Method, system and device for evaluating actual capacity factor of wind power under medium and long term scale

Abstract

The application discloses a kind of medium and long-term scale under wind power actual capacity coefficient evaluation method, system and equipment, belong to wind power prediction evaluation technical field, method includes based on correlation selection wind power historical power generation data, and wind power historical power generation data is converted into fan capacity coefficient sample value;Statistical calculation fan capacity coefficient sample value frequency of occurrence;On the basis of fan capacity coefficient sample value frequency of occurrence, respectively statistical calculation left direction probability distribution and right direction probability distribution;Based on given confidence, obtain wind power minimum guaranteed output capacity coefficient and maximum output capacity coefficient by left direction probability distribution and right direction probability distribution;Based on adjustment factor, adjust wind power minimum guaranteed output capacity coefficient and maximum output capacity coefficient, obtain the minimum guaranteed output and maximum output of specific unit under corresponding confidence degree.The application can obtain the capacity coefficient of wind power more real, evaluation result is more targeted, with comprehensiveness and convenience and other advantages.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of wind power generation prediction and evaluation, and particularly relates to a method, system and equipment for evaluating actual capacity coefficient of wind power under a medium and long term scale. BACKGROUND

[0002] With the development of new power systems, the penetration rate of new energy continues to increase, which not only improves the environmental performance of the energy system, but also brings the problem of increased randomness of the power system, which has an important impact on the safety of the power grid, the power supply, and the economy of the operation of the energy system. At present, wind power prediction can achieve a high confidence level (some application reports can reach more than 95%) in the short and ultra-short term time scale, but it is difficult to guarantee accuracy in a longer time scale. In fact, the evaluation of the reliability of wind turbine power generation capacity in the medium and long term time scale is of great significance, and is needed in the application of medium and long term power balance, new energy market trading, etc. With the development of new power systems and the continuous improvement of new energy penetration, the importance and urgency of this evaluation are also increasing.

[0003] Here, the reliable power generation capacity refers to the minimum guaranteed output and the maximum output under a certain confidence level. The reliability of wind turbine power generation capacity can be evaluated by the wind turbine power generation capacity coefficient. The so-called capacity coefficient is defined as: the ratio of the actual power generated by the wind turbine to the maximum power that can be generated if it is always running at full power in a certain time period. This definition is equivalent to the ratio of the average power generated by the wind turbine to the rated power in a certain time period.

[0004] The existing technology mainly has the following technical problems: (1) The current wind turbine power generation capacity coefficient is mainly based on wind speed and unit parameters to obtain through theoretical calculation, but there is a difference between the actual capacity coefficient and the theoretical calculation value. (2) The historical power generation data of the wind turbine is the main basis for evaluating its capacity coefficient, but the power generation of the wind turbine is affected by many factors such as terrain, season, wind speed, air density, etc. Different historical data has different correlation with the evaluated wind turbine power generation capacity coefficient. (3) Under a certain confidence level, the actual need is the interval value of the wind turbine power generation capacity coefficient, not just the minimum or maximum value, because the current wind power is not only an important power source for power supply, but also an important object for preferential consumption. (4) The evaluation of wind turbine capacity coefficient is often the average value of all wind turbines in a certain type or region, and when it comes to a specific 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 reliable power generation capacity evaluation result. SUMMARY

[0005] The present application aims at the problems in the prior art, and provides a method, system and equipment for evaluating actual capacity coefficient of wind power in a medium and long term scale, which can obtain a more real capacity coefficient of wind power, and the evaluation result is more targeted, and can obtain minimum guaranteed output capacity coefficient and maximum output capacity coefficient of wind power under a given confidence, and can also obtain minimum guaranteed output and maximum output of a specific unit under a certain confidence.

[0006] To achieve the above-mentioned purpose, the present application has the following technical solutions:

[0007] In a first aspect, a method for evaluating actual capacity coefficient of wind power in a medium and long term scale is provided, comprising:

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

[0009] Statistically calculating frequency of the wind turbine capacity coefficient sample values;

[0010] Statistically calculating leftward probability distribution and rightward probability distribution based on the frequency of the wind turbine capacity coefficient sample values;

[0011] Obtaining minimum guaranteed output capacity coefficient and maximum output capacity coefficient of wind power based on the leftward probability distribution and the rightward probability distribution under a given confidence;

[0012] Adjusting the minimum guaranteed output capacity coefficient and the maximum output capacity coefficient of wind power based on an adjustment factor, to obtain minimum guaranteed output and maximum output of a specific unit under a corresponding confidence.

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

[0014] Collecting a range of wind turbine sites to be evaluated, including a central site and a range of several kilometers around the central site;

[0015] Collecting data of seasons and time periods in which the wind turbine to be evaluated is in, wherein the time period includes set peak, peak, valley, flat time period, or one or more hours in a day;

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

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

[0018]

[0019] In the formula, p irepresents the ith historical output power sample value, P i represents the rated power corresponding to the ith historical output power sample value, x i represents the calculated ith fan capacity coefficient sample value; x i ∈[0, 1].

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

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

[0022]

[0023] In the formula, y0=0, y N =1.

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

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

[0026]

[0027] In the formula: f1(y j ) represents the frequency of the fan capacity coefficient sample value x j-1 appearing in the interval [y j , y i ], j and k represent the subscripts distinguishing the intervals of the fan capacity coefficient sample values, and respectively represent the number of times the fan capacity coefficient sample value x j-1 appears in the interval [y j , y k-1 ] and [y k , y i ], and 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 j ;

[0028] Draw a fan capacity coefficient sample value frequency curve with the fan capacity coefficient sample values as the abscissa and the frequency of the fan capacity coefficient sample values as the ordinate; f1(y j ) represents a series of discrete values, and the discrete points (y j)) the probability density curve approximating 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].

[0029] As a preferred scheme, the leftward probability distribution is calculated based on the frequency of the fan capacity coefficient sample values according to the following formula:

[0030]

[0031] In the formula, y' is the capacity coefficient value to be evaluated, and the value range is [0, 1], and F1(y') represents the probability corresponding to the capacity coefficient less than or equal to y'.

[0032] According to the frequency f1(y j ) of the fan capacity coefficient sample values, F1(y') is calculated using the numerical integration method.

[0033] The rightward probability distribution is calculated according to the following formula:

[0034]

[0035] In the formula, F2(y') represents the probability corresponding to the capacity coefficient greater than or equal to y'.

[0036] In the same two-dimensional rectangular coordinate system, the capacity coefficient value is taken as the horizontal coordinate, and the leftward probability distribution and the rightward probability distribution of the capacity coefficient are taken as the vertical coordinates. The leftward probability distribution curve and the rightward probability distribution curve are drawn respectively.

[0037] As a preferred scheme, the step of obtaining the minimum guaranteed output capacity coefficient and the maximum output capacity coefficient of the wind power based on the given confidence level through the leftward probability distribution and the rightward probability distribution comprises:

[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 coefficient under the confidence level p, and the probability of the actual capacity coefficient value of the wind power falling in [y l , y r ] is 2p-1.

[0041] As a preferred scheme, the expression of the minimum guaranteed output and the maximum output of the specific unit under the corresponding confidence level is obtained based on the adjustment factor, and the adjustment of the minimum guaranteed output capacity coefficient and the maximum output capacity coefficient of the wind power is as follows:

[0042]

[0043] In the formula, Y represents the capacity coefficient of a specific unit, y represents the average capacity coefficient of wind turbine in the corresponding region and season, w1, w2, w3 and w4 all represent weight coefficients, and w1+w2+w3+w4=1, v represents the annual average wind speed of the location where the corresponding unit is located, V represents the annual average wind speed of the corresponding region, d represents the air density of the location where the corresponding unit is located, 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, and s represents the average equivalent forced outage rate of the corresponding unit.

[0044] The minimum value y of the wind turbine capacity coefficient under the confidence level p is substituted into the expression to obtain the minimum guaranteed output capacity coefficient Y of the corresponding unit. l The maximum value y of the wind turbine capacity coefficient under the confidence level p is substituted into the expression to obtain the maximum capacity coefficient Y of the corresponding unit. r l r ;

[0045] The minimum guaranteed output and the maximum output of the wind power under the confidence level p are calculated by 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 represent the minimum guaranteed output and the maximum output of the corresponding unit, respectively, and P R is the rated power of the corresponding unit.

[0049] In a second aspect, a system for evaluating the actual capacity coefficient of wind power on a medium and long term scale is provided, comprising:

[0050] A data acquisition module is configured to select wind power historical generation data based on correlation and convert the wind power historical generation data into wind turbine capacity coefficient sample values.

[0051] A sample value frequency statistical module is configured to statistically calculate the frequency of wind turbine capacity coefficient sample values.

[0052] A left and right probability distribution calculation module is configured to statistically calculate the left and right probability distributions based on the frequency of wind turbine capacity coefficient sample values.

[0053] ​​The capacity coefficient calculation module is configured to obtain the minimum guaranteed output capacity coefficient and the maximum output capacity coefficient of wind power based on a given confidence level through the leftward probability distribution and the rightward probability distribution.

[0054] The capacity coefficient adjustment and output calculation module is configured to adjust the minimum guaranteed output capacity coefficient and the maximum output capacity coefficient of wind power to obtain the minimum guaranteed output and the maximum output of a specific unit under a corresponding confidence level.

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

[0056] As a preferred solution, the calculation expression of converting the wind power historical generation data collected by the data collection module into wind turbine capacity coefficient 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 wind turbine capacity coefficient sample value; x i ∈[0, 1].

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

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

[0061]

[0062] In the formula, y0=0, y N =1.

[0063] All the calculated wind turbine capacity coefficient sample values x i are sorted, and the number of values falling into [y j-1 , y j ] is recorded, denoted as

[0064] The frequency of the wind turbine capacity coefficient sample values is calculated according to the following formula:

[0065]

[0066] In the formula, f1(y j ) represents that the wind turbine capacity coefficient sample value x i appears in [y j-1 , y jThe frequency of the interval, j and k represent the subscripts that distinguish the interval of sample values ​​for the wind turbine capacity coefficient. and These represent the sample values ​​of the wind turbine capacity coefficient, x and x, respectively. i Appears in [y j-1 y j ] and [y k-1 y k The number of intervals is N, where N represents the total number of intervals for all wind turbine capacity coefficient sample values. This represents the sample values ​​of the capacity coefficient for all wind turbines, x. i The number of;

[0067] Plot a curve showing the frequency of wind turbine capacity coefficient sample values ​​on the x-axis and the frequency of these sample values ​​on the y-axis; f1(y j ) represents a series of discrete values, connected by discrete points (y j ,f1(y j The probability density curve approximately represents the capacity coefficient of a wind turbine; the probability density of the actual wind turbine capacity coefficient is defined as f(y), where y represents any capacity coefficient value between [0, 1].

[0068] As a preferred embodiment, the left-right probability distribution calculation module calculates the left-right probability distribution based on the frequency of occurrence of the wind turbine capacity coefficient sample values ​​using the following formula:

[0069]

[0070] In the formula: y' is the capacity coefficient value to be evaluated, and its value range is [0,1]. F1(y') represents the probability that the corresponding capacity coefficient is less than or equal to y'.

[0071] Based on the frequency of occurrence f1(y) of the sample values ​​of the wind turbine capacity coefficient j F1(y') is calculated using numerical integration.

[0072] Calculate the rightward probability distribution using the following formula:

[0073]

[0074] In the formula: F2(y') represents the probability that the corresponding capacity coefficient is greater than or equal to y';

[0075] In the same two-dimensional rectangular coordinate system, the capacity coefficient is plotted on the x-axis, and the left and right probability distributions of the capacity coefficient are plotted on the y-axis, respectively, to draw the left probability distribution curve and the right probability distribution curve.

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

[0077]

[0078] wherein y l and y r represent the minimum and maximum values of the wind turbine capacity factor at a confidence level p, respectively, and the probability that the actual capacity factor value of the wind power falls in the range [y l , y r ] is 2p-1.

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

[0080]

[0081] wherein Y represents the capacity factor of a specific unit, y represents the average capacity factor of the wind turbine in the corresponding region and season, w1, w2, w3, and 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, and s represents the average equivalent forced outage rate of the corresponding unit, S represents the average equivalent forced outage rate of all units in the corresponding region;

[0082] The minimum value y l and the maximum value y r of the wind turbine capacity factor at a confidence level p are substituted into the expression, respectively, to calculate the minimum guaranteed power capacity factor Y l and the maximum capacity factor Y r of the corresponding unit;

[0083] The minimum guaranteed power and the maximum power of the wind power at a confidence level p are calculated by the following expression:

[0084] P min = PY R Y l

[0085] P max = PY R Y r

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

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

[0088] In a fourth aspect, a computer readable storage medium is provided, the computer readable storage medium storing at least one instruction, the at least one instruction being configured to implement the method for evaluating actual capacity factor of wind power under medium-long term scale when executed by a processor.

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

[0090] The application provides a solution for evaluating the reliability generation capacity of a fan on a medium and long time scale, and has the advantages of objectivity, pertinence, comprehensiveness, and convenience. By collecting historical wind power generation data, converting the historical wind power generation data into fan capacity coefficient sample values, statistically calculating the frequency of the fan capacity coefficient sample values, and respectively statistically calculating the left and right probability distributions based on the frequency of the fan capacity coefficient sample values, the minimum guaranteed output capacity coefficient and the maximum output capacity coefficient of the wind power are obtained through the left and right probability distributions under a given confidence level. For a specific unit, the minimum guaranteed output capacity coefficient and the maximum output capacity coefficient of the wind power are adjusted based on an adjustment factor, and the minimum guaranteed output and the maximum output of the specific unit under the corresponding confidence level are obtained. As for objectivity, the application is evaluated based on actual historical data of the wind power, avoiding significant differences between the actual capacity coefficient and the theoretical calculation value. Since the technology is progressive, the progress of the technology is gradually reflected in the wind power output data, and is reflected in the historical data to some extent, so the application can obtain a more realistic capacity coefficient of the wind power. As for pertinence, the evaluation results of the evaluation method of the application are based on selected data, and the selected dimensions cover many factors such as geographical location, season, time period, and weather conditions, so that the evaluation results are more targeted. As for comprehensiveness, since the wind power is an important support for power supply during the peak power consumption period and an object for guaranteeing consumption during the off-peak power consumption period, the minimum guaranteed output and the maximum output of the wind power are both needed in practical applications. The application provides a minimum guaranteed output capacity coefficient and a maximum output capacity coefficient evaluation method, and different confidence requirements can be selected according to application needs. As for convenience, since there are many fans, each fan needs to be evaluated separately, which requires a large amount of work and high data requirements. The application can actually evaluate a single fan or several fans, or evaluate all the fans in a region uniformly to obtain an average capacity coefficient, and then adjust the average capacity coefficient through an adjustment factor to obtain differentiated evaluation results for each fan. In summary, the wind power actual capacity coefficient evaluation method in the application can provide important support parameters for power grid regulation and control, and can provide 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 aspect to the fourth aspect described above can be referred to the related description in the first aspect described above, and will not be repeated here. BRIEF DESCRIPTION OF DRAWINGS

[0092] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description only constitute some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort.

[0093] Figure 1 The flow chart of the method for evaluating the actual capacity coefficient of wind power in the long-term scale in the embodiments of the present application;

[0094] Figure 2 The schematic diagram of the frequency curve of the sample value of the capacity coefficient of the wind turbine in the embodiments of the present application;

[0095] Figure 3 The schematic diagram of the leftward probability distribution curve and the rightward probability distribution curve in the embodiments of the present application;

[0096] Figure 4 The schematic diagram of obtaining the minimum guaranteed output capacity coefficient and the maximum output capacity coefficient of wind power under a given confidence in the embodiments of the present application;

[0097] Figure 5 The schematic diagram of the system structure for evaluating the actual capacity coefficient of wind power in the long-term scale in the embodiments of the present application;

[0098] Figure 6 The schematic diagram of the physical structure of the electronic device in the embodiments of the present application. DETAILED DESCRIPTION

[0099] In the following description, specific details are set forth in order to provide a thorough understanding of the embodiments of the present application for purposes of explanation and not limitation. It will be obvious to those skilled in the art that other embodiments can be practiced apart from these specific details. In some instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.

[0100] Please refer to Figure 1 The embodiments of the present application propose a method for evaluating the actual capacity coefficient of wind power in the long-term scale, which comprises the following steps:

[0101] S1, selecting wind power historical generation data based on correlation, and converting the wind power historical generation data into sample values of the capacity coefficient of wind turbines;

[0102] S2, statistically calculating the frequency of the sample values of the capacity coefficient of wind turbines;

[0103] S3, respectively statistically calculating leftward probability distribution and rightward probability distribution based on the frequency of the sample values of the capacity coefficient of wind turbines;

[0104] S4, based on the given confidence, obtaining the minimum guaranteed output capacity coefficient and the maximum output capacity coefficient of the wind power through the leftward probability distribution and the rightward probability distribution;

[0105] S5, based on the adjustment factor, adjusting the minimum guaranteed output capacity coefficient and the maximum output capacity coefficient of the wind power to obtain the minimum guaranteed output and the maximum output of the specific unit under the corresponding confidence.

[0106] In a possible implementation, step S1 collects the range of wind power unit sites that need to be evaluated, where the range of sites can be the central site and the range within a certain number of kilometers; collects the data of the season and time period of the wind turbine that needs to be evaluated, where the time period specifically refers to the set peak, peak, valley, flat period, or one or several hours in a day; collects the capacity coefficient of the wind turbine under what weather conditions, including the wind speed range and air density range.

[0107] Further, step S1 converts the wind power historical power generation data into the calculation expression of the wind turbine capacity coefficient sample value 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 coefficient sample value; it is assumed that all historical output power sample values must be greater than or equal to 0 and less than or equal to the corresponding rated power, otherwise it is not counted in the statistics, that is, x i ∈[0, 1].

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

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

[0112]

[0113] In the formula, y0=0, y N =1.

[0114] All calculated wind turbine capacity coefficient sample values x i are sorted, and the number of values falling into [y j-1 , y j ] is recorded, denoted as

[0115] The frequency of the wind turbine capacity coefficient sample value is calculated as follows:

[0116]

[0117] wherein f1(y j ) represents the frequency of fan capacity coefficient sample value x i appearing in the interval [y j-1 , y j ], j and k represent the index distinguishing the interval of fan capacity coefficient sample value, and represent the number of times of fan capacity coefficient sample value x i appearing in the interval [y j-1 , y j ] and [y k-1 , y k ] respectively, and N represents the number of all intervals of fan capacity coefficient sample value; represents the number of all fan capacity coefficient sample values x i ;

[0118] A fan capacity coefficient sample value appearance frequency curve is drawn with fan capacity coefficient sample value as the horizontal coordinate and the frequency of fan capacity coefficient sample value as the vertical coordinate. The specific shape depends on the data, Figure 2 An example is given. f1(y j ) represents a series of discrete values, and the probability density curve of fan capacity coefficient is approximately expressed by connecting the discrete points (y j , f1(y j )). The actual probability density of fan capacity coefficient is defined as f(y), and y represents an arbitrary capacity coefficient value between 0 and 1.

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

[0120]

[0121] wherein y' is the capacity coefficient value to be evaluated, the value range is [0, 1], and F1(y') represents the probability corresponding to the capacity coefficient less than or equal to y'.

[0122] F1(y') is calculated using the numerical integration method according to the frequency f1(y j ) of fan capacity coefficient sample value. The numerical integration method can be selected from the trapezoidal method, the rectangular method and the Simpson method.

[0123] The right probability distribution is calculated according to the following formula:

[0124]

[0125] wherein F2(y') represents the probability corresponding to the capacity coefficient greater than or equal to y'.

[0126] In the same two-dimensional rectangular coordinate system, the leftward probability distribution curve and the rightward probability distribution curve are drawn respectively with the capacity coefficient value as the horizontal coordinate and the leftward probability distribution and the rightward probability distribution of the capacity coefficient as the vertical coordinate. The specific shape depends on the data, Figure 3 An example is given.

[0127] In a possible implementation, referring to Figure 4 , a confidence level p is selected in step S3, p≥0.5, and the intersection points of the straight line with the probability distribution p on the leftward probability distribution curve and the rightward probability distribution curve are respectively corresponding to the horizontal coordinate values of the leftward probability distribution curve and the rightward probability distribution curve.

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

[0129] The calculation expression is as follows:

[0130]

[0131] The probability that the actual capacity coefficient value of the wind power falls in [y l , y r ] is 2p-1.

[0132] In a possible implementation, step S5 adjusts the minimum guaranteed output capacity coefficient and the maximum output capacity coefficient of the wind power based on the adjustment factor, to obtain the minimum guaranteed output and the maximum output of a specific unit under the corresponding confidence level. When the capacity coefficient of a specific unit needs to be evaluated, it is assumed that the minimum guaranteed output capacity coefficient and the maximum capacity coefficient of the unit are Y l and Y r .

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

[0134] If the data selected in step S1 is the historical data of the wind power of a certain region, a certain season, or a certain period, the values of y l and y rThe value represents the average situation of the region, when applied to a specific unit, the obtained capacity coefficient value also needs to be adjusted according to the following expression:

[0135]

[0136] In the formula: Y represents the capacity coefficient of a specific unit, y represents the average capacity coefficient of the wind turbine in the corresponding region and season, w1, w2, w3 and w4 all represent weight coefficients, and w1+w2+w3+w4=1, v represents the annual average wind speed of the corresponding unit, V represents the annual average wind speed of the corresponding region, d represents the air density 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, and s represents the average equivalent forced outage rate of the corresponding unit.

[0137] The minimum value y of the wind turbine capacity coefficient value under the confidence level p l and the maximum value y r are substituted into the expression respectively to obtain the minimum guaranteed output capacity coefficient Y l and the maximum capacity coefficient Y r of the corresponding unit.

[0138] The minimum guaranteed output and the maximum output of the wind power under the confidence level p are calculated by 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 represent the minimum guaranteed output and the maximum output of the corresponding unit respectively, and P R is the rated power of the corresponding unit.

[0142] Please refer to Figure 5 , the embodiment of the present application also provides a wind power actual capacity coefficient evaluation system under a medium and long term scale, comprising:

[0143] A data acquisition module is used for selecting wind power historical power generation data based on correlation, and converting the wind power historical power generation data into wind turbine capacity coefficient sample values;

[0144] A sample value frequency statistical module is used for statistically calculating the frequency of wind turbine capacity coefficient sample values;

[0145] The left-to-right probability distribution calculation module is configured to calculate the left-to-right probability distribution and the right-to-left probability distribution based on the frequency of the fan capacity coefficient sample values.

[0146] The capacity coefficient calculation module is configured to obtain the minimum guaranteed output capacity coefficient and the maximum output capacity coefficient of the wind power based on the given confidence level by using the left-to-right probability distribution and the right-to-left probability distribution.

[0147] The capacity coefficient adjustment and output calculation module is configured to adjust the minimum guaranteed output capacity coefficient and the maximum output capacity coefficient of the wind power to obtain the minimum guaranteed output and the maximum output of the specific unit under the corresponding confidence level.

[0148] In a possible implementation, the data acquisition module stores the collected wind power historical generation data in a historical database, and the embodiment of the present application uses a MySql open source database as the historical database to save the relevant data.

[0149] In a possible implementation, the long-term scale wind power actual capacity coefficient evaluation system of the embodiment of the present application receives the evaluation requirements through the man-machine interaction module, confirms the relevant information of the wind turbine to be evaluated, and converts the evaluation requirements into evaluation demands, which are sent to the data selection module, the capacity coefficient calculation module and the capacity coefficient adjustment and output calculation module.

[0150] The data selection module selects the historical data required for this evaluation from the wind power related historical database by using SQL statements, and forwards the historical data to the capacity coefficient calculation module.

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

[0152] The capacity coefficient adjustment and output calculation module adjusts the calculation result of the capacity coefficient calculation module according to the unit information sent by the man-machine interaction module, and forwards the result to the man-machine interaction module.

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

[0154] In a possible implementation, the calculation expression of the wind turbine capacity coefficient sample value converted from the wind power historical generation data collected by the data acquisition module 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 irepresents the calculated i-th fan capacity coefficient sample value; x i ∈ [0, 1].

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

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

[0159]

[0160] In the formula, y0=0, y N =1.

[0161] All the calculated fan capacity coefficient sample values x i are sorted, and the number of values falling into [y j-1 , y j ] is recorded, denoted as

[0162] The fan capacity coefficient sample value frequency is calculated according to the following formula:

[0163]

[0164] In the formula, f1(y j ) represents the frequency of the fan capacity coefficient sample value x i appearing in the interval [y j-1 , y j ], j and k represent the subscripts 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 the intervals [y j-1 , y j ] and [y k-1 , y k ], and N represents the number of all fan capacity coefficient sample value intervals; represents the number of all fan capacity coefficient sample values x i ;

[0165] The fan capacity coefficient sample value frequency curve is drawn with the fan capacity coefficient sample value as the horizontal coordinate and the fan capacity coefficient sample value frequency as the vertical coordinate. f1(y j ) represents a series of discrete values, and the probability density curve of the fan capacity coefficient is approximately expressed by connecting the discrete points (y j , f1(y j )). The actual probability density of the fan capacity coefficient is defined as f(y), and y represents any capacity coefficient value between 0 and 1.

[0166] In a possible implementation, the leftward probability distribution calculation module calculates the leftward probability distribution based on the frequency of the fan capacity coefficient sample values according to the following formula:

[0167]

[0168] In the formula, y' is the capacity coefficient value to be evaluated, and the value range is [0, 1], and F1(y') represents the probability corresponding to the capacity coefficient less than or equal to y'.

[0169] According to the frequency f1(y j ) of the fan capacity coefficient sample values, the F1(y') is calculated by using the numerical integration method.

[0170] The rightward probability distribution is calculated according to the following formula:

[0171]

[0172] In the formula, F2(y') represents the probability corresponding to the capacity coefficient greater than or equal to y'.

[0173] In the same two-dimensional rectangular coordinate system, the leftward probability distribution curve and the rightward probability distribution curve are drawn respectively by taking the capacity coefficient value as the horizontal coordinate and taking the leftward probability distribution and the rightward probability distribution of the capacity coefficient as the vertical coordinate.

[0174] In a possible implementation, the capacity coefficient calculation module selects a confidence degree 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 coefficient under the confidence degree p, and the probability of the actual capacity coefficient value of the wind power falling in the range of [y l , y r ] is 2p-1.

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

[0178]

[0179] In the formula: Y represents the capacity coefficient of a specific unit, y represents the average capacity coefficient of wind turbines in the corresponding region and season, w1, w2, w3, and w4 all represent weighting 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;

[0180] The minimum value y of the fan capacity coefficient under confidence level p. l and maximum value y r Substituting the values ​​into the expressions, the minimum guaranteed output capacity coefficient Y for the corresponding units is calculated. l and maximum capacity factor Y r ;

[0181] The minimum guaranteed output and maximum output of wind power at confidence level p are calculated using 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 P represents the minimum guaranteed output and maximum output of the corresponding generator unit, respectively. R This refers to the rated power of the corresponding unit.

[0185] The following specific examples illustrate the method for evaluating the actual wind power capacity factor over a long-term timescale in this invention.

[0186] Suppose we need to assess the capacity factor of numerous wind turbines in a certain region. Due to the large number of units and their significant differences in conditions, conducting individual assessments would be time-consuming and labor-intensive, and the theoretical and actual values ​​of the calculated capacity factor would differ considerably.

[0187] The evaluation is performed using the method described in this embodiment of the invention, and the process is as follows:

[0188] 1) Collect historical power generation data for the region over the past 5 years and classify the data according to the table below.

[0189]

[0190] This yields 96 data subsets representing different seasons and time periods.

[0191] Respectively on each data subset, the calculation of the capacity coefficient sample value is performed according to step S1 in the embodiment of the present application.

[0192] 2) Respectively on each data subset, the calculation of the frequency of the fan capacity coefficient sample value is performed according to the capacity coefficient sample value, and the frequency curve of the fan capacity coefficient sample value is drawn.

[0193] 3) Respectively on each data subset, the leftward probability distribution curve and the rightward probability distribution curve are drawn by using the rectangular numerical integration method on the basis of the frequency curve of the fan capacity coefficient sample value.

[0194] In the embodiment of the present application, the leftward probability distribution calculation expression in step S3 is approximately calculated as follows:

[0195]

[0196] In the formula, m represents the maximum y falling in the range of [0, y'] among the defined N+1 numbers j .

[0197] In the embodiment of the present application, the rightward probability distribution calculation expression in step S3 is approximately calculated as follows:

[0198]

[0199] In the formula, n represents the minimum y falling in the range of [y', 1] among the defined N+1 numbers j .

[0200] 4) Respectively on each data subset, the minimum guaranteed output capacity coefficient value and the maximum capacity coefficient value are solved by using the approximate calculation on the leftward probability distribution curve and the rightward probability distribution curve with the confidence of 0.95. The minimum guaranteed output capacity coefficient value and the maximum capacity coefficient value can also be directly cut off on the leftward probability distribution curve and the rightward probability distribution curve with the probability distribution of 0.95, for example, the minimum guaranteed output capacity coefficient value of the fan in the spring night from 2:00 to 3:00 is 0.203, and the maximum capacity coefficient value is 0.375.

[0201] 5) When each unit is specified, the minimum capacity coefficient and the maximum capacity coefficient of the fan in the region are also needed to be corrected according to the fan parameters, the environmental conditions, the grid-connected conditions and the like on the basis of the average minimum capacity coefficient and the average maximum capacity coefficient of the fan in the region obtained with a certain confidence.

[0202] According to the fact that the geographical environment conditions of the region are not different, the grid-connection conditions are good (there are less problems of limiting grid-connection, etc.), and the main cause of the difference of the different wind turbine capacity coefficients is the difference of the unit operation parameters, etc., the adjustment factors are set as follows: w1=0.2, w2=0.1, w3=0.5, w4=0.2, and the capacity coefficients of all units are corrected and confirmed in turn.

[0203] For example, the relevant parameters of the unit and the average parameters of the region are as follows in the table below, in which the average wind speed and the average air density are based on the sampling data of 2:00-3:00 in the spring night.

[0204]

[0205] The above parameters are substituted into the calculation expression to obtain the following under the confidence of 0.95:

[0206] The minimum capacity coefficient is:

[0207]

[0208] The maximum capacity coefficient is:

[0209]

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

[0211] Figure 6 An example of an entity structure diagram of an electronic device is shown in FIG. 1. Figure 6As shown, the electronic device can include a processor 510, a communications interface 520, a memory 530, and a communications bus 540, wherein the processor 510, the communications interface 520, and the memory 530 complete mutual communication through the communications bus 540. The processor 510 can invoke a logic instruction in the memory 530 to execute the long-term scale wind power actual capacity coefficient evaluation method in the embodiment of the application, and the method includes: selecting wind power historical power generation data based on correlation, and converting the wind power historical power generation data into wind turbine capacity coefficient sample values; statistically calculating the wind turbine capacity coefficient sample value frequency; respectively statistically calculating the left probability distribution and the right probability distribution based on the wind turbine capacity coefficient sample value frequency; obtaining the minimum guaranteed output capacity coefficient and the maximum output capacity coefficient of the wind power based on the given confidence level through the left probability distribution and the right probability distribution; and adjusting the minimum guaranteed output capacity coefficient and the maximum output capacity coefficient of the wind power based on the adjustment factor to obtain the minimum guaranteed output and the maximum output of the specific unit under the corresponding confidence level.

[0212] In addition, the logic instruction in the memory 530 described above can be realized in the form of a software functional unit and sold or used as an independent product, and can be stored in a computer readable storage medium.

[0213] Another embodiment of the application further 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, and the computer program is executed by a processor, so that a computer can execute the long-term scale wind power actual capacity coefficient evaluation method provided by the above-mentioned embodiment.

[0214] The embodiment of the application further provides a computer readable storage medium, the computer readable storage medium stores at least one instruction, and the at least one instruction is executed by a processor to realize the long-term scale wind power actual capacity coefficient evaluation method.

[0215] The computer program includes computer program code, which can be in the form of source code, object code, executable code, or some intermediate form. The computer readable storage medium can include any entity or device capable of carrying the computer program code, media, U disk, 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 computer readable medium contains contents which can be appropriately added or reduced according to the requirements of legislation and patent practice in 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 convenience of description, only the part related to the embodiment of the present application is shown above, and the specific technical details not disclosed are referred to the method part of the embodiment of the present application. The computer readable storage medium is non-transitory and can be stored in the storage device formed by various electronic devices, and can realize the execution process described in the method of the embodiment of the present application.

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

[0217] The present application is described with reference to flowcharts and / or block diagrams of the method, device (system) and computer program product according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be realized 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 device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one or more flows and / or blocks. Figure 1 The device for realizing the functions specified in one or more flows and / or blocks.

[0218] These computer program instructions can also be stored in a computer readable memory capable of guiding the computer or other programmable data processing device to work in a specific way, so that the instructions stored in the computer readable memory produce a product including instruction device, which realizes the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one or more flows and / or blocks. Figure 1 The device for realizing the functions specified in one or more flows and / or blocks.

[0219] These computer program instructions can also be loaded into computer or other programmable data processing devices, so that a series of operational steps are performed on the computer or other programmable data processing devices to generate computer-implemented processes, thus the instructions executed on the computer or other programmable data processing devices provide processes for implementing the functions specified in the flowcharts Figure 1 one flow or multiple flows and / or the functions specified in one block or multiple blocks in the flowcharts. Figure 1 one flow or multiple flows and / or the functions specified in one block or multiple blocks in the flowcharts.

[0220] Finally, it should be noted that: the above examples are only used to illustrate the technical solutions of the present application, but not to limit it, although the present application has been described in detail with reference to the above examples, those skilled in the art should understand that: the specific embodiments of the present application can still be modified or replaced by the equivalent, without departing from the spirit and scope of the present application, any modification or equivalent replacement, which should be covered within the protection scope of the claims of the present application.

Claims

1. A method for evaluating the actual capacity factor of wind power on a medium- to long-term timescale, characterized in that, include: Historical wind power generation data was selected based on correlation, and the historical wind power generation data was converted into sample values ​​of wind turbine capacity coefficient; Statistical calculation of the frequency of sample values ​​for wind turbine capacity coefficient; Based on the frequency of occurrence of sample values ​​of wind turbine capacity coefficient, the left-hand probability distribution and the right-hand probability distribution are calculated respectively. Based on a given confidence level, the minimum guaranteed output capacity coefficient and the maximum output capacity coefficient 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 a specific unit at the corresponding confidence level.

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

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

4. The method for evaluating the actual capacity factor of wind power on a medium- to long-term timescale according to claim 1, characterized in that, The step of statistically calculating the frequency of sample values ​​for wind turbine capacity coefficient includes: Divide the interval [0, 1] into N intervals using N+1 numbers; the N+1 numbers are defined as follows: In the formula, y0=0, y N =1; For all calculated sample values ​​of wind turbine capacity coefficient x i Organize and record the entries that fall into [y] j-1 y j The number of ] is denoted as The frequency of sample values ​​for the fan capacity coefficient is calculated using the following formula: In the formula: f1(y j () represents the sample value of the wind turbine capacity coefficient x i Appears in [y j-1 y j The frequency of the interval, j and k represent the subscripts that distinguish the interval of sample values ​​for the wind turbine capacity coefficient. and These represent the sample values ​​of the wind turbine capacity coefficient, x and x, respectively. i Appears in [y j-1 y j ] and [y k-1 y k The number of intervals is N, where N represents the total number of intervals for all wind turbine capacity coefficient sample values. This represents the sample values ​​of the capacity coefficient for all wind turbines, x. i The number of; Plot a curve showing the frequency of wind turbine capacity coefficient sample values ​​on the x-axis and the frequency of these sample values ​​on the y-axis; f1(y j ) represents a series of discrete values, connected by discrete points (y j ,f1(y j The probability density curve approximately represents the capacity coefficient of a wind turbine; the probability density of the actual wind turbine capacity coefficient is defined as f(y), where y represents any capacity coefficient value between [0, 1].

5. The method for evaluating the actual capacity factor of wind power on a medium- to long-term timescale according to claim 4, characterized in that, Based on the frequency of occurrence of sample values ​​for wind turbine capacity coefficient, the left-hand probability distribution is calculated using the following formula: In the formula: y' is the capacity coefficient value to be evaluated, and its value range is [0,1]. F1(y') represents the probability that the corresponding capacity coefficient is less than or equal to y'. Based on the frequency of occurrence f1(y) of the sample values ​​of the wind turbine capacity coefficient j F1(y') is calculated using numerical integration. Calculate the rightward probability distribution using the following formula: In the formula: F2(y') represents the probability that the corresponding capacity coefficient is greater than or equal to y'; In the same two-dimensional rectangular coordinate system, with the capacity coefficient value as the abscissa and the left and right probability distributions of the capacity coefficient as the ordinates, the left probability distribution curve and the right probability distribution curve are plotted respectively.

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

7. The method for evaluating the actual capacity factor of wind power on a medium- to long-term timescale according to claim 6, characterized in that, The expressions for adjusting the minimum guaranteed output capacity coefficient and maximum output capacity coefficient of wind power based on the adjustment factor to obtain the minimum guaranteed output and maximum output of a specific unit at the corresponding confidence level are as follows: In the formula: Y represents the capacity coefficient of a specific unit, y represents the average capacity coefficient of wind turbines in the corresponding region and season, w1, w2, w3, and w4 all represent weighting 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 fan capacity coefficient under confidence level p. l and maximum value y r Substituting the values ​​into the expressions, the minimum guaranteed output capacity coefficient Y for the corresponding units is calculated. l and maximum capacity factor Y r ; The minimum guaranteed output and maximum output of wind power at confidence level p are calculated using the following formula: P min =P R Y l P max =P R Y r In the formula: P min and P max P represents the minimum guaranteed output and maximum output of the corresponding generator unit, respectively. R This refers to the rated power of the corresponding unit.

8. A system for evaluating the actual capacity factor of wind power on a medium- to long-term timescale, characterized in that, include: The data acquisition module is used to select historical wind power generation data based on correlation and convert the historical wind power generation data into sample values ​​of wind turbine capacity coefficient; The sample value frequency statistics module is used to statistically calculate the frequency of sample values ​​for the wind turbine capacity coefficient. The left and right probability distribution calculation module is used to statistically calculate the left and right probability distributions respectively based on the frequency of occurrence of sample values ​​of wind turbine capacity coefficient; The capacity factor calculation module is used to obtain the minimum guaranteed output capacity factor and the maximum output capacity factor of wind power based on a given confidence level through left-hand probability distribution and right-hand probability distribution. 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 to 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 on a medium- to long-term timescale according to claim 8, characterized in that, The data acquisition module stores the collected historical wind power generation data in a historical database.

10. The wind power actual capacity factor evaluation system on a medium- to long-term timescale 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 sample values ​​of wind turbine capacity coefficient is as follows: In the formula: p i P represents the i-th historical power output sample value. i x represents the rated power corresponding to the i-th historical power output sample value. i x represents the calculated sample value of the i-th wind turbine capacity coefficient; i ∈[0,1].

11. The wind power actual capacity factor evaluation system on a medium- to long-term timescale according to claim 8, characterized in that, The sample value frequency statistics module divides the [0, 1] interval into N intervals with N+1 numbers on average; The N+1 numbers are defined as follows: In the formula, y0=0, y N =1; For all calculated sample values ​​of wind turbine capacity coefficient x i Organize and record the entries that fall into [y] j-1 y j The number of ] is denoted as The frequency of sample values ​​for the fan capacity coefficient is calculated using the following formula: In the formula: f1(y j () represents the sample value of the wind turbine capacity coefficient x i Appears in [y j-1 y j The frequency of the interval, j and k represent the subscripts that distinguish the interval of sample values ​​for the wind turbine capacity coefficient. and These represent the sample values ​​of the wind turbine capacity coefficient, x and x, respectively. i Appears in [y j-1 y j ] and [y k -1, y k The number of intervals is N, where N represents the total number of intervals for all wind turbine capacity coefficient sample values. This represents the sample values ​​of the capacity coefficient for all wind turbines, x. i The number of; Plot a curve showing the frequency of wind turbine capacity coefficient sample values ​​with the sample values ​​of wind turbine capacity coefficient on the x-axis and the frequency of occurrence of wind turbine capacity coefficient sample values ​​on the y-axis. f1(y j ) represents a series of discrete values, connected by discrete points (y j ,f1(y j The probability density curve approximately represents the capacity coefficient of a wind turbine; the probability density of the actual wind turbine capacity coefficient is defined as f(y), where y represents any capacity coefficient value between [0, 1].

12. The wind power actual capacity factor evaluation system on a medium- to long-term timescale 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 wind turbine capacity coefficient sample values ​​using the following formula: In the formula: y' is the capacity coefficient value to be evaluated, and its value range is [0,1]. F1(y') represents the probability that the corresponding capacity coefficient is less than or equal to y'. Based on the frequency of occurrence f1(y) of the sample values ​​of the wind turbine capacity coefficient j F1(y') is calculated using numerical integration. Calculate the rightward probability distribution using the following formula: In the formula: F2(y') represents the probability that the corresponding capacity coefficient is greater than or equal to y'; In the same two-dimensional rectangular coordinate system, with the capacity coefficient value as the abscissa and the left and right probability distributions of the capacity coefficient as the ordinates, the left probability distribution curve and the right probability distribution curve are plotted respectively.

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

14. The wind power actual capacity factor evaluation system on a medium- to long-term timescale according to claim 13, characterized in that, The capacity coefficient adjustment and output calculation module calculates according to the following expression: In the formula: Y represents the capacity coefficient of a specific unit, y represents the average capacity coefficient of wind turbines in the corresponding region and season, w1, w2, w3, and w4 all represent weighting 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 fan capacity coefficient under confidence level p. l and maximum value y r Substituting the values ​​into the expressions, the minimum guaranteed output capacity coefficient Y for the corresponding units is calculated. l and maximum capacity factor Y r ; The minimum guaranteed output and maximum output of wind power at confidence level p are calculated using the following formula: P min =P R Y l P max =P R Y r In the formula: P min and P max P represents the minimum guaranteed output and maximum output of the corresponding generator unit, respectively. R This refers to the rated power of the corresponding unit.

15. An electronic device, characterized in that, It includes a processor and a memory, the processor being used to execute a computer program stored in the memory to implement the method for evaluating the actual capacity factor of wind power on a medium- to long-term scale as described 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, which, when executed by a processor, implements the method for evaluating the actual capacity factor of wind power on a medium- to long-term scale as described in any one of claims 1 to 7.

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