Hybrid energy storage system capacity configuration method and system

Abstract

The invention discloses a hybrid energy storage system capacity configuration method and system, and the method comprises the steps: obtaining historical wind power output data of a hybrid energy storage system, analyzing the historical wind power output data, and obtaining a power curve of the hybrid energy storage system, the annual average cost of the hybrid energy storage system, and the wind power fluctuation opportunity compensation cost; based on the hybrid energy storage system power curve, the annual average cost of the hybrid energy storage system and the wind power fluctuation opportunity compensation cost, and in combination with the charge state constraint and the power constraint, a capacity configuration model is constructed, and the minimum sum of the annual average cost of the hybrid energy storage system and the wind power fluctuation opportunity compensation cost is taken as a target; obtaining target capacity configuration through the capacity configuration model; by adopting the method, the problem of unreasonable capacity configuration of the hybrid energy storage system is solved, high power grid cost or short service life caused by frequent switching of charging and discharging states of the energy storage system due to too large or too small energy storage capacity is avoided, and benefit maximization when the hybrid energy storage system stabilizes wind power fluctuation is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of power system control, and in particular to a method and system for configuring the capacity of a hybrid energy storage system. Background Art

[0002] With the development of energy storage technology, the combination of wind-solar power generation systems and energy storage devices will become an effective measure for utilizing new energy; by giving play to the charge-discharge characteristics of the energy storage system, not only can stable, safe and economic new energy power generation be achieved, but also the operation efficiency of the power system can be improved, the cycle life of equipment can be extended, and the large-scale utilization of renewable energy can be promoted;

[0003] In the related art, for a complementary power generation system composed of wind-solar renewable energy, the instantaneous power mutation caused by power supply or load fluctuations often leads to voltage and frequency fluctuations of the power supply and distribution system and instability of the new energy power generation system; in order to maximize the benefits of the hybrid energy storage system when suppressing wind power fluctuations and simultaneously ensure the feasibility of the operation of the hybrid energy storage system, how to reasonably plan the capacity of the energy storage system is a technical problem that needs to be solved urgently at present; in view of the problem of unreasonable capacity configuration of the hybrid energy storage system in the related art, the traditional technical design has not proposed an effective solution. Summary of the Invention

[0004] (I) Technical Problem to be Solved

[0005] In view of the deficiencies of the prior art, the present invention provides a method and system for configuring the capacity of a hybrid energy storage system, and by adopting this configuration method, the problems raised in the background art are solved.

[0006] (II) Technical Solution

[0007] To achieve the above object, the present invention is realized through the following technical solutions:

[0008] A method for configuring the capacity of a hybrid energy storage system, the steps of the method include:

[0009] The steps of the method include:

[0010] Obtain the historical wind power output data of the hybrid energy storage system, analyze the historical wind power output data, and obtain a summary power cost data set; wherein, the summary power cost data at least includes: the power curve of the hybrid energy storage system, the annual average cost of the hybrid energy storage system, and the wind power fluctuation opportunity compensation cost;

[0011] Based on the summary power cost data set, and in combination with the state of charge constraint and power constraint of the hybrid energy storage system, construct a capacity configuration model;

[0012] With the goal of minimizing the sum of the annual average cost of the hybrid energy storage system and the opportunity compensation cost for wind power fluctuations, through the operation capacity configuration model, the preliminary target capacity configuration is obtained;

[0013] According to the preliminary target capacity configuration, the preset simulation model is run, and the fluctuations of key indicators of the hybrid energy storage system during the operation cycle in the simulation model are recorded. Whether the preliminary target capacity configuration is feasible is determined based on the fluctuation situation;

[0014] If it is feasible, the preliminary target capacity configuration is taken as the actual target capacity configuration;

[0015] If it is not feasible, the evaluation and correction strategy is triggered to correct the preliminary target capacity configuration to obtain the actual target capacity configuration.

[0016] Furthermore, the process of analyzing the historical wind power output data to obtain the power curve of the hybrid energy storage system is as follows:

[0017] Cluster the historical wind power output data to obtain different wind power output scenarios;

[0018] Determine the target grid connection fluctuation quantity limit corresponding to each wind power output scenario from the grid connection fluctuation limits of the wind power output scenarios;

[0019] According to the historical wind power output data and the target grid connection fluctuation quantity limit, the power curve of the hybrid energy storage system is obtained.

[0020] Furthermore, the process of obtaining the power curve of the hybrid energy storage system according to the historical wind power output data and the target grid connection fluctuation quantity limit is as follows:

[0021] Perform frequency domain decomposition on the historical wind power output data to obtain at least two order IMF components and a residual component;

[0022] Based on the low-frequency reconstruction algorithm, perform low-frequency reconstruction on the IMF components and the residual component to obtain at least two low-frequency reconstruction components;

[0023] Based on the high-frequency reconstruction algorithm, perform high-frequency reconstruction on the IMF components and the residual component to obtain at least two high-frequency reconstruction components;

[0024] Determine the power curve of the hybrid energy storage system according to the low-frequency reconstruction components, the high-frequency reconstruction components and the target grid connection fluctuation quantity limit.

[0025] Furthermore, the annual average cost of the hybrid energy storage system includes the annual average cost of the flywheel;

[0026] The process of analyzing the historical wind power output data to obtain the annual average cost of the flywheel is as follows:

[0027] Based on the preset initial cost model of the flywheel, the initial investment cost of the flywheel is obtained according to the system operation cycle, the flywheel charge and discharge power, and the flywheel rated capacity in the historical wind power output data;

[0028] Among them, the preset initial cost model of the flywheel includes:

[0029]

[0030] In the formula, is the initial investment cost of the flywheel, is the flywheel power investment cost coefficient, is the flywheel capacity investment cost coefficient, r is the discount rate, T is the system operation cycle, PFESS is the flywheel charge and discharge power, and EFESS is the flywheel rated capacity;

[0031] Based on the initial investment cost of the flywheel and the preset flywheel maintenance coefficient, the operation and maintenance cost of the flywheel is obtained;

[0032] According to the initial investment cost of the flywheel and the operation and maintenance cost of the flywheel, the annual average cost of the flywheel is obtained.

[0033] Furthermore, the annual average cost of the hybrid energy storage system also includes the annual average cost of the chemical battery;

[0034] The process of analyzing the historical wind power output data to obtain the annual average cost of the flywheel is as follows:

[0035] Based on the preset initial cost model of the chemical battery, the initial investment cost of the chemical battery is obtained according to the system operation cycle, the chemical battery charge and discharge power, and the chemical battery rated capacity in the historical wind power output data;

[0036] Among them, the preset initial cost model of the chemical battery includes:

[0037]

[0038] In the formula, is the initial investment cost of the chemical battery, is the chemical battery power investment cost coefficient, is the chemical battery capacity investment cost coefficient, PBAT is the chemical battery charge and discharge power, and EBAT is the chemical battery rated capacity;

[0039] Based on the initial investment cost of the chemical battery and the preset chemical battery maintenance coefficient, the operation and maintenance cost of the chemical battery is obtained;

[0040] According to the initial investment cost of the chemical battery and the operation and maintenance cost of the chemical battery, the annual average cost of the chemical battery is obtained.

[0041] Furthermore, the process of analyzing the historical wind power output data to obtain the wind power fluctuation opportunity compensation cost is as follows:

[0042] Based on a preset compensation cost model, the compensation cost for wind power fluctuation opportunities is obtained according to historical wind power output data;

[0043] The preset compensation cost model includes:

[0044]

[0045] In the formula, Fcomp is the compensation cost for wind power fluctuation opportunities, h is the opportunity compensation cost coefficient, N is the time length, Pposun,n is the positive under-compensation amount at time n, and Pnegun,n is the negative under-compensation amount at time n.

[0046] Furthermore, the state of charge constraint includes the state of charge constraint of the flywheel energy storage system and the state of charge constraint of the chemical battery;

[0047] Among them, the state of charge constraint of the flywheel energy storage system includes:

[0048]

[0049] In the formula, SOCFESS(n) is the state of charge of the flywheel energy storage system at time n, SOCFESS(n + Δn) is the state of charge of the flywheel energy storage system at time (n + Δn), μFESS(n) is the state of charge coefficient of the flywheel energy storage system at time n, PFESS(n) is the charging and discharging power of the flywheel energy storage system at time n, EFESS is the rated capacity of the flywheel energy storage system, and Δn is the time span;

[0050] The state of charge constraint of the chemical battery includes:

[0051]

[0052] In the formula, SOCBAT(n) is the state of charge of the chemical battery at time n, SOCBAT(n + Δn) is the state of charge of the chemical battery at time (n + Δn), μBAT(n) is the state of charge coefficient of the chemical battery at time n, PBAT(n) is the charging and discharging power of the chemical battery at time n, and EBAT is the rated capacity of the chemical battery.

[0053] Furthermore, the power constraint includes:

[0054]

[0055] In the formula, PHESS is the charging and discharging power of the hybrid energy storage system, is the charging power of the flywheel energy storage system at time n, is the discharging power of the flywheel energy storage system at time n, is the charging power of the chemical battery at time n, The discharge power of the chemical battery at time n, SOCFESS is the state of charge of the flywheel, SOCBAT is the state of charge of the chemical battery, PFESS is the charge and discharge power of the flywheel, PBAT is the charge and discharge power of the chemical battery, PFESS,max is the rated power of the flywheel, and PBAT,max is the rated power of the chemical battery.

[0056] Furthermore, the output power of the hybrid energy storage system and the energy loss during the operation of the hybrid energy storage system;

[0057] The fluctuation of key indicators is represented by: the fluctuation coefficient of each key indicator at each moment within the operation cycle, including the output power fluctuation coefficient and the energy loss fluctuation coefficient;

[0058] Among them, the calculation formula for the output power fluctuation coefficient is:

[0059]

[0060] In the formula, ΔP represents the output power fluctuation coefficient, t represents the number at different moments within the operation cycle T, t = 1, 2, 3, 4,..., T, T is a positive integer, prt represents the output power corresponding to moment t, and pravg represents the average value of the output power within the operation cycle T;

[0061] The calculation formula for the energy loss fluctuation coefficient is:

[0062]

[0063] In the formula, ΔE represents the energy loss fluctuation coefficient, ert represents the energy loss corresponding to moment t, and eravg represents the average value of the energy loss within the operation cycle T;

[0064] When determining whether the preliminary target capacity configuration is feasible based on the fluctuation, if both the output power fluctuation coefficient and the energy loss fluctuation coefficient are within the corresponding fluctuation threshold ranges, it is determined to be feasible;

[0065] When only the output power fluctuation coefficient exceeds the corresponding fluctuation threshold, it is determined to be infeasible, and the content of the triggered evaluation and correction strategy is: based on the output power fluctuation coefficient and the corresponding fluctuation threshold, construct a first correction model to generate a correction value;

[0066] When only the energy loss fluctuation coefficient exceeds the corresponding fluctuation threshold, it is determined to be infeasible, and the content of the triggered evaluation and correction strategy is: based on the energy loss fluctuation coefficient and the corresponding fluctuation threshold, construct a second correction model to generate a correction value;

[0067] When both the output power fluctuation coefficient and the energy loss fluctuation coefficient exceed their corresponding fluctuation thresholds, it is determined to be infeasible, and the content of the triggered evaluation and correction strategy is as follows: Based on the output power fluctuation coefficient, the energy loss fluctuation coefficient, and their corresponding fluctuation thresholds, a third correction model is constructed to generate a correction value;

[0068] The correction value includes:

[0069]

[0070] In the formula, C represents the correction value, Pth and Eth respectively represent the fluctuation threshold corresponding to the output power fluctuation coefficient and the fluctuation threshold corresponding to the energy loss fluctuation coefficient, P_max and E_max respectively represent the maximum value of the output power fluctuation coefficient and the maximum value of the energy loss fluctuation coefficient; CM represents the currently running model, and M1, M2, and M3 respectively represent the first correction model, the second correction model, and the third correction model;

[0071] Based on the generated correction value, multiply the correction value by the preliminary target capacity configuration, and the resulting value is the actual target capacity configuration.

[0072] A hybrid energy storage system capacity configuration system, which includes:

[0073] A data analysis module, which obtains the historical wind power output data of the hybrid energy storage system, analyzes the historical wind power output data, and obtains a summary power cost data set; among them, the summary power cost data at least includes: the power curve of the hybrid energy storage system, the average annual cost of the hybrid energy storage system, and the wind power fluctuation opportunity compensation cost;

[0074] A model construction module, which constructs a capacity configuration model based on the summary power cost data set and in combination with the state of charge constraint and power constraint of the hybrid energy storage system;

[0075] A capacity configuration module, with the goal of minimizing the sum of the average annual cost of the hybrid energy storage system and the wind power fluctuation opportunity compensation cost, obtains a preliminary target capacity configuration by running the capacity configuration model;

[0076] A model correction module, based on the preliminary target capacity configuration, runs a preset simulation model, records the fluctuation conditions of the key indicators of the hybrid energy storage system during the operation cycle in the simulation model, and determines whether the preliminary target capacity configuration is feasible based on the fluctuation conditions;

[0077] If it is feasible, the preliminary target capacity configuration is used as the actual target capacity configuration;

[0078] If it is not feasible, the evaluation and correction strategy is triggered to correct the preliminary target capacity configuration to obtain the actual target capacity configuration.

[0079] (III) Beneficial effects

[0080] The present invention provides a method and a system for capacity configuration of a hybrid energy storage system, having the following beneficial effects:

[0081] 1. By obtaining the historical wind power output data of the hybrid energy storage system, analyzing the historical wind power output data, the power curve of the hybrid energy storage system, the average annual cost of the hybrid energy storage system, and the compensation cost for the opportunity of wind power fluctuation are obtained;

[0082] Based on the power curve of the hybrid energy storage system, the average annual cost of the hybrid energy storage system, and the compensation cost for the opportunity of wind power fluctuation, and combining the state of charge constraint and power constraint of the hybrid energy storage system, a capacity configuration model is constructed. With the goal of minimizing the sum of the average annual cost of the hybrid energy storage system and the compensation cost for the opportunity of wind power fluctuation, the target capacity configuration is obtained through the capacity configuration model, initially solving the problem of unreasonable capacity configuration of the hybrid energy storage system;

[0083] By analyzing the historical wind power output data to construct a capacity configuration model to determine the capacity configuration of the hybrid energy storage system when suppressing the wind power output, avoiding excessive energy storage capacity resulting in high grid costs, or too small energy storage capacity resulting in frequent switching of the charge and discharge states of the energy storage system and shortening the service life, so as to maximize the benefit when the hybrid energy storage system suppresses wind power fluctuations;

[0084] 2. When running the capacity configuration model to obtain the preliminary target capacity configuration, considering the cost factor at this time, in order to ensure the feasibility or reliability of subsequent actual operation, and based on the simulation model to obtain the result, according to the result, it is selected whether to further introduce a correction model to obtain the final required actual target capacity configuration, so as to achieve the effect of synchronously meeting the benefit and feasibility, thus ensuring the stability of the hybrid energy storage system and further solving the problem of unreasonable configuration of the existing hybrid energy storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] Figure 1 is a flowchart of the method for capacity configuration of the hybrid energy storage system in the present invention;

[0086] Figure 2 is a modular schematic diagram of the system for capacity configuration of the hybrid energy storage system in the present invention;

[0087] Figure 3 is a schematic structural diagram of the electronic device used in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0088] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0089] Embodiment 1:

[0090] Please refer to Figure 1 , this embodiment provides a method for configuring the capacity of a hybrid energy storage system, and the method includes the following steps:

[0091] S101. Obtain the historical wind power output data of the hybrid energy storage system, analyze the historical wind power output data, and obtain a summary power cost data set; wherein, the summary power cost data at least includes: the power curve of the hybrid energy storage system, the annual average cost of the hybrid energy storage system, and the wind power fluctuation opportunity compensation cost;

[0092] Specifically, the time intervals for obtaining the historical wind power output data are the same, and it can be the annual wind power output data, including but not limited to wind speed, wind direction, and wind farm output data;

[0093] Analyzing the historical wind power output data to obtain the power curve of the hybrid energy storage system includes:

[0094] S1011. Cluster the historical wind power output data to obtain different wind power output scenarios;

[0095] According to different wind speeds, wind directions, wind farm layouts, and the performance of wind turbines, classify the possible power output situations of the wind farm to obtain different wind power output scenarios; optionally, use the K-means clustering algorithm to cluster the annual wind power output data obtained to obtain a wind power output scenario set; then analyze each wind power output scenario in the wind power output scenario set respectively

[0096] S1012. Determine the target grid connection fluctuation quantity limit corresponding to each wind power output scenario from the grid connection fluctuation limits of the wind power output scenarios;

[0097] Determine the target grid connection fluctuation limit value based on the wind power output data of any day in the target wind power output scenario and multiple grid connection fluctuation limit values; specifically, in the process of determining the target grid connection fluctuation limit value, first, for the target wind power output scenario, select the wind power output data of any day as the analysis basis; the data of this day contains the wind power output values at multiple time points, reflecting the real-time change of wind power output; next, consider the grid's requirements for the grid connection fluctuation limit, which are usually given in the form of multiple grid connection fluctuation limit values, and may include limit values in different time periods or different fluctuation situations; in order to determine the target grid connection fluctuation limit value, it is necessary to comprehensively consider the characteristics of the wind power output data and the requirements for the grid connection fluctuation limit; the specific approach may be: analyze the fluctuation characteristics of the wind power output data, such as the fluctuation amplitude and frequency; at the same time, combine the limit values of the grid connection fluctuation and evaluate the grid connection feasibility of the wind power output under different limit values; through comparative analysis, a grid connection fluctuation limit value that can not only meet the grid connection requirements of the wind power output but also comply with the grid fluctuation limit can be found as the target value; this process involves complex numerical calculations and evaluations, and professional analysis tools (such as any one of MATLAB and Python) and methods (such as any one of clustering analysis and frequency domain decomposition) need to be used; the finally determined target grid connection fluctuation limit value will be used as an important basis for the subsequent power curve design and capacity configuration of the hybrid energy storage system to ensure that the hybrid energy storage system can effectively suppress wind power fluctuations and meet the grid connection requirements of the grid;

[0098] S1013. Obtain the power curve of the hybrid energy storage system according to the historical wind power output data and the target grid connection fluctuation limit value;

[0099] Specifically included in S1013:

[0100] S201. Perform frequency domain decomposition on the historical wind power output data to obtain at least two order IMF components and a residual component;

[0101] Decompose the wind power output data into at least two intrinsic mode functions (IMFs) and a residual component through a decomposition algorithm (such as the variational mode decomposition algorithm or the empirical mode decomposition algorithm); the IMF intrinsic mode function is used to process non-linear data, and in this embodiment, it is used to process the wind power output data, and the residual component refers to the remaining trend or non-oscillatory part after the output data is decomposed;

[0102] S202. Based on the low-frequency reconstruction algorithm, perform low-frequency reconstruction on the IMF components and the residual component to obtain at least two low-frequency reconstruction components;

[0103] Optionally, perform low-frequency reconstruction on the IMF components and the residual component according to Formula 1;

[0104] Formula 1:

[0105]

[0106] Among them, c2f(1), c2f(2), and c2f(p + 1) are all low-frequency reconstruction components, res is the residual component, IMF1 is the first-order IMF component, IMF2 is the second-order IMF component, IMFp is the p-order IMF component, and p is the order of the IMF components obtained by performing frequency-domain decomposition on historical wind power output data;

[0107] S203. Based on the high-frequency reconstruction algorithm, perform high-frequency reconstruction on the IMF components and the residual component to obtain at least two high-frequency reconstruction components;

[0108] Optionally, perform high-frequency reconstruction on the IMF components and the residual component according to Formula 2;

[0109] Formula 2:

[0110]

[0111] Among them, f2c(1), f2c(2), and f2c(p + 1) are high-frequency reconstruction components;

[0112] S204. Determine the power curve of the hybrid energy storage system according to the low-frequency reconstruction components, the high-frequency reconstruction components, and the target grid connection fluctuation limit value;

[0113] The process of determining the power curve of the hybrid energy storage system according to the low-frequency reconstruction components, the high-frequency reconstruction components, and the target grid connection fluctuation limit value can be as follows: First, decompose the historical wind power output data into low-frequency reconstruction components and high-frequency reconstruction components through the frequency-domain decomposition algorithm; then, in combination with the target grid connection fluctuation limit value, adjust the high-frequency reconstruction components; the target grid connection fluctuation limit value sets the acceptable range of wind power output fluctuations. By controlling the amplitude of the high-frequency reconstruction components, it can be ensured that the wind power output does not exceed this limit value when connected to the grid; finally, add the adjusted high-frequency reconstruction components to the low-frequency reconstruction components to obtain the power curve that the hybrid energy storage system needs to output; this power curve takes into account both the main trend of the wind power output and meets the requirements of the grid connection fluctuation limit value by controlling the high-frequency fluctuation part; the power curve of the hybrid energy storage system determined in this way can effectively suppress the fluctuations of the wind power output, improve the stability and reliability of the wind power grid connection, and at the same time optimize the operation efficiency of the hybrid energy storage system;

[0114] In some of the embodiments, the annual average cost of the hybrid energy storage system includes the annual average cost of the flywheel;

[0115] The analysis of the historical wind power output data in S101 to obtain the annual average cost of the hybrid energy storage system includes:

[0116] S1014. Based on a preset initial flywheel cost model, obtain the initial flywheel investment cost according to the system operation cycle, flywheel charge and discharge power, and flywheel rated capacity in the historical wind power output data.

[0117] The preset initial flywheel cost model includes:

[0118]

[0119] Among them, is the initial flywheel investment cost, is the flywheel power investment cost coefficient, is the flywheel capacity investment cost coefficient, r is the discount rate, T is the system operation cycle, P FESS is the flywheel charge and discharge power, E FESS is the flywheel rated capacity;

[0120] S1015. Based on the initial flywheel investment cost and a preset flywheel maintenance coefficient, obtain the flywheel operation and maintenance cost.

[0121] The calculation formula for the flywheel operation and maintenance cost is:

[0122]

[0123] Among them, is the flywheel operation and maintenance cost, s is the flywheel operation and maintenance coefficient, that is, the proportion of the flywheel operation and maintenance cost in the initial flywheel investment cost;

[0124] S1016. According to the initial flywheel investment cost and the flywheel operation and maintenance cost, obtain the annual average cost of the flywheel.

[0125] The annual average cost F FESS of the flywheel is calculated as:

[0126]

[0127] Among them, is the initial flywheel investment cost, is the flywheel operation and maintenance cost;

[0128] In some embodiments, the annual average cost of the hybrid energy storage system includes the annual average cost of the chemical battery.

[0129] In S101, analyzing the historical wind power output data, the annual average cost of the hybrid energy storage system includes:

[0130] S1017. Based on a preset initial chemical battery cost model, obtain the initial investment cost of the chemical battery according to the system operation cycle, chemical battery charge and discharge power, and chemical battery rated capacity in the historical wind power output data.

[0131] The preset initial cost model of the chemical battery includes:

[0132]

[0133] Among them, is the initial investment cost of the chemical battery, is the power investment cost coefficient of the chemical battery, is the capacity investment cost coefficient of the chemical battery, r is the discount rate, T is the system operation period, P BAT is the charge and discharge power of the chemical battery, E BAT is the rated capacity of the chemical battery;

[0134] S1018. Based on the initial investment cost of the chemical battery and the preset chemical battery maintenance coefficient, obtain the operation and maintenance cost of the chemical battery;

[0135] Calculate the operation and maintenance cost of the chemical battery according to the following formula:

[0136]

[0137] Among them, is the operation and maintenance cost of the chemical battery, b is the operation and maintenance coefficient of the chemical battery, that is, the proportion of the operation and maintenance cost of the chemical battery in the initial investment cost of the chemical battery;

[0138] S1019. According to the initial investment cost of the chemical battery and the operation and maintenance cost of the chemical battery, obtain the annual average cost of the chemical battery;

[0139] The annual average cost F of the chemical battery BAT The calculation formula is:

[0140]

[0141] Among them, is the initial investment cost of the chemical battery, is the operation and maintenance cost of the chemical battery;

[0142] In some embodiments, in S101, analyzing the historical wind power output data, the obtained wind power fluctuation opportunity compensation cost includes:

[0143] Based on the preset compensation cost model, according to the historical wind power output data, obtain the wind power fluctuation opportunity compensation cost;

[0144] The preset compensation cost model includes:

[0145]

[0146] Among them, F comp is the wind power fluctuation opportunity compensation cost, h is the opportunity compensation cost coefficient, N is the time length, Pposun,n is the positive under-compensation amount at time n, P negun,n is the negative under-compensation amount at time n.

[0147] S102. Based on the aggregated power cost data set and combined with the state-of-charge constraint and power constraint of the hybrid energy storage system, a capacity configuration model is constructed;

[0148] Among them, the state-of-charge constraint includes the state-of-charge constraint of the flywheel and the state-of-charge constraint of the chemical battery;

[0149] The state-of-charge constraint of the flywheel includes:

[0150]

[0151] Among them, SOC FESS (n) is the state of charge of the flywheel at time n, SOC FESS (n + Δn) is the state of charge of the flywheel at time (n + Δn), μ FESS (n) is the flywheel charge coefficient at time n, P FESS (n) is the flywheel charge and discharge power at time n, E FESS is the rated capacity of the flywheel, and Δn is the time span;

[0152] The flywheel charge coefficient μ FESS (n) is calculated as follows:

[0153]

[0154] Among them, η Fc is the flywheel charging efficiency, η Fd is the flywheel discharging efficiency;

[0155] The state-of-charge constraint of the chemical battery includes:

[0156]

[0157] Among them, SOC BAT (n) is the state of charge of the chemical battery at time n, SOC BAT (n + Δn) is the state of charge of the chemical battery at time (n + Δn), μ BAT (n) is the chemical battery charge coefficient at time n, P BAT (n) is the chemical battery charge and discharge power at time n, E BAT is the rated capacity of the chemical battery;

[0158] The chemical battery charge coefficient μ BAT (n) is calculated as follows:

[0159]

[0160] Among them, η Bc is the charging efficiency of the chemical battery, and η Bd is the discharging efficiency of the chemical battery;

[0161] The power constraints include:

[0162]

[0163] Among them, P HESS is the charging and discharging power of the hybrid energy storage system, is the flywheel charging power at the nth moment, is the flywheel discharging power at the nth moment, is the chemical battery charging power at the nth moment, is the chemical battery discharging power at the nth moment, P posun,n is the positive under-compensation amount at the nth moment, P negun,n is the negative under-compensation amount at the nth moment, SOC FESS is the state of charge of the flywheel, SOC BAT is the state of charge of the chemical battery, P FESS is the flywheel charging and discharging power, P BAT is the chemical battery charging and discharging power, P FESS，max is the flywheel rated power, P BAT，max is the chemical battery rated power;

[0164] It should be noted that the capacity configuration model is a mathematical optimization model that takes into account the power curves, annual average cost, wind power fluctuation opportunity compensation cost of the hybrid energy storage system (usually including flywheel and chemical battery, etc.), as well as the state of charge constraint and power constraint of the system; the goal of the model is to find the optimal capacity configuration of the flywheel and chemical battery to minimize the total cost of the system (annual average cost plus wind power fluctuation opportunity compensation cost);

[0165] The steps are as follows:

[0166] Calculate the annual average cost: According to the initial cost models of the flywheel and chemical battery, calculate their respective initial investment costs; add the operation and maintenance costs to obtain the annual average cost; Calculate the wind power fluctuation opportunity compensation cost: According to the preset compensation cost model, calculate the opportunity compensation cost brought by wind power fluctuation; Construct the objective function: Objective function = flywheel annual average cost + chemical battery annual average cost + wind power fluctuation opportunity compensation cost; Set the constraint conditions: According to the state of charge constraint and power constraint, set the constraint conditions of the model; Solve the model: Use mathematical optimization software (such as MATLAB, CPLEX, etc.) to solve this optimization problem; Obtain the optimal rated capacity configuration of the flywheel and chemical battery, that is, the target capacity configuration;

[0167] Among them, the above specific steps only briefly describe the relevant or similar steps for constructing the capacity configuration model to assist understanding;

[0168] Hypothetical results:

[0169] The rated capacity E of the flywheel FESS = 50 kWh;

[0170] The rated capacity E of the chemical battery BAT = 100 kWh;

[0171] These results indicate that, under the given conditions, in order to minimize the total cost of the system, the optimal capacity configurations of the flywheel and the chemical battery are 50 kWh and 100 kWh respectively; through such a process, we can obtain the optimal capacity configuration of the hybrid energy storage system based on specific system parameters and historical data, thereby optimizing the economy and performance of the system.

[0172] S103. With the goal of minimizing the sum of the annual average cost of the hybrid energy storage system and the compensation cost for wind power fluctuation opportunities, through operating the capacity configuration model, obtain the preliminary target capacity configuration;

[0173] Among them, if the energy storage capacity of the hybrid energy storage system is too large, it will lead to an increase in the economic cost of the power grid;

[0174] If the energy storage capacity is too small, it will lead to frequent switching of the charge and discharge states of the energy storage system and shorten the service life;

[0175] Through the capacity configuration model, obtain the target capacity configuration when the hybrid energy storage system suppresses the wind power output, and avoid the energy storage capacity of the hybrid energy storage system being too large or too small, so as to maximize the benefit when the hybrid energy storage system suppresses wind power fluctuations.

[0176] By obtaining the historical wind power output data of the hybrid energy storage system, analyzing the historical wind power output data, obtain the power curve of the hybrid energy storage system, the annual average cost of the hybrid energy storage system, and the compensation cost for wind power fluctuation opportunities;

[0177] By adopting the above steps, the method given in this embodiment can construct a capacity configuration model based on the power curve of the hybrid energy storage system, the annual average cost of the hybrid energy storage system, and the compensation cost for wind power fluctuation opportunities, and in combination with the state of charge constraint and power constraint of the hybrid energy storage system, with the goal of minimizing the sum of the annual average cost of the hybrid energy storage system and the compensation cost for wind power fluctuation opportunities, obtain the target capacity configuration through the capacity configuration model, and initially solve the problem of unreasonable capacity configuration of the hybrid energy storage system;

[0178] By analyzing historical wind power output data, a capacity configuration model is constructed to determine the capacity configuration of the hybrid energy storage system when suppressing wind power output, avoiding excessive energy storage capacity, which may lead to high grid costs, or insufficient energy storage capacity, which may cause frequent switching of the charge and discharge states of the energy storage system and shorten its service life, so as to maximize the benefits of the hybrid energy storage system in suppressing wind power fluctuations.

[0179] S104. According to the preliminary target capacity configuration, run the preset simulation model, record the fluctuations of key indicators of the hybrid energy storage system during the operation cycle in the simulation model, and determine whether the preliminary target capacity configuration is feasible based on the fluctuations;

[0180] If it is feasible, take the preliminary target capacity configuration as the actual target capacity configuration;

[0181] If it is not feasible, trigger an evaluation and correction strategy to correct the preliminary target capacity configuration to obtain the actual target capacity configuration;

[0182] Among them, the simulation model will simulate the performance of the hybrid energy storage system in actual operation. By inputting necessary parameters, the operation of the system can be recorded, and this situation can accurately reflect the situation under the actual hybrid energy storage system;

[0183] Set the operation cycle corresponding to the simulation model:

[0184] Set the time cycle of the simulation operation according to actual needs, such as one day, one week or one month; among them, during the operation of the simulation model, for the external environment and output parameters, their relative stability can be guaranteed to ensure the stability or representativeness of the operation results of the simulation model during the operation cycle;

[0185] The key indicators at least include:

[0186] The output power of the hybrid energy storage system to the outside (i.e., the instantaneous power corresponding to each moment during the operation cycle) and the energy loss of the hybrid energy storage system during the operation process (i.e., electrical loss);

[0187] Then, the reasons for selecting the above two parameters as key indicators are as follows:

[0188] The external output power directly reflects the ability of the system to meet the load demand and is an important indicator for evaluating the system performance; while the energy loss reflects the efficiency of the system during operation; the two indicators can comprehensively and intuitively reflect the performance of the hybrid energy storage system in actual operation; the stability of the output power is crucial for ensuring the reliable operation of the system, and the magnitude of the energy loss directly affects the economy and sustainability of the system; therefore, by monitoring and analyzing the fluctuations of these two key indicators, the feasibility of the preliminary target capacity configuration can be accurately evaluated and a strong basis can be provided for subsequent correction strategies, so as to ensure that the finally determined actual target capacity configuration can meet the performance requirements and energy efficiency standards of the system.

[0189] The fluctuations of the key indicators refer to: the fluctuation coefficients of each key indicator at each moment within the operation cycle, including the output power fluctuation coefficient and the energy loss fluctuation coefficient;

[0190] The calculation formula for the output power fluctuation coefficient is:

[0191]

[0192] In the formula, ΔP represents the output power fluctuation coefficient, t represents the serial number at different moments within the operation cycle T, t = 1, 2, 3, 4, ……, T, T is a positive integer, pr t represents the output power corresponding to the moment t, pr avg represents the average value of the output power within the operation cycle T;

[0193] The calculation formula for the energy loss fluctuation coefficient is:

[0194]

[0195] In the formula, ΔE represents the energy loss fluctuation coefficient, er t represents the energy loss corresponding to the moment t, er avg represents the average value of the energy loss within the operation cycle T;

[0196] When determining whether the preliminary target capacity configuration is feasible based on the fluctuations, if both the output power fluctuation coefficient and the energy loss fluctuation coefficient are within the corresponding fluctuation threshold ranges, it is determined to be feasible;

[0197] When only the output power fluctuation coefficient exceeds the corresponding fluctuation threshold, it is determined to be infeasible, and the content of the triggered evaluation and correction strategy is: based on the output power fluctuation coefficient and the corresponding fluctuation threshold, a first correction model is constructed to generate a correction value;

[0198] When only the energy loss fluctuation coefficient exceeds the corresponding fluctuation threshold, it is determined to be infeasible, and the content of the triggered evaluation correction strategy is: based on the energy loss fluctuation coefficient and the corresponding fluctuation threshold, construct a second correction model to generate a correction value;

[0199] When both the output power fluctuation coefficient and the energy loss fluctuation coefficient exceed the corresponding fluctuation thresholds, it is determined to be infeasible, and the content of the triggered evaluation correction strategy is: based on the output power fluctuation coefficient, the energy loss fluctuation coefficient, and the corresponding fluctuation thresholds, construct a third correction model to generate a correction value;

[0200] According to the finally generated correction value, multiply the correction value by the preliminary target capacity configuration, and the obtained result is the actual target capacity configuration; the result of this actual target capacity configuration increases;

[0201] If after the preliminary target capacity configuration runs in the simulation model, the fluctuation conditions of the key indicators indicate that the system performance does not meet the requirements (that is, the fluctuation coefficient exceeds the corresponding fluctuation threshold range), generally the preliminary target capacity configuration is increased;

[0202] Reason:

[0203] When the preliminary target capacity configuration is too small, the hybrid energy storage system may not be able to maintain a stable output power and low energy loss in the face of load changes or external environmental fluctuations; increasing the capacity configuration can provide more energy reserves and regulation capabilities, which helps the system better cope with these fluctuations, thereby reducing the fluctuation coefficients of the output power and energy loss and enabling the system performance to meet the design requirements; therefore, when the preliminary target capacity configuration is infeasible, increasing the capacity configuration is a reasonable correction strategy;

[0204]

[0205] In the formula, C represents the correction value, P th 、E th respectively represent the fluctuation threshold corresponding to the output power fluctuation coefficient and the fluctuation threshold corresponding to the energy loss fluctuation coefficient, and P_max and E_max respectively represent the maximum value of the output power fluctuation coefficient and the maximum value of the energy loss fluctuation coefficient (determined according to the simulation model or actual experience);

[0206] if represents the prerequisite for the current formula to run, CM represents the currently running model, and M1, M2, and M3 respectively represent the first correction model, the second correction model, and the third correction model;

[0207] Logical explanation: The formula principles of the first and second correction models are the same. When ΔP is exactly equal to P thWhen the correction value C is 1, it means no correction. When ΔP increases, the correction value C also increases accordingly. For the third correction model, when either one or both increase, the correction value C also increases. However, due to the use of the square root of the sum of squares, its increasing speed will be slower than when considering only one factor, ensuring the rationality of the obtained results.

[0208] For example:

[0209] Suppose P th = 0.05, P_max = 0.2;

[0210] E th = 0.02, E_max = 0.1;

[0211] The simulation results show that: ΔP = 0.1, ΔE = 0.05;

[0212] When considering only the output power fluctuation coefficient: C = 1 + 0.05 / 0.15 ≈ 1.33 (rounded to two decimal places);

[0213] When considering only the energy loss fluctuation coefficient: C = 1 + 0.03 / 0.08 ≈ 1.38 (rounded to two decimal places);

[0214] When considering both: C = 1 + √0.33 2 + 0.38 2 ≈ 1.48 (rounded to two decimal places);

[0215] In addition, after obtaining the actual target capacity configuration as above, the simulation model can be further operated twice. According to the fluctuation situation, it can be determined whether the current actual target capacity configuration is feasible. If it is still determined to be infeasible, the loop operation is performed until it is feasible.

[0216] By adopting the above steps, the method given in this embodiment can also run the capacity configuration model to obtain a preliminary target capacity configuration. At this time, the cost factor is considered. To ensure the feasibility or reliability of subsequent actual operation and based on the results obtained from the simulation model, it is determined whether to further introduce the correction model according to the results to obtain the final required actual target capacity configuration, so as to achieve the effect of simultaneously meeting the benefits and feasibility, thus ensuring the stability of the hybrid energy storage system and further solving the problem of unreasonable configuration of the existing hybrid energy storage system.

[0217] Embodiment 2:

[0218] Please refer to Figure 2 , based on Embodiment 1, this embodiment also provides a capacity configuration system for a hybrid energy storage system. The system includes the following modules that run in sequence, specifically:

[0219] The data analysis module obtains the historical wind power output data of the hybrid energy storage system, analyzes the historical wind power output data, and obtains a summary power cost data set; wherein, the summary power cost data at least includes: the power curve of the hybrid energy storage system, the average annual cost of the hybrid energy storage system, and the wind power fluctuation opportunity compensation cost;

[0220] The model construction module constructs a capacity configuration model based on the summary power cost data set and in combination with the state of charge constraint and power constraint of the hybrid energy storage system;

[0221] The capacity configuration module aims to minimize the sum of the average annual cost of the hybrid energy storage system and the wind power fluctuation opportunity compensation cost, and obtains a preliminary target capacity configuration by running the capacity configuration model;

[0222] The model correction module runs a preset simulation model according to the preliminary target capacity configuration, records the fluctuation conditions of the key indicators of the hybrid energy storage system during the operation period in the simulation model, and determines whether the preliminary target capacity configuration is feasible according to the fluctuation conditions;

[0223] If it is feasible, the preliminary target capacity configuration is used as the actual target capacity configuration;

[0224] If it is not feasible, an evaluation and correction strategy is triggered to correct the preliminary target capacity configuration to obtain the actual target capacity configuration;

[0225] By running the system in this embodiment, the data analysis module obtains the historical wind power output data of the hybrid energy storage system, analyzes the historical wind power output data, and obtains the power curve of the hybrid energy storage system, the average annual cost of the hybrid energy storage system, and the wind power fluctuation opportunity compensation cost; the model construction is based on the power curve of the hybrid energy storage system, the average annual cost of the hybrid energy storage system, and the wind power fluctuation opportunity compensation cost, and in combination with the state of charge constraint and power constraint of the hybrid energy storage system, a capacity configuration model is constructed; the capacity configuration module aims to minimize the sum of the average annual cost of the hybrid energy storage system and the wind power fluctuation opportunity compensation cost, and obtains the target capacity configuration through the capacity configuration model, solving the problem of unreasonable capacity configuration of the hybrid energy storage system; by analyzing the historical wind power output data to construct a capacity configuration model to determine the capacity configuration of the hybrid energy storage system when suppressing wind power output, avoiding too large energy storage capacity leading to high grid costs, or too small energy storage capacity leading to frequent switching of the charge and discharge states of the energy storage system and shortening the service life, so as to maximize the benefits of the hybrid energy storage system when suppressing wind power fluctuations.

[0226] Embodiment 3:

[0227] Please refer to Figure 3, this embodiment also provides an electronic device, including a memory and a processor. A computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps of any one of the methods in Embodiment 1;

[0228] Specifically, the above electronic device may further include a transmission device and an input / output device;

[0229] Among them, the transmission device is connected to the above processor, and the input / output device is connected to the above processor;

[0230] In this embodiment, the above processor may be configured to execute the steps in Embodiment 1 through a computer program;

[0231] It should be noted that the specific examples in this embodiment may refer to the examples described in the above embodiments and alternative embodiments, and will not be elaborated here;

[0232] In one embodiment, Figure 3 is a schematic internal structure diagram of an electronic device according to an embodiment of the present application. As Figure 3 shown, an electronic device is provided. The electronic device may be a server, and its internal structure diagram may be as Figure 3 shown; the electronic device includes a processor, a memory, a network interface, and a database connected through a system bus; among them, the processor of the electronic device is used to provide computing and control capabilities; the memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database; the internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium; the database of the electronic device is used to store data. The network interface of the electronic device is used to communicate with an external terminal through a network connection; when the computer program is executed by the processor, it implements a method for configuring the capacity of a hybrid energy storage system.

[0233] Those skilled in the art can understand that Figure 3 the structure shown in

[0234] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those of ordinary skill in the art will appreciate that the units and algorithm steps of the examples described in connection with the embodiments disclosed herein can be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution.

[0235] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units. They may be located in one place or distributed over multiple network units. Some or all of the units can be selected according to actual needs to achieve the objectives of the solution of this embodiment.

[0236] As described above, the specific implementation manners of the present application are only described, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed in the present application can easily think of changes or substitutions, which should be covered within the protection scope of the present application.

Claims

1. Method for capacity configuration of hybrid energy storage system, characterized in that: The steps of this method include: Obtain the historical wind power output data of the hybrid energy storage system, analyze the historical wind power output data, and obtain a summary power cost data set; wherein, the summary power cost data at least includes: the power curve of the hybrid energy storage system, the annual average cost of the hybrid energy storage system, and the wind power fluctuation opportunity compensation cost; Based on the summary power cost data set, and combined with the state of charge constraint and power constraint of the hybrid energy storage system, construct a capacity configuration model; Aim at minimizing the sum of the annual average cost of the hybrid energy storage system and the wind power fluctuation opportunity compensation cost, and obtain a preliminary target capacity configuration by operating the capacity configuration model; According to the preliminary target capacity configuration, operate a preset simulation model, record the fluctuation conditions of the key indicators of the hybrid energy storage system during the operation cycle in the simulation model, and determine whether the preliminary target capacity configuration is feasible according to the fluctuation conditions; If it is feasible, use the preliminary target capacity configuration as the actual target capacity configuration; If it is not feasible, trigger an evaluation and correction strategy to correct the preliminary target capacity configuration to obtain the actual target capacity configuration.

2. The method for configuring the capacity of the hybrid energy storage system according to claim 1, wherein: The process of analyzing the historical wind power output data to obtain the power curve of the hybrid energy storage system is: Cluster the historical wind power output data to obtain different wind power output scenarios; Determine the target grid connection fluctuation quantity limit corresponding to each wind power output scenario from the grid connection fluctuation limits of the wind power output scenarios; According to the historical wind power output data and the target grid connection fluctuation quantity limit, obtain the power curve of the hybrid energy storage system.

3. The method for configuring the capacity of the hybrid energy storage system according to claim 2, wherein: The process of obtaining the power curve of the hybrid energy storage system according to the historical wind power output data and the target grid connection fluctuation quantity limit is: Perform frequency domain decomposition on the historical wind power output data to obtain at least two-order IMF components and a residual component; Based on the low-frequency reconstruction algorithm, perform low-frequency reconstruction on the IMF components and the residual component to obtain at least two low-frequency reconstruction components; Based on the high-frequency reconstruction algorithm, perform high-frequency reconstruction on the IMF components and the residual component to obtain at least two high-frequency reconstruction components; Determine the power curve of the hybrid energy storage system according to the low-frequency reconstruction components, the high-frequency reconstruction components, and the target grid connection fluctuation quantity limit.

4. The method for configuring the capacity of the hybrid energy storage system according to claim 1, wherein: The annual average cost of the hybrid energy storage system includes the annual average cost of the flywheel; The process of analyzing the historical wind power output data to obtain the annual average cost of the flywheel is: Based on a preset flywheel initial cost model, obtain the initial investment cost of the flywheel according to the system operation cycle, the flywheel charge and discharge power, and the flywheel rated capacity in the historical wind power output data; Wherein, the preset flywheel initial cost model includes: In the formula, is the initial investment cost of the flywheel, is the investment cost coefficient of the flywheel power, is the investment cost coefficient of the flywheel capacity, r is the discount rate, T is the system operation period, P FESS is the charging and discharging power of the flywheel, E FESS is the rated capacity of the flywheel; Based on the initial investment cost of the flywheel and a preset flywheel maintenance coefficient, obtain the flywheel operation and maintenance cost; According to the initial investment cost of the flywheel and the flywheel operation and maintenance cost, obtain the annual average cost of the flywheel.

5. The method for configuring the capacity of the hybrid energy storage system according to claim 4, wherein: The annual average cost of the hybrid energy storage system also includes the annual average cost of the chemical battery; The process of analyzing the historical wind power output data to obtain the annual average cost of the flywheel is: Based on a preset chemical battery initial cost model, obtain the initial investment cost of the chemical battery according to the system operation cycle, the chemical battery charge and discharge power, and the chemical battery rated capacity in the historical wind power output data; Wherein, the preset chemical battery initial cost model includes: In the formula, is the initial investment cost of the chemical battery, is the investment cost coefficient of the chemical battery power, is the investment cost coefficient of the chemical battery capacity, P BAT is the charge and discharge power of the chemical battery, E BAT is the rated capacity of the chemical battery; Based on the initial investment cost of the chemical battery and the preset chemical battery maintenance coefficient, the operation and maintenance cost of the chemical battery is obtained; According to the initial investment cost of the chemical battery and the operation and maintenance cost of the chemical battery, the annual average cost of the chemical battery is obtained.

6. The method for configuring the capacity of the hybrid energy storage system according to claim 1, wherein: The process of analyzing historical wind power output data to obtain the opportunity compensation cost for wind power fluctuation is as follows: Based on the preset compensation cost model, according to the historical wind power output data, the opportunity compensation cost for wind power fluctuation is obtained; The preset compensation cost model includes: Where, F comp is the opportunity compensation cost for wind power fluctuation, h is the opportunity compensation cost coefficient, N is the time length, P posun,n is the positive under-compensation amount at time n, and P negun,n is the negative under-compensation amount at time n.

7. The method for configuring the capacity of the hybrid energy storage system according to claim 1, wherein: The state of charge constraint includes the state of charge constraint of the flywheel and the state of charge constraint of the chemical battery; Among them, the state of charge constraint of the flywheel includes: Wherein, SOC FESS (n) is the state of charge of the flywheel at the nth moment, SOC FESS (n + Δn) is the state of charge of the flywheel at the (n + Δn)th moment, μ FESS (n) is the charge coefficient of the flywheel at the nth moment, P FESS (n) is the charging and discharging power of the flywheel at the nth moment, E FESS is the rated capacity of the flywheel, and Δn is the time span; The state of charge constraint of the chemical battery includes: Where, SOC BAT (n) is the state of charge of the chemical battery at the nth moment, SOC BAT (n + Δn) is the state of charge of the chemical battery at the (n + Δn)th moment, μ BAT (n) is the charge coefficient of the chemical battery at the nth moment, P BAT (n) is the charge and discharge power of the chemical battery at the nth moment, E BAT is the rated capacity of the chemical battery.

8. The method for configuring the capacity of the hybrid energy storage system according to claim 1, wherein: The power constraint includes: Where, P HESS is the charge and discharge power of the hybrid energy storage system, is the flywheel charging power at the nth moment, is the flywheel discharging power at the nth moment, is the chemical battery charging power at the nth moment, is the chemical battery discharging power at the nth moment, SOC FESS is the state of charge of the flywheel, SOC BAT is the state of charge of the chemical battery, P FESS is the charge and discharge power of the flywheel, P BAT is the charge and discharge power of the chemical battery, P FESS，max is the rated power of the flywheel, P BAT，max is the rated power of the chemical battery.

9. The method for configuring the capacity of the hybrid energy storage system according to claim 1, wherein: The key indicators at least include: the output power of the hybrid energy storage system and the energy loss during the operation of the hybrid energy storage system; The fluctuation conditions of the key indicators indicate: the fluctuation coefficients of each key indicator at each moment during the operation cycle, including the output power fluctuation coefficient and the energy loss fluctuation coefficient; Among them, the calculation formula of the output power fluctuation coefficient is: Wherein, ΔP represents the output power fluctuation coefficient, t represents the serial number at different moments within the operation period T, t = 1, 2, 3, 4, ……, T, T is a positive integer, and pr t represents the output power corresponding to the moment t, and pr avg represents the average value of the output power within the operation period T; The calculation formula of the energy loss fluctuation coefficient is: Wherein, ΔE represents the energy loss fluctuation coefficient, and er t represents the energy loss corresponding to the moment t, and er avg represents the average value of the energy loss within the operation period T; When determining whether the preliminary target capacity configuration is feasible based on the fluctuation conditions, when both the output power fluctuation coefficient and the energy loss fluctuation coefficient are within the corresponding fluctuation threshold ranges, it is determined to be feasible; When only the output power fluctuation coefficient exceeds the corresponding fluctuation threshold, it is determined to be infeasible, and the content of the triggered evaluation and correction strategy is: based on the output power fluctuation coefficient and the corresponding fluctuation threshold, a first correction model is constructed to generate a correction value; When only the energy loss fluctuation coefficient exceeds the corresponding fluctuation threshold, it is determined to be infeasible, and the content of the triggered evaluation and correction strategy is: based on the energy loss fluctuation coefficient and the corresponding fluctuation threshold, a second correction model is constructed to generate a correction value; When both the output power fluctuation coefficient and the energy loss fluctuation coefficient exceed the corresponding fluctuation threshold, it is determined to be infeasible, and the content of the triggered evaluation and correction strategy is: based on the output power fluctuation coefficient, the energy loss fluctuation coefficient and the corresponding fluctuation threshold, a third correction model is constructed to generate a correction value; The correction value includes: where C represents the correction value, P th , E th respectively represent the fluctuation threshold corresponding to the output power fluctuation coefficient and the fluctuation threshold corresponding to the energy loss fluctuation coefficient, P_max and E_max respectively represent the maximum value of the output power fluctuation coefficient and the maximum value of the energy loss fluctuation coefficient; CM represents the currently running model, and M1, M2, and M3 respectively represent the first correction model, the second correction model, and the third correction model; Based on the generated correction value, multiply the correction value by the preliminary target capacity configuration, and the obtained result is the actual target capacity configuration.

10. Hybrid energy storage system capacity configuration system, characterized in that: The system includes: The data analysis module obtains the historical wind power output data of the hybrid energy storage system, analyzes the historical wind power output data, and obtains the aggregated power cost data set; among them, the aggregated power cost data at least includes: the power curve of the hybrid energy storage system, the annual average cost of the hybrid energy storage system, and the opportunity compensation cost for wind power fluctuation; The model construction module constructs a capacity configuration model based on the aggregated power cost data set and in combination with the state of charge constraint and power constraint of the hybrid energy storage system; The capacity configuration module aims to minimize the sum of the annual average cost of the hybrid energy storage system and the opportunity compensation cost for wind power fluctuation, and obtains the preliminary target capacity configuration by running the capacity configuration model; The model correction module runs the preset simulation model based on the preliminary target capacity configuration, records the fluctuation conditions of the key indicators of the hybrid energy storage system during the operation cycle in the simulation model, and determines whether the preliminary target capacity configuration is feasible based on the fluctuation conditions; If feasible, use the preliminary target capacity configuration as the actual target capacity configuration; If not feasible, trigger the evaluation and correction strategy to correct the preliminary target capacity configuration to obtain the actual target capacity configuration.

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