Wind power plant energy storage capacity configuration evaluation method and device

Abstract

The invention relates to the technical field of wind power plant energy storage, and discloses a wind power plant energy storage capacity configuration evaluation method and device. The method comprises the following steps: respectively constructing a wind speed fluctuation probability density function, a temperature fluctuation probability density function, a power grid missing power fluctuation probability density function and a power grid missing capacity fluctuation probability density function based on wind power plant data in a target time period; performing Monte Carlo simulation based on the wind speed fluctuation probability density function, the temperature fluctuation probability density function, the power grid missing power fluctuation probability density function and the power grid missing capacity fluctuation probability density function to obtain wind power plant energy storage parameter data; and evaluating the reliability performance, the frequency modulation performance, the peak regulation performance and the economic performance of the energy storage capacity configuration of the wind power plant based on the energy storage parameter data of the wind power plant to obtain an evaluation result of the energy storage capacity configuration of the wind power plant. According to the method, comprehensive evaluation of the reliability performance, the frequency modulation performance, the peak regulation performance and the economic performance of hybrid energy storage capacity configuration is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of wind farm energy storage, and in particular to a method and device for evaluating the configuration of wind farm energy storage capacity. Background Art

[0002] The configuration of wind farm energy storage capacity is of great significance to improving the operating efficiency and power supply quality of wind farms. Through reasonable configuration of energy storage capacity, the role of energy storage devices can be fully utilized, the operating efficiency and power supply quality of wind farms can be improved, the operating cost of power systems can be reduced, and it can also help promote the widespread application and development of renewable energy such as wind power.

[0003] The relevant methods for configuring energy storage capacity in wind farms do not take into account the probability distribution of hybrid energy storage damage, the probability distribution of wind speed, the probability distribution of grid missing power fluctuations, the probability distribution of grid missing capacity fluctuations and the probability distribution of temperature, and are unable to comprehensively evaluate the reliability, frequency regulation, peak regulation and economic performance of the energy storage capacity configuration. Summary of the invention

[0004] In view of this, the present invention provides a method and device for evaluating the energy storage capacity configuration of a wind farm, so as to solve the problem that the related methods for configuring the energy storage capacity of a wind farm cannot comprehensively evaluate the reliability performance, frequency regulation performance, peak regulation performance and economic performance of the energy storage capacity configuration.

[0005] In a first aspect, the present invention provides a method for evaluating the configuration of energy storage capacity of a wind farm, the method comprising:

[0006] Collect wind farm data within the target time period, and construct wind speed fluctuation probability density functions, temperature fluctuation probability density functions, grid missing power fluctuation probability density functions, and grid missing capacity fluctuation probability density functions based on the wind farm data within the target time period;

[0007] Monte Carlo simulation is performed based on the probability density function of wind speed fluctuation, temperature fluctuation, grid missing power fluctuation and grid missing capacity fluctuation to obtain the energy storage parameter data of the wind farm;

[0008] Based on the wind farm energy storage parameter data, the reliability performance, frequency regulation performance, peak regulation performance and economic performance of the wind farm energy storage capacity configuration are evaluated respectively, and the evaluation results of the wind farm energy storage capacity configuration are obtained.

[0009] The present embodiment provides a method for evaluating the configuration of energy storage capacity in a wind farm. The method constructs a probability density function of wind speed fluctuation, a probability density function of temperature fluctuation, a probability density function of grid missing power fluctuation, and a probability density function of grid missing capacity fluctuation, and uses Monte Carlo simulation to obtain wind farm energy storage parameter data. The wind farm energy storage parameter data is then used to evaluate the reliability performance, frequency regulation performance, peak regulation performance, and economic performance of the wind farm energy storage capacity configuration, and obtains an evaluation result of the wind farm energy storage capacity configuration. The Monte Carlo simulation is introduced to fully consider the probability distribution of hybrid energy storage damage, the probability distribution of wind speed, the probability distribution of grid missing power fluctuation, the probability distribution of grid missing capacity fluctuation, and the probability distribution of temperature, and achieves a comprehensive evaluation of the reliability performance, frequency regulation performance, peak regulation performance, and economic performance of the hybrid energy storage capacity configuration.

[0010] In an optional implementation, based on the wind farm data within the target time period, a wind speed fluctuation probability density function, a temperature fluctuation probability density function, a grid missing power fluctuation probability density function, and a grid missing capacity fluctuation probability density function are respectively constructed, including:

[0011] Based on the wind farm data in the target time period, a wind farm wind speed dataset, a temperature dataset, a grid missing power dataset, and a grid missing capacity dataset are constructed;

[0012] Based on the wind speed data of each month in the wind farm wind speed data set, parameter identification is performed to obtain the probability density function parameters, and the wind speed fluctuation probability density function is constructed based on the probability density function parameters;

[0013] Parameter identification is performed based on the temperature data of each month in the temperature data set to obtain the first normal distribution parameters, and a temperature fluctuation probability density function is constructed based on the first normal distribution parameters;

[0014] Parameter identification is performed based on the power grid missing power data of each month in the power grid missing power data set to obtain the second normal distribution parameters, and a power grid missing power fluctuation probability density function is constructed based on the second normal distribution parameters;

[0015] Parameter identification is performed based on the power grid missing capacity data of each month in the power grid missing capacity dataset to obtain the third normal distribution parameters, and the power grid missing capacity fluctuation probability density function is constructed based on the third normal distribution parameters.

[0016] The present embodiment provides a method for evaluating the configuration of energy storage capacity of a wind farm. By performing parameter identification on the wind farm data of each month, the parameters in each probability density function are accurately calculated. Furthermore, by constructing a probability density function of wind speed fluctuation, a probability density function of temperature fluctuation, a probability density function of grid missing power fluctuation, and a probability density function of grid missing capacity fluctuation, the probability distribution of data fluctuation corresponding to the hybrid energy storage is accurately grasped, laying a foundation for the subsequent calculation of the energy storage parameter data of the wind farm.

[0017] In an optional implementation, the wind farm energy storage parameter data includes: wind speed simulation data, temperature simulation data, grid missing power simulation data, grid missing capacity simulation data, energy storage annual value and energy storage damage status data.

[0018] In an optional implementation, a Monte Carlo simulation is performed based on a wind speed fluctuation probability density function, a temperature fluctuation probability density function, a grid missing power fluctuation probability density function, and a grid missing capacity fluctuation probability density function to obtain wind farm energy storage parameter data, including:

[0019] Randomly obtain the Monte Carlo test month, integrate the wind speed fluctuation probability density function, temperature fluctuation probability density function, grid missing power fluctuation probability density function and grid missing capacity fluctuation probability density function corresponding to the Monte Carlo test month, and obtain the wind speed fluctuation cumulative distribution function, temperature fluctuation cumulative distribution function, grid missing power fluctuation cumulative distribution function and grid missing capacity fluctuation cumulative distribution function;

[0020] A random number generation function is used to obtain a random number, and based on the random number, wind speed fluctuation cumulative distribution function, temperature fluctuation cumulative distribution function, grid missing power fluctuation cumulative distribution function and grid missing capacity fluctuation cumulative distribution function are used to respectively determine wind speed simulation data, temperature simulation data, grid missing power simulation data and grid missing capacity simulation data;

[0021] The energy storage damage probability density function is obtained, and the energy storage annual value is randomly obtained within the energy storage life cycle. Based on the energy storage annual value, the energy storage damage probability density function is used to determine the energy storage damage status data.

[0022] The present embodiment provides a method for evaluating the energy storage capacity configuration of a wind farm. The method randomly obtains Monte Carlo test months and random numbers, and uses Monte Carlo simulation to accurately calculate wind speed simulation data, temperature simulation data, grid missing power simulation data, and grid missing capacity simulation data. The energy storage damage probability density function is used to accurately obtain energy storage damage status data, and the reliability performance, frequency regulation performance, peak regulation performance, and economic performance of the energy storage capacity configuration can be better evaluated.

[0023] In an optional implementation, the reliability performance, frequency regulation performance, peak regulation performance and economic performance of the wind farm energy storage capacity configuration are evaluated based on the wind farm energy storage parameter data to obtain the wind farm energy storage capacity configuration evaluation result, including:

[0024] Based on the energy storage damage status data, the reliability performance of the wind farm energy storage capacity configuration is evaluated to obtain the reliability performance evaluation results;

[0025] Based on the wind farm data in the target time period, a temperature performance curve and a wind turbine wind speed power curve are constructed, and based on the wind speed simulation data and the temperature simulation data, the wind turbine power generation data, the energy storage power simulation data and the energy storage capacity simulation data are determined in combination with the temperature performance curve and the wind turbine wind speed power curve;

[0026] The sum of the wind turbine power generation data and the energy storage power simulation data is compared with the grid missing power simulation data, and the frequency regulation performance of the wind farm energy storage capacity configuration is evaluated based on the comparison result to obtain the frequency regulation performance evaluation result;

[0027] The energy storage capacity simulation data is compared with the power grid missing capacity simulation data, and the peak-shaving performance of the wind farm energy storage capacity configuration is evaluated based on the comparison result to obtain the peak-shaving performance evaluation result;

[0028] The energy storage cost of the wind farm is obtained, and the economic performance of the energy storage capacity configuration of the wind farm is evaluated based on the energy storage cost of the wind farm and the annual value of energy storage to obtain the economic performance evaluation result.

[0029] A wind farm energy storage capacity configuration assessment method provided in this embodiment utilizes wind speed simulation data, temperature simulation data, grid missing power simulation data, grid missing capacity simulation data, energy storage annual value, and energy storage damage status data to achieve a comprehensive assessment of the reliability performance, frequency regulation performance, peak regulation performance, and economic performance of a hybrid energy storage capacity configuration.

[0030] In an optional implementation, a temperature performance curve and a wind turbine wind speed power curve are constructed based on wind farm data within a target time period, and wind turbine power generation data, energy storage power simulation data, and energy storage capacity simulation data are determined based on wind speed simulation data and temperature simulation data in combination with the temperature performance curve and the wind turbine wind speed power curve, including:

[0031] With the goal of optimizing the energy storage benefits of wind farms, a wind farm energy storage capacity configuration model is constructed based on wind farm data within the target time period;

[0032] Based on the wind farm energy storage capacity configuration model, the energy storage configuration power and energy storage configuration capacity are obtained using the optimization algorithm;

[0033] Construct temperature performance curve and wind turbine wind speed power curve based on energy storage configuration power and energy storage configuration capacity;

[0034] Based on the wind speed simulation data, the wind turbine power data is determined using the wind turbine wind speed power curve;

[0035] Based on the temperature simulation data, the energy storage power simulation data and the energy storage capacity simulation data are determined using the temperature performance curve.

[0036] The present embodiment provides a method for evaluating the energy storage capacity configuration of a wind farm. By constructing a wind farm energy storage capacity configuration model, and obtaining the energy storage configuration power and energy storage configuration capacity based on the wind farm energy storage capacity configuration model, and then constructing a temperature performance curve and a wind turbine wind speed power curve, the method achieves accurate calculation of the actual energy storage power and capacity configuration, improves the economy during the energy storage operation period, and lays a foundation for the subsequent evaluation of the frequency regulation performance and peak regulation performance of the wind farm energy storage capacity configuration.

[0037] In a second aspect, the present invention provides a wind farm energy storage capacity configuration assessment device, the device comprising:

[0038] A construction module is used to collect wind farm data within a target time period, and to construct a wind speed fluctuation probability density function, a temperature fluctuation probability density function, a power grid missing power fluctuation probability density function, and a power grid missing capacity fluctuation probability density function based on the wind farm data within the target time period;

[0039] A simulation module is used to perform Monte Carlo simulation based on a wind speed fluctuation probability density function, a temperature fluctuation probability density function, a power grid missing power fluctuation probability density function, and a power grid missing capacity fluctuation probability density function to obtain wind farm energy storage parameter data;

[0040] The evaluation module is used to evaluate the reliability performance, frequency regulation performance, peak regulation performance and economic performance of the wind farm energy storage capacity configuration based on the wind farm energy storage parameter data, and obtain the wind farm energy storage capacity configuration evaluation result.

[0041] In a third aspect, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the wind farm energy storage capacity configuration assessment method of the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0042] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the method for evaluating the configuration of energy storage capacity of a wind farm according to the first aspect or any corresponding embodiment thereof.

[0043] In a fifth aspect, the present invention provides a computer program product, comprising computer instructions for causing a computer to execute the method for evaluating the configuration of energy storage capacity of a wind farm according to the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0045] Figure 1 is a schematic flow chart of a method for evaluating the configuration of energy storage capacity of a wind farm according to an embodiment of the present invention;

[0046] Figure 2 is a flow chart of another method for evaluating the configuration of energy storage capacity of a wind farm according to an embodiment of the present invention;

[0047] Figure 3 is a flow chart of another method for evaluating the configuration of energy storage capacity of a wind farm according to an embodiment of the present invention;

[0048] Figure 4 is a structural block diagram of a wind farm energy storage capacity configuration assessment device according to an embodiment of the present invention;

[0049] Figure 5 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are 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 those skilled in the art without creative work are within the scope of protection of the present invention.

[0051] As a renewable energy source, wind energy has three characteristics: volatility, randomness, and intermittency, which leads to large fluctuations in the output power of wind power generation. When the installed capacity and grid-connected capacity of wind power continue to increase, the instability of the output power will have a huge impact on the frequency of the power grid, causing a serious impact on the safe and stable operation of the power grid. The energy storage system can adjust the load, absorb power peaks, and inject power when the power supply suddenly decreases. On-site energy storage can alleviate the power supply fluctuations caused by the output of renewable energy production and achieve peak shaving and valley filling. In addition, the energy storage system can smooth the active power fluctuations of wind and solar power generation, improve the quality of electricity, and improve the ability to track planned output, thereby reducing the impact of distributed wind and solar power generation on the power grid and promoting the ability of the power grid to accept high-penetration distributed renewable energy generation.

[0052] By establishing optimization functions for comprehensive frequency regulation effects, energy storage costs, energy storage life, power grid assessment, and other aspects, and using different types of continuous optimization algorithms (such as the Harris Eagle optimization algorithm and the particle swarm optimization algorithm), energy storage power and energy storage capacity are optimized. Then, based on preset examples or using complete historical load data, and combined with the characteristics of energy storage operation, accurate typical daily load data is obtained to accurately evaluate capacity configuration to improve the economy of energy storage operation period.

[0053] The relevant control methods do not take into account the probability distribution of hybrid energy storage damage, the probability distribution of wind speed, the probability distribution of grid missing power fluctuations, the probability distribution of grid missing capacity fluctuations, and the probability distribution of temperature, and are unable to comprehensively evaluate the reliability, frequency regulation, peak regulation, and economic performance of the energy storage capacity configuration.

[0054] The embodiment of the present invention provides a method for evaluating the configuration of energy storage capacity in a wind farm, which is applied to server-type equipment. Since the reliability performance, frequency regulation performance, peak regulation performance, and economic performance are closely related to the probability distribution of energy storage damage, the probability distribution of wind speed, the probability distribution of grid-side frequency fluctuation, and the probability distribution of temperature, and it is difficult to determine the mathematical model between the above data, Monte Carlo is introduced and random simulation is used to realize hybrid energy storage capacity configuration evaluation, which can better evaluate the reliability performance, frequency regulation performance, peak regulation performance, and economic performance of the energy storage capacity configuration.

[0055] According to an embodiment of the present invention, an embodiment of a method for evaluating the configuration of energy storage capacity of a wind farm is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0056] In this embodiment, a method for evaluating the configuration of energy storage capacity of a wind farm is provided, which can be used for the above-mentioned server-type devices. Figure 1is a flow chart of a method for evaluating the configuration of energy storage capacity of a wind farm according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0057] Step S101, collecting wind farm data within a target time period, and constructing wind speed fluctuation probability density functions, temperature fluctuation probability density functions, grid missing power fluctuation probability density functions, and grid missing capacity fluctuation probability density functions based on the wind farm data within the target time period.

[0058] Step S102, performing Monte Carlo simulation based on the wind speed fluctuation probability density function, the temperature fluctuation probability density function, the grid missing power fluctuation probability density function and the grid missing capacity fluctuation probability density function to obtain wind farm energy storage parameter data.

[0059] Specifically, the wind farm energy storage parameter data includes: wind speed simulation data, temperature simulation data, grid missing power simulation data, grid missing capacity simulation data, energy storage annual value and energy storage damage status data.

[0060] Step S103, based on the wind farm energy storage parameter data, respectively evaluate the reliability performance, frequency regulation performance, peak regulation performance and economic performance of the wind farm energy storage capacity configuration to obtain a wind farm energy storage capacity configuration evaluation result.

[0061] The present embodiment provides a method for evaluating the configuration of energy storage capacity in a wind farm. The method constructs a probability density function of wind speed fluctuation, a probability density function of temperature fluctuation, a probability density function of grid missing power fluctuation, and a probability density function of grid missing capacity fluctuation, and uses Monte Carlo simulation to obtain wind farm energy storage parameter data. The wind farm energy storage parameter data is then used to evaluate the reliability performance, frequency regulation performance, peak regulation performance, and economic performance of the wind farm energy storage capacity configuration, and obtains an evaluation result of the wind farm energy storage capacity configuration. The Monte Carlo simulation is introduced to fully consider the probability distribution of hybrid energy storage damage, the probability distribution of wind speed, the probability distribution of grid missing power fluctuation, the probability distribution of grid missing capacity fluctuation, and the probability distribution of temperature, and achieves a comprehensive evaluation of the reliability performance, frequency regulation performance, peak regulation performance, and economic performance of the hybrid energy storage capacity configuration.

[0062] In this embodiment, a method for evaluating the configuration of energy storage capacity of a wind farm is provided, which can be used for the above-mentioned server-type devices. Figure 2 is a flow chart of a method for evaluating the configuration of energy storage capacity of a wind farm according to an embodiment of the present invention. Figure 2 As shown, the process includes the following steps:

[0063] Step S201, collecting wind farm data within a target time period, and constructing wind speed fluctuation probability density functions, temperature fluctuation probability density functions, grid missing power fluctuation probability density functions, and grid missing capacity fluctuation probability density functions based on the wind farm data within the target time period.

[0064] Specifically, the above step S201 includes:

[0065] Step S2011, constructing a wind farm wind speed data set, a temperature data set, a grid missing power data set, and a grid missing capacity data set based on the wind farm data in the target time period.

[0066] Specifically, the wind speed data, temperature data, grid missing power data and grid missing capacity data of the current wind farm can be collected every 10 minutes for 10 years, and then the wind farm wind speed data set, temperature data set, grid missing power data set and grid missing capacity data set can be constructed based on the time series.

[0067] Step S2012, performing parameter identification based on the wind speed data of each month in the wind farm wind speed data set to obtain probability density function parameters, and constructing a wind speed fluctuation probability density function based on the probability density function parameters.

[0068] Specifically, the wind speed fluctuation probability density function adopts the Weibull function, and its expression is as follows:

[0069] f(x)=(k / λ)*(x / λ) (k-1) *e -(x / λ)^k (1)

[0070] Where x represents time, f(x) represents wind speed data, k represents shape parameter, λ represents scale parameter, and k and λ are probability density function parameters.

[0071] Furthermore, k and λ in the above formula (1) are set as initial quantities to obtain the initial wind speed. Then, the wind speed data of January of each year in 10 years are selected, and the least squares method is used to fit the discrete data for parameter identification to obtain the probability density function parameters of January. The objective function of the least squares method is as follows:

[0072]

[0073] Among them, y m is the actual wind speed, y n is the wind speed calculated by the Weibull function, and E represents the values ​​of k and λ.

[0074] Furthermore, the obtained values ​​of k and λ are input into the above formula (1) to obtain the probability density function of wind speed fluctuation in January.

[0075] Furthermore, the same steps as those for calculating the probability density function of wind speed fluctuation in January are used to obtain the probability density function of wind speed fluctuation from January to December.

[0076] Step S2013, performing parameter identification based on the temperature data of each month in the temperature data set to obtain first normal distribution parameters, and constructing a temperature fluctuation probability density function based on the first normal distribution parameters.

[0077] Specifically, the expression of the temperature fluctuation probability density function is as follows:

[0078]

[0079] Wherein, z(x) represents temperature data, μ is the average temperature, σ is the standard deviation of temperature, and μ and σ are taken as the first normal distribution parameters.

[0080] Furthermore, the temperature data of January of each year in the past 10 years are selected for parameter identification to obtain the first normal distribution parameters, and then the temperature fluctuation probability density function of January is obtained. The calculation steps of the first normal distribution parameters are the same as the calculation steps of the above probability density function parameters.

[0081] Furthermore, the temperature fluctuation probability density function from January to December is obtained by adopting the same steps as those for calculating the temperature fluctuation probability density function in January.

[0082] Step S2014, performing parameter identification based on the grid missing power data of each month in the grid missing power data set to obtain second normal distribution parameters, and constructing a grid missing power fluctuation probability density function based on the second normal distribution parameters.

[0083] Specifically, the expression of the probability density function of power grid missing power fluctuation is as follows:

[0084]

[0085] Wherein, w(x) represents the grid missing power data, α is the average value of the grid missing power, δ is the standard deviation of the grid missing power, and α and δ are used as the second normal distribution parameters.

[0086] Furthermore, the grid missing power data in January of each year in the past 10 years are selected for parameter identification to obtain the second normal distribution parameters, and then the grid missing power data in January are obtained. The calculation steps of the second normal distribution parameters are the same as the calculation steps of the above-mentioned probability density function parameters.

[0087] Furthermore, the same steps as those for calculating the power grid missing power data for January are used to obtain the power grid missing power data for January to December.

[0088] Step S2015, performing parameter identification based on the power grid missing capacity data of each month in the power grid missing capacity data set to obtain third normal distribution parameters, and constructing a power grid missing capacity fluctuation probability density function based on the third normal distribution parameters.

[0089] Specifically, the expression of the probability density function of the power grid missing capacity fluctuation is as follows:

[0090]

[0091] Among them, z(x) represents the missing capacity data of the power grid, is the average value of the missing capacity of the power grid, γ is the standard deviation of the missing capacity of the power grid, and γ as the third normal distribution parameter.

[0092] Furthermore, the grid missing capacity data in January of each year in the past 10 years are selected for parameter identification to obtain the third normal distribution parameters, and then the grid missing capacity fluctuation probability density function in January is obtained. The calculation steps of the third normal distribution parameters are the same as the calculation steps of the above probability density function parameters.

[0093] Furthermore, the same steps of calculating the power grid missing capacity fluctuation probability density function in January are used to obtain the power grid missing capacity fluctuation probability density function from January to December.

[0094] Step S202, Monte Carlo simulation is performed based on the probability density function of wind speed fluctuation, the probability density function of temperature fluctuation, the probability density function of grid missing power fluctuation and the probability density function of grid missing capacity fluctuation to obtain wind farm energy storage parameter data. Figure 1 Step S102 of the illustrated embodiment will not be described in detail here.

[0095] Step S203, based on the wind farm energy storage parameter data, respectively evaluate the reliability performance, frequency regulation performance, peak regulation performance and economic performance of the wind farm energy storage capacity configuration to obtain the wind farm energy storage capacity configuration evaluation result. Figure 1 Step S103 of the illustrated embodiment will not be described in detail here.

[0096] The present embodiment provides a method for evaluating the configuration of energy storage capacity of a wind farm. By performing parameter identification on the wind farm data of each month, the parameters in each probability density function are accurately calculated. Furthermore, by constructing a probability density function of wind speed fluctuation, a probability density function of temperature fluctuation, a probability density function of grid missing power fluctuation, and a probability density function of grid missing capacity fluctuation, the probability distribution of data fluctuation corresponding to the hybrid energy storage is accurately grasped, laying a foundation for the subsequent calculation of the energy storage parameter data of the wind farm.

[0097] In this embodiment, a method for evaluating the configuration of energy storage capacity of a wind farm is provided, which can be used for the above-mentioned server-type devices. Figure 3 is a flow chart of a method for evaluating the configuration of energy storage capacity of a wind farm according to an embodiment of the present invention. Figure 3 As shown, the process includes the following steps:

[0098] Step S301, collect wind farm data within the target time period, and construct wind speed fluctuation probability density functions, temperature fluctuation probability density functions, grid missing power fluctuation probability density functions, and grid missing capacity fluctuation probability density functions based on the wind farm data within the target time period. Figure 2 Step S201 of the illustrated embodiment will not be described in detail here.

[0099] Step S302 , performing Monte Carlo simulation based on the wind speed fluctuation probability density function, the temperature fluctuation probability density function, the grid missing power fluctuation probability density function and the grid missing capacity fluctuation probability density function to obtain wind farm energy storage parameter data.

[0100] Specifically, the above step S302 includes:

[0101] Step S3021, randomly obtain the Monte Carlo test month, and integrate the wind speed fluctuation probability density function, temperature fluctuation probability density function, grid missing power fluctuation probability density function and grid missing capacity fluctuation probability density function corresponding to the Monte Carlo test month, respectively, to obtain the wind speed fluctuation cumulative distribution function, temperature fluctuation cumulative distribution function, grid missing power fluctuation cumulative distribution function and grid missing capacity fluctuation cumulative distribution function.

[0102] Specifically, the randomly obtained month is used as the current Monte Carlo test month.

[0103] Step S3022, use the random number generation function to obtain a random number, and based on the random number, use the wind speed fluctuation cumulative distribution function, temperature fluctuation cumulative distribution function, grid missing power fluctuation cumulative distribution function and grid missing capacity fluctuation cumulative distribution function to respectively determine the wind speed simulation data, temperature simulation data, grid missing power simulation data and grid missing capacity simulation data.

[0104] Specifically, a random number is obtained using a random number generation function within the range of 0-1.

[0105] Step S3023, obtaining an energy storage damage probability density function, and randomly obtaining an energy storage annual value within the energy storage life cycle, and determining energy storage damage status data based on the energy storage annual value using the energy storage damage probability density function.

[0106] Specifically, the probability of energy storage damage increases with annual growth, and in a fixed year it can be equivalent to an energy storage damage probability density function that obeys a 0-1 distribution, and then the annual value b of the energy storage within the life cycle B is randomly obtained, and the energy storage damage probability density function is used to obtain energy storage damage status data that conforms to the probability of energy storage damage.

[0107] The present embodiment provides a method for evaluating the energy storage capacity configuration of a wind farm. The method randomly obtains Monte Carlo test months and random numbers, and uses Monte Carlo simulation to accurately calculate wind speed simulation data, temperature simulation data, grid missing power simulation data, and grid missing capacity simulation data. The energy storage damage probability density function is used to accurately obtain energy storage damage status data, and the reliability performance, frequency regulation performance, peak regulation performance, and economic performance of the energy storage capacity configuration can be better evaluated.

[0108] Step S303, based on the wind farm energy storage parameter data, respectively evaluate the reliability performance, frequency regulation performance, peak regulation performance and economic performance of the wind farm energy storage capacity configuration to obtain the wind farm energy storage capacity configuration evaluation result.

[0109] Specifically, the above step S303 includes:

[0110] Step S3031, evaluating the reliability performance of the wind farm energy storage capacity configuration based on the energy storage damage status data to obtain a reliability performance evaluation result.

[0111] Specifically, if the current energy storage damage status data indicates that there is no energy storage damage, the reliability performance is set to 1, otherwise the reliability performance is set to 0; if the reliability index corresponding to 10,000 Monte Carlo tests is i times, the probability of reliability performance (i.e., the reliability performance evaluation result) is i / 10,000.

[0112] Step S3032, constructing a temperature performance curve and a wind turbine wind speed power curve based on the wind farm data within the target time period, and determining the wind turbine power generation data, energy storage power simulation data and energy storage capacity simulation data based on the wind speed simulation data and the temperature simulation data in combination with the temperature performance curve and the wind turbine wind speed power curve.

[0113] In some optional implementations, the above step S3032 includes:

[0114] Step a1, with the goal of optimizing the energy storage benefits of the wind farm, construct a wind farm energy storage capacity configuration model based on the wind farm data within the target time period.

[0115] Specifically, with the goal of optimizing the energy storage benefits of the wind farm, an objective function is constructed based on the wind farm data within the target time period. The expression of the objective function is as follows:

[0116] M=max(C 3-C 1 -C 2 ) (6)

[0117] Among them, M represents the total economic data, C 3 Indicates income, C 1 Indicates the cost of a development, C 2 Indicates the running cost.

[0118] Furthermore, income C 3 Including direct income and indirect income generated by reducing wind power loss, the calculation formula is as follows:

[0119] C 3 =(λ T +β)Q T +U s Q s (7)

[0120] Among them, λ T is the frequency regulation electricity price, β is the compensation electricity price for auxiliary frequency regulation facilities set by the power grid company, Q T is the frequency modulation power, U s In order to reduce the unit cost of wind power loss, Q s To reduce the amount of electricity lost due to wind power abandonment.

[0121] Furthermore, the cost of a development is C 1 The calculation formula is as follows:

[0122] C 1 =C p P+C E E (8)

[0123] Among them, C p is the unit power cost, P is the energy storage power, C E is the unit capacity cost, and E is the energy storage capacity.

[0124] Furthermore, the operating cost mainly includes the equipment maintenance cost and the battery replacement cost. The equipment maintenance cost is very small and can be ignored. The battery replacement cost mainly includes the battery life loss. Therefore, the operating cost C 2 The calculation formula is as follows:

[0125]

[0126] Where N is the number of cycles of energy storage life; N t The number of energy storage cycles on a typical working day, T is the number of days the energy storage is put into operation in a year.

[0127] Furthermore, the energy storage power constraint and energy storage capacity constraint are used as the constraint conditions of the objective function to construct a wind farm energy storage capacity configuration model, where the constraint conditions are expressed as follows:

[0128]

[0129] Among them, Q is the state of charge of the energy storage unit, Q min is the minimum value of the allowed state of charge, Q max is the maximum value of the allowed state of charge, P max is the missing power of the power grid, that is, the total demand response power of the output power of the units in the power grid and the load in the power grid in the frequency regulation area, η is the discharge efficiency of the energy storage unit, where the calculation formula of the charge state Q of the energy storage unit is as follows:

[0130]

[0131] Among them, Q 0 is the initial charge state of the energy storage unit, t 1 Frequency modulation time for energy storage unit.

[0132] Step a2: Based on the wind farm energy storage capacity configuration model, an optimization algorithm is used to obtain the energy storage configuration power and energy storage configuration capacity.

[0133] Step a3, constructing a temperature performance curve and a wind turbine wind speed power curve based on the energy storage configuration power and the energy storage configuration capacity.

[0134] Step a4, based on the wind speed simulation data, using the wind speed power curve of the wind turbine to determine the wind turbine power generation data.

[0135] Step a5, based on the temperature simulation data, determine the energy storage power simulation data and the energy storage capacity simulation data using the temperature performance curve.

[0136] Specifically, the wind speed and temperature are randomly obtained through 10,000 Monte Carlo tests, and combined with the temperature performance curve and the wind speed power curve of the wind turbine, 10,000 sets of energy storage corresponding performance (i.e., energy storage power simulation data P2 and energy storage capacity simulation data S2) and wind turbine power generation data P3 are obtained.

[0137] The present embodiment provides a method for evaluating the energy storage capacity configuration of a wind farm. By constructing a wind farm energy storage capacity configuration model, and obtaining the energy storage configuration power and energy storage configuration capacity based on the wind farm energy storage capacity configuration model, and then constructing a temperature performance curve and a wind turbine wind speed power curve, an accurate calculation of the actual energy storage power and capacity configuration is achieved, the economy during the energy storage operation period is improved, and a foundation is laid for the subsequent evaluation of the frequency regulation performance and peak regulation performance of the wind farm energy storage capacity configuration.

[0138] Step S3033, comparing the sum of the wind turbine power generation data and the energy storage power simulation data with the grid missing power simulation data, and evaluating the frequency regulation performance of the wind farm energy storage capacity configuration based on the comparison result to obtain a frequency regulation performance evaluation result.

[0139] Specifically, if P2+P3>P1, the frequency regulation meets the requirements and the frequency regulation performance is set to 1, otherwise the frequency regulation performance is 0; if 10,000 Monte Carlo tests meet m times, the frequency regulation performance evaluation result is m / 10,000; where P1 represents the power grid missing power simulation data.

[0140] Step S3034, comparing the energy storage capacity simulation data with the grid missing capacity simulation data, and evaluating the peak-shaving performance of the wind farm energy storage capacity configuration based on the comparison result to obtain a peak-shaving performance evaluation result.

[0141] Specifically, if the absolute value of S2 > the absolute value of S1, the peak shaving meets the requirements and the peak shaving performance is set to 1, otherwise the peak shaving performance is 0; if 10,000 tests meet n times, the peak shaving performance evaluation result is n / 10,000.

[0142] Step S3035, obtaining the energy storage cost of the wind farm, and evaluating the economic performance of the energy storage capacity configuration of the wind farm based on the energy storage cost of the wind farm and the annual value of energy storage to obtain an economic performance evaluation result.

[0143] Specifically, the energy storage cost of a wind farm includes the investment cost y and the annual operation and maintenance cost w. The cost of all Monte Carlo tests with damaged energy storage is calculated to be (y+w*b) / b, and the cost of other undamaged Monte Carlo tests is (y+w*B) / B. Therefore, the economic performance evaluation result is: (y+w*b) / b*j / 10000+(y+w*B) / B*(1-j / 10000); where j represents the number of economic performance evaluations corresponding to 10,000 Monte Carlo tests.

[0144] A wind farm energy storage capacity configuration assessment method provided in this embodiment utilizes wind speed simulation data, temperature simulation data, grid missing power simulation data, grid missing capacity simulation data, energy storage annual value, and energy storage damage status data to achieve a comprehensive assessment of the reliability performance, frequency regulation performance, peak regulation performance, and economic performance of a hybrid energy storage capacity configuration.

[0145] In this embodiment, a wind farm energy storage capacity configuration assessment device is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0146] This embodiment provides a wind farm energy storage capacity configuration assessment device, such as Figure 4 As shown, including:

[0147] A construction module 401 is used to collect wind farm data within a target time period, and to construct a wind speed fluctuation probability density function, a temperature fluctuation probability density function, a power grid missing power fluctuation probability density function, and a power grid missing capacity fluctuation probability density function based on the wind farm data within the target time period;

[0148] A simulation module 402 is used to perform Monte Carlo simulation based on a wind speed fluctuation probability density function, a temperature fluctuation probability density function, a power grid missing power fluctuation probability density function, and a power grid missing capacity fluctuation probability density function to obtain wind farm energy storage parameter data;

[0149] The evaluation module 403 is used to evaluate the reliability performance, frequency regulation performance, peak regulation performance and economic performance of the wind farm energy storage capacity configuration based on the wind farm energy storage parameter data, and obtain the wind farm energy storage capacity configuration evaluation result.

[0150] In some optional implementations, the building block 401 includes:

[0151] A first construction unit is used to construct a wind farm wind speed data set, a temperature data set, a power grid missing power data set, and a power grid missing capacity data set based on wind farm data within a target time period;

[0152] The second construction unit is used to perform parameter identification based on the wind speed data of each month in the wind farm wind speed data set, obtain probability density function parameters, and construct a wind speed fluctuation probability density function based on the probability density function parameters;

[0153] A third construction unit is used to perform parameter identification based on the temperature data of each month in the temperature data set to obtain a first normal distribution parameter, and to construct a temperature fluctuation probability density function based on the first normal distribution parameter;

[0154] A fourth construction unit is used to perform parameter identification based on the power grid missing power data of each month in the power grid missing power data set to obtain second normal distribution parameters, and to construct a power grid missing power fluctuation probability density function based on the second normal distribution parameters;

[0155] The fifth construction unit is used to perform parameter identification based on the power grid missing capacity data of each month in the power grid missing capacity data set to obtain third normal distribution parameters, and to construct a power grid missing capacity fluctuation probability density function based on the third normal distribution parameters.

[0156] In some optional implementations, the wind farm energy storage parameter data in the simulation module 402 includes: wind speed simulation data, temperature simulation data, grid missing power simulation data, grid missing capacity simulation data, energy storage annual value and energy storage damage status data.

[0157] In some optional implementations, the simulation module 402 includes:

[0158] An integration unit is used to randomly obtain a Monte Carlo test month, and integrate the wind speed fluctuation probability density function, the temperature fluctuation probability density function, the grid missing power fluctuation probability density function, and the grid missing capacity fluctuation probability density function corresponding to the Monte Carlo test month to obtain a wind speed fluctuation cumulative distribution function, a temperature fluctuation cumulative distribution function, a grid missing power fluctuation cumulative distribution function, and a grid missing capacity fluctuation cumulative distribution function;

[0159] a first determination unit, configured to obtain a random number using a random number generation function, and determine wind speed simulation data, temperature simulation data, grid missing power simulation data, and grid missing capacity simulation data based on the random number using a wind speed fluctuation cumulative distribution function, a temperature fluctuation cumulative distribution function, a grid missing power fluctuation cumulative distribution function, and a grid missing capacity fluctuation cumulative distribution function;

[0160] The second determination unit is used to obtain the energy storage damage probability density function and randomly obtain the energy storage annual value within the energy storage life cycle, and determine the energy storage damage status data based on the energy storage annual value using the energy storage damage probability density function.

[0161] In some optional implementations, the evaluation module 403 includes:

[0162] A first evaluation unit is used to evaluate the reliability performance of the wind farm energy storage capacity configuration based on the energy storage damage state data to obtain a reliability performance evaluation result;

[0163] A third determination unit is used to construct a temperature performance curve and a wind turbine wind speed power curve based on the wind farm data within the target time period, and determine the wind turbine power generation data, energy storage power simulation data and energy storage capacity simulation data respectively based on the wind speed simulation data and the temperature simulation data in combination with the temperature performance curve and the wind turbine wind speed power curve;

[0164] The second evaluation unit is used to compare the sum of the wind turbine power generation data and the energy storage power simulation data with the power grid missing power simulation data, and evaluate the frequency regulation performance of the wind farm energy storage capacity configuration based on the comparison result to obtain a frequency regulation performance evaluation result;

[0165] A third evaluation unit is used to compare the energy storage capacity simulation data with the power grid missing capacity simulation data, and evaluate the peak-shaving performance of the wind farm energy storage capacity configuration based on the comparison result to obtain a peak-shaving performance evaluation result;

[0166] The fourth evaluation unit is used to obtain the energy storage cost of the wind farm, evaluate the economic performance of the energy storage capacity configuration of the wind farm based on the energy storage cost of the wind farm and the annual value of energy storage, and obtain an economic performance evaluation result.

[0167] In some optional implementations, the third determining unit includes:

[0168] The first construction subunit is used to construct a wind farm energy storage capacity configuration model based on wind farm data within a target time period with the goal of optimizing the wind farm energy storage benefits;

[0169] An optimization subunit, used to obtain energy storage configuration power and energy storage configuration capacity by using an optimization algorithm based on a wind farm energy storage capacity configuration model;

[0170] A second construction subunit is used to construct a temperature performance curve and a wind turbine wind speed power curve based on the energy storage configuration power and the energy storage configuration capacity;

[0171] A first determination subunit is used to determine the wind turbine power data based on the wind speed simulation data and using the wind turbine wind speed power curve;

[0172] The second determination subunit is used to determine the energy storage power simulation data and the energy storage capacity simulation data based on the temperature simulation data and using the temperature performance curve.

[0173] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0174] In this embodiment, a wind farm energy storage capacity configuration assessment device is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0175] The embodiment of the present invention also provides a computer device having the above Figure 4 A wind farm energy storage capacity configuration assessment device is shown.

[0176] See also Figure 5 , Figure 5 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Figure 5As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 5 A processor 10 is taken as an example.

[0177] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0178] The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0179] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0180] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0181] The computer device further comprises a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0182] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0183] A part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the existence of the computer program instruction in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc., and accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium accessible to the computer.

[0184] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for evaluating the configuration of energy storage capacity of a wind farm, characterized in that: The method comprises: Collect wind farm data within a target time period, and construct a wind speed fluctuation probability density function, a temperature fluctuation probability density function, a power grid missing power fluctuation probability density function, and a power grid missing capacity fluctuation probability density function based on the wind farm data within the target time period; Performing Monte Carlo simulation based on the wind speed fluctuation probability density function, the temperature fluctuation probability density function, the grid missing power fluctuation probability density function, and the grid missing capacity fluctuation probability density function to obtain wind farm energy storage parameter data; Based on the wind farm energy storage parameter data, the reliability performance, frequency regulation performance, peak regulation performance and economic performance of the wind farm energy storage capacity configuration are evaluated respectively to obtain a wind farm energy storage capacity configuration evaluation result.

2. The method according to claim 1, characterized in that The wind speed fluctuation probability density function, the temperature fluctuation probability density function, the power grid missing power fluctuation probability density function and the power grid missing capacity fluctuation probability density function are constructed based on the wind farm data in the target time period, respectively, including: Building a wind farm wind speed data set, a temperature data set, a power grid missing power data set, and a power grid missing capacity data set based on the wind farm data within the target time period; Performing parameter identification based on the wind speed data of each month in the wind farm wind speed data set to obtain probability density function parameters, and constructing the wind speed fluctuation probability density function based on the probability density function parameters; Perform parameter identification based on the temperature data of each month in the temperature data set to obtain first normal distribution parameters, and construct the temperature fluctuation probability density function based on the first normal distribution parameters; Performing parameter identification based on the grid missing power data of each month in the grid missing power data set to obtain second normal distribution parameters, and constructing the grid missing power fluctuation probability density function based on the second normal distribution parameters; Parameter identification is performed based on the power grid missing capacity data of each month in the power grid missing capacity data set to obtain third normal distribution parameters, and the power grid missing capacity fluctuation probability density function is constructed based on the third normal distribution parameters.

3. The method according to claim 1, characterized in that The wind farm energy storage parameter data includes: wind speed simulation data, temperature simulation data, grid missing power simulation data, grid missing capacity simulation data, energy storage annual value and energy storage damage status data.

4. The method according to claim 3, characterized in that The Monte Carlo simulation is performed based on the wind speed fluctuation probability density function, the temperature fluctuation probability density function, the grid missing power fluctuation probability density function and the grid missing capacity fluctuation probability density function to obtain wind farm energy storage parameter data, including: Randomly obtain a Monte Carlo test month, and integrate the wind speed fluctuation probability density function, the temperature fluctuation probability density function, the grid missing power fluctuation probability density function, and the grid missing capacity fluctuation probability density function corresponding to the Monte Carlo test month, respectively, to obtain a wind speed fluctuation cumulative distribution function, a temperature fluctuation cumulative distribution function, a grid missing power fluctuation cumulative distribution function, and a grid missing capacity fluctuation cumulative distribution function; Obtain a random number using a random number generation function, and based on the random number, determine the wind speed simulation data, the temperature simulation data, the grid missing power simulation data, and the grid missing capacity simulation data using the wind speed fluctuation cumulative distribution function, the temperature fluctuation cumulative distribution function, the grid missing power fluctuation cumulative distribution function, and the grid missing capacity fluctuation cumulative distribution function, respectively; The energy storage damage probability density function is obtained, and the energy storage annual value is randomly obtained within the energy storage life cycle, and based on the energy storage annual value, the energy storage damage status data is determined using the energy storage damage probability density function.

5. The method according to claim 4, characterized in that The reliability performance, frequency regulation performance, peak regulation performance and economic performance of the wind farm energy storage capacity configuration are evaluated based on the wind farm energy storage parameter data to obtain the wind farm energy storage capacity configuration evaluation result, including: Based on the energy storage damage status data, the reliability performance of the wind farm energy storage capacity configuration is evaluated to obtain a reliability performance evaluation result; Constructing a temperature performance curve and a wind turbine wind speed power curve based on the wind farm data within the target time period, and determining wind turbine power generation data, energy storage power simulation data, and energy storage capacity simulation data based on the wind speed simulation data and the temperature simulation data, in combination with the temperature performance curve and the wind turbine wind speed power curve; The sum of the wind turbine power generation data and the energy storage power simulation data is compared with the grid missing power simulation data, and the frequency regulation performance of the wind farm energy storage capacity configuration is evaluated based on the comparison result to obtain a frequency regulation performance evaluation result; Comparing the energy storage capacity simulation data with the power grid missing capacity simulation data, and evaluating the peak-shaving performance of the wind farm energy storage capacity configuration based on the comparison result to obtain a peak-shaving performance evaluation result; The energy storage cost of the wind farm is obtained, and the economic performance of the energy storage capacity configuration of the wind farm is evaluated based on the energy storage cost of the wind farm and the annual value of energy storage to obtain an economic performance evaluation result.

6. The method according to claim 5, characterized in that The step of constructing a temperature performance curve and a wind turbine wind speed power curve based on the wind farm data within the target time period, and determining wind turbine power generation data, energy storage power simulation data, and energy storage capacity simulation data based on the wind speed simulation data and the temperature simulation data in combination with the temperature performance curve and the wind turbine wind speed power curve, respectively, includes: Taking the optimization of wind farm energy storage benefits as the goal, building a wind farm energy storage capacity configuration model based on wind farm data within the target time period; Based on the wind farm energy storage capacity configuration model, an optimization algorithm is used to obtain energy storage configuration power and energy storage configuration capacity; Constructing the temperature performance curve and the wind turbine wind speed power curve based on the energy storage configuration power and the energy storage configuration capacity; Based on the wind speed simulation data, determining the wind turbine power generation data using the wind turbine wind speed power curve; Based on the temperature simulation data, the energy storage power simulation data and the energy storage capacity simulation data are determined using the temperature performance curve.

7. A wind farm energy storage capacity configuration assessment device, characterized in that: The device comprises: A construction module is used to collect wind farm data within a target time period, and to construct a wind speed fluctuation probability density function, a temperature fluctuation probability density function, a power grid missing power fluctuation probability density function, and a power grid missing capacity fluctuation probability density function based on the wind farm data within the target time period; A simulation module, used for performing Monte Carlo simulation based on the wind speed fluctuation probability density function, the temperature fluctuation probability density function, the grid missing power fluctuation probability density function and the grid missing capacity fluctuation probability density function to obtain wind farm energy storage parameter data; The evaluation module is used to evaluate the reliability performance, frequency regulation performance, peak regulation performance and economic performance of the wind farm energy storage capacity configuration based on the wind farm energy storage parameter data, and obtain the wind farm energy storage capacity configuration evaluation result.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the wind farm energy storage capacity configuration assessment method according to any one of claims 1 to 6 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the wind farm energy storage capacity configuration assessment method according to any one of claims 1 to 6.

10. A computer program product, characterized in that The method comprises computer instructions, wherein the computer instructions are used to enable a computer to execute the wind farm energy storage capacity configuration assessment method according to any one of claims 1 to 6.

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