Method, system and equipment for evaluating actual wind power capacity coefficient under medium and long term scale

Abstract

The invention discloses a method, system and equipment for evaluating a wind power actual capacity coefficient under a medium and long term scale, and belongs to the technical field of wind power generation prediction and evaluation, and the method comprises the steps: selecting wind power historical power generation data based on correlation, and converting the wind power historical power generation data into a fan capacity coefficient sample value; the occurrence frequency of fan capacity coefficient sample values is counted and calculated; on the basis of the appearance frequency of the fan capacity coefficient sample values, left-direction probability distribution and right-direction probability distribution are calculated in a statistical mode; on the basis of the given confidence coefficient, the minimum guaranteed wind power output capacity coefficient and the maximum wind power output capacity coefficient are obtained through the left-direction probability distribution and the right-direction probability distribution; and based on the adjustment factor, adjusting the minimum guaranteed output capacity coefficient and the maximum output capacity coefficient of the wind power, and obtaining the minimum guaranteed output and the maximum output of the specific unit under the corresponding confidence coefficient. According to the method, the real capacity coefficient of wind power can be obtained, the evaluation result is more targeted, and the method has the advantages of comprehensiveness, convenience and the like.

Description

Technical Field

[0001] The present invention belongs to the technical field of wind power generation prediction and evaluation, and particularly relates to a method, system and device for evaluating the actual capacity factor of wind power on a medium- and long-term scale. Background Art

[0002] With the development of the new power system, the penetration rate of new energy continues to increase. While improving the environmental protection of the energy system, it also brings the problem of enhanced randomness of the power supply system, which has an important impact on grid security, power supply guarantee, and the economy of the operation of the energy system. At present, wind power generation prediction can achieve a high confidence level on short-term and ultra-short-term time scales (some application reports can reach more than 95%), but it is difficult to ensure accuracy on longer time scales. In fact, the evaluation of the reliable power generation capacity of wind turbines on a medium- and long-term scale is of great significance and is required in applications such as power and electricity balance on a medium- and long-term scale and new energy participating in market transactions. Moreover, with the development of the new power system and the continuous increase in the penetration rate of new energy, the importance and urgency of this evaluation are also constantly increasing.

[0003] The reliable power generation capacity here refers to the minimum guaranteed output and the maximum output under a certain confidence level. The reliable power generation capacity of a wind turbine can be evaluated by the capacity factor of the wind turbine. The so-called capacity factor is defined as the ratio of the actual electricity generated by a wind turbine during a certain period of time to the maximum electricity that could be generated if it had been operating at full power during the corresponding period. This definition is equivalent to the ratio of the average power generation of the wind turbine to the rated power over a period of time.

[0004] The existing technologies mainly have the following technical problems: (1) At present, the capacity factor of wind turbines is mainly obtained through theoretical calculation based on wind speed and unit parameters, but there is a difference between the actual capacity factor and the theoretical calculated value. (2) The historical power generation data of wind turbines is the main basic data for evaluating their capacity factors, but the power generation of wind turbines is affected by many factors such as terrain, season, wind speed, and air density, and the correlation between different historical data and the capacity factor of the wind turbine being evaluated is different. (3) Under a certain confidence level, what is actually needed is the interval value of the capacity factor of wind turbine power generation, not just the minimum value or the maximum value, because currently wind power is not only an important power source for power supply guarantee but also an important object for priority consumption. (4) The evaluation of the capacity factor of wind turbines is often the average value of all wind turbines in a class or a region. When it comes to a specific wind turbine unit or a specific power plant, it is necessary to combine the specific environment and physical parameters of the wind turbine unit to obtain a more practical and reliable power generation capacity evaluation result. Summary of the Invention

[0005] The object of the present invention is to provide a method, a system and a device for evaluating the actual capacity factor of wind power on a medium- and long-term scale in view of the above problems in the prior art, which can obtain a more realistic capacity factor of wind power, and the evaluation result is more targeted. It can not only obtain the minimum guaranteed output capacity factor and the maximum output capacity factor of wind power at a given confidence level, but also obtain the minimum guaranteed output and the maximum output of a specific unit at a certain confidence level.

[0006] To achieve the above object, the present invention has the following technical solutions:

[0007] In the first aspect, a method for evaluating the actual capacity factor of wind power on a medium- and long-term scale is provided, including:

[0008] Selecting historical wind power generation data based on correlation and converting the historical wind power generation data into sample values of the turbine capacity factor;

[0009] Statistically calculating the occurrence frequency of the sample values of the turbine capacity factor;

[0010] Based on the occurrence frequency of the sample values of the turbine capacity factor, statistically calculate the left probability distribution and the right probability distribution respectively;

[0011] Based on a given confidence level, obtain the minimum guaranteed output capacity factor and the maximum output capacity factor of wind power through the left probability distribution and the right probability distribution;

[0012] Based on an adjustment factor, adjust the minimum guaranteed output capacity factor and the maximum output capacity factor of wind power to obtain the minimum guaranteed output and the maximum output of a specific unit at the corresponding confidence level.

[0013] As a preferred solution, the step of selecting historical wind power generation data based on correlation includes:

[0014] Collecting the location range of the wind turbines to be evaluated, including the central location and within a certain number of kilometers around the central location;

[0015] Collecting data on which season and time period of the wind turbines to be evaluated, where the time period includes the set peak, valley, and normal time periods, or one or several hours in 24 hours of a day;

[0016] Collecting the capacity factor of the wind turbines under what meteorological conditions to be evaluated, including the wind speed range and the air density range.

[0017] As a preferred solution, the calculation expression for converting the historical wind power generation data into sample values of the turbine capacity factor is as follows:

[0018]

[0019] In the formula: p iDenote the i-th historical output power sample value, P i Denote the rated power corresponding to the i-th historical output power sample value, x i Denote the calculated i-th fan capacity coefficient sample value; x i ∈[0, 1].

[0020] As a preferred solution, the step of statistically calculating the occurrence frequency of the fan capacity coefficient sample values includes:

[0021] Divide the interval [0, 1] into N intervals evenly with N + 1 numbers; the N + 1 numbers are defined as:

[0022]

[0023] In the formula, y 0 = 0, y N = 1;

[0024] Sort out all the calculated fan capacity coefficient sample values x i and record the number falling into [y j-1 , y j , denoted as

[0025] Calculate the occurrence frequency of the fan capacity coefficient sample values according to the following formula:

[0026]

[0027] In the formula: f 1 (y j ) represents the frequency that the fan capacity coefficient sample value xi appears in [y j-1 , y j , j and k represent the subscripts for distinguishing the fan capacity coefficient sample value intervals, and respectively represent the number of times the fan capacity coefficient sample value x i appears in [y j-1 , y j and [y k-1 , y k ; N represents the number of all fan capacity coefficient sample value intervals; represents the number of all fan capacity coefficient sample values x i ;

[0028] Taking the fan capacity coefficient sample value as the abscissa and the occurrence frequency of the fan capacity coefficient sample value as the ordinate, draw the occurrence frequency curve of the fan capacity coefficient sample value; f 1 (y j ) represents a series of discrete values, by connecting the discrete points (y j , f1 (y j )) approximately represents the probability density curve of the fan capacity factor; define the probability density of the actual fan capacity factor as f(y), where y represents any capacity factor value between [0, 1].

[0029] As a preferred solution, based on the occurrence frequency of the fan capacity factor sample values, calculate the left - hand probability distribution according to the following formula:

[0030]

[0031] In the formula: y' is the capacity factor value to be evaluated, and its value range is [0, 1], F 1 (y') represents the probability that the corresponding capacity factor is less than or equal to y';

[0032] According to the occurrence frequency f 1 (y j ) of the fan capacity factor sample values, use numerical integration to calculate F 1 (y');

[0033] Calculate the right - hand probability distribution according to the following formula:

[0034]

[0035] In the formula: F 2 (y') represents the probability that the corresponding capacity factor is greater than or equal to y';

[0036] In the same two - dimensional rectangular coordinate system, with the capacity factor value as the abscissa and the left - hand probability distribution and right - hand probability distribution of the capacity factor as the ordinates, plot the left - hand probability distribution curve and the right - hand probability distribution curve respectively.

[0037] As a preferred solution, the steps of obtaining the minimum guaranteed output capacity factor and the maximum output capacity factor of wind power through the left - hand probability distribution and the right - hand probability distribution based on a given confidence level include:

[0038] Select a confidence level p, p ≥ 0.5, and solve the following equations respectively:

[0039]

[0040] In the formula, y l and y r respectively represent the minimum value and the maximum value of the fan capacity factor under the confidence level p. Then the probability that the actual wind power capacity factor value falls within [y l , y r is: 2p - 1.

[0041] As a preferred solution, based on the adjustment factor, the minimum guaranteed output capacity factor and the maximum output capacity factor of the wind power are adjusted to obtain the expressions for the minimum guaranteed output and the maximum output of a specific unit at the corresponding confidence level as follows:

[0042]

[0043] In the formula: Y represents the capacity factor of a specific unit, y represents the average capacity factor of the wind turbines in the corresponding region and corresponding season, w 1 、w 2 、w 3 、w 4 all represent weight coefficients, and w 1 +w 2 +w 3 +w 4 =1, v represents the annual average wind speed at the location of the corresponding unit, V represents the annual average wind speed of the corresponding region, d represents the air density at the location of the corresponding unit, D represents the average air density of the corresponding region, h represents the tower height of the corresponding unit, H represents the average tower height of all units in the corresponding region, s represents the average equivalent forced outage rate of the corresponding unit, and S represents the average equivalent forced outage rate of all units in the corresponding region;

[0044] The minimum value y l and the maximum value y r of the wind turbine capacity factor at the confidence level p are respectively substituted into the expression to calculate the minimum guaranteed output capacity factor Y l and the maximum capacity factor Y r of the corresponding unit;

[0045] The minimum guaranteed output and the maximum output of the wind power at the confidence level p are calculated through the following formula:

[0046] P min =P R Y l

[0047] P max =P R Y r

[0048] In the formula: P min and P max respectively represent the minimum guaranteed output and the maximum output of the corresponding unit, and P R is the rated power of the corresponding unit.

[0049] Second, a medium- and long-term scale wind power actual capacity factor evaluation system is provided, including:

[0050] A data acquisition module, configured to select historical wind power generation data based on correlation and convert the historical wind power generation data into fan capacity factor sample values;

[0051] A sample value occurrence frequency statistics module, configured to statistically calculate the occurrence frequency of the fan capacity factor sample values;

[0052] A left - right probability distribution calculation module, configured to respectively statistically calculate the left - hand probability distribution and the right - hand probability distribution based on the occurrence frequency of the fan capacity factor sample values;

[0053] A capacity factor calculation module, configured to obtain the minimum guaranteed output capacity factor and the maximum output capacity factor of the wind power based on a given confidence level through the left - hand probability distribution and the right - hand probability distribution;

[0054] A capacity factor adjustment and output calculation module, configured to adjust the minimum guaranteed output capacity factor and the maximum output capacity factor of the wind power to obtain the minimum guaranteed output and the maximum output of a specific unit at the corresponding confidence level.

[0055] As a preferred solution, the data acquisition module stores the collected historical wind power generation data through a historical database.

[0056] As a preferred solution, the calculation expression for converting the historical wind power generation data collected by the data acquisition module into fan capacity factor sample values is as follows:

[0057]

[0058] In the formula: p i represents the i - th historical output power sample value, P i represents the rated power corresponding to the i - th historical output power sample value, x i represents the calculated i - th fan capacity factor sample value; x i ∈[0, 1].

[0059] As a preferred solution, the sample value occurrence frequency statistics module evenly divides the interval [0, 1] into N intervals with N + 1 numbers;

[0060] The N + 1 numbers are defined as:

[0061]

[0062] In the formula, y 0 = 0, y N = 1;

[0063] Sort all the calculated fan capacity factor sample values x i and record the ones falling into [y j-1 , y jThe number of them is denoted as

[0064] Calculate the frequency of the sample value of the fan capacity coefficient according to the following formula:

[0065]

[0066] In the formula: f 1 (y j ) represents the frequency that the sample value x of the fan capacity coefficient i appears in the interval [y j-1 , y j . j and k represent the subscripts for distinguishing the intervals of the sample values of the fan capacity coefficient. and respectively represent the number of times that the sample value x of the fan capacity coefficient i appears in the intervals [y j-1 , y j and [y k-1 , y k . N represents the number of all intervals of the sample values of the fan capacity coefficient; represents the number of all sample values x of the fan capacity coefficient i ;

[0067] Taking the sample value of the fan capacity coefficient as the abscissa and the frequency of the sample value of the fan capacity coefficient as the ordinate, draw the frequency curve of the sample value of the fan capacity coefficient; f 1 (y j ) represents a series of discrete values. By connecting the discrete points (y j , f 1 (y j )) approximately represents the probability density curve of the fan capacity coefficient; define the probability density of the actual fan capacity coefficient as f(y), and y represents any capacity coefficient value between [0, 1].

[0068] As a preferred solution, based on the frequency of the sample value of the fan capacity coefficient, the left - right probability distribution calculation module calculates the left - hand probability distribution according to the following formula:

[0069]

[0070] In the formula: y' is the capacity coefficient value to be evaluated, and its value range is [0, 1]. F 1 (y') represents the probability that the corresponding capacity coefficient is less than or equal to y';

[0071] According to the frequency f 1 (y j ) of the sample value of the fan capacity coefficient, use the numerical integration method to calculate F 1 (y');

[0072] Calculate the right - hand probability distribution according to the following formula:

[0073]

[0074] In the formula: F 2 (y') represents the probability that the corresponding capacity factor is greater than or equal to y';

[0075] In the same two - dimensional rectangular coordinate system, with the capacity factor value as the abscissa and the left - hand probability distribution and right - hand probability distribution of the capacity factor as the ordinate, draw the left - hand probability distribution curve and the right - hand probability distribution curve respectively.

[0076] As a preferred solution, the capacity factor calculation module selects a confidence level p, p≥0.5, and solves the following equations respectively:

[0077]

[0078] In the formula, y l and y r respectively represent the minimum value and the maximum value of the fan capacity factor under the confidence level p. Then the probability that the actual wind power capacity factor value falls within [y l , y r is: 2p - 1.

[0079] As a preferred solution, the capacity factor adjustment and output calculation module calculates according to the following expression:

[0080]

[0081] In the formula: Y represents the capacity factor of a specific unit, y represents the average capacity factor of wind turbines in the corresponding region and corresponding season, w 1 , w 2 , w 3 , w 4 all represent weight coefficients, and w 1 +w 2 +w 3 +w 4 =1, v represents the annual average wind speed at the location of the corresponding unit, V represents the annual average wind speed of the corresponding region, d represents the air density at the location of the corresponding unit, D represents the average air density of the corresponding region, h represents the tower height of the corresponding unit, H represents the average tower height of all units in the corresponding region, s represents the average equivalent forced outage rate of the corresponding unit, and S represents the average equivalent forced outage rate of all units in the corresponding region;

[0082] Substitute the minimum value y l and the maximum value y r of the fan capacity factor under the confidence level p into the expression respectively, and calculate to obtain the minimum guaranteed output capacity factor Y of the corresponding unitl and the maximum capacity factor Y r ;

[0083] Calculate the minimum guaranteed output and maximum output of wind power at confidence level p through the following formula:

[0084] P min = P R Y l

[0085] P max = P R Y r

[0086] In the formula: P min and P max respectively represent the minimum guaranteed output and maximum output of the corresponding unit, and P R is the rated power of the corresponding unit.

[0087] In a third aspect, an electronic device is provided, including a processor and a memory. The processor is configured to execute a computer program stored in the memory to implement the method for evaluating the actual capacity factor of wind power at the medium- and long-term scales.

[0088] In a fourth aspect, a computer-readable storage medium is provided. The computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by a processor, the method for evaluating the actual capacity factor of wind power at the medium- and long-term scales is implemented.

[0089] Compared with the prior art, the first aspect of the present invention has at least the following beneficial effects:

[0090] The present invention provides a solution for the evaluation of the reliable power generation capacity of wind turbines on a medium- and long-term time scale, and has advantages such as objectivity, pertinence, comprehensiveness, and convenience. By collecting historical wind power generation data, converting the historical wind power generation data into sample values of the turbine capacity factor, statistically calculating the occurrence frequency of the sample values of the turbine capacity factor, and on the basis of the occurrence frequency of the sample values of the turbine capacity factor, respectively statistically calculating the leftward probability distribution and the rightward probability distribution. At a given confidence level, the minimum guaranteed output capacity factor and the maximum output capacity factor of the wind power are obtained through the leftward probability distribution and the rightward probability distribution. Specifically for a certain unit, based on the adjustment factor, the minimum guaranteed output capacity factor and the maximum output capacity factor of the wind power are adjusted to obtain the minimum guaranteed output and the maximum output of the specific unit at the corresponding confidence level. Regarding objectivity, the present invention is evaluated based on the actual historical data of the wind power, avoiding the significant differences that may exist between the actual capacity factor and the theoretically calculated value. Due to the gradual progress of technology, the progress of technology is gradually reflected in the wind power output data and must be reflected in the historical data. Therefore, the present invention can obtain a relatively real capacity factor of the wind power. Regarding pertinence, the evaluation results given by the evaluation method of the present invention are based on the selected data, and the selected dimensions cover many factors such as geographical location, season, time period, meteorological conditions, etc., making the evaluation results more targeted. Regarding comprehensiveness, since wind power is not only an important support for power supply during peak electricity consumption periods but also an object to ensure consumption during low electricity consumption periods, both the minimum guaranteed output and the maximum output of wind power are required for practical applications. The present invention simultaneously provides evaluation methods for the minimum guaranteed output capacity factor and the maximum output capacity factor, and according to the application requirements, different confidence level requirements can be selected. Regarding convenience, due to the large number of wind turbines, if each one is evaluated separately, the workload is large and the data requirements are high. In fact, the present invention can either evaluate one or several wind turbines separately or evaluate all the wind turbines in a region uniformly to obtain an average capacity factor, and then adjust the average capacity factor according to the specific conditions of different wind turbines through the adjustment factor to obtain the differentiated evaluation results of each wind turbine. In summary, the evaluation method for the actual capacity factor of wind power on a medium- and long-term scale according to the present invention can provide important support parameters for power grid regulation and important references for power market transactions, and has significant value in practical applications.

[0091] It can be understood that the beneficial effects of the second to fourth aspects above can refer to the relevant descriptions in the first aspect above and will not be repeated here. Description of the Drawings

[0092] To more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the accompanying drawings required for use in the embodiments or the description of the prior art. Obviously, the accompanying drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other accompanying drawings can be obtained based on these drawings without creative efforts.

[0093] Figure 1 Flowchart of the method for evaluating the actual capacity factor of wind power in the long-term scale in the embodiments of the present invention;

[0094] Figure 2 Schematic diagram of the frequency curve of the sample values of the fan capacity factor in the embodiments of the present invention;

[0095] Figure 3 Schematic diagram of the leftward probability distribution curve and the rightward probability distribution curve in the embodiments of the present invention;

[0096] Figure 4 Schematic diagram of obtaining the minimum guaranteed output capacity factor and the maximum output capacity factor of wind power at a given confidence level in the embodiments of the present invention;

[0097] Figure 5 Schematic diagram of the structure of the system for evaluating the actual capacity factor of wind power in the long-term scale in the embodiments of the present invention;

[0098] Figure 6 Schematic diagram of the physical structure of the electronic device in the embodiments of the present invention. Detailed implementation manners

[0099] In the following description, specific details such as specific system structures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present application. However, those skilled in the art should clearly understand that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present application.

[0100] Please refer to Figure 1 , an embodiment of the present invention proposes a method for evaluating the actual capacity factor of wind power in the long-term scale, including:

[0101] S1. Select the historical power generation data of wind power based on correlation and convert the historical power generation data of wind power into sample values of the fan capacity factor;

[0102] S2. Statistically calculate the frequency of occurrence of the sample values of the fan capacity factor;

[0103] S3. Based on the frequency of occurrence of the sample values of the fan capacity factor, statistically calculate the leftward probability distribution and the rightward probability distribution respectively;

[0104] S4. Obtain the minimum guaranteed output capacity factor and the maximum output capacity factor of the wind power based on the given confidence level through the left - hand probability distribution and the right - hand probability distribution;

[0105] S5. Adjust the minimum guaranteed output capacity factor and the maximum output capacity factor of the wind power based on the adjustment factor to obtain the minimum guaranteed output and the maximum output of the specific unit at the corresponding confidence level.

[0106] In a possible implementation manner, in step S1, collect the location range of the wind turbine units to be evaluated. Here, the location range can be the central location and within a certain number of kilometers around it; collect the data of the wind turbines in which season and time period. Here, the time period specifically refers to the set peak, valley, flat periods, or one or several hours in 24 hours of a day; collect the capacity factor of the wind turbines under what meteorological conditions, including the wind speed range and the air density range.

[0107] Further, the calculation expression for converting the historical wind power generation data into the sample values of the wind turbine capacity factor in step S1 is as follows:

[0108]

[0109] In the formula: p i represents the i - th historical output power sample value, P i represents the rated power corresponding to the i - th historical output power sample value, x i represents the calculated i - th wind turbine capacity factor sample value; here it is assumed that all historical output power sample values must be greater than or equal to 0 and less than or equal to their corresponding rated powers, otherwise they are not included in the statistics, that is, x i ∈[0, 1].

[0110] In a possible implementation manner, in step S2, divide the interval [0, 1] into N intervals evenly with N + 1 numbers;

[0111] The N + 1 numbers are defined as:

[0112]

[0113] In the formula, y 0 = 0, y N = 1;

[0114] Sort out all the calculated wind turbine capacity factor sample values x i and record the number of those falling into [y j-1 , y j , denoted as

[0115] Calculate the occurrence frequency of the wind turbine capacity factor sample value according to the following formula:

[0116]

[0117] Where: f 1 (y j ) represents the sample value of the fan capacity coefficient, and x i appears in the frequency of the interval [y j-1 , y j . j and k represent the subscripts for distinguishing the intervals of the fan capacity coefficient sample values. and respectively represent the number of times the fan capacity coefficient sample value x i appears in the intervals [y j-1 , y j and [y k-1 , y k . N represents the number of all intervals of the fan capacity coefficient sample values; represents the number of all fan capacity coefficient sample values x i ;

[0118] Taking the fan capacity coefficient sample value as the abscissa and the occurrence frequency of the fan capacity coefficient sample value as the ordinate, draw the occurrence frequency curve of the fan capacity coefficient sample value; the specific shape depends on the data, Figure 2 and an example is given. f 1 (y j ) represents a series of discrete values. By connecting the discrete points (y j , f 1 (y j )) approximately represents the probability density curve of the fan capacity coefficient; define the probability density of the actual fan capacity coefficient as f(y), where y represents any capacity coefficient value between [0, 1].

[0119] In a possible implementation manner, in step S3, based on the occurrence frequency of the fan capacity coefficient sample value, calculate the left - hand probability distribution according to the following formula:

[0120]

[0121] Where: y' is the capacity coefficient value to be evaluated, and its value range is [0, 1]. F 1 (y') represents the probability that the corresponding capacity coefficient is less than or equal to y';

[0122] According to the occurrence frequency f 1 (y j ), use the numerical integration method to calculate F 1 (y'). Here, the numerical integration method can be selected from methods such as the trapezoidal method, the rectangular method, and the Simpson method.

[0123] Calculate the right - hand probability distribution according to the following formula:

[0124]

[0125] Where: F 2 (y') represents the probability that the corresponding capacity factor is greater than or equal to y'.

[0126] In the same two-dimensional rectangular coordinate system, with the capacity factor value as the abscissa and the left and right probability distributions of the capacity factor as the ordinate, the left and right probability distribution curves are respectively plotted. The specific shape depends on the data, Figure 3 An example is given.

[0127] In a possible implementation, please refer to Figure 4 , in step S3, a confidence level p is selected, p≥0.5. On the left and right probability distribution curves, the abscissa values corresponding to the intersection points of the straight line with a probability distribution of p and the left and right probability distribution curves are:

[0128] The minimum and maximum values of the fan capacity factor value under the confidence level p are respectively denoted as y l and y r ;

[0129] The calculation expressions are as follows:

[0130]

[0131] Then the probability that the actual wind power capacity factor value falls within [y l , y r is: 2p - 1.

[0132] In a possible implementation, in step S5, based on the adjustment factor, the minimum guaranteed output capacity factor and the maximum output capacity factor of the wind power are adjusted to obtain the minimum guaranteed output and the maximum output of a specific unit under the corresponding confidence level. When evaluating the capacity factor of a specific unit, assuming that its minimum guaranteed output capacity factor and the maximum capacity factor are respectively Y l and Y r .

[0133] If the data selected in step S1 is itself the data of this unit, then the y l and y r values obtained in step S4 can be directly used as the minimum and maximum capacity factor values of this unit, that is: Y l = y l , Y r = y r , but this value is only applicable to this unit.

[0134] If the data selected in step S1 belongs to the historical wind power data of a certain region, a certain season, and a certain time period, then the y l and y r values represent the average situation of the region. When applied to a specific unit, the obtained capacity factor value needs to be adjusted according to the following expression:

[0135]

[0136] In the formula: Y represents the capacity factor of a specific unit, y represents the average capacity factor of wind turbines in the corresponding region and corresponding season, w 1 、w 2 、w 3 、w 4 all represent weight coefficients, and w 1 +w 2 +w 3 +w 4 =1, v represents the annual average wind speed at the location of the corresponding unit, V represents the annual average wind speed of the corresponding region, d represents the air density at the location of the corresponding unit, D represents the average air density of the corresponding region, h represents the tower height of the corresponding unit, H represents the average tower height of all units in the corresponding region, s represents the average equivalent forced outage rate of the corresponding unit, and S represents the average equivalent forced outage rate of all units in the corresponding region;

[0137] Substitute the minimum value y l and the maximum value y r of the wind turbine capacity factor value under the confidence level p into the expression respectively, and calculate to obtain the minimum guaranteed output capacity factor Y l and the maximum capacity factor Y r of the corresponding unit;

[0138] Calculate the minimum guaranteed output and the maximum output of wind power under the confidence level p through the following formula:

[0139] P min =P R Y l

[0140] P max =P R Y r

[0141] In the formula: P min and P max respectively represent the minimum guaranteed output and the maximum output of the corresponding unit, and P R is the rated power of the corresponding unit.

[0142] Please refer to Figure 5 , this embodiment of the present invention also proposes a medium- and long-term scale wind power actual capacity factor evaluation system, including:

[0143] A data acquisition module, configured to select historical wind power generation data based on relevance and convert the historical wind power generation data into fan capacity factor sample values;

[0144] A sample value occurrence frequency statistics module, configured to statistically calculate the occurrence frequency of the fan capacity factor sample values;

[0145] A left - right probability distribution calculation module, configured to respectively statistically calculate the left - hand probability distribution and the right - hand probability distribution based on the occurrence frequency of the fan capacity factor sample values;

[0146] A capacity factor calculation module, configured to obtain the minimum guaranteed output capacity factor and the maximum output capacity factor of wind power based on a given confidence level through the left - hand probability distribution and the right - hand probability distribution;

[0147] A capacity factor adjustment and output calculation module, configured to adjust the minimum guaranteed output capacity factor and the maximum output capacity factor of wind power to obtain the minimum guaranteed output and the maximum output of a specific unit at the corresponding confidence level.

[0148] In a possible implementation manner, the data acquisition module stores the collected historical wind power generation data through a historical database. In the embodiment of the present invention, the MySql open - source database is used as the historical database to save relevant data.

[0149] In a possible implementation manner, the medium - and long - term scale wind power actual capacity factor evaluation system in the embodiment of the present invention receives an evaluation requirement through a human - machine interaction module, confirms the relevant information of the fan to be evaluated, and converts it into an evaluation requirement, and sends it to the data selection module, the capacity factor calculation module, and the capacity factor adjustment and output calculation module.

[0150] The data selection module uses SQL statements in the wind power - related historical database to select and obtain the historical data required for this evaluation, and forwards it to the capacity factor calculation module.

[0151] The capacity factor calculation module completes the evaluation calculation of the wind power capacity factor according to the data forwarded by the data selection module, and forwards the result to the capacity factor adjustment and output calculation module.

[0152] The capacity factor adjustment and output calculation module makes targeted adjustments to the calculation result of the capacity factor calculation module according to the unit information sent by the human - machine interaction module, and forwards the result to the human - machine interaction module.

[0153] Finally, the human - machine interaction module returns the capacity factor evaluation result to the user.

[0154] In a possible implementation manner, the calculation expression for converting the historical wind power generation data collected by the data acquisition module into the sample value of the fan capacity factor is as follows:

[0155]

[0156] In the formula: p i represents the i-th historical output power sample value, P i represents the rated power corresponding to the i-th historical output power sample value, x i represents the calculated i-th fan capacity factor sample value; x i ∈[0, 1].

[0157] In a possible implementation manner, the sample value frequency statistics module evenly divides the interval [0, 1] into N intervals with N + 1 numbers;

[0158] The N + 1 numbers are defined as:

[0159]

[0160] In the formula, y 0 = 0, y N = 1;

[0161] Sort all the calculated fan capacity factor sample values x i and record the number of those falling into [y j-1 , y j , denoted as

[0162] Calculate the frequency of the fan capacity factor sample value according to the following formula:

[0163]

[0164] In the formula: f 1 (y j ) represents the frequency of the fan capacity factor sample value x i appearing in the interval [y j-1 , y j , j and k represent the subscripts for distinguishing the intervals of the fan capacity factor sample value, and respectively represent the number of times the fan capacity factor sample value x i appears in the intervals [y j-1 , y j and [y k-1 , y k , N represents the number of all intervals of the fan capacity factor sample value; represents the number of all fan capacity factor sample values x i ;

[0165] Taking the sample values of the fan capacity factor as the abscissa and the occurrence frequency of the fan capacity factor sample values as the ordinate, plot the occurrence frequency curve of the fan capacity factor sample values; f 1 (y j ) represents a series of discrete values. By connecting the discrete points (y j , f 1 (y j )) approximately represents the probability density curve of the fan capacity factor; define the probability density of the actual fan capacity factor as f(y), where y represents any capacity factor value between [0, 1].

[0166] In a possible implementation manner, the left - right probability distribution calculation module calculates the left - hand probability distribution based on the occurrence frequency of the fan capacity factor sample values according to the following formula:

[0167]

[0168] In the formula: y' is the capacity factor value to be evaluated, and its value range is [0, 1], and F 1 (y') represents the probability that the corresponding capacity factor is less than or equal to y';

[0169] According to the occurrence frequency f 1 (y j ), use the numerical integration method to calculate F 1 (y');

[0170] Calculate the right - hand probability distribution according to the following formula:

[0171]

[0172] In the formula: F 2 (y') represents the probability that the corresponding capacity factor is greater than or equal to y';

[0173] In the same two - dimensional rectangular coordinate system, taking the capacity factor value as the abscissa and the left - hand probability distribution and the right - hand probability distribution of the capacity factor as the ordinate, plot the left - hand probability distribution curve and the right - hand probability distribution curve respectively.

[0174] In a possible implementation manner, the capacity factor calculation module selects a confidence level p, p ≥ 0.5, and solves the following equations respectively:

[0175]

[0176] In the formula, y l and y r respectively represent the minimum value and the maximum value of the fan capacity factor under the confidence level p. Then the actual wind power capacity factor value falls within [y l , y rThe probability of is: 2p - 1.

[0177] In a possible implementation, the capacity factor adjustment and output calculation module calculates according to the following expression:

[0178]

[0179] In the formula: Y represents the capacity factor of a specific unit, y represents the average capacity factor of wind turbines in the corresponding region and corresponding season, w 1 , w 2 , w 3 , w 4 all represent weight coefficients, and w 1 + w 2 + w 3 + w 4 = 1, v represents the annual average wind speed at the location of the corresponding unit, V represents the annual average wind speed of the corresponding region, d represents the air density at the location of the corresponding unit, D represents the average air density of the corresponding region, h represents the tower height of the corresponding unit, H represents the average tower height of all units in the corresponding region, s represents the average equivalent forced outage rate of the corresponding unit, and S represents the average equivalent forced outage rate of all units in the corresponding region;

[0180] Substitute the minimum value y l and the maximum value y r of the wind turbine capacity factor under the confidence level p into the expression respectively, and calculate to obtain the minimum guaranteed output capacity factor Y l and the maximum capacity factor Y r of the corresponding unit;

[0181] Calculate the minimum guaranteed output and maximum output of wind power under the confidence level p through the following formula:

[0182] P min = P R Y l

[0183] P max = P R Y r

[0184] In the formula: P min and P max respectively represent the minimum guaranteed output and maximum output of the corresponding unit, and P R is the rated power of the corresponding unit.

[0185] The following uses a specific case to illustrate the method for evaluating the actual capacity factor of wind power in the long term in the embodiments of the present invention.

[0186] Assume that it is necessary to evaluate the capacity factor for a large number of wind turbines in a certain area. Due to the large number of units and significant differences in conditions, separate evaluations are time-consuming and laborious, and there are significant differences between the theoretical and actual values of the capacity factor calculation.

[0187] Using the method of the embodiment of the present invention for evaluation, the process is as follows:

[0188] 1) Collect the historical power generation data of the area in the past 5 years, and classify these data according to the following table.

[0189]

[0190] In this way, 96 data subsets representing different seasons and different time periods are obtained.

[0191] On each data subset, calculate the sample value of the capacity factor according to step S1 in the embodiment of the present invention.

[0192] 2) For each data subset, calculate the frequency of occurrence of the sample value of the wind turbine capacity factor based on the sample value of the capacity factor, and draw the frequency curve of the sample value of the wind turbine capacity factor.

[0193] 3) For each data subset, based on the frequency curve of the sample value of the wind turbine capacity factor, use the rectangular method numerical integration method, and draw the leftward probability distribution curve and the rightward probability distribution curve respectively.

[0194] Among them, the approximate calculation of the leftward probability distribution calculation expression in step S3 of the embodiment of the present invention is as follows:

[0195]

[0196] In the formula, m represents the largest y among the defined N + 1 numbers that fall within the range of [0, y']. j .

[0197] The approximate calculation of the rightward probability distribution calculation expression in step S3 of the embodiment of the present invention is as follows::

[0198]

[0199] In the formula, n represents the smallest y among the defined N + 1 numbers that fall within the range of [y', 1]. j .

[0200] 4) For each data subset, with a confidence level of 0.95, on the left and right probability distribution curves, approximately calculate to obtain the minimum guaranteed output capacity factor value and the maximum capacity factor value. The process can also be simplified by directly intercepting the minimum guaranteed output capacity factor value and the maximum capacity factor value on the left and right probability distribution curves with a probability distribution of 0.95. For example, for the minimum guaranteed output capacity factor value from 2:00 to 3:00 in the spring night, it is 0.203, and the maximum capacity factor value is 0.375.

[0201] 5) When it comes to each specific unit, it is also necessary to make corrections based on the average minimum capacity factor and the maximum capacity factor of the wind turbines in this area obtained with a certain confidence level mentioned above, according to the turbine parameters, environmental conditions, grid connection conditions, etc.

[0202] According to the fact that the geographical environment conditions in this area do not vary much and the grid connection conditions are good (there are few problems such as restricted grid connection), and the main reason for the differences in the capacity factors of different wind turbines is the differences in the operation parameters of the units and other actual situations, in the setting of the adjustment factors: w 1 = 0.2, w 2 = 0.1, w 3 = 0.5, w 4 = 0.2, and sequentially correct and confirm the capacity factors of all units.

[0203] For example, the relevant parameters of this unit and the average parameters of the area are as shown in the following table, where the average wind speed and the average air density are statistics based on the sampling data from 2:00 to 3:00 in the spring night.

[0204]

[0205] Substitute the above parameters into the calculation expression to obtain, with a confidence level of 0.95:

[0206] Minimum capacity factor:

[0207]

[0208] Maximum capacity factor:

[0209]

[0210] In this way, the capacity factor evaluation values of this unit in each season and time period are obtained. For example, based on the above calculation, from 2:00 to 3:00 in the spring night, the probability that the actual capacity factor of this unit is greater than or equal to 0.209 is 0.95, the probability that the actual capacity factor of this unit is less than or equal to 0.386 is 0.95, and the probability that the actual capacity factor of this unit is in the interval [0.203, 0.375] is: 2×0.95 - 1 = 0.9.

[0211] Figure 6 Illustrates a schematic diagram of the physical structure of an electronic device, as Figure 6 shown. The electronic device may include: a processor 510, a communications interface 520, a memory 530, and a communication bus 540. Among them, the processor 510, the communications interface 520, and the memory 530 communicate with each other through the communication bus 540. The processor 510 can call the logical instructions in the memory 530 to execute the method for evaluating the actual capacity factor of wind power in the long-term scale in the embodiments of the present invention. The method includes: selecting historical wind power generation data based on correlation and converting the historical wind power generation data into sample values of the fan capacity factor; statistically calculating the occurrence frequency of the sample values of the fan capacity factor; based on the occurrence frequency of the sample values of the fan capacity factor, statistically calculating the left probability distribution and the right probability distribution respectively; based on a given confidence level, obtaining the minimum guaranteed output capacity factor and the maximum output capacity factor of wind power through the left probability distribution and the right probability distribution; based on an adjustment factor, adjusting the minimum guaranteed output capacity factor and the maximum output capacity factor of wind power to obtain the minimum guaranteed output and the maximum output of a specific unit at the corresponding confidence level.

[0212] In addition, when the logical instructions in the above-mentioned memory 530 are implemented in the form of software functional units and sold or used as an independent product, they can be stored in a computer-readable storage medium.

[0213] Another embodiment of the present invention also provides a computer program product. The computer program product includes a computer program. The computer program can be stored on a computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the method for evaluating the actual capacity factor of wind power in the long-term scale provided in the above embodiments.

[0214] The embodiments of the present invention also provide a computer-readable storage medium. The computer-readable storage medium stores at least one instruction. When the at least one instruction is executed by a processor, the method for evaluating the actual capacity factor of wind power in the long-term scale is implemented.

[0215] The computer program includes computer program code, which can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable storage medium may include: any entity or device, medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory, random access memory, electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice within the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals. For the sake of convenience of description, only the part related to the embodiments of the present invention is shown above. For the specific technical details not disclosed, please refer to the method part of the embodiments of the present invention. This computer-readable storage medium is non-transitory and can be stored in the storage devices formed by various electronic devices, and can implement the execution process recorded in the method of the embodiments of the present invention.

[0216] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) containing computer-usable program code.

[0217] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for realizing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0218] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device realizes the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0219] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are performed on the computer or other programmable apparatus to produce a computer-implemented process, and thus the instructions executed on the computer or other programmable apparatus provide steps for realizing the functions specified in one process or multiple processes and / or one block or multiple blocks. Figure 1 one process or multiple processes and / or blocks Figure 1 steps for realizing the functions specified in one block or multiple blocks.

[0220] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications or equivalent replacements can still be made to the specific embodiments of the present invention. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention shall be covered by the protection scope of the claims of the present invention.

Claims

1. A method for evaluating the actual capacity factor of wind power in the medium and long term, characterized in that: include: Selecting historical wind power generation data based on correlation, and converting the historical wind power generation data into sample values ​​of wind turbine capacity coefficients; Statistically calculate the frequency of occurrence of fan capacity factor sample values; Based on the frequency of occurrence of the sample values ​​of the wind turbine capacity factor, the left-hand probability distribution and the right-hand probability distribution are statistically calculated respectively; Based on a given confidence level, the minimum guaranteed output capacity factor and the maximum output capacity factor of wind power are obtained through left-hand probability distribution and right-hand probability distribution; Based on the adjustment factor, the minimum guaranteed output capacity coefficient and the maximum output capacity coefficient of wind power are adjusted to obtain the minimum guaranteed output and maximum output of the specific unit under the corresponding confidence level.

2. The method for evaluating the actual capacity factor of wind power in the medium and long term according to claim 1 is characterized in that: The step of selecting historical wind power generation data based on correlation includes: Collect the location range of wind turbines that need to be evaluated, including the central location and the area within a certain radius of the central location; The data collected is about the season and time period of the wind turbine that needs to be evaluated. The time period includes the set peak, valley, and normal time periods, or one or several hours in a 24-hour day; What needs to be evaluated is the capacity factor of the wind turbine under what meteorological conditions, including the wind speed range and air density range.

3. The method for evaluating the actual capacity factor of wind power in the medium and long term according to claim 1 is characterized in that: The calculation expression for converting the wind power historical power generation data into the wind turbine capacity factor sample value is as follows: Where: p i represents the i-th historical output power sample value, P i represents the rated power corresponding to the i-th historical output power sample value, x i represents the calculated sample value of the i-th wind turbine capacity factor; x i ∈[0, 1].

4. The method for evaluating the actual capacity factor of wind power in the medium and long term according to claim 1 is characterized in that: The step of statistically calculating the frequency of occurrence of fan capacity factor sample values ​​comprises: The interval [0, 1] is divided into N intervals by N+1 numbers; the N+1 numbers are defined as: In the formula, y0=0, y N =1; For all calculated fan capacity factor sample values ​​x i Sort and record the items that fall into [y j-1 ,y j ], recorded as The frequency of occurrence of fan capacity factor sample values ​​is calculated as follows: In the formula: f1(y j ) represents the fan capacity factor sample value x i Appears in [y j-1 ,y j ] interval, j and k represent the subscripts that distinguish the sample value intervals of the fan capacity factor. and Respectively represent the fan capacity factor sample value x i Appears in [y j-1 ,y j ] and [y k-1 ,y k ] interval, N represents the number of sample value intervals of all wind turbine capacity factors; Represents the sample value x of all wind turbine capacity factors i The number of With the fan capacity coefficient sample value as the horizontal axis and the fan capacity coefficient sample value occurrence frequency curve as the vertical axis, the fan capacity coefficient sample value occurrence frequency curve is drawn; f1(y j ) represents a series of discrete values, by connecting discrete points (y j ,f1(y j )) Approximately express the probability density curve of the wind turbine capacity factor; define the actual probability density of the wind turbine capacity factor as f(y), where y represents any capacity factor value between [0, 1].

5. The method for evaluating the actual capacity factor of wind power in the medium and long term according to claim 4 is characterized in that: Based on the frequency of occurrence of the fan capacity factor sample value, the left-hand probability distribution is calculated as follows: Where: y' is the capacity factor value to be evaluated, ranging from [0,1], and F1(y') represents the probability that the corresponding capacity factor is less than or equal to y'; According to the frequency of occurrence of the fan capacity factor sample value f1(y j ), and use the numerical integration method to calculate F1(y'); The right-hand probability distribution is calculated as follows: Where: F2(y') represents the probability that the corresponding capacity factor is greater than or equal to y'; In the same two-dimensional rectangular coordinate system, the capacity coefficient value is used as the horizontal coordinate, and the left-ward probability distribution and right-ward probability distribution of the capacity coefficient are used as the vertical coordinate to draw the left-ward probability distribution curve and the right-ward probability distribution curve respectively.

6. The method for evaluating the actual capacity factor of wind power in the medium and long term according to claim 5 is characterized in that: The step of obtaining the minimum guaranteed wind power output capacity factor and the maximum wind power output capacity factor through the left-hand probability distribution and the right-hand probability distribution based on the given confidence level includes: Select a confidence level p, p ≥ 0.5, and solve the following equations respectively: In the formula, y l and r They represent the minimum and maximum values ​​of the wind turbine capacity factor under confidence level p, respectively. The actual wind power capacity factor value falls within [y l ,y r ] is: 2p-1.

7. The method for evaluating the actual capacity factor of wind power in the medium and long term according to claim 6 is characterized in that: Based on the adjustment factor, the minimum guaranteed output capacity coefficient and the maximum output capacity coefficient of wind power are adjusted to obtain the expressions of the minimum guaranteed output and the maximum output of the specific unit under the corresponding confidence level as follows: Where: Y represents the capacity factor of a specific unit, y represents the average capacity factor of wind turbines in the corresponding region and season, w1, w2, w3, w4 all represent weight coefficients, and w1+w2+w3+w4=1, v represents the annual average wind speed at the location of the corresponding unit, V represents the annual average wind speed in the corresponding region, d represents the air density at the location of the corresponding unit, D represents the average air density in the corresponding region, h represents the tower height of the corresponding unit, H represents the average tower height of all units in the corresponding region, s represents the average equivalent forced outage rate of the corresponding unit, and S represents the average equivalent forced outage rate of all units in the corresponding region; The minimum value y of the wind turbine capacity factor under confidence level p l and the maximum value y r , respectively, into the expression, and calculate the minimum guaranteed output capacity coefficient Y of the corresponding unit l and the maximum capacity factor Y r ; The minimum guaranteed output and maximum output of wind power under confidence level p are calculated by the following formula: P min =P R Y l P max =P R Y r Where: P min and P max Respectively represent the minimum guaranteed output and maximum output of the corresponding unit, P R is the rated power of the corresponding unit.

8. A wind power actual capacity factor evaluation system in the medium and long term, characterized in that: include: A data acquisition module, used for selecting historical wind power generation data based on correlation, and converting the historical wind power generation data into sample values ​​of wind turbine capacity coefficient; The sample value occurrence frequency statistics module is used to calculate the frequency of occurrence of the fan capacity factor sample values; The left and right probability distribution calculation module is used to statistically calculate the left probability distribution and the right probability distribution respectively based on the frequency of occurrence of the fan capacity factor sample value; A capacity factor calculation module is used to obtain the minimum guaranteed output capacity factor and the maximum output capacity factor of wind power through left-hand probability distribution and right-hand probability distribution based on a given confidence level; The capacity factor adjustment and output calculation module is used to adjust the minimum guaranteed output capacity factor and the maximum output capacity factor of wind power, and obtain the minimum guaranteed output and maximum output of a specific unit under the corresponding confidence level.

9. The wind power actual capacity factor evaluation system in the medium and long term according to claim 8 is characterized in that: The data acquisition module stores the collected wind power historical generation data in a historical database.

10. The wind power actual capacity factor evaluation system in the medium and long term according to claim 8, characterized in that: The calculation expression for converting the historical wind power generation data collected by the data acquisition module into a sample value of the wind turbine capacity factor is as follows: Where: p i represents the i-th historical output power sample value, P i represents the rated power corresponding to the i-th historical output power sample value, x i represents the calculated sample value of the i-th wind turbine capacity factor; x i ∈[0, 1].

11. The wind power actual capacity factor evaluation system in the medium and long term according to claim 8, characterized in that: The sample value occurrence frequency statistics module divides the interval [0, 1] into N intervals with N+1 numbers on average; The N+1 number is defined as: In the formula, y0=0, y N =1; For all calculated fan capacity factor sample values ​​x i Sort and record the items that fall into [y j-1 ,y j ], recorded as The frequency of occurrence of fan capacity factor sample values ​​is calculated as follows: In the formula: f1(y j ) represents the fan capacity factor sample value x i Appears in [y j-1 ,y j ] interval, j and k represent the subscripts that distinguish the sample value intervals of the fan capacity factor. and Respectively represent the fan capacity factor sample value x i Appears in [y j-1 ,y j ] and [y k -1,y k ] interval, N represents the number of sample value intervals of all wind turbine capacity factors; Represents the sample value x of all wind turbine capacity factors i The number of With the fan capacity coefficient sample value as the horizontal axis and the fan capacity coefficient sample value occurrence frequency curve as the vertical axis; f1(y j ) represents a series of discrete values, by connecting discrete points (y j ,f1(y j )) Approximately express the probability density curve of the wind turbine capacity factor; define the actual probability density of the wind turbine capacity factor as f(y), where y represents any capacity factor value between [0, 1].

12. The wind power actual capacity factor evaluation system in the medium and long term according to claim 11, characterized in that: The left-right probability distribution calculation module calculates the left-right probability distribution based on the frequency of occurrence of the fan capacity coefficient sample value according to the following formula: Where: y' is the capacity factor value to be evaluated, ranging from [0,1], and F1(y') represents the probability that the corresponding capacity factor is less than or equal to y'; According to the frequency of occurrence of the fan capacity factor sample value f1(y j ), and use the numerical integration method to calculate F1(y'); The right-hand probability distribution is calculated as follows: Where: F2(y') represents the probability that the corresponding capacity factor is greater than or equal to y'; In the same two-dimensional rectangular coordinate system, the capacity coefficient value is used as the horizontal coordinate, and the left-ward probability distribution and right-ward probability distribution of the capacity coefficient are used as the vertical coordinate to draw the left-ward probability distribution curve and the right-ward probability distribution curve respectively.

13. The wind power actual capacity factor evaluation system in the medium and long term according to claim 12, characterized in that: The capacity factor calculation module selects a confidence level p, p≥0.5, and solves the following equations respectively: In the formula, y l and r They represent the minimum and maximum values ​​of the wind turbine capacity factor under confidence level p, respectively. The actual wind power capacity factor value falls within [y l ,y r ] is: 2p-1.

14. The wind power actual capacity factor evaluation system in the medium and long term according to claim 13, characterized in that: The capacity factor adjustment and output calculation module performs calculations according to the following expression: Where: Y represents the capacity factor of a specific unit, y represents the average capacity factor of wind turbines in the corresponding region and season, w1, w2, w3, w4 all represent weight coefficients, and w1+w2+w3+w4=1, v represents the annual average wind speed at the location of the corresponding unit, V represents the annual average wind speed in the corresponding region, d represents the air density at the location of the corresponding unit, D represents the average air density in the corresponding region, h represents the tower height of the corresponding unit, H represents the average tower height of all units in the corresponding region, s represents the average equivalent forced outage rate of the corresponding unit, and S represents the average equivalent forced outage rate of all units in the corresponding region; The minimum value y of the wind turbine capacity factor under confidence level p l and the maximum value y r , respectively, into the expression, and calculate the minimum guaranteed output capacity coefficient Y of the corresponding unit l and the maximum capacity factor Y r ; The minimum guaranteed output and maximum output of wind power under confidence level p are calculated by the following formula: P min =P R Y l P max =P R Y r Where: P min and P max Respectively represent the minimum guaranteed output and maximum output of the corresponding unit, P R is the rated power of the corresponding unit.

15. An electronic device, characterized in that: It comprises a processor and a memory, wherein the processor is used to execute a computer program stored in the memory to implement a method for evaluating the actual capacity factor of wind power in the medium and long term as claimed in any one of claims 1 to 7.

16. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by the processor, the method for evaluating the actual capacity factor of wind power in the medium and long term scales as described in any one of claims 1 to 7 is implemented.

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