A power system planning method and device considering wind turbine wake effect, a terminal device and a storage medium

Abstract

This invention discloses a power system planning method, apparatus, terminal equipment, and storage medium that considers the wake effect of wind turbines. Belonging to the field of power systems, the method includes: predicting forecast meteorological data and forecast load data for the target planning period based on historical meteorological data, historical load data, and preset maximum and minimum temperature thresholds; determining the wake influence area of ​​the wind turbines and the predicted wind speed at each wind turbine based on wind turbine coordinates, forecast meteorological data, and wind turbine parameters; correcting the predicted wind speed at each wind turbine based on a wind turbine wake obstruction logic matrix constructed from the wake influence area and wind turbine parameters, wind turbine parameters, and ground roughness; and planning the power system to be planned based on the wind farm output and forecast load data for the target planning period calculated using the corrected wind speed, forecast meteorological data, and wind turbine parameters. By implementing this invention, the problem of deviations in power system planning due to wind turbine wake effects is solved.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and in particular to a power system planning method, apparatus, terminal equipment and storage medium that takes into account the wake effect of wind turbines. Background Technology

[0002] In recent years, with the rapid development of wind power technology, the scale and number of wind farms have been increasing, and their proportion in the power system has also been gradually rising. Against the backdrop of the increasing proportion of renewable energy, wind power, as an important clean energy source, is significantly affected by extreme weather conditions in terms of output stability and reliability. The output of a wind farm 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 reduction in wind speed downstream of a wind turbine due to energy loss when the turbine captures wind energy, significantly affecting the power generation efficiency of downstream turbines. Currently, power system planning focuses primarily on simulating the wind power output of a single wind farm or a specific region, with insufficient research on the impact of the wake effect on the power system. In high-density wind farms, changes in wind speed and direction further exacerbate the complexity of the wake, affecting its 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] This invention provides a power system planning method, apparatus, terminal equipment, and storage medium that considers the wake effect of wind turbines, which can effectively solve the problem of low accuracy in power system planning caused by the wake effect of wind turbines in existing wind farms.

[0004] An embodiment of the present invention provides a power system planning method considering the wake effect of wind turbines, comprising:

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

[0006] Regression prediction is performed based on the historical meteorological data, the historical load data, the preset highest temperature threshold, and the lowest temperature threshold to obtain the predicted meteorological data and predicted load data for the target planning period.

[0007] Based on 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.

[0008] Based on the wake influence area of ​​each wind turbine and the wind turbine parameters, a wind turbine wake blocking logic matrix is ​​constructed.

[0009] Based on the wind turbine wake obstruction logic matrix, the wind turbine parameters, and the ground roughness, the predicted wind speed at each wind turbine is corrected to obtain the corrected wind speed at the wind turbine.

[0010] Based on the corrected wind speed of the wind turbine, the predicted meteorological data, and the wind turbine parameters, the wind farm output during the target planning period is calculated.

[0011] The power system to be planned is based on 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, and preset maximum and minimum temperature thresholds to obtain predicted meteorological data and predicted load data for the target planning period. This includes: using historical meteorological data where the historical temperature is greater than the preset maximum temperature threshold or less than the preset minimum temperature threshold as target meteorological data, and using the historical load data corresponding to the target meteorological data as target load data; calculating the hourly average of each target meteorological data point, and using the hourly average of each target meteorological data point as a typical daily scene meteorological dataset for the target planning period; calculating the daily average of meteorological data based on the typical daily scene meteorological dataset to obtain a daily average meteorological dataset; performing regression prediction based on the largest daily target meteorological data point among all target meteorological data points to obtain the maximum daily predicted meteorological data for the target planning period; correcting the maximum daily predicted meteorological data based on the typical daily scene meteorological dataset and the daily average meteorological dataset to obtain the predicted meteorological data for the target planning period; and 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] Further, 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, 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 dataset for the target planning period; calculating the daily average of the load data based on the typical daily scenario load dataset to obtain a daily average load dataset; performing regression prediction based on the daily average load dataset 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 dataset and the daily average load dataset 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] Based on 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 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 for the 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; rotating and correcting the initial wind farm coordinate system based on the rotation matrix to obtain the rotated target wind farm coordinate system; calculating the envelope equation for 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.

[0017] Furthermore, based on the wake influence area of ​​each wind turbine and the turbine parameters, a wind turbine wake shading logic matrix is ​​constructed, including: calculating the first distance between the current rotation coordinate of the current wind turbine and the envelope based on the envelope 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; determining the shading relationship based on the first distance, blade diameter, the rotation coordinate of the current wind turbine, the rotation coordinates of the remaining wind turbines, and the envelope equations of the remaining wind turbines; and identifying the wind turbine whose rotation coordinates and envelope equations satisfy the first condition as the first wind turbine with a semi-shading relationship with the current wind turbine. Based on the partial occlusion relationship between the current wind turbine and the first wind turbine, a partial occlusion logic matrix is ​​constructed. Wind turbines whose rotational coordinates and envelope equations satisfy the second condition are designated as second wind turbines with an occlusion relationship with the current wind turbine. The occlusion relationship includes both full and partial occlusion relationships. Based on the occlusion relationship between the current wind turbine and the second wind turbine, a first occlusion logic matrix is ​​constructed to represent the existence of an occlusion relationship. The first occlusion logic matrix is ​​then modified based on the partial occlusion logic matrix to obtain a full occlusion logic matrix. The partial occlusion logic matrix and the full occlusion logic matrix are used as the wind turbine wake occlusion logic matrix. The first condition is: And y j ≥y i ; where d i，j D0 represents the first distance; D0 represents the blade diameter; y j The y-coordinate represents the current rotational coordinate of the wind turbine; i The ordinate represents the rotational coordinates of the remaining wind turbines; the second condition is: and Among them, X j b represents the x-coordinate of the current wind turbine's rotational coordinates; 1,i The first parameter represents the envelope equation of the remaining wind turbines; b 2,ik1 represents the second parameter of the envelope equation of the remaining wind turbines; k2 represents the first slope of the envelope equation of the remaining wind turbines; k2 represents the second slope of the envelope equation of the remaining wind turbines.

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

[0019] The predicted wind speed at each wind turbine is corrected based on the wind turbine wake shading logic matrix, the wind turbine parameters, and the ground roughness to obtain the corrected wind speed. This includes: calculating the first projected distance of the wind turbine in the horizontal wind direction and the second projected distance in the vertical wind direction based on the rotation coordinates of the other wind turbines and the current wind turbine; calculating the diameter of the affected area based on the wind turbine installation height, the ground roughness, the first projected distance, and the blade diameter; and, if the wind turbine wake shading logic matrix indicates a full shading relationship between the current wind turbine and the other wind turbines, adjusting the predicted wind speed at the current wind turbine with the full shading relationship based on the diameter of the affected area, the blade diameter, and the predicted wind speed. The process involves several steps: First, a full occlusion velocity matrix is ​​obtained. Then, based on the wake occlusion logic matrix, if a partial occlusion relationship is determined between the current wind turbine and other turbines, the wake partial occlusion area is calculated using the second projection distance, the diameter of the affected area, and the blade diameter. Next, the predicted wind speed is corrected using the wake partial occlusion area, the blade diameter, and the diameter of the affected area to obtain a partial occlusion velocity matrix. Finally, a wind speed correction matrix is ​​calculated using the full occlusion velocity matrix, the full occlusion logic matrix, the partial occlusion velocity matrix, and the partial occlusion logic matrix. Finally, the corrected wind speed at each turbine is calculated using the corrected wind speed matrix, the predicted wind speed, and the number of turbines.

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

[0022] The data acquisition module is used to acquire historical hourly meteorological data, historical load data, wind turbine parameters, wind turbine coordinates, and ground roughness of wind farms in the power system to be planned; the data prediction module is used to perform regression prediction based on the historical meteorological data, the historical load data, and preset maximum and minimum temperature thresholds to obtain 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; and the wind turbine shading logic module... The system comprises the following modules: a wake obstruction logic matrix for each wind turbine, constructed based on the wake influence area of ​​each turbine and the turbine parameters; a wind turbine wind speed correction module for correcting the predicted wind speed at each turbine based on the wake obstruction logic matrix, the turbine parameters, and the ground roughness, to obtain the corrected wind speed; a wind farm output calculation module for calculating the wind farm output during the target planning period based on the corrected wind speed, the predicted meteorological data, and the turbine parameters; and a power system planning module for planning 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 considering the wind turbine wake effect as described in the above embodiments.

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

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

[0026] This invention provides a power system planning method, apparatus, terminal equipment, and storage medium that considers the wake effect of wind turbines. The method filters historical meteorological and load data using preset maximum and minimum temperature thresholds, and makes predictions for extreme hot or cold weather to obtain predicted meteorological and load data. Considering the wake effect of each wind turbine in the wind farm, a wind turbine wake obstruction logic matrix is ​​constructed to represent the wake influence relationship between wind turbines in the wind farm. The predicted wind speed at the wind turbine is then corrected based on the wind turbine wake obstruction logic matrix. By obtaining the corrected wind speed for wind turbines, we can better consider the wake interaction between wind turbines in high-density wind farms under extreme cold or heat weather conditions. This allows for a more accurate simulation of wind turbine operation under extreme cold or heat weather conditions, improving the efficiency of wind turbine operation. Then, based on the corrected wind speed, predicted meteorological data, and wind turbine parameters, we can calculate the wind farm output for the target planning period. Finally, based on the wind farm output and predicted load data, we can plan the power system to be planned. By combining the predicted load data with the wind farm output, we can improve the accuracy of wind farm output under extreme weather conditions, thereby reducing power system planning deviations and improving the accuracy of power system planning. Attached Figure Description

[0027] Figure 1 This is a flowchart illustrating a power system planning method considering the wake effect of wind turbines, provided by an embodiment of the present invention.

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

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

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

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

[0032] Figure 6 This is a schematic diagram of a fan j being partially blocked by the wake of fan 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 extremely cold source load scenario provided in an embodiment of the present invention;

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

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

[0037] Figure 11 This is a schematic diagram of the U-axis wind speed curve at a height of 100m under extreme heat, provided by an embodiment of the present invention.

[0038] Figure 12 This is a schematic diagram of the v-axis wind speed curve at a typical sub-sunlight altitude of 100m provided in an embodiment of the present invention.

[0039] Figure 13 This is a schematic diagram of the U-axis wind speed curve at a typical altitude of 100m in extremely cold weather, provided by an embodiment of the present invention.

[0040] Figure 14 This is a schematic diagram of the v-axis wind speed curve at a typical altitude of 100m in extremely cold weather, provided by an embodiment of the present invention.

[0041] Figure 15 This is a schematic diagram of the load curve under 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 the load curve under 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 the wind farm power curve under a typical day of extreme heat in a planned year, provided by an embodiment of the present invention.

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

[0045] Figure 19 This is a schematic diagram of the structure of a power system planning device that takes into account the wake effect of wind turbines, provided in an embodiment of the present invention. Detailed Implementation

[0046] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0047] See Figure 1To address the problem of low accuracy in power system planning due to the wake effect of wind turbines in existing technologies, an embodiment of the present invention provides a flowchart illustrating a power system planning method considering the wake effect of wind turbines, comprising:

[0048] S1. Obtain historical meteorological data, historical load data, wind turbine parameters, wind turbine coordinates, and ground roughness of wind farms in the planned power system for each 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 turbines, turbine installation height, and turbine efficiency. Both historical meteorological data and historical load data are annual historical data on an hourly scale.

[0050] In a preferred embodiment of the present invention, the hourly U-axis wind speed (historical east-west wind speed) at a height of 100m for the m-th historical year. 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. Based on the historical meteorological data, the historical load data, the preset highest temperature threshold and the lowest temperature threshold, regression prediction is performed to obtain the 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, and preset maximum and minimum temperature thresholds to obtain predicted meteorological data and predicted load data for the target planning period. This includes: using historical meteorological data where the historical temperature is greater than the preset maximum temperature threshold or less than the preset minimum temperature threshold as target meteorological data, and using the historical load data corresponding to the target meteorological data as 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; and calculating the hourly average of each of the target meteorological data. The following steps are taken: The hourly average of each target meteorological data point is used as the typical daily scene meteorological dataset for the target planning period; the daily average meteorological dataset is calculated based on the typical daily scene meteorological dataset; regression prediction is performed based on the largest daily target meteorological data among all target meteorological data points to obtain the maximum daily predicted meteorological data for the target planning period; the maximum daily predicted meteorological data is corrected based on the typical daily scene meteorological dataset and the daily average meteorological dataset to obtain the 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 the predicted load data for the target planning period.

[0053] Preferably, the standard for judging extreme heat and extreme cold temperatures is obtained according to a preset 98th percentile, with the highest temperature threshold being... And the preset minimum temperature threshold is That is, when the highest temperature on a certain day is greater than At that time, the day was an extremely hot day, and the index set of extremely hot days was obtained as C. hot When the lowest temperature on a certain day is lower than At that time, the day was an extremely cold day, and the index set of extremely cold days was obtained as C. cold Therefore, the target meteorological data for the hottest day in year m are obtained as follows: The target meteorological data for the extreme cold day in year m can be obtained as follows: and The target is the east-west wind speed; and Target north-south wind speed; and The target temperature; and Let the target air density be denoted. Calculate the hourly average of each of the aforementioned target meteorological data points to obtain the hourly average of the meteorological data for extremely hot days: The average of hourly meteorological data during extremely cold weather: Among them, Y cN represents the set of indexes for the historical years under consideration. hot N represents the number of historically hottest days. cold This indicates the number of historically extreme cold days. The hourly average of each of the aforementioned target meteorological data points is used as the typical daily scene meteorological dataset for the target planning period; that is, the average of each moment during the aforementioned extremely hot and extremely cold days is used as the typical daily scene meteorological dataset for the planning year (target planning period). The daily average meteorological data is calculated based on the typical daily scene meteorological dataset to obtain the daily average meteorological dataset. Based on the daily maximum target meteorological data in the target meteorological data, a regression fitting method is used to perform regression prediction to obtain the daily maximum predicted meteorological data for the target planning period. Then, based on the typical daily scene meteorological dataset and the daily average meteorological dataset, the daily maximum predicted meteorological data is corrected to obtain the predicted meteorological data for the target planning period: Meteorological data for typical days of extreme heat and cold in the planning year are used. 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, the process of 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 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 dataset for the target planning period; calculating the daily average of the load data based on the typical daily scenario load dataset to obtain a daily average load dataset; performing regression prediction based on the daily average load dataset 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 dataset and the daily average load dataset to obtain the predicted load data for the target planning period.

[0055] In a preferred embodiment of the present invention, historical load data is processed in the same way as historical meteorological data to obtain a typical daily scene load dataset and a daily average load dataset. Then, regression prediction is performed based on the daily average load dataset and the predicted meteorological data to obtain the daily maximum predicted load data for the target planning period. Next, the daily maximum predicted load data is corrected based on the typical daily scene load dataset and the daily average load dataset to obtain the predicted load data for the target planning period, i.e., the load data for the hottest and coldest days of the planning year. and

[0056] By performing regression prediction based on the daily maximum meteorological data in the target meteorological data, the maximum predicted meteorological data for each day is obtained. This data is then corrected using typical daily scene meteorological datasets and daily average meteorological datasets. By integrating multiple data features for prediction and correction, the accuracy of predicted meteorological data for the target planning period can be effectively improved, more accurately reflecting the changing patterns of meteorological conditions under extreme weather. Load data prediction is also achieved: by performing regression prediction based on predicted meteorological data and target load data, predicted load data for the target planning period is obtained. This establishes a link between meteorological conditions and load data, taking into account the impact of extreme weather on power load. This allows for more accurate prediction of the power system's load demand under extreme weather conditions, providing a more scientific basis for power system dispatching and planning, and contributing to improving the operational stability and reliability of the power system under extreme weather conditions.

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

[0058] Specifically, the predicted meteorological data includes: predicted east-west wind speed and predicted north-south wind speed; based on the wind turbine coordinates, the predicted meteorological data, and the wind turbine parameters, determining the wake influence area of ​​each wind turbine and the predicted wind speed at each wind turbine includes: 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 for the 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; rotating and correcting the initial wind farm coordinate system based on the rotation matrix to obtain the rotated target wind farm coordinate system; calculating the envelope equation for 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, based on the wind turbine coordinates, a u-axis and a v-axis coordinate system are established, where the u-axis points east and the v-axis points north, and then an initial wind farm coordinate system is constructed. Assume the wind farm has N... w Typhoon machines, such as Figure 2 As shown in (a), the wind farms are numbered as follows: 1, 2, 3, ..., N w Based on the relative relationships of the wind turbines within the wind farm, the abscissa of each wind farm is obtained as follows: The vertical axis is like Figure 2 As shown in (b), (u) i v i The coordinates of the wind turbines are represented by (). The origin of this coordinate axis can be chosen arbitrarily, as long as the relative positions of each wind turbine remain consistent.

[0060] Preferably, it is assumed that the predicted meteorological data for a certain moment on a typical day of extreme heat or extreme cold is V. u,100 V v,100 ,T,ρ air Then, the actual wind speed at a height of 100m (predicted wind speed) can be obtained: The angle between the wind at a height of 100m and the u-axis (wind direction angle) is: θ = arctan(V v,100 / V u,100 Since rotation does not change the relative positions of the fans, the rotation center can be freely chosen. 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 wind turbine i, the coordinates before rotation are: (u i ,v i ), rotated coordinates (x) i ,y i )for: The rotated 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. A schematic diagram of the wind turbine coordinate rotation is shown below. Figure 3 As shown, Figure 3 (a) indicates the relationship between the wind direction before rotation and the wind direction at a height of 100m. Figure 3 (b) indicates that the y-axis of the target wind farm coordinate system after rotation is aligned with the direction of the wind at a height of 100m. Finally, based on the rotation coordinates of each wind turbine in the target wind farm coordinate system and the blade diameter, the envelope equation of each wind turbine is calculated, and the wake influence area of ​​each wind turbine is determined based on the envelope equation.

[0061] In a preferred embodiment of the present invention, let the rotation coordinate of fan i be (x... i ,y i The wake effect diagram is shown below. Figure 4 As shown in (a). Figure 4 (b) shows that the rotation coordinates of fan i are (x... i ,y i Given a blade radius of D0 / 2, the linear equations of the envelope of the wake influence region 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, therefore we have: k1=arccot(k), k2=-arccot(k); for the line l1: y=k1x+b 1,i Since it is tangent to fan i, we can deduce from the formula for the distance from a point to a line: Solving the above equation, we can obtain b. 1,iTwo solutions: or Clearly, the smaller solution should be chosen, i.e.: Similarly, for line l2, we can obtain b 2,i : Therefore, the equations of the lines l1 and l2 are as follows: l1: l2:

[0062] In actual operation, changes in wind direction can alter the wake effect between wind turbines. This rotational correction allows for a more accurate description of the relative positions of the turbines and the propagation direction of the wake, providing a precise spatial reference for analyzing the wake's influence area. In high-density wind farms, the distance between turbines is relatively close, making the wake effect more complex. This embodiment accurately defines the wake influence range of each turbine, facilitating a deeper study of the wake's impact on the power generation efficiency of surrounding turbines.

[0063] S4. Construct a wake obstruction logic matrix for each wind turbine based on the wake influence area of ​​each wind turbine and the wind turbine parameters.

[0064] Specifically, based on the wake influence area of ​​each wind turbine and the turbine parameters, a wind turbine wake shading logic matrix is ​​constructed, including: calculating the first distance between the current rotation coordinate of the current wind turbine and the envelope line based on the envelope 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; determining the shading relationship based on the first distance, blade diameter, the rotation coordinate of the current wind turbine, the rotation coordinates of the remaining wind turbines, and the envelope equations of the remaining wind turbines; and identifying the wind turbines whose rotation coordinates and envelope equations satisfy the first condition as having a semi-shading relationship with the current wind turbine. A wind turbine is used. Based on the partial occlusion relationship between the current wind turbine and the first wind turbine, a partial occlusion logic matrix is ​​constructed. Among the remaining wind turbines, those whose rotational coordinates and envelope equations satisfy a second condition are designated as the second wind turbines with an occlusion relationship with the current wind turbine. The occlusion relationship includes both full and partial occlusion relationships. Based on the occlusion relationship between the current wind turbine and the second wind turbine, a first occlusion logic matrix is ​​constructed to represent the existence of an occlusion relationship. The first occlusion logic matrix is ​​then modified based on the partial occlusion logic matrix to obtain a full occlusion logic matrix. The partial occlusion logic matrix and the full occlusion logic matrix are used as the wind turbine wake occlusion logic matrix.

[0065] The first condition is: And y j ≥y i ; where d i，j D0 represents the first distance; D0 represents the blade diameter; y j The y-coordinate represents the current rotational coordinate of the wind turbine; iThe ordinate represents the rotational coordinates of the remaining wind turbines; the second condition is: and Among them, X j b represents the x-coordinate of the current wind turbine's rotational coordinates; 1,i The first parameter represents the envelope equation of the remaining wind turbines; b 2,i k1 represents the second parameter of the envelope equation of the remaining wind turbines; k2 represents the first slope of the envelope equation of the remaining wind turbines; k2 represents the second slope of the envelope equation of the remaining wind turbines.

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

[0067] Furthermore, it must also satisfy the condition that the ordinate y corresponding to the rotation coordinate of the current wind turbine j is... j The ordinate y of the other wind turbine i is not less than that of the other wind turbine i. i y j ≥y i Based on the current partial shading relationship between the wind turbine and another wind turbine, construct the partial shading logic matrix: The partial occlusion logic matrix represents the logic of whether wind turbine j will be partially occluded by the wake of wind turbine i, h ij =1 indicates that fan j is partially blocked by the wake of fan i. For fan j to be completely blocked by the wake of fan i, the following condition must be met: and From this, we can initially obtain the initial full occlusion logic matrix A′, which is the first occlusion logic matrix; when the above X is satisfied... j At this time, wind turbine j may also be partially blocked by the wake of wind turbine i. That is, among the other wind turbines, there is a blocking relationship with the current wind turbine, which may be a full blocking relationship or a partial blocking relationship. Therefore, it is necessary to modify the initial full blocking logic matrix A′ according to the partial 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 blocking logic matrix includes the partial blocking logic matrix and the final full blocking logic matrix. Therefore, the impact of other wind turbine wakes on a particular wind turbine can be clearly and intuitively obtained through the A and H matrices.

[0068] S5. Correct the predicted wind speed at each wind turbine according to the wind turbine wake obstruction logic matrix, the wind turbine parameters and the ground roughness to obtain the corrected wind speed of the wind turbine.

[0069] Specifically, the predicted wind speed at each wind turbine is corrected based on the wind turbine wake shading logic matrix, the wind turbine parameters, and the ground roughness to obtain the corrected wind speed. This includes: calculating the first projected distance of the wind turbine in the horizontal wind direction and the second projected distance in the vertical wind direction based on the rotation coordinates of the other wind turbines and the current wind turbine; calculating the diameter of the affected area based on the wind turbine installation height, the ground roughness, the first projected distance, and the blade diameter; and, if the wind turbine wake shading logic matrix indicates a full shading relationship between the current wind turbine and the other wind turbines, adjusting the predicted wind speed at the current wind turbine with the full shading relationship based on the diameter of the affected area, the blade diameter, and the predicted wind speed. The following steps are performed to obtain a full occlusion velocity matrix: If, based on the wind turbine wake occlusion logic matrix, it is determined that the current wind turbine has a partial occlusion relationship with other wind turbines, the wake partial occlusion area is calculated based on the second projection distance, the diameter of the affected area, and the blade diameter; the predicted wind speed is corrected based on the wake partial occlusion area, the blade diameter, and the diameter of the affected area to obtain a partial occlusion velocity matrix; a wind speed correction velocity matrix is ​​calculated based on the full occlusion velocity matrix, the full occlusion logic matrix, the partial occlusion velocity matrix, and the partial occlusion logic matrix; and the wind speed correction velocity at each wind turbine is calculated based on the wind speed correction velocity matrix, the predicted wind speed, and the number of wind turbines.

[0070] Preferably, the first projection distance x ij Let x represent the distance projected onto the hubs of wind turbines j and i in the direction parallel to the wind at a height of 100m. Since the wind turbine coordinates have been rotated, x can be calculated clearly and easily. ij For: x ij =y j -y i Second projection distance d ij This represents the distance projected onto the hubs of wind turbines j and i in the vertical direction at a height of 100m. Since the wind turbine coordinates have been rotated, d can be calculated clearly and easily. ij For: d ij =|x j -x i |. Diameter of the affected area Let x represent the distance of the wake of wind turbine i projected in the direction parallel to the wind at a height of 100m. ij The diameter of the area affected at that time k is a physical quantity related to the wind turbine installation height h and the ground roughness h0, which can be expressed as: Assuming that fan j is completely blocked by the wake of fan i, the schematic diagram is as follows. Figure 5 As shown. Based on the wind turbine wake shading logic matrix, if any two wind turbines are in a state of complete shading, the wind speed correction value for wind turbine j being completely shaded under the influence of wind turbine i's wake can be obtained. for: in C T This represents the turbine thrust coefficient. Based on the diameter of the affected area, the blade diameter, and the predicted wind speed, the predicted wind speed at each turbine with a complete shading relationship is corrected. After calculating the corrected velocities of the wake of all turbines at their respective locations, a complete shading velocity matrix V can be formed. a : When it is determined that any two wind turbines have a partial shading relationship based on the wind turbine wake shading logic matrix, the wake partial shading area is calculated based on the second projection distance, the diameter of the affected area, and the blade diameter. Figure 6 As shown, the wake partial obstruction area S ij Let x represent the distance of the wake of wind turbine i projected in the direction parallel to the wind at a height of 100m. ij The area enclosed by the affected area and the swept area of ​​the turbine blades. Based on the sector area formula and Heron's formula: The wind speed correction value for fan j being partially blocked under the influence of the wake of fan i can be obtained. for:

[0071] After calculating the corrected velocities of the wakes of all wind turbines relative to their respective locations, a partial obstruction velocity matrix V can be formed. h :

[0072] Then, based on the full occlusion velocity matrix, the full occlusion logic matrix, the partial occlusion velocity matrix, and the partial occlusion logic matrix, the wind speed correction velocity matrix is ​​calculated: V = V a ⊙A+V h ⊙H, where the element in the i-th row and j-th column of V is This represents the speed correction value for fan j affected by the wake of fan i. Finally, when fan j is affected by the wakes of other fans, the actual wind speed at the fan's location can be calculated as the sum of kinetic energies. Therefore, the actual corrected wind speed V for fan j is... j It can be expressed as:

[0073] S6. Calculate the wind farm output during the target planning period based on the wind turbine corrected wind speed, the predicted meteorological data, and the wind turbine parameters.

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

[0075] The wind farm output during the target planning period is calculated based on the corrected wind speed, the predicted meteorological data, and the wind turbine parameters, including: calculating the swept area of ​​the blades based on the blade diameter; calculating the maximum output power of the wind turbine based on the swept area of ​​the blades, the corrected wind speed, the wind turbine efficiency, and the predicted air density; and calculating the wind farm output during 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 area of ​​the blade sweeping region is... This represents the area enclosed by the swept area of ​​the wind turbine blades. Based on the area S of the swept area... i The corrected wind speed of the fan, and the fan efficiency η w Based on the predicted air density, the maximum output power of the wind turbine is calculated. Then, based on the maximum output power of the wind turbine and the number of wind turbines, the wind farm output P during the target planning period is calculated. W for:

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

[0078] In a preferred embodiment of the present invention, given the speed and direction of the wind at 100m height at various times on typical days of extreme heat and cold reconstructing in the planning year, the actual wind turbine corrected wind speed considering the wake effect is obtained. Combined with meteorological elements at each time, the maximum output power of the considered wind farm is obtained. Then, calculations are performed at various times within the typical day, and the wind farm output power is combined to obtain the wind farm output on the extreme heat and cold days of the planning year. Finally, by combining the predicted load data of the extreme heat and cold days, a source-load scenario for the extreme heat and cold days of the planning year is constructed. Based on the source-load scenario for the extreme heat and cold days of the planning year, the power system power planning scheme can be further verified under the extreme heat and cold day scenario to determine whether the system meets the N-1 constraint condition under this scenario, and feedback can be 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 for the extreme heat and cold days of the planning year is as follows: Figure 7As shown, annual meteorological data on an hourly scale is first collected, and meteorological data for extreme heat and cold days in each year are extracted. Then, meteorological and load data for typical days of extreme heat and cold are reconstructed. First, hourly averages are calculated based on historical meteorological and load data to generate basic data for typical days of meteorological and load data. Then, the maximum temperature of extreme heat and cold is fitted to correct the meteorological and load data for typical days of meteorological and load data to obtain predicted meteorological and load data. The coordinates are rotated according to the initial coordinates of the wind turbines, and the envelope line equation of the wake effect influence area is solved to construct a wake full shading logic matrix and a wake partial shading logic matrix. According to the wake shading judgment criteria, it is determined whether wind turbine j is fully or partially shaded by wind turbine i to determine the wake influence range of the wind turbines. Then, the wind speed of the wind turbines is corrected according to the wind speed correction values ​​under full shading and partial shading. Finally, the maximum output power of the wind farm on a typical day is calculated, and the source-load scenario for typical days of extreme heat and cold is constructed based on the maximum output power and predicted load data.

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

[0080] With the intensification of global climate change, the frequency and intensity of extreme weather events have increased significantly, posing unprecedented challenges to the stable operation of power systems. Against the backdrop of a continuously increasing proportion of renewable energy, wind power, as an important clean energy source, is significantly affected by extreme weather in terms of output stability and reliability. The output of wind farms depends not only on meteorological conditions such as wind speed and direction but also on 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 been continuously increasing, and their proportion in the power system has also gradually risen. However, the intermittency and uncertainty of wind power output pose significant challenges to the operation and dispatch of power systems. Especially under extreme weather conditions, such as extreme heat, extreme cold, strong winds, and torrential rain, the volatility of wind power output is even more pronounced. For example, in hot weather, wind speed may decrease, leading to insufficient wind power output; while in cold weather, wind speed may increase, but at the same time, low temperatures may cause wind turbines to freeze, affecting their normal operation. Furthermore, extreme weather can also trigger grid equipment failures, further exacerbating the operational risks of the power system. The wake effect is a crucial component of the complex flow phenomena within wind farms, and its impact on overall wind farm output cannot be ignored. The wake effect not only reduces wind speeds downstream but also increases turbulence intensity between turbines, thereby reducing power generation efficiency and increasing mechanical fatigue. In high-density wind farms, the wake effect is particularly significant, especially under extreme weather conditions, where changes in wind speed and direction further exacerbate its complexity. Therefore, to accurately assess wind farm output under extreme weather conditions, the wake effect must be comprehensively considered. Existing research largely focuses on the wake effect under normal weather conditions, lacking in-depth analysis of its variation under extremely hot and cold weather. Extreme weather can lead to significant changes in meteorological parameters such as wind speed and direction, thus affecting the propagation and intensity of the wake, 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 often neglect the impact of the wind turbine wake effect on wind power output when constructing source-load scenarios for extremely hot and cold days, resulting in insufficient accuracy and reliability of the scenarios. This embodiment establishes a wake effect model considering wind direction changes and turbulence intensity for extreme hot and cold weather. Coordinate axis rotation facilitates the establishment of envelope equations, accurately calculating the influence range and intensity of each wind turbine's wake, thus improving the accuracy of wind power output reconstruction. Simultaneously, by combining the wake effect model with meteorological data reconstruction results, source-load scenarios for extreme hot and cold days are dynamically constructed, reflecting real-time changes in wind farm output under extreme weather conditions and providing a more reliable decision-making basis for power system planning.

[0081] By implementing this embodiment, historical meteorological and load data are filtered using preset maximum and minimum temperature thresholds. For extreme hot or cold weather, predictions are made to obtain predicted meteorological and load data. Considering the wake effect of each wind turbine in the wind farm, a wind turbine wake obstruction logic matrix is ​​constructed to represent the wake influence relationship between wind turbines in the wind farm. The predicted wind speed at the wind turbine is corrected on the wind turbine wake obstruction logic matrix to obtain the corrected wind speed. This better considers the wake interaction between wind turbines in high-density wind farms under extreme cold or hot weather, more accurately simulates the wind turbine operation under extreme cold or hot weather, and improves the wind turbine's performance. 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 planned power system is planned based on the wind farm output and predicted load data. Combining the predicted load data with the wind farm output improves the accuracy of wind farm output under extreme weather conditions, thereby reducing power system planning deviations and improving the accuracy of power system planning.

[0082] See Figure 19 This is a schematic diagram of a power system planning device considering the wake effect of wind turbines, provided in an embodiment of the present invention, comprising:

[0083] The data acquisition module is used to acquire historical hourly meteorological data, historical load data, wind turbine parameters, wind turbine coordinates, and ground roughness of wind farms in the power system to be planned; the data prediction module is used to perform regression prediction based on the historical meteorological data, the historical load data, and preset maximum and minimum temperature thresholds to obtain 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; and the wind turbine shading logic module... The system comprises the following modules: a wake obstruction logic matrix for each wind turbine, constructed based on the wake influence area of ​​each turbine and the turbine parameters; a wind turbine wind speed correction module for correcting the predicted wind speed at each turbine based on the wake obstruction logic matrix, the turbine parameters, and the ground roughness, to obtain the corrected wind speed; a wind farm output calculation module for calculating the wind farm output during the target planning period based on the corrected wind speed, the predicted meteorological data, and the turbine parameters; and a power system planning module for planning the power system to be planned based on the wind farm output and the predicted load data.

[0084] This invention provides a power system planning device that considers the wake effect of wind turbines. The device includes a data acquisition module that acquires historical hourly meteorological data, historical load data, wind turbine parameters, wind turbine coordinates, and ground roughness of wind farms in the power system to be planned. A data prediction module performs regression prediction based on the historical meteorological data, the historical load data, and preset maximum and minimum temperature thresholds to obtain predicted meteorological and load data for the target planning period. A wind turbine wake prediction module determines the wake influence area of ​​each wind turbine and the location of each wind turbine based on the wind turbine coordinates, the predicted meteorological data, and the wind turbine parameters. The system calculates the predicted wind speed. In the wind turbine shading logic construction module, a wind turbine wake shading logic matrix is ​​constructed based on the wake influence area of ​​each wind turbine and the turbine parameters. Then, in the wind turbine wind speed correction module, the predicted wind speed at each wind turbine is corrected based on the wind turbine wake shading logic matrix, the turbine parameters, and the ground roughness to obtain the corrected wind speed. Next, in the wind farm output calculation module, the wind farm output during the target planning period is calculated based on the corrected wind speed, the predicted meteorological data, and the turbine parameters. Finally, in the power system planning module, the power system to be planned is planned based on the wind farm output and the predicted load data. Historical meteorological and load data are filtered using preset maximum and minimum temperature thresholds. For extreme hot or cold weather, forecasts are generated to obtain predicted meteorological and load data. Considering the wake effect of each wind turbine in the wind farm, a wake obstruction logic matrix is ​​constructed to represent the wake influence relationship between wind turbines. The predicted wind speed at the turbine is corrected based on this matrix to obtain the corrected wind speed. This better accounts for the wake interaction between wind turbines in high-density wind farms under extreme cold or heat, more accurately simulating wind turbine operation under these conditions. Then, based on the corrected wind speed, predicted meteorological data, and turbine parameters, the wind farm output for the target planning period is calculated. Finally, the planned power system is designed based on the wind farm output and predicted load data. Combining the predicted load data with the wind farm output improves the accuracy of wind farm output under extreme weather conditions, thereby reducing power system planning deviations and improving overall power system planning accuracy.

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

[0086] 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 considering wind turbine wake effects as described in the above embodiments. The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. 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 including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute a power system planning method considering wind turbine wake effect as described in the above embodiment.

[0088] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for power system planning considering wind turbine wake effect, characterized in that, include: Acquire historical meteorological data, historical load data, wind turbine parameters, wind turbine coordinates, and ground roughness of wind farms in the planned power system for each hour of the year; Regression prediction is performed based on the historical meteorological data, the historical load data, the preset highest temperature threshold, and the lowest temperature threshold to obtain the predicted meteorological data and predicted load data for the target planning period. Based on 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. Based on the wake influence area of ​​each wind turbine and the wind turbine parameters, a wind turbine wake blocking logic matrix is ​​constructed. Based on the wind turbine wake obstruction logic matrix, the wind turbine parameters, and the ground roughness, the predicted wind speed at each wind turbine is corrected to obtain the corrected wind speed at the wind turbine. Based on the corrected wind speed of the wind turbine, the predicted meteorological data, and the wind turbine parameters, the wind farm output during the target planning period is calculated. The power system to be planned is based on the wind farm output and the predicted load data. The predicted meteorological data includes: predicted east-west wind speed and predicted north-south wind speed; the wind turbine parameters include blade diameter. Based on 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: Based on the predicted east-west wind speed and the predicted north-south wind speed, the predicted wind speed at each wind turbine is calculated, and the predicted wind direction angle at each wind turbine is determined based on the wind direction angle corresponding to the predicted wind speed. Based on the predicted wind direction angle at each wind turbine, construct the rotation matrix of the wind turbine coordinates; Based on the wind turbine coordinates, an initial wind farm coordinate system is constructed; The initial wind farm coordinate system is rotated and corrected according to the rotation matrix to obtain the rotated target wind farm coordinate system. The envelope equation for each wind turbine is calculated based on the target wind farm coordinate system and the blade diameter. Based on the envelope equation, the wake influence area of ​​each wind turbine is determined; Based on the wake influence area of ​​each wind turbine and the turbine parameters, a wind turbine wake blocking logic matrix is ​​constructed, including: Based on the envelope 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 first distance between the current rotation coordinate of the current wind turbine and the envelope is calculated. The occlusion relationship is determined based on the first distance, 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. Among the remaining wind turbines, the wind turbine whose rotational coordinates and envelope equations satisfy the first condition is taken as the first wind turbine that has a semi-blocking relationship with the current wind turbine. Construct a partial shading logic matrix based on the current partial shading relationship between the wind turbine and the first wind turbine; Among the remaining wind turbines, those whose rotational coordinates and envelope equations satisfy the second condition are considered as the second wind turbines that have an occlusion relationship with the current wind turbine; where the occlusion relationship includes full occlusion and partial occlusion. Based on the shading relationship between the current wind turbine and the second wind turbine, a first shading logic matrix is ​​constructed to represent the existence of shading relationships; The first occlusion logic matrix is ​​modified based on the half-occlusion logic matrix to obtain the full occlusion logic matrix; The half-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 ; wherein, represents the first distance; represents the diameter of the blade; represents the longitudinal coordinate of the rotational coordinate of the current fan; represents the longitudinal coordinate of the rotational coordinate of the remaining fan; The second condition is: ,in, The x-coordinate represents the current rotational coordinate of the wind turbine; The first parameter represents the envelope equation of the remaining wind turbines; The second parameter represents the envelope equation of the remaining wind turbines; The first slope of the envelope equation for the remaining wind turbines is represented; This represents the second slope of the envelope equation for the remaining wind turbines.

2. The power system planning method considering wind turbine wake effect as described in 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, and preset maximum and minimum temperature thresholds to obtain the predicted meteorological data and predicted load data for the target planning period, including: Historical meteorological data corresponding to historical temperatures greater than a preset maximum temperature threshold or historical temperatures less than a preset minimum temperature threshold are used as target meteorological data, and historical load data corresponding to the target meteorological data are used as target load data. Calculate the hourly average of each target meteorological data point, and use the hourly average of each target meteorological data point as a typical daily scene meteorological dataset for the target planning period; The daily average value of meteorological data is calculated based on the typical daily scene meteorological dataset to obtain the daily average meteorological dataset; Regression prediction is performed 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; Based on the typical daily scene meteorological dataset and the daily average meteorological dataset, the daily maximum predicted meteorological data is corrected to obtain the 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 the predicted load data for the target planning period.

3. The power system planning method considering wind turbine wake effect as described in claim 2, characterized in that, 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, including: Calculate the hourly average of the target load data, and use the hourly average of the target load data as the typical daily scenario load dataset for the target planning period; The daily average load data is calculated based on the typical daily scenario load dataset to obtain the daily average load dataset; Regression prediction is performed based on the daily average load dataset and the predicted meteorological data to obtain the daily maximum predicted load data for the target planning period; The daily maximum predicted load data is corrected based on the typical daily scenario load dataset and the daily average load dataset to obtain the predicted load data for the target planning period.

4. The power system planning method considering wind turbine wake effect as described in claim 1, characterized in that, The fan parameters also include: the number of fans and the fan installation height; The predicted wind speed at each wind turbine is corrected based on the wind turbine wake obstruction logic matrix, the wind turbine parameters, and the ground roughness, resulting in the corrected wind speed for the wind turbine, including: Based on the rotation coordinates of the remaining wind turbines and the current wind turbine, the first projected distance of the wind turbine in the horizontal wind direction and the second projected distance in the vertical wind direction are calculated. The diameter of the affected area is calculated based on the installation height of the wind turbine, the ground roughness, the first projection distance, and the blade diameter. If, based on the wind turbine wake shading logic matrix, it is determined that the current wind turbine and other wind turbines have a full shading relationship, the predicted wind speed at the current wind turbine with the full shading relationship is corrected according to the diameter of the affected area, the blade diameter, and the predicted wind speed, to obtain the full shading speed matrix. If it is determined that the current wind turbine has a partial shading relationship with other wind turbines based on the wind turbine wake shading logic matrix, the wake partial shading area is calculated based on the second projection distance, the diameter of the affected area, and the blade diameter. The predicted wind speed is corrected based on the wake partial obstruction area, the blade diameter, and the diameter of the affected area to obtain the partial obstruction speed matrix; The wind speed correction matrix is ​​calculated based on the full occlusion velocity matrix, the full occlusion logic matrix, the partial occlusion velocity matrix, and the partial occlusion logic matrix. Based on the wind speed correction velocity matrix, the predicted wind speed, and the number of wind turbines, the wind turbine correction wind speed at each wind turbine is calculated.

5. A power system planning method considering wind turbine wake effect as described in claim 4, characterized in that, The wind turbine parameters also include wind turbine efficiency; the predicted meteorological data also includes predicted air density. Based on the corrected wind speed of the wind turbine, the predicted meteorological data, and the wind turbine parameters, the wind farm output during the target planning period is calculated, including: Calculate the area of ​​the blade sweeping zone based on the blade diameter; The maximum output power of the fan is calculated based on the area of ​​the blade sweeping zone, the corrected wind speed of the fan, the fan efficiency, and the predicted air density. The wind farm output during the target planning period is calculated based on the maximum output power of the wind turbine and the number of wind turbines.

6. A power system planning device considering the wake effect of wind turbines, characterized in that, include: The data acquisition module is used to acquire historical meteorological data, historical load data, wind turbine parameters, wind turbine coordinates, and ground roughness of wind farms in the power system to be planned, for each 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 shading logic construction module is used to construct a wind turbine wake shading logic matrix based on the wake influence area of ​​each wind turbine and the wind turbine parameters. The wind turbine wind speed correction module is used to correct the predicted wind speed at each wind turbine based on the wind turbine wake obstruction logic matrix, the wind turbine parameters, and the ground roughness, so as to obtain the corrected wind speed of the wind turbine. The wind farm output calculation module is used to calculate the wind farm output during the 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 based on the wind farm output and the predicted load data. The predicted meteorological data includes: predicted east-west wind speed and predicted north-south wind speed; the wind turbine parameters include blade diameter. 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, including: Based on the predicted east-west wind speed and the predicted north-south wind speed, the predicted wind speed at each wind turbine is calculated, and the predicted wind direction angle at each wind turbine is determined based on the wind direction angle corresponding to the predicted wind speed. Based on the predicted wind direction angle at each wind turbine, construct the rotation matrix of the wind turbine coordinates; Based on the wind turbine coordinates, an initial wind farm coordinate system is constructed; The initial wind farm coordinate system is rotated and corrected according to the rotation matrix to obtain the rotated target wind farm coordinate system. The envelope equation for each wind turbine is calculated based on the target wind farm coordinate system and the blade diameter. Based on the envelope equation, the wake influence area of ​​each wind turbine is determined; A wind turbine wake obstruction logic construction module is used to construct a wind turbine wake obstruction logic matrix based on the wake influence area of ​​each wind turbine and the wind turbine parameters, including: Based on the envelope 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 first distance between the current rotation coordinate of the current wind turbine and the envelope is calculated. The occlusion relationship is determined based on the first distance, 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. Among the remaining wind turbines, the wind turbine whose rotational coordinates and envelope equations satisfy the first condition is taken as the first wind turbine that has a semi-blocking relationship with the current wind turbine. Construct a partial shading logic matrix based on the current partial shading relationship between the wind turbine and the first wind turbine; Among the remaining wind turbines, those whose rotational coordinates and envelope equations satisfy the second condition are considered as the second wind turbines that have an occlusion relationship with the current wind turbine; where the occlusion relationship includes full occlusion and partial occlusion. Based on the shading relationship between the current wind turbine and the second wind turbine, a first shading logic matrix is ​​constructed to represent the existence of shading relationships; The first occlusion logic matrix is ​​modified based on the half-occlusion logic matrix to obtain the full occlusion logic matrix; The half-blocking logic matrix and the full-blocking logic matrix are used as the wind turbine wake blocking logic matrix; The first condition is: and ;in, Indicates the first distance; Indicates the blade diameter; The vertical coordinate represents the current rotational coordinate of the wind turbine; The ordinate represents the rotational coordinates of the remaining wind turbines; The second condition is: ,in, The x-coordinate represents the current rotational coordinate of the wind turbine; The first parameter represents the envelope equation of the remaining wind turbines; The second parameter represents the envelope equation of the remaining wind turbines; The first slope of the envelope equation for the remaining wind turbines is represented; This represents the second slope of the envelope equation for the remaining wind turbines.

7. A terminal device, characterized in that, The system includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement a power system planning method considering wind turbine wake effects as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device on which the computer-readable storage medium is located to perform a power system planning method considering wind turbine wake effects as described in any one of claims 1 to 5.

Images