the supercapacitor output power, is the lithium-ion battery output power;

[0165] Grid-connected volatility constraints:

[0166] ; ;

[0167] Where, is the grid-connected power standard deviation, is the instantaneous value of grid-connected power, is the average grid-connected power, is the grid power change rate, 、 are the maximum and minimum power values during the grid connection period, is the upper limit of the grid-connected power standard deviation, is the maximum power change rate that the grid can withstand, is the grid connection period;

[0168] The constraints of the lower model include:

[0169] System power balance constraints:

[0170] ;

[0171] Where, for The discharge power of the hybrid energy storage system at each moment, for The load that the wind-solar hybrid power generation system meets at all times, for The charging power of the hybrid energy storage system at all times, for The photovoltaic power station outputs power at all times. for Wind turbine output at all times;

[0172] Wind and solar power output constraints:

[0173] ;

[0174] Where, For wind power, photovoltaic t Maximum output at all times.

[0175] S4. Determine the constraints of the upper model and the lower model, input the allocation data into the upper model and the lower model, and iteratively update and solve according to the constraints to obtain the optimal configuration scheme of the energy storage capacity of the multi-energy complementary power generation system.

[0176] The step S4 comprises:

[0177] S41. Input the allocation data into the upper model to perform volatility analysis to obtain a preliminary energy storage capacity configuration result.

[0178] S42: Input the preliminary energy storage capacity configuration result into the lower-level model and correct the lower-level model to obtain a lower-level corrected model.

[0179] S43. The lower-level correction model uses actual frequency data to calculate the hybrid energy storage system's participation in primary frequency regulation, and inputs the data corresponding to the preliminary energy storage capacity configuration results into the lithium-ion battery and supercapacitor systems respectively. With the goal of maximizing the operating benefits over the entire life cycle, an iterative solution is performed under the aforementioned constraints to obtain the optimal configuration plan for the energy storage capacity of the multi-energy complementary power generation system.

[0180] The first embodiment of the present invention provides a method for optimizing the energy storage capacity configuration of a multi-energy complementary power generation system. The present invention uses an improved fuzzy clustering algorithm to divide wind and solar output reduction scenarios into typical scenarios. The elbow rule is introduced to improve the shortcomings of traditional methods in that the number of clusters is unclear. This method can accurately cluster the complex changes in wind and photovoltaic output scenarios, reduce computational complexity, and reduce the impact of complex output scenario changes on output fluctuation smoothing. The present invention uses Kalman filtering to process the wind and photovoltaic output power under typical scenarios to obtain a low-frequency signal that meets the grid connection standard. The high-frequency signal is then Kalman filtered to achieve reasonable distribution between supercapacitors and lithium-ion batteries to achieve power smoothing. The introduction of a regulation factor achieves effective decomposition of wind and photovoltaic power, which is beneficial for extending the life of hybrid energy storage and improving the economic efficiency of the system. The present invention proposes a model for optimizing the energy storage capacity configuration of a multi-energy complementary power generation system. The operation of the multi-energy complementary power generation system is divided into two stages. The coupling characteristics of the hierarchical model are fully considered. The upper and lower layers are iteratively solved to obtain the optimal capacity optimization configuration of the hybrid energy storage, which can effectively leverage the advantages of each layer and improve the overall operating efficiency.

[0181] Example 2

[0182] like Figure 2 As shown, in a second embodiment of the present invention, a system for optimizing energy storage capacity configuration of a multi-energy complementary power generation system is provided, the system comprising:

[0183] Processing module 1 is used to obtain the original data of wind power and photovoltaic power output at the target location, and perform clustering processing on the original data of wind power and photovoltaic power output to obtain representative data of wind power and photovoltaic power;

[0184] Decomposition module 2 is used to perform variable gain Kalman filtering on the representative data of wind and photovoltaic power to obtain high-frequency data and low-frequency data, select lithium-ion batteries and supercapacitors to form a hybrid energy storage system, use the low-frequency data as a reference value for grid-connected power, and distribute the high-frequency data within the hybrid energy storage system to obtain distribution data;

[0185] Configuration module 3, for constructing a hierarchical model for optimizing the configuration of energy storage capacity of a multi-energy complementary power generation system based on the allocation data, wherein the hierarchical model for optimizing the configuration of energy storage capacity of a multi-energy complementary power generation system includes an upper model and a lower model;

[0186] A solution module 4 is configured to determine the constraints of the upper model and the lower model, input the allocation data into the upper model and the lower model, and perform iterative update and solution according to the constraints to obtain an optimal configuration scheme for the energy storage capacity of the multi-energy complementary power generation system;

[0187] The processing module 1 includes:

[0188] The normalization submodule is used to normalize the raw data of wind power and photovoltaic output power to obtain normalized data:

[0189] ;

[0190] Where, To normalize the data, is the original data, 、 Represent the maximum and minimum values in the original data respectively;

[0191] Error submodule, used to introduce the elbow method and calculate the average error sum of clusters :

[0192] ;

[0193] Where, For the clusters, for The data points in for The mean of all data points in , is the number of clusters;

[0194] The cluster number submodule is used to determine the average sum of errors under different cluster numbers and the change range of the average sum of errors until the change range of the average sum of errors tends to be stable, and output the number of clusters corresponding to the average sum of errors and use it as the optimal number of clusters;

[0195] A clustering submodule is used to select corresponding objects in the normalized data as initial cluster centers based on the optimal number of clusters, determine the Euclidean distance of each normalized data to the initial cluster center, assign each normalized data to the initial cluster center with the smallest Euclidean distance, and repeat the clustering process until the change of the initial cluster center is less than a preset threshold to obtain representative data of wind and photovoltaic power.

[0196] The decomposition module 2 includes:

[0197] The first construction submodule is used to use the wind and photovoltaic power representative data as the prediction quantity of the Kalman filter to perform state estimation and establish a mathematical model of a multi-energy complementary power generation system based on a variable gain Kalman filter;

[0198] The first time update submodule is used to determine the first time update equation of the mathematical model of the multi-energy complementary power generation system based on the variable gain Kalman filter:

[0199] ;

[0200] ;

[0201] The first state update submodule is used to determine the first state update equation of the mathematical model of the multi-energy complementary power generation system based on the variable gain Kalman filter:

[0202] ;

[0203] ;

[0204] ;

[0205] Where, It is the output power of wind power and photovoltaic power station after adding hybrid energy storage. yes The grid-connected power smoothing value after adding the hybrid energy storage system at all times, yes Hybrid energy storage output power at all times, After grid connection The power smoothing value at the moment, The current moment of wind power and photovoltaic power station Obtained The prior estimate of the state at time t, It is the output power of wind power and photovoltaic power station before adding energy storage. yes The first covariance of the moment estimate, is the first a priori estimate of the covariance, yes The first covariance of the moment estimate, is the first process noise covariance, is the first gain value of the Kalman filter, is the first measurement noise covariance, is the regulating factor, Optimize variables for state of charge;

[0206] The first prediction submodule is used to predict a first current value based on the wind and photovoltaic output power at the previous moment, substitute the first current value into the first state update equation for correction to obtain a first current correction value, substitute the first current correction value into the first time update equation to predict the wind and photovoltaic output power at the next moment to obtain high-frequency data and low-frequency data.

[0207] The decomposition module 2 includes:

[0208] A hybrid submodule is used to select lithium-ion batteries and supercapacitors to form a hybrid energy storage system, and use the low-frequency data as a reference value for grid-connected power;

[0209] A second construction module is configured to use the high-frequency data as the output power of the hybrid energy storage system, wherein the high-frequency data is the difference between the output power of the wind power plant and the photovoltaic power plant after adding the hybrid energy storage and the reference value of the grid-connected power, use the output power of the hybrid energy storage system as the predicted value of the Kalman filter for state estimation, and establish a hybrid energy storage system power distribution model based on the Kalman filter;

[0210] The second time update submodule is used to determine the second time update equation of the hybrid energy storage system power distribution model based on Kalman filtering:

[0211] ;

[0212] ;

[0213] The second state update submodule is used to determine the second state update equation of the hybrid energy storage system power distribution model based on Kalman filtering:

[0214] ;

[0215] ;

[0216] ;

[0217] Where, yes Lithium-ion battery output power at all times, yes The supercapacitor output power at each moment, yes The output power of lithium-ion battery at any moment, Is the lithium-ion battery at the moment The current The prior estimate of the state at time t, The current moment of the hybrid energy storage system The current The prior estimate of the state at time t, yes The second covariance of the moment estimate, is the second a priori estimated covariance, yes The output power of the hybrid energy storage system at all times, yes The second covariance of the moment estimate, is the second process noise covariance, is the second gain value of the Kalman filter, is the second measurement noise covariance;

[0218] a second prediction submodule, configured to predict a second current value based on the high-frequency data at a previous moment, substitute the second current value into the second state update equation for correction to obtain a second current correction value, and substitute the second current correction value into the second time update equation to predict the high-frequency data at a next moment;

[0219] Filter submodule, used to calculate the filter coefficient :

[0220] ;

[0221] Where, For OK Column gain matrix No. Rank Take the absolute value of the column elements and sum them continuously;

[0222] The allocation submodule is configured to determine an allocation principle based on the filter coefficient and allocate the predicted high-frequency data of the next moment according to the allocation principle to obtain allocated data, wherein the allocation principle is:

[0223] ;

[0224] ;

[0225] Where, yes Lithium-ion battery output power at all times.

[0226] The solution module 4 includes:

[0227] A first solving submodule is configured to input the allocation data into the upper model to perform a volatility analysis to obtain a preliminary energy storage capacity configuration result;

[0228] a second solving submodule, configured to input the preliminary energy storage capacity configuration result into the lower-layer model and correct the lower-layer model to obtain a lower-layer corrected model;

[0229] The third solution submodule is used to calculate the hybrid energy storage system's participation in primary frequency regulation using actual frequency data in the lower-level correction model. The data corresponding to the preliminary energy storage capacity configuration results are input into the lithium-ion battery and supercapacitor systems respectively. With the goal of maximizing operating benefits over the entire life cycle, an iterative solution is performed under the aforementioned constraints to obtain the optimal energy storage capacity configuration scheme for the multi-energy complementary power generation system.

[0230] In other embodiments of the present invention, embodiments of the present invention provide the following technical solutions: a computer comprising a memory 102, a processor 101, and a computer program stored on the memory 102 and executable on the processor 101; the processor 101 implements the above-described method for optimizing the configuration of energy storage capacity of a multi-energy complementary power generation system when executing the computer program.

[0231] Specifically, the processor 101 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured as one or more integrated circuits for implementing the embodiments of the present invention.

[0232] Memory 102 may include a large-capacity memory for data or instructions. By way of example, and not limitation, memory 102 may include a hard disk drive (HDD), a floppy disk drive, a solid-state drive (SSD), flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 102 may include removable or non-removable (or fixed) media. Where appropriate, memory 102 may be internal or external to the data processing device. In certain embodiments, memory 102 is non-volatile memory. In certain embodiments, memory 102 includes read-only memory (ROM) and random access memory (RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically alterable ROM (EAROM) or a flash memory (FLASH), or a combination of two or more of these. Under appropriate circumstances, the RAM can be a static random access memory (SRAM) or a dynamic random access memory (DRAM), where the DRAM can be a fast page mode dynamic random access memory (FPMDRAM), an extended data output dynamic random access memory (EDODRAM), a synchronous dynamic random access memory (SDRAM), etc.

[0233] The memory 102 may be used to store or cache various data files that need to be processed and / or used for communication, as well as possible computer program instructions executed by the processor 101 .

[0234] The processor 101 reads and executes computer program instructions stored in the memory 102 to implement the above-mentioned method for optimizing the configuration of energy storage capacity of the multi-energy complementary power generation system.

[0235] In some embodiments, the computer may further include a communication interface 103 and a bus 100. Figure 3 As shown, the processor 101 , the memory 102 , and the communication interface 103 are connected via a bus 100 and communicate with each other.

[0236] The communication interface 103 is used to implement communication between the various modules, devices, units, and / or equipment in the embodiments of the present invention. The communication interface 103 can also implement data communication with other components such as external devices, image / data acquisition equipment, databases, external storage, and image / data processing workstations.

[0237] Bus 100 includes hardware, software, or both, and couples components of a computer device to each other. Bus 100 includes, but is not limited to, at least one of the following: a data bus, an address bus, a control bus, an expansion bus, and a local bus. By way of example and not limitation, bus 100 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses, or a combination of two or more of these. Bus 100 may include one or more buses, where appropriate. Although embodiments of the present invention describe and illustrate a particular bus, the present invention contemplates any suitable bus or interconnect.

[0238] The computer can execute the multi-energy complementary power generation system energy storage capacity optimization configuration method of the present invention based on the obtained multi-energy complementary power generation system energy storage capacity optimization configuration system, thereby realizing the multi-energy complementary power generation system energy storage capacity optimization configuration.

[0239] In some further embodiments of the present invention, in combination with the above-mentioned method for optimizing the configuration of the energy storage capacity of a multi-energy complementary power generation system, the embodiments of the present invention provide the following technical solutions: a storage medium having a computer program stored thereon, which implements the above-mentioned method for optimizing the configuration of the energy storage capacity of a multi-energy complementary power generation system when the computer program is executed by a processor.

[0240] Those skilled in the art will appreciate that the logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device), or in conjunction with such instruction execution system, apparatus, or device. For purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by an instruction execution system, apparatus, or device, or in conjunction with such instruction execution system, apparatus, or device.

[0241] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0242] It should be understood that various components of the present invention may be implemented using hardware, software, firmware, or a combination thereof. In the aforementioned embodiments, multiple steps or methods may be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one or a combination of the following technologies known in the art may be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0243] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0244] The above-described embodiments merely represent several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent. It should be noted that a person of ordinary skill in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, and these variations and improvements fall within the scope of protection of the present invention. Therefore, the scope of protection of the patent for this invention shall be determined by the appended claims.

Claims

1. A method for optimizing energy storage capacity configuration of a multi-energy complementary power generation system, characterized in that: include: Obtaining original data of wind power and photovoltaic power output at the target location, and performing clustering processing on the original data of wind power and photovoltaic power output to obtain representative data of wind power and photovoltaic power; performing variable gain Kalman filtering on the representative data of wind and photovoltaic power to obtain high-frequency data and low-frequency data, selecting a hybrid energy storage system composed of lithium-ion batteries and supercapacitors, using the low-frequency data as a reference value for grid-connected power, and distributing the high-frequency data within the hybrid energy storage system to obtain distribution data; Constructing a hierarchical model for optimizing the energy storage capacity configuration of a multi-energy complementary power generation system based on the allocation data, wherein the hierarchical model for optimizing the energy storage capacity configuration of a multi-energy complementary power generation system includes an upper model and a lower model; Determining the constraints of the upper model and the lower model, inputting the allocation data into the upper model and the lower model, and performing iterative updates and solutions according to the constraints to obtain an optimal configuration scheme for the energy storage capacity of the multi-energy complementary power generation system; Wherein, the upper model is: ； Where, is the wind and solar fluctuation rate, is the grid connection period, for The wind and photovoltaic output power after hybrid energy storage compensation at all times, for Wind and photovoltaic output power after hybrid energy storage compensation at all times; The lower layer model is: ； ； ； Where, For the system grid connection income, is the life cycle cost of the hybrid energy storage unit, is the initial investment cost, For operation and maintenance costs, For scrapping costs, Penalty costs for curtailing wind and solar power, 、 are the unit capacity prices of supercapacitors and lithium-ion batteries in hybrid energy storage systems, 、 are the operation and maintenance costs per unit power of supercapacitors and lithium-ion batteries in the hybrid energy storage system, 、 are the capacities of supercapacitor and lithium-ion battery in the hybrid energy storage system, is the supercapacitor output power, is the lithium-ion battery output power, 、 are the processing coefficients of supercapacitors and lithium-ion batteries in the hybrid energy storage system, is the penalty coefficient for curtailing wind and solar power, For the The amount of wind and solar power abandoned in the year is the discount rate, For the environmental benefits of wind and solar grid integration and energy storage systems, For the on-grid electricity price, is the instantaneous value of grid-connected power, is the environmental benefit coefficient of hybrid energy storage, yes Hybrid energy storage output power at all times, is the photovoltaic environmental benefit coefficient, is the wind power environmental benefit coefficient, for The photovoltaic power station outputs power at all times. for Wind turbine output at all times, Operational benefits throughout the system's life cycle; The constraints of the upper model include: Lithium-ion battery state of charge constraints: ； Where, Respectively represent the lower limit and upper limit of the lithium-ion battery charge state, is the state of charge of the lithium-ion battery; Supercapacitor terminal voltage constraint: ； Where, Respectively represent the lower and upper limits of the supercapacitor terminal voltage, is the supercapacitor terminal voltage; Hybrid energy storage charging and discharging power constraints: ； Where, is the maximum instantaneous power loss, 、 are the lower and upper limits of supercapacitor charging and discharging power, respectively. 、 They are the lower and upper limits of the charge and discharge power of lithium-ion batteries, yes The supercapacitor output power at each moment, yes Lithium-ion battery output power at all times, is the supercapacitor output power, is the lithium-ion battery output power; Grid-connected volatility constraints: ； ； Where, is the grid-connected power standard deviation, is the instantaneous value of grid-connected power, is the average grid-connected power, is the grid power change rate, 、 are the maximum and minimum power values during the grid connection period, is the upper limit of the grid-connected power standard deviation, is the maximum power change rate that the grid can withstand, is the grid connection period; The constraints of the lower model include: System power balance constraints: ； Where, for The discharge power of the hybrid energy storage system at each moment, for The load that the wind-solar hybrid power generation system meets at all times, for The charging power of the hybrid energy storage system at all times, for The photovoltaic power station outputs power at all times. for Wind turbine output at all times; Wind and solar power output constraints: ； Where, For wind power, photovoltaic t Maximum output at all times.

2. The method for optimizing energy storage capacity configuration of a multi-energy complementary power generation system according to claim 1, characterized in that: The step of clustering the original data of wind power and photovoltaic power output to obtain representative data of wind power and photovoltaic power includes: The raw data of wind power and photovoltaic output power are normalized to obtain normalized data: ； Where, To normalize the data, is the original data, 、 Represent the maximum and minimum values in the original data respectively; Introduce the elbow method and calculate the average sum of cluster errors : ； Where, For the clusters, for The data points in for The mean of all data points in , is the number of clusters; Determine the average sum of errors under different numbers of clusters and determine the variation of the average sum of errors until the variation of the average sum of errors tends to be stable, output the number of clusters corresponding to the average sum of errors and use it as the optimal number of clusters; Based on the optimal number of clusters, corresponding objects are selected from the normalized data as initial cluster centers, the Euclidean distance from each normalized data to the initial cluster center is determined, each normalized data is assigned to the initial cluster center with the smallest Euclidean distance, and the clustering process is repeated until the change in the initial cluster center is less than a preset threshold to obtain representative data of wind and photovoltaic power.

3. The method for optimizing energy storage capacity configuration of a multi-energy complementary power generation system according to claim 1, characterized in that: The step of performing variable gain Kalman filtering on the wind and photovoltaic power representative data to obtain high-frequency data and low-frequency data includes: Using the wind and photovoltaic power representative data as the prediction quantity of the Kalman filter to perform state estimation and establish a mathematical model of a multi-energy complementary power generation system based on a variable gain Kalman filter; Determine the first time update equation of the mathematical model of the multi-energy complementary power generation system based on the variable gain Kalman filter: ； ； Determine the first state update equation of the mathematical model of the multi-energy complementary power generation system based on the variable gain Kalman filter: ； ； ； Where, It is the output power of wind power and photovoltaic power station after adding hybrid energy storage. yes The grid-connected power smoothing value after adding the hybrid energy storage system at all times, yes Hybrid energy storage output power at all times, After grid connection The power smoothing value at the moment, The current moment of wind power and photovoltaic power station Obtained The prior estimate of the state at time t, It is the output power of wind power and photovoltaic power station before adding energy storage. yes The first covariance of the moment estimate, is the first a priori estimate of the covariance, yes The first covariance of the moment estimate, is the first process noise covariance, is the first gain value of the Kalman filter, is the first measurement noise covariance, is the regulating factor, Optimize variables for state of charge; A first current value is predicted based on the wind and photovoltaic output power at the previous moment, the first current value is substituted into the first state update equation for correction to obtain a first current correction value, and the first current correction value is substituted into the first time update equation to predict the wind and photovoltaic output power at the next moment to obtain high-frequency data and low-frequency data.

4. The method for optimizing energy storage capacity configuration of a multi-energy complementary power generation system according to claim 1, characterized in that: The steps of selecting a lithium-ion battery and a supercapacitor to form a hybrid energy storage system, using the low-frequency data as a reference value of the grid-connected power, and distributing the high-frequency data within the hybrid energy storage system to obtain distributed data include: A hybrid energy storage system is composed of lithium-ion batteries and supercapacitors, and the low-frequency data is used as a reference value for grid-connected power; The high-frequency data is used as the output power of the hybrid energy storage system, and the high-frequency data is the difference between the output power of the wind power plant and the photovoltaic power plant after adding the hybrid energy storage and the reference value of the grid-connected power. The output power of the hybrid energy storage system is used as the predicted value of the Kalman filter for state estimation, and a hybrid energy storage system power distribution model based on the Kalman filter is established; Determine the second time update equation of the hybrid energy storage system power distribution model based on Kalman filtering: ； ； Determine the second state update equation of the hybrid energy storage system power distribution model based on Kalman filtering: ； ； ； Where, yes Lithium-ion battery output power at all times, yes The supercapacitor output power at each moment, yes The output power of lithium-ion battery at any moment, Is the lithium-ion battery at the moment The current The prior estimate of the state at time t, The current moment of the hybrid energy storage system The current The prior estimate of the state at time t, yes The second covariance of the moment estimate, is the second a priori estimated covariance, yes The output power of the hybrid energy storage system at all times, yes The second covariance of the moment estimate, is the second process noise covariance, is the second gain value of the Kalman filter, is the second measurement noise covariance; Predicting a second current value based on the high-frequency data at the previous moment, substituting the second current value into the second state update equation for correction to obtain a second current correction value, and substituting the second current correction value into the second time update equation to predict the high-frequency data at the next moment; Calculate filter coefficients : ； Where, For OK Column gain matrix No. Rank Take the absolute value of the column elements and sum them continuously; An allocation principle is determined based on the filter coefficient, and the predicted high-frequency data at the next moment is allocated according to the allocation principle to obtain allocated data, wherein the allocation principle is: ； ； Where, yes Lithium-ion battery output power at all times.

5. The method for optimizing energy storage capacity configuration of a multi-energy complementary power generation system according to claim 1, characterized in that: The step of inputting the allocation data into the upper model and the lower model and performing iterative update and solution according to the constraint conditions to obtain the optimal configuration scheme of the energy storage capacity of the multi-energy complementary power generation system includes: Inputting the allocation data into the upper model to perform volatility analysis to obtain a preliminary energy storage capacity configuration result; Inputting the preliminary energy storage capacity configuration result into the lower-layer model and correcting the lower-layer model to obtain a lower-layer corrected model; The lower-level correction model uses actual frequency data to calculate the hybrid energy storage system's participation in primary frequency regulation. The data corresponding to the preliminary energy storage capacity configuration results are input into the lithium-ion battery and supercapacitor systems respectively. With the goal of maximizing operating benefits over the entire life cycle, an iterative solution is performed under the aforementioned constraints to obtain the optimal energy storage capacity configuration scheme for the multi-energy complementary power generation system.

6. A system for optimizing the energy storage capacity configuration of a multi-energy complementary power generation system, the system adopting the method for optimizing the energy storage capacity configuration of a multi-energy complementary power generation system as claimed in claim 1, characterized in that: The system comprises: a processing module for acquiring raw data of wind power and photovoltaic power output at a target location, and performing clustering processing on the raw data of wind power and photovoltaic power output to obtain representative data of wind power and photovoltaic power; a decomposition module for performing variable gain Kalman filtering on the representative data of wind and photovoltaic power to obtain high-frequency data and low-frequency data, selecting a hybrid energy storage system composed of lithium-ion batteries and supercapacitors, using the low-frequency data as a reference value for grid-connected power, and distributing the high-frequency data within the hybrid energy storage system to obtain distribution data; A configuration module, configured to construct a hierarchical model for optimizing the configuration of energy storage capacity of a multi-energy complementary power generation system based on the allocation data, wherein the hierarchical model for optimizing the configuration of energy storage capacity of a multi-energy complementary power generation system includes an upper model and a lower model; A solution module is used to determine the constraints of the upper model and the lower model, input the allocation data into the upper model and the lower model, and iteratively update and solve according to the constraints to obtain the optimal configuration plan of the energy storage capacity of the multi-energy complementary power generation system.

7. A computer comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method for optimizing the configuration of energy storage capacity of a multi-energy complementary power generation system according to any one of claims 1 to 5 is implemented.

8. A storage medium, characterized in that: The storage medium stores a computer program, which, when executed by a processor, implements the method for optimizing energy storage capacity configuration of a multi-energy complementary power generation system according to any one of claims 1 to 5.

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