Flywheel energy storage-power generation system capacity configuration method, device, medium and product

Abstract

The present invention discloses a method, device, medium and product for configuring the capacity of a flywheel energy storage-generation system, which belongs to the field of energy storage capacity configuration. The method screens the disturbance data of each wind turbine from the output power historical data sequence under different wind power penetration rates, and extracts the probability distribution of the disturbance and the distribution of the disturbance amplitude, thereby fitting the scene disturbance data under each wind power penetration rate as the primary frequency regulation demand of the power grid under each wind power penetration rate; considering the full life cycle of flywheel energy storage and the frequency regulation power of flywheel energy storage, a flywheel energy storage-generation system configuration optimization model with the maximum annual average power as the optimization target is established; based on the primary frequency regulation demand of the power grid under each wind power penetration rate, a particle swarm algorithm is used to solve the energy storage-generation system configuration optimization model, and the optimal capacity configuration of flywheel energy storage under each wind power penetration rate is obtained. The present invention can accurately configure the capacity of the flywheel energy storage-generation system under different stages of wind power scenarios.

Description

Technical Field

[0001] This invention relates to the field of energy storage capacity configuration, and in particular to a method, apparatus, medium and product for configuring the capacity of a flywheel energy storage-power generation system. Background Art

[0002] With the continued growth of global energy demand and increasing emphasis on environmental protection, traditional fossil fuels are gradually failing to meet the needs of sustainable development. Renewable energy sources such as wind and solar power have become the focus of energy development due to their cleanliness, environmental friendliness, and renewability. However, the intermittency and instability of renewable energy sources pose challenges to the stable power supply of the power grid and the efficient utilization of energy.

[0003] Energy storage technology has received widespread attention as a key technology for balancing grid supply and demand, improving energy efficiency, and enhancing the ability of renewable energy to be integrated into the grid. Among them, flywheel energy storage technology has shown unique application potential in frequency regulation, grid peak shaving and valley filling, and renewable energy power generation systems due to its advantages such as fast response speed, long cycle life, and environmental friendliness.

[0004] With the increasing application of flywheel energy storage technology, it is necessary to scientifically and rationally configure the capacity of flywheel energy storage-generation systems according to different wind power scenarios in order to maximize the economy and reliability of the system and guide the incremental configuration of future energy storage. Summary of the Invention

[0005] The purpose of this invention is to provide a method, device, medium, and product for configuring the capacity of a flywheel energy storage-power generation system, which can accurately configure the capacity of the flywheel energy storage-power generation system in different stages of wind power scenarios.

[0006] To achieve the above objectives, the present invention provides the following solution:

[0007] A method for configuring the capacity of a flywheel energy storage-generation system includes: acquiring historical data sequences of the output power of each wind turbine in a wind farm over a preset day under different wind power penetration rates; filtering disturbance data of each wind turbine under different wind power penetration rates from the historical output power data sequences; extracting the probability distribution and amplitude distribution of disturbances from the disturbance data of all wind turbines under each wind power penetration rate; fitting scenario disturbance data under each wind power penetration rate based on the probability distribution and amplitude distribution of disturbances, as the primary frequency regulation requirement of the power grid under each wind power penetration rate; considering the entire life cycle of flywheel energy storage and the frequency regulation power of flywheel energy storage, establishing a flywheel energy storage-generation system configuration optimization model with the maximum annual average power generation as the optimization objective; and using a particle swarm optimization algorithm to solve the energy storage-generation system configuration optimization model based on the primary frequency regulation requirement of the power grid under each wind power penetration rate, to obtain the optimal capacity configuration of flywheel energy storage under each wind power penetration rate.

[0008] A computer device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the flywheel energy storage-generation system capacity configuration method described in any of the preceding claims.

[0009] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the flywheel energy storage-generation system capacity configuration method described in any of the preceding claims.

[0010] A computer program product includes a computer program that, when executed by a processor, implements the flywheel energy storage-generation system capacity configuration method described in any of the preceding claims.

[0011] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0012] This invention takes into account the uncertainty and variability of renewable energy, as well as the fluctuations in grid demand, and generates scenario disturbance data for different stages. It clarifies the demand for primary frequency regulation in different scenarios, and then establishes a flywheel energy storage-generation system configuration optimization model with the maximum annual average power generation as the optimization objective. This enables accurate configuration of the flywheel energy storage-generation system capacity in wind power scenarios at different stages. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 This is a flowchart illustrating the flywheel energy storage-power generation system capacity configuration method provided in Embodiment 1 of the present invention.

[0015] Figure 2 This is a diagram of the internal structure of a computer device. Detailed Implementation

[0016] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0017] This invention proposes a scenario generation method that establishes an energy storage-generation system configuration optimization model based on different scenarios with the goal of maximizing benefits, guiding future incremental energy storage configurations. This method analyzes and simulates different operating scenarios, considering the uncertainty and variability of renewable energy, as well as fluctuations in grid demand, thereby more accurately determining the optimal capacity configuration of the flywheel energy storage-generation system. This method aims to improve the system's economics and reliability while optimizing energy resource utilization.

[0018] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0019] Example 1

[0020] like Figure 1 As shown, the flywheel energy storage-power generation system capacity configuration method in this embodiment includes the following steps.

[0021] Step 1: Obtain the historical data sequence of the output power of each wind turbine in the wind farm on a preset day under different wind power penetration rates.

[0022] The flywheel energy storage-generation system is a frequency regulation system comprising multiple 48MW wind turbines, multiple 315MW thermal power units, and flywheel energy storage. When the grid experiences a primary frequency regulation demand, the thermal power units and flywheel energy storage work together to stabilize the grid frequency. Wind power, however, is only related to different penetration rates and does not participate in the frequency regulation process.

[0023] Obtain historical output power data P of a typical day at a wind farm wind1 P wind2 , ..., P windn Let n be the number of wind turbine units. Obtain the grid frequency data f. Define the wind power penetration rate α as: α = installed wind power capacity / total installed wind and thermal power capacity of the system. Assume that the wind power penetration rate is only related to the number of wind turbine units. The historical output power data of a typical wind farm day is collected every minute, resulting in 1440 historical output power data points collected per day, forming a historical output power data sequence, i.e., P. wind1 P wind2 , ..., P windn Each of them is a sequence.

[0024] Step 2: Filter the disturbance data of each wind turbine under different wind power penetration rates from the historical output power data series.

[0025] The Chinese national standard "Technical Regulations for Wind Farm Connection to Power Systems" clearly stipulates the maximum limits for the output power variation of wind farms over 1 minute and 10 minutes. Wind power grid connection standards are shown in Table 1. insThis indicates the installed capacity. The installed capacity of a wind farm refers to the total power of the wind turbine generators installed in a particular wind farm.

[0026] Table 1 Wind Power Grid Connection Standards

[0027]

[0028] Screening method: First look at the installed capacity of the wind farm. If it is less than 30MW, then P wind1 For each unit, the maximum fluctuation rate over 10 minutes must not exceed 10MW and the maximum fluctuation rate over 1 minute must not exceed 3MW to be connected to the grid. Data with a maximum fluctuation rate exceeding 10MW over 10 minutes or exceeding 3MW over 1 minute cannot be connected to the grid. These data that cannot be connected to the grid are the final P values. wr1 (Each unit)

[0029] Therefore, the disturbance to the power grid from a single wind turbine does not meet the above standards. Each P... wind1 P wind2 , ..., P windn After filtering, the disturbance data P for each wind turbine was obtained. wr1 P wr2 , ..., P wrn Put P wr The slip disturbance is calculated by applying a 5% slip inequality rate, and the calculation formula is as follows:

[0030] R wr =-P wr ×3000×5% / 48

[0031] Record and statistically analyze the slip disturbances corresponding to wind power disturbances to obtain the distribution of wind power disturbance slip values ​​on a typical day. The typical day data differs for different wind power penetration rates. Scenario generation can then be performed based on the typical day disturbance data for wind power.

[0032] The distribution of typical daily wind power disturbance slip values ​​varies depending on the wind power penetration rate. For example, the higher the wind power penetration rate, the greater the slip disturbance generally is. When the wind power penetration rate is 0%, the slip disturbance corresponds to the grid frequency data f obtained in step 1. When the wind power penetration rate is i, data is generated based on the number of wind turbine units, and the generated disturbance data is P. rand-i .

[0033] The relationship between frequency data f and slip disturbance is as follows:

[0034] In other words, the implementation process of step 2 can be summarized as follows: Filter data from the historical output power data series that have a maximum output power fluctuation rate greater than 1 minute or 10 minutes; then, based on the conversion formula R...wr =-P wr ×3000×5% / 48, the selected data is converted into slip disturbance according to a 5% slip inequality rate, which serves as the disturbance data for each wind turbine under different wind power penetration rates; where P wr For the filtered data, R wr This is a slip disturbance.

[0035] Step 3: Extract the probability distribution of disturbance occurrence and the distribution of disturbance amplitude from the disturbance data of all wind turbines at each wind power penetration rate.

[0036] By analyzing disturbance data from selected typical wind power days, we extract statistical distribution characteristics, including the distribution of disturbance amplitude and the probability distribution of duration. Based on these distribution characteristics, we employ a probabilistic model to fit the final scenario disturbance data.

[0037] The principle behind generating the horizontal axis is as follows:

[0038] (1) Generation of non-zero perturbation points and zero value perturbation points.

[0039] Based on the dead zone probability statistically obtained from typical daily data, the probability of occurrence of non-zero disturbance points and zero-value disturbance points is defined. This step aims to simulate disturbances caused by natural variations in wind power output, ensuring that the generated disturbance data reflects the intermittency and randomness of actual wind power output.

[0040] The probabilities of non-zero perturbation points and zero-value perturbation points are based on statistical analysis of typical daily data. Let P... non-zero The probability of a non-zero disturbance point occurring can be calculated using the dead zone probability P obtained from typical daily data. dead zone To determine, "the dead zone probability P calculated from typical daily data" dead zone "That is, the probability of a disturbance point with a value of 0."

[0041] P non-zero =1-P dead zone

[0042] (2) Setting the duration of the disturbance point.

[0043] The duration of the disturbance point is divided into short-term duration T. short =30s and intermediate duration T medium =60s, the probability distribution function f(T) for the duration is set as follows:

[0044]

[0045] Where m represents the proportion of the short-term duration.

[0046] The principle behind generating the vertical axis is as follows:

[0047] Statistical analysis of typical daily disturbance data amplitudes yields the probability distribution function F(x) of the disturbance amplitude, where x represents the disturbance amplitude, reflecting its statistical characteristics. This distribution function can be obtained by fitting the distribution of the actual statistically observed disturbance data amplitudes. It is assumed that the disturbance amplitude follows a certain statistical distribution, the form and parameters of which are determined by the statistical analysis of the actual data. For example, if the statistical analysis of the disturbance amplitude indicates that it approximately follows a specific distribution (such as a Bernoulli distribution, a Poisson distribution, or any other suitable distribution), then:

[0048] F(x) = statistical distribution(x; θ)

[0049] Here, x represents the disturbance amplitude, and θ represents the parameter set of the statistical distribution, which is obtained by fitting the actual disturbance data.

[0050] Step 3 can be summarized as follows: Based on the disturbance data of all wind turbines at each wind power penetration rate, generate a wind power disturbance slip distribution map for each wind power penetration rate; determine the dead zone probability in the wind power disturbance slip distribution map and use it as the probability of a 0-value disturbance point; based on the probability of a 0-value disturbance point, use the formula P... non-zero =1-P dead zone Determine the probability of non-zero perturbation points; set the probability distribution function for the duration of the perturbation as: By combining the probability distribution functions of the zero-value disturbance point, the probability of the non-zero disturbance point, and the disturbance duration, the probability distribution of the disturbance occurrence is determined; the disturbance data amplitude of all wind turbines under each wind power penetration rate is statistically analyzed to obtain the probability distribution function of the disturbance amplitude, so as to characterize the distribution of the disturbance amplitude.

[0051] Step 2 involves compiling statistics on actual wind power data that cannot be directly connected to the grid. Since this data is not a regular, step-like disturbance type similar to primary frequency regulation, it is used to generate the data in Step 3. Different wind power penetration rates correspond to different data compiled in Step 2, and therefore, the data generated in Step 3 will also differ.

[0052] Step 4: Based on the probability distribution of disturbance occurrence and the distribution of disturbance amplitude, fit the scenario disturbance data for each wind power penetration rate as the primary frequency regulation requirement of the power grid for each wind power penetration rate.

[0053] Using the probability distribution of disturbance occurrence on the horizontal axis, we can determine the time point of the disturbance. Using the distribution of disturbance amplitude on the vertical axis, we can determine the amplitude at the time of the disturbance. This allows us to fit scenario disturbance data for each wind power penetration rate.

[0054] Step 5: Considering the entire life cycle of flywheel energy storage and the frequency regulation power of flywheel energy storage, establish a flywheel energy storage-power generation system configuration optimization model with the goal of maximizing the annual average power generation.

[0055] At P respectively rand-i Capacity configuration is then performed. The system's total frequency regulation output consists of the combined frequency regulation output from thermal power and energy storage. Considering the entire lifecycle of energy storage and the amount of electricity regulated by energy storage, an objective function is established to maximize the average annual electricity generation:

[0056] maxJ = -C LCC +Y s

[0057] Where J is the average annual electricity consumption, and C LCC Y represents the total electricity generated over the entire lifespan. s This is for the primary frequency regulation of energy storage.

[0058] 1) Total lifecycle power consumption

[0059] (1) Present value of maintenance electricity N CO,M

[0060] The net present value (NPV) method can be used to convert the total electricity cost over the life cycle into the present value N of the electricity cost for operation and maintenance. CO,M The conversion calculation formula is as follows:

[0061]

[0062] In the formula: C O,M T represents the total cost of electricity generated over its lifecycle. LCC The flywheel lifespan is 20 years, as used in this invention. r is the discount rate.

[0063] (2) Electricity loss C el

[0064] Flywheel energy storage experiences some energy loss during its charging and discharging process, as shown below:

[0065]

[0066] In the formula: P FESS (t) represents the flywheel's output at time t, i.e., the flywheel's real-time output (MW); P ef η is the coefficient for energy loss during flywheel operation. c η d The charge / discharge efficiency is expressed as a percentage.

[0067] The power loss is also generated annually, and its present value N is... cel It is calculated by the following formula:

[0068]

[0069] In summary, the total electrical energy C of the flywheel energy storage system over its entire lifespan can be obtained. LCC for:

[0070] C LCC =N CO,M +N cel

[0071] 2) Energy storage frequency regulation power

[0072] The present invention considers flywheel energy storage participating in the primary frequency regulation power calculation as follows:

[0073] Y S =A f ∫|P FESS (t)|dt

[0074] In the formula: A f The energy coefficient for energy storage to participate in primary frequency regulation.

[0075] Constraints:

[0076] This invention considers using a traditional general method for calculating rated power to limit the upper limit of the flywheel energy storage system's power. The maximum charging and discharging power of the flywheel energy storage system can be set to the maximum value of the energy storage power command. Therefore, its rated power constraint is as follows:

[0077] 0 < P rated <P fmax

[0078] In the formula: P rated P represents the power of the flywheel energy storage system. fmax This represents the maximum absolute value (MW) of the power command for the flywheel energy storage system.

[0079] The power grid's assessment cycle for primary frequency regulation is generally 60 seconds. To fully utilize the flywheel's frequency regulation capability, the capacity configuration should ensure that the flywheel can participate in primary frequency regulation multiple times when there is sufficient power. Therefore, this invention considers leaving a certain frequency regulation margin for the flywheel, and sets its rated capacity constraint as shown in the following formula:

[0080] 0 < E rated <E fmax

[0081] In the formula: E rated For the capacity of the flywheel energy storage system, E fmax This represents the maximum capacity of the flywheel energy storage system.

[0082] Step 6: Based on the primary frequency regulation requirements of the power grid under each wind power penetration rate, the particle swarm optimization algorithm is used to solve the energy storage-generation system configuration optimization model to obtain the optimal capacity configuration of flywheel energy storage under each wind power penetration rate.

[0083] Using the particle swarm optimization algorithm, under constraints, the optimal solution for the objective function is obtained for different scenarios, yielding the optimal energy storage capacity and optimal power, i.e., P. rated and E rated The optimal value.

[0084] This invention establishes an objective function that maximizes average annual revenue by considering the entire lifecycle cost of energy storage and the revenue from energy storage frequency regulation. The particle swarm optimization algorithm is then used to solve the capacity optimization problem. This addresses the issue that existing configurations do not consider energy storage area planning.

[0085] This invention fully considers the capacity configuration of energy storage-generation systems under different demand conditions and diverse scenarios, clarifies different stage scenarios, analyzes frequency fluctuations in different scenarios, and identifies the frequency regulation requirements for different scenarios. For different frequency regulation requirements under diverse scenarios, and considering grid planning, incremental configuration of energy storage is achieved under different wind power penetration rates. The scenario generation method proposed in this invention comprehensively considers the multidimensional characteristics of wind power disturbance data, including the frequency of disturbance occurrence, amplitude distribution, and duration, thereby providing simulation data based on actual wind power output characteristics for the optimal configuration of energy storage systems. This method helps guide future incremental energy storage.

[0086] Example 2

[0087] A computer device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the flywheel energy storage-power generation system capacity configuration method in Embodiment 1.

[0088] Example 3

[0089] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the flywheel energy storage-power generation system capacity configuration method of Embodiment 1.

[0090] Example 4

[0091] A computer program product includes a computer program that, when executed by a processor, implements the flywheel energy storage-power generation system capacity configuration method of Embodiment 1.

[0092] Example 5

[0093] A computer device, the internal structure of which can be shown in the diagram below. Figure 2As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores pending transactions. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network connection. When the computer program is executed by the processor, it implements the flywheel energy storage-generation system capacity configuration method in Embodiment 1.

[0094] It should be noted that the object information (including but not limited to object device information, object personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this invention are all information and data authorized by the object or fully authorized by all parties.

[0095] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided by this invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided by this invention may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided by this invention may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, etc., and are not limited to these.

[0096] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above 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.

[0097] Specific examples have been used to illustrate the principles and implementation methods of this invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of this invention. Furthermore, those skilled in the art will recognize that, based on the ideas of this invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this invention.

Claims

1. A method for configuring the capacity of a flywheel energy storage-power generation system, characterized in that, include: To obtain historical data sequences of the output power of each wind turbine in a wind farm over a preset day under different wind power penetration rates; Disturbance data for each wind turbine under different wind power penetration rates were filtered from the historical output power data series. Extract the probability distribution of disturbance occurrence and the distribution of disturbance amplitude from the disturbance data of all wind turbines at each wind power penetration rate; Based on the probability distribution of disturbance occurrence and the distribution of disturbance amplitude, the scenario disturbance data under each wind power penetration rate is fitted to serve as the primary frequency regulation requirement of the power grid under each wind power penetration rate. Considering the entire life cycle of flywheel energy storage and the frequency regulation power of flywheel energy storage, an optimization model for flywheel energy storage-power generation system configuration is established with the maximum annual average power generation as the optimization objective. Based on the primary frequency regulation requirements of the power grid under each wind power penetration rate, the particle swarm optimization algorithm is used to solve the configuration optimization model of the energy storage-generation system and obtain the optimal capacity configuration of flywheel energy storage under each wind power penetration rate. The flywheel energy storage-generation system configuration optimization model includes: objective function and constraints; The objective function is: ； in, This represents the average annual electricity consumption. C LCC The total electricity consumption over the entire lifespan. Y s For energy storage, primary frequency regulation power; The constraints are as follows: ； ； in, P rated For the power of the flywheel energy storage system, This represents the maximum absolute value of the power command for the flywheel energy storage system. E rated For the capacity of the flywheel energy storage system, This represents the maximum capacity of the flywheel energy storage system. The formula for calculating the total electricity consumption over the entire lifespan is as follows: ； in, N CO,M The present value of maintenance electricity consumption. N cel The present value of the lost electricity; ； ； ； In the formula, C O,M Cost of all electricity used over the product's lifespan. T LCC For the flywheel life cycle, r The discount rate; C el To consume electricity; P ef The coefficient for energy loss during flywheel operation. P FESS ( t For the flywheel in t Constant effort For charging efficiency, This refers to the discharge efficiency.

2. The flywheel energy storage-power generation system capacity configuration method according to claim 1, characterized in that, Disturbance data for each wind turbine at different wind power penetration rates was selected from the historical output power data series, specifically including: Filter data from historical output power data sequences that have a maximum output power fluctuation rate greater than 1 minute or 10 minutes of the wind farm's output power fluctuation rate. According to the conversion formula The selected data were converted into slip disturbances according to a 5% slip inequality rate, which were then used as disturbance data for each wind turbine under different wind power penetration rates; among them, P wr For the filtered data, R wr This is a slip disturbance.

3. The flywheel energy storage-power generation system capacity configuration method according to claim 1, characterized in that, Extract the probability distribution of disturbance occurrence and the distribution of disturbance amplitude from the disturbance data of all wind turbines at each wind power penetration rate, specifically including: Based on the disturbance data of all wind turbine units at each wind power penetration rate, generate a distribution map of wind power disturbance slip value at each wind power penetration rate; Determine the dead zone probability in the wind power disturbance slip distribution map and use it as the probability of a 0-value disturbance point; Based on the probability of the occurrence of the 0-value perturbation point, the formula is used. Determine the probability of non-zero perturbation points; where, The probability of a disturbance point with a value of 0 appearing. The probability of being a non-zero perturbation point; The probability distribution function for the duration of the disturbance is defined as follows: ;in, Let be the probability distribution function for duration, and m represent the proportion of short-term durations. T short For a short duration, T medium For the medium term duration; By combining the probability distribution functions of the zero-value perturbation point, the probability of the non-zero perturbation point, and the duration of the perturbation, the probability distribution of the perturbation occurrence is determined. The disturbance data amplitudes of all wind turbines under each wind power penetration rate are statistically analyzed to obtain the probability distribution function of the disturbance amplitude, which characterizes the distribution of the disturbance amplitude.

4. The flywheel energy storage-power generation system capacity configuration method according to claim 1, characterized in that, The formula for calculating the primary frequency regulation power of the energy storage is as follows: ； in, A f The energy coefficient for energy storage participating in primary frequency regulation. P FESS ( t For the flywheel in t Efforts are made at all times.

5. The flywheel energy storage-power generation system capacity configuration method according to claim 1, characterized in that, The optimal capacity configuration includes the optimal capacity and optimal power of the flywheel energy storage system.

6. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the flywheel energy storage-generation system capacity configuration method according to any one of claims 1-5.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the flywheel energy storage-power generation system capacity configuration method according to any one of claims 1-5.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the flywheel energy storage-power generation system capacity configuration method according to any one of claims 1-5.

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