Power system planning method and device considering fan wake effect, terminal equipment and storage medium

Abstract

The invention discloses a power system planning method and device considering a fan wake effect, terminal equipment and a storage medium, and belongs to the field of power systems. The method comprises the steps of performing prediction according to historical meteorological data, historical load data, a preset highest temperature threshold value and a preset lowest temperature threshold value to obtain predicted meteorological data and predicted load data of a target planning time period; according to the fan coordinates, the predicted meteorological data and the fan parameters, wake flow influence areas of the fans and predicted wind speeds of the fans are determined; according to a fan wake flow shielding logic matrix constructed by the wake flow influence area and the fan parameters, the fan parameters and the ground roughness, the predicted wind speed of each fan is corrected, and the corrected wind speed of the fan is obtained; and planning the to-be-planned power system according to the wind power plant output and the predicted load data in the target planning time period obtained by calculating the corrected wind speed of the fan, the predicted meteorological data and the fan parameters, and by implementing the method, the problem of deviation of power system planning due to the wake flow of the fan is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a power system planning method, apparatus, terminal equipment and storage medium taking into account wind turbine wake effects. Background Art

[0002] In recent years, with the rapid development of wind power technology, the scale and number of wind farms have continued to increase, and their share in the power system has also gradually increased. Against the backdrop of the increasing proportion of renewable energy, wind power, as an important clean energy source, faces significant impacts on the stability and reliability of its output, particularly due to extreme weather. Wind farm output depends not only on meteorological conditions such as wind speed and direction, but also on the wake effect of wind turbines. The wake effect refers to the fact that when a wind turbine captures wind energy, wind speed in the downstream area is reduced due to energy losses, significantly affecting the power generation efficiency of the downstream wind turbines. Current power system planning focuses on wind power output simulations for single wind farms or specific regions, with insufficient research on the impact of wake effects on power systems. In densely populated wind farms, variations in wind speed and direction further exacerbate the complexity of wakes, affecting their propagation and intensity, thereby reducing the accuracy of wind turbine power generation and leading to deviations in power system planning. Summary of the Invention

[0003] The embodiments of the present invention provide a power system planning method, apparatus, terminal device and storage medium that consider the wake effect of wind turbines, which can effectively solve the problem of low accuracy of power system planning in the prior art due to the influence of the wake effect of wind turbines in wind farms.

[0004] An embodiment of the present invention provides a power system planning method considering wind turbine wake effects, including:

[0005] Obtain historical meteorological data and load data for each hour of the historical year, wind turbine parameters and coordinates of the wind farm in the planned power system, and ground roughness;

[0006] Perform regression prediction based on the historical meteorological data, the historical load data, a preset maximum temperature threshold, and a preset minimum temperature threshold to obtain predicted meteorological data and predicted load data for the target planning period;

[0007] Determining the wake impact area of ​​each wind turbine and the predicted wind speed at each wind turbine based on the wind turbine coordinates, the predicted meteorological data, and the wind turbine parameters;

[0008] Constructing a wind turbine wake shielding logic matrix based on the wake impact area of ​​each wind turbine and the wind turbine parameters;

[0009] Correcting the predicted wind speed at each wind turbine according to the wind turbine wake shielding logic matrix, the wind turbine parameters, and the ground roughness to obtain a corrected wind speed for the wind turbine;

[0010] Calculating the wind farm output within a target planning period based on the wind turbine corrected wind speed, the predicted meteorological data, and the wind turbine parameters;

[0011] The power system to be planned is planned according to the wind farm output and the predicted load data.

[0012] Furthermore, the historical meteorological data includes: historical east-west wind speed, historical north-south wind speed, historical temperature, and historical air density;

[0013] Regression prediction is performed based on the historical meteorological data, the historical load data, the preset maximum temperature threshold and the minimum temperature threshold to obtain predicted meteorological data and predicted load data for the target planning period, including: taking the historical meteorological data corresponding to the historical temperature being greater than the preset maximum temperature threshold, or the historical temperature being less than the preset minimum temperature threshold as the target meteorological data, and taking the historical load data corresponding to the target meteorological data as the target load data; calculating the hourly mean of each target meteorological data, and taking the hourly mean of each target meteorological data as the typical daily scene meteorological data set for the target planning period; calculating the daily average of meteorological data based on the typical daily scene meteorological data set to obtain the daily average meteorological data set; performing regression prediction based on the daily maximum target meteorological data among all target meteorological data to obtain the daily maximum predicted meteorological data for the target planning period; correcting the daily maximum predicted meteorological data based on the typical daily scene meteorological data set and the daily average meteorological data set to obtain the predicted meteorological data for the target planning period; performing regression prediction based on the predicted meteorological data and the target load data to obtain the predicted load data for the target planning period.

[0014] Furthermore, regression prediction is performed based on the predicted meteorological data and the target load data to obtain predicted load data for the target planning period, including: calculating the hourly average of the target load data, and using the hourly average of the target load data as the typical daily scenario load data set for the target planning period; calculating the daily average of the load data based on the typical daily scenario load data set to obtain the daily average load data set; performing regression prediction based on the daily average load data set and the predicted meteorological data to obtain the daily maximum predicted load data for the target planning period; and correcting the daily maximum predicted load data based on the typical daily scenario load data set and the daily average load data set to obtain the predicted load data for the target planning period.

[0015] Furthermore, the predicted meteorological data includes: predicted east-west wind speed and predicted north-south wind speed; the wind turbine parameters include blade diameter;

[0016] According to the wind turbine coordinates, the predicted meteorological data and the wind turbine parameters, the wake influence area of ​​each wind turbine and the predicted wind speed at each wind turbine are determined, including: calculating the predicted wind speed at each wind turbine according to the predicted east-west wind speed and the predicted north-south wind speed, and determining the predicted wind direction angle at each wind turbine according to the wind direction angle corresponding to the predicted wind speed; constructing a rotation matrix of wind turbine coordinates according to the predicted wind direction angle at each wind turbine; constructing an initial wind farm coordinate system according to the wind turbine coordinates; performing rotation correction on the initial wind farm coordinate system according to the rotation matrix to obtain a rotated target wind farm coordinate system; calculating the envelope equation of each wind turbine according to the target wind farm coordinate system and the blade diameter; and determining the wake influence area of ​​each wind turbine according to the envelope equation.

[0017] Furthermore, based on the wake influence area of ​​each wind turbine and the wind turbine parameters, a wind turbine wake shielding logic matrix is ​​constructed, including: calculating a first distance between the current rotation coordinate of the current wind turbine and the envelope line according to the envelope line equation corresponding to the wake influence area of ​​the current wind turbine and the current rotation coordinate of the current wind turbine in the target wind farm coordinate system; judging the shielding relationship according to the first distance, the blade diameter, the rotation coordinate of the current wind turbine, the rotation coordinates of the remaining wind turbines, and the envelope line equations of the remaining wind turbines; and determining the wind turbine among the remaining wind turbines whose rotation coordinates and envelope line equations meet the first condition as the first wind turbine that has a semi-shielding relationship with the current wind turbine; According to the semi-blocking relationship between the current wind turbine and the first wind turbine, a semi-blocking logic matrix is ​​constructed; among the remaining wind turbines, the wind turbines whose rotation coordinates and envelope equations satisfy the second condition are used as the second wind turbines that have a blocking relationship with the current wind turbine; wherein the blocking relationship includes a full blocking relationship and a semi-blocking relationship; according to the blocking relationship between the current wind turbine and the second wind turbine, a first blocking logic matrix for indicating the existence of a blocking relationship is constructed; according to the semi-blocking logic matrix, the first blocking logic matrix is ​​corrected to obtain a full blocking logic matrix; the semi-blocking logic matrix and the full blocking logic matrix are used as the wind turbine wake blocking logic matrix; wherein the first condition is: And y j ≥y i ; Among them, d i，j represents the first distance; D0 represents the blade diameter; y j Indicates the vertical coordinate of the current fan rotation coordinate; y i The ordinate represents the rotation coordinate of the remaining fans; the second condition is: and Among them, X j Indicates the horizontal coordinate of the current fan rotation coordinate; b 1,i The first parameter of the envelope equation of the remaining fans; b 2,irepresents the second parameter of the envelope equation of the remaining fans; k1 represents the first slope of the envelope equation of the remaining fans; k2 represents the second slope of the envelope equation of the remaining fans.

[0018] Furthermore, the fan parameters also include: the number of fans and the installation height of the fans;

[0019] The predicted wind speed at each wind turbine is corrected according to the wind turbine wake shielding logic matrix, the wind turbine parameters and the ground roughness to obtain the corrected wind speed of the wind turbine, including: calculating the first projection distance of the wind turbine in the horizontal wind direction and the second projection distance in the vertical wind direction according to the rotation coordinates of the remaining wind turbines and the current wind turbine; calculating the diameter of the affected area according to the installation height of the wind turbine, the ground roughness, the first projection distance and the blade diameter; when it is determined that the current wind turbine has a full shielding relationship with the remaining wind turbines according to the wind turbine wake shielding logic matrix, the predicted wind speed at the current wind turbine with a full shielding relationship is corrected according to the diameter of the affected area, the blade diameter and the predicted wind speed. Correction is performed to obtain a full-blocking speed matrix; when it is judged that the current wind turbine has a semi-blocking relationship with the other wind turbines according to the wind turbine wake blockage logic matrix, the wake semi-blocking area is calculated according to the second projection distance, the diameter of the affected area and the blade diameter; the predicted wind speed is corrected according to the wake semi-blocking area, the blade diameter and the diameter of the affected area to obtain a semi-blocking speed matrix; the wind speed correction speed matrix is ​​calculated according to the full-blocking speed matrix, the full-blocking logic matrix, the semi-blocking speed matrix and the semi-blocking logic matrix; the wind speed correction wind speed at each wind turbine is calculated according to the wind speed correction speed matrix, the predicted wind speed and the number of wind turbines.

[0020] Furthermore, the wind turbine parameters also include wind turbine efficiency; the predicted meteorological data also include predicted air density; the wind farm output within the target planning period is calculated based on the wind turbine corrected wind speed, the predicted meteorological data and the wind turbine parameters, including: calculating the blade swept area area based on the blade diameter; calculating the maximum output power of the wind turbine based on the blade swept area area, the wind turbine corrected wind speed, the wind turbine efficiency and the predicted air density; calculating the wind farm output within the target planning period based on the wind turbine maximum output power and the number of wind turbines.

[0021] As an improvement to the above solution, another embodiment of the present invention provides a power system planning device that considers the wind turbine wake effect, including:

[0022] The data acquisition module is used to obtain the historical meteorological data, historical load data, wind turbine parameters, wind turbine coordinates and ground roughness of the wind farm in the power system to be planned every hour of the historical year; the data prediction module is used to perform regression prediction based on the historical meteorological data, the historical load data, the preset maximum temperature threshold and the minimum temperature threshold to obtain the predicted meteorological data and predicted load data for the target planning period; the wind turbine wake prediction module is used to determine the wake influence area of ​​each wind turbine and the predicted wind speed at each wind turbine based on the wind turbine coordinates, the predicted meteorological data and the wind turbine parameters; the wind turbine shielding logic An editor construction module is used to construct a wind turbine wake shielding logic matrix based on the wake influence area of ​​each wind turbine and the wind turbine parameters; a wind turbine wind speed correction module is used to correct the predicted wind speed at each wind turbine based on the wind turbine wake shielding logic matrix, the wind turbine parameters and the ground roughness to obtain the wind turbine corrected wind speed; a wind farm output calculation module is used to calculate the wind farm output within the target planning period based on the wind turbine corrected wind speed, the predicted meteorological data and the wind turbine parameters; a power system planning module is used to plan the power system to be planned based on the wind farm output and the predicted load data.

[0023] Another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a power system planning method that considers the wind turbine wake effect as described in the above embodiment.

[0024] Another embodiment of the present invention provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the power system planning method considering the wind turbine wake effect described in the above embodiment.

[0025] By implementing the present invention, at least the following beneficial effects are achieved:

[0026] The present invention provides a power system planning method, device, terminal device and storage medium that considers the wake effect of wind turbines. The method can filter historical meteorological data and historical load data through a preset maximum temperature threshold and a preset minimum temperature threshold, and make predictions for extremely hot or extremely cold weather to obtain predicted meteorological data and predicted load data; considering the wake influence area formed by the wake effect of each wind turbine in the wind farm, a wind turbine wake shielding logic matrix is ​​constructed to represent the wake influence relationship between each wind turbine in the wind farm, and the predicted wind speed at the wind turbine is corrected based on the wind turbine wake shielding logic matrix. Obtain the corrected wind speed of the wind turbine, better consider the wake interaction between wind turbines in high-density wind farms under extremely cold or extremely hot weather, more accurately simulate the operation of wind turbines in wind farms under extremely cold or extremely hot weather, and improve the wind turbine. Then, according to the corrected wind speed of the wind turbine, predicted meteorological data and wind turbine parameters, calculate the wind farm output in the target planning period, and finally plan the power system to be planned according to the wind farm output and predicted load data, combine the predicted load data with the wind farm output, and improve the output accuracy of the wind farm under extreme weather, thereby reducing the planning deviation of the power system and improving the accuracy of the power system planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is a flow chart of a power system planning method considering wind turbine wake effects provided by one embodiment of the present invention;

[0028] Figure 2 is a schematic diagram of an initial wind farm coordinate system provided by an embodiment of the present invention;

[0029] Figure 3 This is a schematic diagram of wind farm rotation coordinates provided by an embodiment of the present invention;

[0030] Figure 4 This is a schematic diagram of a wind turbine wake effect provided by an embodiment of the present invention;

[0031] Figure 5 Schematic diagram of a wind turbine j being completely blocked by the wake of wind turbine i according to an embodiment of the present invention;

[0032] Figure 6 Schematic diagram of a wind turbine j being partially blocked by the wake of wind turbine i according to an embodiment of the present invention;

[0033] Figure 7 This is a schematic diagram of the construction process of an extremely hot and cold source-load scenario provided by one embodiment of the present invention;

[0034] Figure 8 This is a schematic diagram of wind turbine locations in a wind farm provided by an embodiment of the present invention;

[0035] Figure 9This is a schematic diagram of a daily temperature curve for a typical extremely hot day in a planned year provided by an embodiment of the present invention;

[0036] Figure 10 This is a schematic diagram of a daily temperature curve on a typical extremely cold day in a planned year provided by an embodiment of the present invention;

[0037] Figure 11 1. A schematic diagram of a U-axis wind speed curve at an altitude of 100 m on a typical extremely hot day provided by an embodiment of the present invention;

[0038] Figure 12 This is a schematic diagram of the V-axis wind speed curve at 100m altitude on a typical extremely hot day provided by an embodiment of the present invention.

[0039] Figure 13 This is a schematic diagram of a U-axis wind speed curve at an altitude of 100m on a typical extremely cold day provided by one embodiment of the present invention;

[0040] Figure 14 This is a schematic diagram of a V-axis wind speed curve at an altitude of 100m on a typical extremely cold day provided by one embodiment of the present invention;

[0041] Figure 15 This is a schematic diagram of a load curve for a typical extremely hot day in a planned year provided by an embodiment of the present invention;

[0042] Figure 16 This is a schematic diagram of a load curve on a typical extremely cold day in a planned year provided by an embodiment of the present invention;

[0043] Figure 17 This is a schematic diagram of a wind farm power curve on a typical extremely hot day in a planned year provided by an embodiment of the present invention;

[0044] Figure 18 This is a schematic diagram of a wind farm power curve on a typical extremely cold day in a planned year provided by an embodiment of the present invention;

[0045] Figure 19 This is a structural diagram of a power system planning device that takes into account wind turbine wake effects, provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0046] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0047] See also Figure 1To address the problem in the prior art of low accuracy in power system planning due to the influence of the wake effect of wind turbines in wind farms, an embodiment of the present invention provides a flow chart of a power system planning method that considers the wake effect of wind turbines, including:

[0048] S1. Obtain historical meteorological data, historical load data, wind turbine parameters, wind turbine coordinates, and ground roughness of the wind farm in the planned power system every hour of the historical year;

[0049] Specifically, historical meteorological data includes historical east-west wind speed, historical north-south wind speed, historical temperature, and historical air density. Wind turbine parameters include blade diameter, number of wind turbines, wind turbine installation height, and wind turbine efficiency. Both historical meteorological data and historical load data are annual, hourly-based data.

[0050] In a preferred embodiment of the present invention, the hourly u-axis wind speed at a height of 100 m in the mth historical year (historical east-west wind speed) is V-axis wind speed at 100m height (historical north-south wind speed) Historical temperature T (m) =[T (m,1) ,T (m,2) ,…,T (m,8760) ], historical air density Ground roughness h0, blade diameter D0, and fan installation height h.

[0051] S2. Perform regression prediction based on the historical meteorological data, the historical load data, a preset maximum temperature threshold, and a preset minimum temperature threshold to obtain predicted meteorological data and predicted load data for the target planning period;

[0052] Specifically, regression prediction is performed based on the historical meteorological data, the historical load data, the preset maximum temperature threshold and the minimum temperature threshold to obtain the predicted meteorological data and predicted load data for the target planning period, including: taking the historical meteorological data corresponding to the historical temperature being greater than the preset maximum temperature threshold, or the historical temperature being less than the preset minimum temperature threshold as the target meteorological data, and taking the historical load data corresponding to the target meteorological data as the target load data; wherein the target meteorological data includes: target east-west wind speed, target north-south wind speed, target temperature and target air density; calculating the hourly average of each target meteorological data , taking the hourly average of each target meteorological data as the typical day scene meteorological data set of the target planning period; calculating the daily average of the meteorological data according to the typical day scene meteorological data set to obtain the daily average meteorological data set; performing regression prediction based on the daily maximum target meteorological data among all target meteorological data to obtain the daily maximum predicted meteorological data of the target planning period; correcting the daily maximum predicted meteorological data according to the typical day scene meteorological data set and the daily average meteorological data set to obtain the predicted meteorological data of the target planning period; performing regression prediction based on the predicted meteorological data and the target load data to obtain the predicted load data of the target planning period.

[0053] Preferably, the criteria for determining extremely hot and cold temperatures are obtained according to the preset 98% quantile, with the highest temperature threshold being and the preset minimum temperature threshold is That is, when the maximum temperature on a certain day is greater than When , the day is an extremely hot day, and the index set of extremely hot days is C hot ; When the minimum temperature on a certain day is lower than When , the day is extremely cold, and the index set of extremely cold days is C cold The target meteorological data for the extremely hot day in year m is obtained as follows: The target meteorological data for the extremely cold day in year m can be obtained as follows: and is the target east-west wind speed; and is the target north-south wind speed; and is the target temperature; and The target air density is calculated. The hourly mean of each target meteorological data is obtained to obtain the hourly mean of the extremely hot day meteorological data: And the mean of hourly meteorological data on extremely cold days: Among them, Y cRepresents the index set of the historical year under consideration, N hot represents the number of extremely hot days in history, N cold The hourly average of each target meteorological data is used as the typical daily scene meteorological data set for the target planning period, that is, the average of each moment of the extremely hot and cold days is used as the typical daily scene meteorological data set for the planning year (target planning period). Calculate the daily average value of meteorological data based on the typical day scene meteorological data set to obtain the daily average meteorological data set According to the target meteorological data, the maximum daily target meteorological data is used to perform regression prediction using the regression fitting method to obtain the maximum daily predicted meteorological data for the target planning period. Then, the daily maximum predicted meteorological data is corrected according to the typical day scene meteorological data set and the daily average meteorological data set to obtain the predicted meteorological data for the target planning period: The meteorological data of typical days of extreme heat and extreme cold in the planning year are used as the meteorological data. Finally, regression prediction is performed based on the predicted meteorological data and the target load data to obtain the predicted load data for the target planning period.

[0054] Specifically, regression prediction is performed based on the predicted meteorological data and the target load data to obtain predicted load data for the target planning period, including: calculating the hourly average of the target load data, and using the hourly average of the target load data as a typical daily scenario load data set for the target planning period; calculating the daily average of the load data based on the typical daily scenario load data set to obtain a daily average load data set; performing regression prediction based on the daily average load data set and the predicted meteorological data to obtain daily maximum predicted load data for the target planning period; and correcting the daily maximum predicted load data based on the typical daily scenario load data set and the daily average load data set to obtain predicted load data for the target planning period.

[0055] In a preferred embodiment of the present invention, the historical load data is processed in the same manner as the historical meteorological data to obtain a typical day scene load data set and a daily average load data set; then, regression prediction is performed based on the daily average load data set and the predicted meteorological data to obtain the daily maximum predicted load data for the target planning period; then, the daily maximum predicted load data is corrected based on the typical day scene load data set and the daily average load data set to obtain the predicted load data for the target planning period, i.e., the load data for the extremely hot and cold days in the planning year. and

[0056] By performing a regression forecast based on the maximum daily meteorological data in the target meteorological data, the maximum daily predicted meteorological data is obtained, and the typical day scenario meteorological data set and the daily average meteorological data set are used for correction. By integrating multiple data characteristics for prediction and correction, the accuracy of the predicted meteorological data for the target planning period can be effectively improved, and the changing patterns of meteorological conditions under extreme weather conditions can be more accurately reflected. Load data forecasting is achieved: Based on the predicted meteorological data and the target load data, the predicted load data for the target planning period is obtained through regression forecasting. The meteorological conditions are linked to the load data, and the impact of extreme weather on power load is taken into account. This allows for more accurate prediction of the load demand of the power system under extreme weather conditions, providing a more scientific basis for the dispatch and planning of the power system, and helping to improve the operational stability and reliability of the power system under extreme weather conditions.

[0057] S3. Determine the wake influence area of ​​each wind turbine and the predicted wind speed at each wind turbine based on the wind turbine coordinates, the predicted meteorological data, and the wind turbine parameters;

[0058] Specifically, the predicted meteorological data includes: predicted east-west wind speed and predicted north-south wind speed; determining the wake influence area of ​​each wind turbine and the predicted wind speed at each wind turbine based on the wind turbine coordinates, the predicted meteorological data and the wind turbine parameters, including: calculating the predicted wind speed at each wind turbine based on the predicted east-west wind speed and the predicted north-south wind speed, and determining the predicted wind direction angle at each wind turbine based on the wind direction angle corresponding to the predicted wind speed; constructing a rotation matrix of wind turbine coordinates based on the predicted wind direction angle at each wind turbine; constructing an initial wind farm coordinate system based on the wind turbine coordinates; performing rotation correction on the initial wind farm coordinate system based on the rotation matrix to obtain a rotated target wind farm coordinate system; calculating the envelope equation of each wind turbine based on the target wind farm coordinate system and the blade diameter; and determining the wake influence area of ​​each wind turbine based on the envelope equation.

[0059] Preferably, according to the wind turbine coordinates, establish u-axis and v-axis coordinate axes, where the u-axis points to the east direction and the v-axis points to the north direction, and then construct the initial wind farm coordinate system. Assume that there are N wind farms in total. w Typhoon machine, such as Figure 2 As shown in (a), each wind farm is numbered: 1, 2, 3, ..., N w According to the relative relationship of wind turbines in the wind farm, the horizontal coordinate of each wind farm is The vertical axis is like Figure 2 As shown in (b), (u i , v i ) represents the wind turbine coordinates. The origin of this coordinate axis can be anywhere, as long as the relative positions of the wind turbines remain consistent.

[0060] Preferably, it is assumed that the forecast weather data at a certain moment on a typical day of extreme heat or extreme cold is V u,100 ,V v,100 ,T,ρ air , we can get the actual wind speed (forecast wind speed) at a height of 100m: The angle between the wind at 100m height and the u-axis (wind direction angle) is: θ = arctan (V v,100 / V u,100 Since the rotation does not change the relative positions of the fans, the rotation center can be freely selected. In this embodiment, the origin of the uv axis is set as the coordinate center, and the rotation matrix R of the fan coordinates is: Where β=π / 2-θ. For fan i, the coordinates before rotation are: (u i ,v i ), the rotated coordinates (x i ,y i )for: The target wind farm coordinate system is obtained based on the coordinates of each wind turbine after rotation. After rotation, the wind direction at a height of 100m is perpendicular to the x(u) axis. The schematic diagram of the wind turbine coordinate rotation is shown in the figure below. Figure 3 As shown, Figure 3 (a) shows the relationship between the wind direction at a height of 100m before rotation. Figure 3 (b) indicates that the y-axis of the target wind farm coordinate system is aligned with the wind direction at a height of 100 meters after rotation. Finally, based on the rotation coordinates of each wind turbine in the target wind farm coordinate system and the blade diameter, the envelope equation for each wind turbine is calculated. Based on this envelope equation, the wake impact area of ​​each wind turbine is determined.

[0061] In a preferred embodiment of the present invention, the rotation coordinate of the fan i is (x i ,y i ), the wake effect diagram is as follows Figure 4 As shown in (a). Figure 4 In (b), we can see that the rotation coordinate of fan i is (x i ,y i ), the blade radius is D0 / 2, and the linear equations of the envelope of the wake influence area of ​​wind turbine i are: l1:y=k1x+b 1,i , l2:y=k2x+b 2,i ,according to Figure 4 As shown in (a), k1>0, k2<0, so: k1=arccot(k), k2=-arccot(k); for line l1: y=k1x+b 1,i It is tangent to fan i, so the distance formula from a point to a straight line shows that: Solving the above formula, we can get b 1,iTwo solutions of : or Obviously, the smaller solution should be taken, that is: Similarly, for the straight line l2, we can get b 2,i : So the equations of the lines l1 and l2 are: l1: l2:

[0062] In actual operation, changes in wind direction will cause changes in the wake effect between wind turbines. Through this rotation correction, the relative position relationship between wind turbines and the propagation direction of the wake can be more accurately described, providing an accurate spatial reference for accurately analyzing the wake impact area. In high-density wind farms, the distance between wind turbines is relatively close, and the wake effect is more complex. Through this embodiment, the wake impact range of each wind turbine can be accurately defined, which is conducive to more in-depth research on the impact of wake on the power generation efficiency of surrounding wind turbines.

[0063] S4. Constructing a wind turbine wake shielding logic matrix based on the wake impact area of ​​each wind turbine and the wind turbine parameters;

[0064] Specifically, according to the wake influence area of ​​each wind turbine and the wind turbine parameters, a wind turbine wake shielding logic matrix is ​​constructed, including: calculating the first distance between the current rotation coordinate of the current wind turbine and the envelope line according to the envelope line equation corresponding to the wake influence area of ​​the current wind turbine and the current rotation coordinate of the current wind turbine in the target wind farm coordinate system; judging the shielding relationship according to the first distance, the blade diameter, the rotation coordinate of the current wind turbine, the rotation coordinates of the remaining wind turbines and the envelope line equations of the remaining wind turbines; and taking the wind turbines whose rotation coordinates and envelope line equations among the remaining wind turbines meet the first condition as the wind turbines that have a semi-shielding relationship with the current wind turbine. A wind turbine; constructing a semi-blocking logic matrix based on the semi-blocking relationship between the current wind turbine and the first wind turbine; taking the wind turbines whose rotation coordinates and envelope equations satisfy the second condition among the remaining wind turbines as the second wind turbines that have a blocking relationship with the current wind turbine; wherein the blocking relationship includes a full blocking relationship and a semi-blocking relationship; constructing a first blocking logic matrix for indicating the existence of a blocking relationship based on the blocking relationship between the current wind turbine and the second wind turbine; modifying the first blocking logic matrix based on the semi-blocking logic matrix to obtain a full blocking logic matrix; taking the semi-blocking logic matrix and the full blocking logic matrix as the wind turbine wake blocking logic matrix;

[0065] Among them, the first condition is: And y j ≥y i ; Among them, d i，j represents the first distance; D0 represents the blade diameter; y j Indicates the vertical coordinate of the current fan rotation coordinate; y iThe ordinate represents the rotation coordinate of the remaining fans; the second condition is: and Among them, X j Indicates the horizontal coordinate of the current fan rotation coordinate; b 1,i The first parameter of the envelope equation of the remaining fans; b 2,i represents the second parameter of the envelope equation of the remaining fans; k1 represents the first slope of the envelope equation of the remaining fans; k2 represents the second slope of the envelope equation of the remaining fans.

[0066] Preferably, the first distance represents the rotation coordinate of the current wind turbine and the envelope, that is, the distances from the center of wind turbine j to the straight lines l1 and l2 are d 1,j and d 2,j : If wind turbine j is partially blocked by the wake of wind turbine i, the first distance must be less than the preset distance condition (D0 / 2): or

[0067] , and also need to satisfy the vertical coordinate y corresponding to the rotation coordinate of the current fan j j Not less than the vertical coordinate y of another fan i i :y j ≥y i According to the semi-shading relationship between the current wind turbine and another wind turbine, a semi-shading logic matrix is ​​constructed: The semi-blocking logic matrix represents the logic of whether wind turbine j will be semi-blocked by the wake of wind turbine i, h ij =1 means that wind turbine j is partially blocked by the wake of wind turbine i. If wind turbine j is completely blocked by the wake of wind turbine i, the following conditions must be met: and From this, we can preliminarily obtain the initial full occlusion logic matrix A′, that is, the first occlusion logic matrix; when the above X is satisfied j When , wind turbine j may also be partially blocked by the wake of wind turbine i, that is, among the remaining wind turbines, there may be a blocking relationship with the current wind turbine, which may be a full blocking relationship or a half blocking relationship. Therefore, it is necessary to correct the initial full blocking logic matrix A' according to the half blocking logic matrix to obtain the final full blocking logic matrix A: A = A'-A'∧H. The full blocking logic matrix represents the logic of whether wind turbine j will be fully blocked by the wake of wind turbine i. a ij = 1 indicates that wind turbine j is completely blocked by the wake of wind turbine i. The final wind turbine wake blockage logic matrix includes a partial blockage logic matrix and a final full blockage logic matrix. The A and H matrices can thus clearly and intuitively determine the impact of the wakes of other wind turbines on a particular wind turbine.

[0068] S5. Correcting the predicted wind speed at each wind turbine according to the wind turbine wake shielding logic matrix, the wind turbine parameters, and the ground roughness to obtain a corrected wind speed for the wind turbine;

[0069] Specifically, the predicted wind speed at each wind turbine is corrected according to the wind turbine wake blocking logic matrix, the wind turbine parameters and the ground roughness to obtain the corrected wind speed of the wind turbine, including: calculating the first projection distance of the wind turbine in the horizontal wind direction and the second projection distance in the vertical wind direction according to the rotation coordinates of the remaining wind turbines and the current wind turbine; calculating the diameter of the affected area according to the installation height of the wind turbine, the ground roughness, the first projection distance and the blade diameter; when it is determined that the current wind turbine has a full blocking relationship with the remaining wind turbines according to the wind turbine wake blocking logic matrix, the predicted wind speed at the current wind turbine with a full blocking relationship is corrected according to the diameter of the affected area, the blade diameter and the predicted wind speed. Correction is performed to obtain a full-blocking speed matrix; when it is determined that the current wind turbine has a semi-blocking relationship with the other wind turbines according to the wind turbine wake blockage logic matrix, the wake semi-blocking area is calculated according to the second projection distance, the diameter of the affected area and the blade diameter; the predicted wind speed is corrected according to the wake semi-blocking area, the blade diameter and the diameter of the affected area to obtain a semi-blocking speed matrix; the wind speed correction speed matrix is ​​calculated according to the full-blocking speed matrix, the full-blocking logic matrix, the semi-blocking speed matrix and the semi-blocking logic matrix; the wind speed correction wind speed at each wind turbine is calculated according to the wind speed correction speed matrix, the predicted wind speed and the number of wind turbines.

[0070] Preferably, the first projection distance x ij It represents the distance between the hubs of wind turbines j and i projected in the direction parallel to the wind at a height of 100 m. Since the coordinates of the wind turbines are rotated, x can be calculated clearly and easily. ij is: x ij =y j -y i ; Second projection distance d ij It represents the distance between the hubs of wind turbines j and i at a height of 100m in the vertical direction of the wind. Since the coordinates of the wind turbines are rotated, d can be calculated clearly and easily. ij For: d ij =|x j -x i |. Diameter of affected area The distance x is the projection of the wake of wind turbine i in the direction parallel to the wind at a height of 100 m. ij The diameter of the affected area is k is a physical quantity related to the fan installation height h and the ground roughness h0, which can be expressed as Assume that wind turbine j is completely blocked by the wake of wind turbine i, as shown in the schematic diagram. Figure 5 When it is determined that any two wind turbines are fully blocked according to the wind turbine wake blocking logic matrix, the wind speed correction value of wind turbine j being fully blocked under the influence of wind turbine i can be obtained. for: in C T Represents the thrust coefficient of the wind turbine. Based on the diameter of the affected area, the blade diameter, and the predicted wind speed, the predicted wind speed at each wind turbine with a full shielding relationship is corrected. After calculating the corrected speed of the wake of all wind turbines at the location of all wind turbines, the full shielding speed matrix V can be formed. a : When it is determined that any two wind turbines have a semi-blocking relationship according to the wind turbine wake blocking logic matrix, the wake semi-blocking area is calculated according to the second projection distance, the impact area diameter and the blade diameter, as follows: Figure 6 As shown, the wake semi-blocking area S ij The distance x is the projection of the wake of wind turbine i in the direction parallel to the wind at a height of 100 m. ij The area enclosed by the affected area and the wind swept area of ​​the fan blade j is: The wind speed correction value of wind turbine j when it is partially blocked under the influence of the wake of wind turbine i can be obtained: for:

[0071] After calculating the corrected speed of all wind turbines’ wakes to the locations of all wind turbines, the semi-blocking speed matrix V can be formed. h :

[0072] Then, the wind speed correction speed matrix is ​​calculated based on the full shading speed matrix, the full shading logic matrix, the half shading speed matrix, and the half shading logic matrix: V = V a ⊙A+V h ⊙H, where the element in row i and column j of V is It represents the speed correction value of wind turbine j affected by the wake of wind turbine i. Finally, when wind turbine j is affected by the wake of other wind turbines, the actual wind speed at the location of the wind turbine can be calculated as the sum of the kinetic energy, so the actual corrected wind speed V of wind turbine j is j It can represent:

[0073] S6. Calculating the wind farm output within a target planning period based on the wind turbine corrected wind speed, the predicted meteorological data, and the wind turbine parameters;

[0074] Specifically, the fan parameters also include fan efficiency; the predicted meteorological data also includes predicted air density;

[0075] The wind farm output within the target planning period is calculated based on the corrected wind speed of the wind turbine, the predicted meteorological data, and the wind turbine parameters, including: calculating the area of ​​the blade swept area based on the blade diameter; calculating the maximum output power of the wind turbine based on the area of ​​the blade swept area, the corrected wind speed of the wind turbine, the wind turbine efficiency, and the predicted air density; and calculating the wind farm output within the target planning period based on the maximum output power of the wind turbine and the number of wind turbines.

[0076] In a preferred embodiment of the present invention, the blade swept area The area of ​​the wind swept by the blades is represented by i , the corrected wind speed of the fan, the fan efficiency η w The wind turbine maximum output power is calculated based on the predicted air density, and then the wind farm output P within the target planning period is calculated based on the wind turbine maximum output power and the number of wind turbines. W for:

[0077] S7. Plan the power system to be planned according to the wind farm output and the predicted load data.

[0078] In a preferred embodiment of the present invention, when the wind speed and direction at a height of 100m at each moment of a typical day of extreme heat and extreme cold in a known planning year are reconstructed, the actual wind turbine corrected wind speed taking into account the wake effect is obtained, and combined with the meteorological elements at each moment, the maximum output power of the wind farm under consideration is obtained, and then the calculation is performed at each moment within a typical day, and the wind farm output power is merged to obtain the wind farm output on the extremely hot and cold days of the planning year. Finally, the source-load scenario of the extremely hot and cold days of the planning year is constructed by comparing it with the predicted load data of the extremely hot and cold days. Based on the source-load scenario of the extremely hot and cold days of the planning year, the power system power supply planning scheme can be further verified under the extremely hot and cold day scenario to determine whether the system meets the N-1 constraint conditions under the scenario, and feedback is provided to the planning scheme, thereby effectively improving the availability and adaptability of the power grid planning scheme. The flowchart for constructing the source-load scenario of the extremely hot and cold days of the planning year is shown in the figure below. Figure 7As shown, first collect annual meteorological data on an hourly scale and extract meteorological data on extremely hot and cold days in each year; then reconstruct the meteorological data and load data for typical extremely hot and cold days, first calculate the hourly average based on historical meteorological data and historical load data to generate basic data for typical meteorological and load days, then fit the maximum temperature for extremely hot and cold days, correct the meteorological and load data for typical days, and obtain predicted meteorological data and predicted load data; rotate the coordinates according to the initial coordinates of the wind turbine, and solve the envelope line equation of the wake effect influence area to construct the wake full blockage logic matrix and the wake half blockage logic matrix, and judge whether wind turbine j is fully or half blocked by wind turbine i according to the wake blockage judgment criterion to judge the influence range of the wind turbine wake; then correct the wind speed of the wind turbine according to the wind speed correction value under full blockage and the wind speed correction value under half blockage; finally, calculate the maximum output power of the wind farm on a typical day, and construct the source-load scenario for typical extremely hot and cold days based on the maximum output power and predicted load data.

[0079] In a preferred embodiment of the present invention, meteorological data at a certain longitude and latitude in a southern coastal province of China and load data of a certain area are collected, and a wind farm is set up, wherein the arrangement position diagram of the wind turbines is as follows Figure 8 As shown. Related parameters k=0.086,D0=70,C T =0.88,η=0.4,ρ air =1.225. The daily temperature curve for a typical hot day in the planned year is as follows Figure 9 As shown in the figure, the temperature must exceed the preset maximum temperature threshold for at least one hour in 24 hours a day; the daily temperature curve for a typical extremely cold day in the planned year is as follows Figure 10 As shown in Figure 2, the temperature must be lower than the preset minimum temperature threshold for at least one hour in a day. The U-axis wind speed curve at a height of 100m on a typical day of extreme heat in the planned year is as follows: Figure 11 As shown; the V-axis wind speed curve at a height of 100m on a typical extremely hot day in the planned year is as follows Figure 12 The U-axis wind speed curve at 100m height on a typical extremely cold day in the planned year is as follows: Figure 13 As shown; the V-axis wind speed curve at a height of 100m on a typical extremely cold day in the planned year is as follows Figure 14 As shown. From the schematic diagram of the 100m height wind speed curve, it can be seen that the wind speed at 100m height on the generated typical day is significantly lower on the extremely hot typical day than on the extremely cold typical day. According to meteorological characteristics: my country's southern coastal areas are mainly affected by the monsoon. In winter, the northeast monsoon prevails, with a northerly wind direction, strong and cold winds; while in summer, the southwest monsoon prevails, with a southerly wind direction and relatively weak winds. The generated forecast meteorological data basically meets this characteristic. The load curve under the extremely hot typical day in the planning year is as follows: Figure 15 As shown in the figure, the load curve under the typical extremely cold day in the planning year is as follows Figure 16As shown in the figure, it can be seen that the generated load curve basically conforms to the characteristics of low load at night and high load during the day, and there is a load trough at noon. In addition, the load power on the typical day of extreme cold is lower than that on the typical day of extreme heat. The wind farm power curve under the typical day of extreme heat in the planned year is as follows: Figure 17 As shown in the figure, the wind farm power curve on a typical extremely cold day in the planned year is as follows Figure 18 shown.

[0080] As global climate change intensifies, the frequency and intensity of extreme weather events have significantly increased, posing unprecedented challenges to the stable operation of power systems. As the proportion of renewable energy continues to rise, wind power, as an important clean energy source, faces significant impacts on the stability and reliability of its output from extreme weather. Wind farm output depends not only on meteorological conditions such as wind speed and direction, but is also constrained by the wake effect of wind turbines. In recent years, with the rapid development of wind power technology, the scale and number of wind farms have continued to increase, and their share of the power system has also gradually increased. However, the intermittent and uncertain nature of wind power output poses significant challenges to the operation and dispatch of power systems. Extreme weather conditions, such as extreme heat and cold, strong winds, and heavy rain, are particularly pronounced, leading to significant fluctuations in wind power output. For example, during high temperatures, wind speeds may decrease, resulting in insufficient wind power output; while during cold snaps, wind speeds may increase, but the low temperatures may also cause wind turbines to freeze, impacting their normal operation. Furthermore, extreme weather can trigger failures in grid equipment, further exacerbating operational risks in the power system. The wind turbine wake effect is a crucial component of the complex flow phenomena within a wind farm, and its impact on the overall output of the wind farm cannot be ignored. The wake effect not only reduces wind speed at downstream wind turbines but also increases turbulence intensity between turbines, thereby reducing turbine power generation efficiency and increasing mechanical fatigue. The impact of the wake effect is particularly significant in densely populated wind farms, especially under extreme weather conditions, where variations in wind speed and direction further exacerbate the complexity of the wake. Therefore, to accurately assess wind farm output under extreme weather conditions, the impact of the wake effect must be comprehensively considered. Existing research has mostly focused on the wake effect of wind farms under normal weather conditions, lacking in-depth analysis of the variations in wake effects under extremely hot and cold weather. Extreme weather can cause significant changes in meteorological parameters such as wind speed and direction, thereby affecting the propagation and intensity of wakes, but current research has not fully considered these factors. Furthermore, in power system planning, the construction of source-load scenarios is crucial for assessing system reliability. However, existing methods for constructing source-load scenarios for extremely hot and cold days often ignore the impact of wind turbine wake effects on wind power output, resulting in insufficient accuracy and reliability of the scenarios. This embodiment establishes a wake effect model for extremely hot and cold weather, taking into account wind direction changes and turbulence intensity. By rotating the coordinate axes to facilitate the establishment of envelope equations, the influence range and intensity of each wind turbine's wake are accurately calculated, improving the accuracy of wind power output reconstruction. Combining the wake effect model with meteorological data reconstruction results, dynamic source-load scenarios for extremely hot and cold days are constructed, reflecting the output changes of wind farms in extreme weather in real time, providing a more reliable decision-making basis for power system planning.

[0081] By implementing this embodiment, historical meteorological data and historical load data are screened using a preset maximum temperature threshold and a preset minimum temperature threshold, and predictions are made for extremely hot or extremely cold extreme weather to obtain predicted meteorological data and predicted load data. Considering the wake influence area formed by the wake effect of each wind turbine in the wind farm, a wind turbine wake shielding logic matrix is ​​constructed to represent the wake influence relationship between the wind turbines in the wind farm. The predicted wind speed at the wind turbine is corrected based on the wind turbine wake shielding logic matrix to obtain a corrected wind speed. This better considers the wake interaction between wind turbines in a high-density wind farm under extremely cold or extremely hot extreme weather, more accurately simulates the operation of wind turbines in the wind farm under extremely cold or extremely hot extreme weather, and improves the wind turbine. Then, based on the corrected wind speed, predicted meteorological data, and wind turbine parameters, the wind farm output for the target planning period is calculated. Finally, the power system to be planned is planned based on the wind farm output and predicted load data. The predicted load data is combined with the wind farm output to improve the output accuracy of the wind farm under extreme weather, thereby reducing power system planning deviations and improving the accuracy of power system planning.

[0082] See also Figure 19 , is a schematic structural diagram of a power system planning device considering wind turbine wake effect provided by one embodiment of the present invention, comprising:

[0083] The data acquisition module is used to obtain the historical meteorological data, historical load data, wind turbine parameters, wind turbine coordinates and ground roughness of the wind farm in the power system to be planned every hour of the historical year; the data prediction module is used to perform regression prediction based on the historical meteorological data, the historical load data, the preset maximum temperature threshold and the minimum temperature threshold to obtain the predicted meteorological data and predicted load data for the target planning period; the wind turbine wake prediction module is used to determine the wake influence area of ​​each wind turbine and the predicted wind speed at each wind turbine based on the wind turbine coordinates, the predicted meteorological data and the wind turbine parameters; the wind turbine shielding logic An editor construction module is used to construct a wind turbine wake shielding logic matrix based on the wake influence area of ​​each wind turbine and the wind turbine parameters; a wind turbine wind speed correction module is used to correct the predicted wind speed at each wind turbine based on the wind turbine wake shielding logic matrix, the wind turbine parameters and the ground roughness to obtain the wind turbine corrected wind speed; a wind farm output calculation module is used to calculate the wind farm output within the target planning period based on the wind turbine corrected wind speed, the predicted meteorological data and the wind turbine parameters; a power system planning module is used to plan the power system to be planned based on the wind farm output and the predicted load data.

[0084] The present invention provides a power system planning device that takes into account the wind turbine wake effect. According to the data acquisition module, historical meteorological data, historical load data, wind turbine parameters of the wind farm in the power system to be planned, wind turbine coordinates and ground roughness are obtained every hour of the historical year; in the data prediction module, regression prediction is performed based on the historical meteorological data, the historical load data, the preset maximum temperature threshold and the minimum temperature threshold to obtain the predicted meteorological data and predicted load data for the target planning period; in the wind turbine wake prediction module, the wake influence area of ​​each wind turbine and the wind turbine location are determined based on the wind turbine coordinates, the predicted meteorological data and the wind turbine parameters. The predicted wind speed is obtained by: in the wind turbine shielding logic construction module, a wind turbine wake shielding logic matrix is ​​constructed according to the wake influence area of ​​each wind turbine and the wind turbine parameters; then, in the wind turbine wind speed correction module, the predicted wind speed at each wind turbine is corrected according to the wind turbine wake shielding logic matrix, the wind turbine parameters and the ground roughness to obtain the wind turbine corrected wind speed; then, in the wind farm output calculation module, the wind farm output within the target planning period is calculated according to the wind turbine corrected wind speed, the predicted meteorological data and the wind turbine parameters; finally, in the power system planning module, the power system to be planned is planned according to the wind farm output and the predicted load data. The historical meteorological data and historical load data are screened by using the preset maximum temperature threshold and the preset minimum temperature threshold, and predictions are made for extremely hot or extremely cold extreme weather to obtain predicted meteorological data and predicted load data. The wake influence area formed by the wake effect of each wind turbine in the wind farm is considered, and a wind turbine wake shielding logic matrix is ​​constructed to represent the wake influence relationship between each wind turbine in the wind farm. The predicted wind speed at the wind turbine is corrected on the wind turbine wake shielding logic matrix to obtain the wind turbine corrected wind speed, so as to better consider the wake interaction between wind turbines in a high-density wind farm under extremely cold or extremely hot extreme weather, more accurately simulate the wind turbine operation of the wind farm under extremely cold or extremely hot extreme weather, and improve the wind turbine. Then, according to the wind turbine corrected wind speed, predicted meteorological data and wind turbine parameters, the wind farm output in the target planning period is calculated. Finally, the planned power system is planned based on the wind farm output and predicted load data. The predicted load data is combined with the wind farm output to improve the output accuracy of the wind farm under extreme weather, thereby reducing the planning deviation of the power system and improving the accuracy of the power system planning.

[0085] Those skilled in the art can clearly understand that, for the sake of convenience and brevity, the specific working process of the device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0086] Another embodiment of the present invention provides a terminal device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a power system planning method that considers wind turbine wake effects, as described in the above embodiment. The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The terminal device may include, but is not limited to, a processor and a memory.

[0087] Another embodiment of the present invention provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the power system planning method considering the wind turbine wake effect described in the above embodiment.

[0088] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A power system planning method considering wind turbine wake effect, characterized in that: include: Obtain historical meteorological data and load data for each hour of the historical year, wind turbine parameters and coordinates of the wind farm in the planned power system, and ground roughness; Perform regression prediction based on the historical meteorological data, the historical load data, a preset maximum temperature threshold, and a preset minimum temperature threshold to obtain predicted meteorological data and predicted load data for the target planning period; Determining the wake impact area of ​​each wind turbine and the predicted wind speed at each wind turbine based on the wind turbine coordinates, the predicted meteorological data, and the wind turbine parameters; Constructing a wind turbine wake shielding logic matrix based on the wake impact area of ​​each wind turbine and the wind turbine parameters; Correcting the predicted wind speed at each wind turbine according to the wind turbine wake shielding logic matrix, the wind turbine parameters, and the ground roughness to obtain a corrected wind speed for the wind turbine; Calculating the wind farm output within a target planning period based on the wind turbine corrected wind speed, the predicted meteorological data, and the wind turbine parameters; The power system to be planned is planned according to the wind farm output and the predicted load data.

2. A power system planning method considering wind turbine wake effect according to claim 1, characterized in that: The historical meteorological data includes: historical east-west wind speed, historical north-south wind speed, historical temperature and historical air density; Regression prediction is performed based on the historical meteorological data, the historical load data, a preset maximum temperature threshold, and a preset minimum temperature threshold to obtain predicted meteorological data and predicted load data for the target planning period, including: The historical meteorological data corresponding to the historical temperature being greater than a preset maximum temperature threshold, or the historical temperature being less than a preset minimum temperature threshold, is used as the target meteorological data, and the historical load data corresponding to the target meteorological data is used as the target load data; Calculating the hourly average of each target meteorological data, and using the hourly average of each target meteorological data as a typical day scene meteorological data set for the target planning period; Calculating a daily average of meteorological data based on the typical day scene meteorological data set to obtain a daily average meteorological data set; Perform regression forecast based on the maximum daily target meteorological data among all target meteorological data to obtain the maximum daily forecast meteorological data for the target planning period; Correcting the daily maximum predicted meteorological data according to the typical day scene meteorological data set and the daily average meteorological data set to obtain predicted meteorological data for the target planning period; Regression prediction is performed based on the predicted meteorological data and the target load data to obtain predicted load data for the target planning period.

3. A power system planning method considering wind turbine wake effect according to claim 2, characterized in that: Performing regression prediction based on the predicted meteorological data and the target load data to obtain predicted load data for the target planning period includes: Calculating the hourly average of the target load data, and using the hourly average of the target load data as a typical daily scenario load data set for a target planning period; Calculating a daily average value of load data based on the typical daily scenario load data set to obtain a daily average load data set; Perform regression prediction based on the daily average load data set and the predicted meteorological data to obtain daily maximum predicted load data for the target planning period; The daily maximum predicted load data is corrected according to the typical day scenario load data set and the daily average load data set to obtain the predicted load data for the target planning period.

4. The power system planning method considering wind turbine wake effect according to claim 1, characterized in that: The predicted meteorological data includes: predicted east-west wind speed and predicted north-south wind speed; the wind turbine parameters include blade diameter; Determining the wake impact area of ​​each wind turbine and the predicted wind speed at each wind turbine according to the wind turbine coordinates, the predicted meteorological data, and the wind turbine parameters, including: Calculating the predicted wind speed at each wind turbine based on the predicted east-west wind speed and the predicted north-south wind speed, and determining the predicted wind direction angle at each wind turbine based on the wind direction angle corresponding to the predicted wind speed; Construct the rotation matrix of the wind turbine coordinates based on the predicted wind direction angle at each wind turbine; Constructing an initial wind farm coordinate system according to the wind turbine coordinates; Performing rotation correction on the initial wind farm coordinate system according to the rotation matrix to obtain a rotated target wind farm coordinate system; Calculating an envelope equation for each wind turbine according to the target wind farm coordinate system and the blade diameter; The wake influence area of ​​each wind turbine is determined according to the envelope equation.

5. A power system planning method considering wind turbine wake effect according to claim 4, characterized in that: Based on the wake impact area of ​​each wind turbine and the wind turbine parameters, a wind turbine wake shielding logic matrix is ​​constructed, including: Calculate a first distance between the current rotation coordinate of the current wind turbine and the envelope line according to an envelope line equation corresponding to the wake influence area of ​​the current wind turbine and the current rotation coordinate of the current wind turbine in the target wind farm coordinate system; The shielding relationship is determined based on the first distance, the blade diameter, the rotation coordinates of the current wind turbine, the rotation coordinates of the remaining wind turbines, and the envelope equations of the remaining wind turbines; The wind turbine whose rotation coordinates and envelope equations satisfy the first condition among the remaining wind turbines is regarded as the first wind turbine in a semi-blocking relationship with the current wind turbine; According to the semi-shading relationship between the current wind turbine and the first wind turbine, a semi-shading logic matrix is ​​constructed; The wind turbines whose rotation coordinates and envelope equations satisfy the second condition are regarded as the second wind turbines that have an obstruction relationship with the current wind turbine; wherein the obstruction relationship includes a full obstruction relationship and a partial obstruction relationship; Constructing a first blocking logic matrix for indicating the existence of a blocking relationship according to the blocking relationship between the current wind turbine and the second wind turbine; Correcting the first occlusion logic matrix according to the semi-occlusion logic matrix to obtain a full occlusion logic matrix; The semi-shading logic matrix and the full-shading logic matrix are used as the wind turbine wake shading logic matrix; Among them, the first condition is: And y j ≥y i ; Among them, d i，j represents the first distance; D0 represents the blade diameter; y j Indicates the vertical coordinate of the current fan rotation coordinate; y i The ordinate representing the rotation coordinates of the remaining fans; The second condition is: Among them, X j Indicates the horizontal coordinate of the current fan rotation coordinate; b 1,i The first parameter of the envelope equation of the remaining fans; b 2,i represents the second parameter of the envelope equation of the remaining fans; k1 represents the first slope of the envelope equation of the remaining fans; k2 represents the second slope of the envelope equation of the remaining fans.

6. A power system planning method considering wind turbine wake effect according to claim 5, characterized in that: The fan parameters also include: the number of fans and the installation height of the fans; Correcting the predicted wind speed at each wind turbine according to the wind turbine wake shielding logic matrix, the wind turbine parameters, and the ground roughness to obtain a wind turbine corrected wind speed includes: Calculate the first projection distance of the wind turbine in the horizontal wind direction and the second projection distance in the vertical wind direction according to the rotation coordinates of the remaining wind turbines and the current wind turbine; Calculating the diameter of the affected area according to the wind turbine installation height, the ground roughness, the first projection distance, and the blade diameter; When it is determined that the current wind turbine has a full obstruction relationship with the other wind turbines according to the wind turbine wake obstruction logic matrix, the predicted wind speed at the current wind turbine with a full obstruction relationship is corrected according to the affected area diameter, the blade diameter, and the predicted wind speed to obtain a full obstruction speed matrix; When it is determined according to the wind turbine wake shielding logic matrix that the current wind turbine is in a semi-shielded relationship with the other wind turbines, a wake semi-shielded area is calculated according to the second projection distance, the affected area diameter, and the blade diameter; Correcting the predicted wind speed according to the wake semi-blocking area, the blade diameter, and the diameter of the affected area to obtain a semi-blocking speed matrix; Calculating a wind speed correction speed matrix according to the full-shading speed matrix, the full-shading logic matrix, the half-shading speed matrix, and the half-shading logic matrix; The wind speed correction matrix, the predicted wind speed, and the number of wind turbines are used to calculate the wind speed correction at each wind turbine.

7. A power system planning method considering wind turbine wake effect according to claim 6, characterized in that: The fan parameters also include fan efficiency; the predicted meteorological data also includes predicted air density; Calculating the wind farm output within a target planning period according to the wind turbine corrected wind speed, the predicted meteorological data, and the wind turbine parameters, including: Calculating the swept area of ​​the blade according to the blade diameter; Calculating the maximum output power of the fan according to the area of ​​the blade swept area, the corrected wind speed of the fan, the fan efficiency, and the predicted air density; The wind farm output within the target planning period is calculated based on the maximum output power of the wind turbine and the number of wind turbines.

8. A power system planning device considering the wind turbine wake effect, characterized in that: include: The data acquisition module is used to obtain historical meteorological data and load data every hour of the historical year, wind turbine parameters and wind turbine coordinates of the wind farm in the power system to be planned, and ground roughness; A data prediction module is used to perform regression prediction based on the historical meteorological data, the historical load data, a preset maximum temperature threshold, and a preset minimum temperature threshold to obtain predicted meteorological data and predicted load data for a target planning period; a wind turbine wake prediction module, configured to determine the wake impact area of ​​each wind turbine and the predicted wind speed at each wind turbine based on the wind turbine coordinates, the predicted meteorological data, and the wind turbine parameters; A wind turbine shielding logic construction module is used to construct a wind turbine wake shielding logic matrix based on the wake influence area of ​​each wind turbine and the wind turbine parameters; a wind turbine wind speed correction module, configured to correct the predicted wind speed at each wind turbine according to the wind turbine wake shielding logic matrix, the wind turbine parameters, and the ground roughness to obtain a wind turbine corrected wind speed; a wind farm output calculation module, configured to calculate the wind farm output within a target planning period based on the wind turbine corrected wind speed, the predicted meteorological data, and the wind turbine parameters; The power system planning module is used to plan the power system to be planned according to the wind farm output and the predicted load data.

9. A terminal device, characterized in that: The invention comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, a power system planning method considering the wind turbine wake effect as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the power system planning method considering the wind turbine wake effect according to any one of claims 1 to 7.

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