A method and system for collaborative planning of energy storage in offshore wind farms considering wake effect

Abstract

The present invention relates to the field of wind power generation technology, and in particular to a method and system for collaborative planning of energy storage in offshore wind farms considering wake effects, including analyzing the wake effects of offshore wind farms according to the original wind speed and direction data of the areas where each wind turbine generator set is located in the offshore wind farm, and establishing an incompletely swept attenuation model for the wind turbine wake; based on the incompletely swept attenuation model for the wind turbine wake, calculating the active output value of each wind turbine generator set under the influence of the wake effect; according to the active output value, using the K-means clustering algorithm to obtain a typical wind turbine output scenario set; using the typical wind turbine output scenario set to perform collaborative planning of offshore wind farms and energy storage systems, and obtain the optimal offshore wind power storage capacity configuration and the optimal grid connection point of the offshore wind farm, so as to control the offshore wind farm energy storage for coordinated and optimized operation. The present invention realizes the collaborative planning of offshore wind farms and energy storage systems by constructing an incompletely swept attenuation model for the wind turbine wake and a clustering algorithm.

Description

Technical Field

[0001] The present invention relates to the technical field of wind power generation, and in particular to a method and system for collaborative planning of energy storage in offshore wind farms taking wake effects into consideration. Background Art

[0002] With the increasing global attention to issues such as energy security, ecological environment and climate change, the rapid development of new energy such as wind power and photovoltaics has become a key measure for the international community to promote energy transformation and respond to climate change. In the field of new energy, wind energy has been rapidly promoted around the world with its advantages of short construction period, low environmental requirements, abundant reserves and high utilization rate. In particular, offshore wind power, as an important part of the renewable energy field, has shown a trend of large-scale development in recent years due to its advantages such as abundant resource reserves and proximity to power load centers. Offshore wind power not only has more stable wind energy resources and high power generation hours, but is also less restricted by terrain and landforms, making it suitable for large-scale development. At the same time, the geographical location of offshore wind power is close to the power load center, which is convenient for local power grid consumption, effectively avoiding the problem of large-scale wind power long-distance transmission and improving energy utilization efficiency.

[0003] However, with the in-depth development of offshore wind power resources, although offshore wind power has many advantages, the output of offshore wind power has significant uncertainty, which brings severe challenges to the safe and stable operation of the power grid. Especially after wind power is connected to the power grid, the output of wind power is greatly affected by natural factors such as wind speed and wind direction, and these factors are random and volatile, which makes it difficult to accurately predict the output of wind power. This uncertainty not only affects the dispatching and operation of the power grid, but also makes it difficult to absorb wind power and cause wind abandonment. Therefore, how to efficiently absorb wind power has become a problem that needs to be solved. At the same time, in the existing technology, the output prediction of offshore wind farms mainly relies on the transmission system. However, these models often ignore the impact of wind turbine wake effect on wind power output. Wind turbine wake effect refers to the change in wind speed and direction after wind passes through a wind turbine generator set, which in turn affects the output of downstream wind turbines. Since offshore wind farms usually adopt a densely arranged wind turbine layout, the impact of the wake effect is particularly significant. Traditional wind turbine wake models are mostly based on the complete pass attenuation assumption, that is, it is assumed that the wake effect is completely transmitted between wind turbines. This assumption often leads to large deviations in output prediction in practical applications, making it difficult to accurately reflect the actual output of wind farms, thereby affecting the reliability of subsequent planning decisions.

[0004] In summary, the existing technology has many shortcomings in terms of offshore wind farm output prediction. The existence of these problems not only limits the efficient development and utilization of offshore wind power, but also poses a potential threat to the safe and stable operation of the power grid. Therefore, developing an offshore wind farm energy storage collaborative planning method that considers the wake effect has become the key to improving wind power absorption capacity and ensuring the safe and stable operation of the power grid. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a method and system for collaborative planning of energy storage in offshore wind farms taking into account the wake effect.

[0006] In a first aspect, the present invention provides a method for coordinated planning of energy storage in an offshore wind farm considering wake effects, the method comprising the following steps:

[0007] The wake effect of offshore wind farms is analyzed based on the original wind speed and direction data of the areas where each wind turbine generator set is located, and an incomplete sweep attenuation model of wind turbine wake is established.

[0008] Based on the wind turbine wake incomplete pass attenuation model, the active output value of each wind turbine generator set is calculated under the influence of the wake effect;

[0009] Constructing a fan output clustering index according to the active output value, and based on the fan output clustering index, using a K-means clustering algorithm to cluster and obtain a typical fan output scenario set;

[0010] The typical wind turbine output scenario set is used to coordinate the planning of offshore wind farms and energy storage systems, and the optimal offshore wind power storage capacity configuration and the optimal grid connection point of the offshore wind farm are obtained;

[0011] The offshore wind farm energy storage is controlled to coordinate and optimize operation according to the optimal offshore wind power storage capacity configuration and the optimal offshore wind farm grid connection point.

[0012] In a further embodiment, the step of analyzing the wake effect of the offshore wind farm based on the original wind speed and direction data of the area where each wind turbine generator set is located in the offshore wind farm and establishing the incomplete sweep attenuation model of the wind turbine wake includes:

[0013] The wake effect is analyzed based on the original wind speed and direction data of the area where each wind turbine generator set is located in the offshore wind farm, and the influence range of the upstream wind turbine on the wind speed of the downstream wind turbine is quantified;

[0014] According to the influence range of the upstream fan on the wind speed of the downstream fan, determine the relative position relationship between the downstream fan and the upstream fan wake range;

[0015] When it is determined that the relative position relationship between the wake range of the downstream fan and the upstream fan is that the wake does not completely pass, the overlapping area of ​​the wake circle and the downstream fan is calculated by a geometric analysis method based on the upstream fan position, the downstream fan position and the wind direction angle;

[0016] According to the overlapping area of ​​the wake circle and the downstream wind turbine and the radius of the wind turbine blades, the actual wind speed of the wind turbine under the influence of the superposition of the wakes of multiple upstream wind turbines is calculated;

[0017] According to the actual wind speed of the wind turbine, the cut-in wind speed of the wind turbine and the cut-out wind speed of the wind turbine, an incomplete pass attenuation model of the wind turbine wake is constructed.

[0018] In a further embodiment, the step of calculating the overlapping area of ​​the wake circle and the downstream wind turbine by a geometric analysis method based on the upstream wind turbine position, the downstream wind turbine position and the wind direction angle comprises:

[0019] Determine the center position and radius of the wake circle according to the upstream wind turbine position, the downstream wind turbine position and the current wind direction angle;

[0020] Based on the center position and radius of the wake circle, the center angle between the wake circle and the downstream wind turbine is calculated through geometric relationship;

[0021] According to the central angle of the circle and the radius of the downstream fan blade, the overlapping area of ​​the wake circle and the downstream fan is calculated.

[0022] In a further embodiment, the step of calculating the active output value of each wind turbine generator set under the influence of the wake effect based on the wind turbine wake incomplete pass attenuation model includes:

[0023] Determine the rated active power of the wind turbine according to the current capacity configuration of the wind turbine generator set;

[0024] When the actual wind speed of the wind turbine is less than the cut-in wind speed of the wind turbine or is greater than or equal to the cut-out wind speed of the wind turbine, the active output value of each wind turbine generator set is determined to be zero;

[0025] When the actual wind speed of the wind turbine is between the cut-in wind speed of the wind turbine and the rated wind speed of the wind turbine, a proportionality coefficient is calculated according to the difference between the actual wind speed of the wind turbine and the cut-in wind speed of the wind turbine, and the difference between the rated wind speed of the wind turbine and the cut-in wind speed of the wind turbine, and the active output value of each wind turbine generator set under the influence of the wake effect is calculated according to the product of the proportionality coefficient and the rated active power of the wind turbine;

[0026] When the actual wind speed of the wind turbine is between the rated wind speed of the wind turbine and the cut-out wind speed of the wind turbine, the rated active power of the wind turbine is used as the active output value of each wind turbine generator set under the influence of the wake effect.

[0027] In a further implementation scheme, the steps of constructing a wind turbine output clustering index according to the active output value, and clustering a typical wind turbine output scene set using a K-means clustering algorithm based on the wind turbine output clustering index include:

[0028] Obtaining the active output value of each wind turbine generator set within a preset target time period, and dividing the target time period into multiple time periods;

[0029] According to the active output value of each wind turbine generator set in each time period within the target time period, a wind turbine output scenario corresponding to each time period is constructed, and the wind turbine output scenarios of all time periods are combined to form a wind turbine output scenario set;

[0030] A wind turbine output clustering index is calculated according to the active output value, and a corresponding clustering index scenario set is generated according to the wind turbine output clustering index; each clustering index scenario set includes an average wind turbine output index, a wind turbine output fluctuation rate index and a wind turbine maximum output index of the wind turbine generator set within a time period;

[0031] Determining the number of clusters, and selecting a number of cluster indicator scenarios from the cluster indicator scenario set as initial cluster centers according to the number of clusters;

[0032] Based on the improved three-scale analytic hierarchy process and entropy weight method, the comprehensive weight of each wind turbine output clustering index is calculated, and the weighted distance from each clustering index scenario to each cluster center is calculated according to the comprehensive weight of each wind turbine output clustering index.

[0033] Assign each clustering indicator scenario to the clustering cluster corresponding to the clustering center with the smallest scenario weighted distance, and iteratively update the clustering center of each clustering cluster according to the clustering indicator scenario in each clustering cluster until the clustering centers converge to obtain the optimal clustering center set;

[0034] According to the optimal cluster center set, calculating the scene weighted distance from the cluster indicator scene in each optimal cluster cluster to the cluster center;

[0035] The wind turbine output scenario corresponding to the clustering index scenario with the smallest scene weighted distance in each optimal clustering cluster is taken as the typical wind turbine output scenario to form a typical wind turbine output scenario set.

[0036] In a further embodiment, the step of calculating the wind turbine output clustering index according to the active output value comprises:

[0037] According to the active output value of each wind turbine generator set in each time period within the target time period, the initial index of the average output of the wind turbine is calculated;

[0038] According to the difference between the active output value of each wind turbine generator set in each time period within the target time period and the average output index of the wind turbine, the initial index of the wind turbine output fluctuation rate is calculated;

[0039] Screening out the maximum active output value from the active output values ​​of each wind turbine generator set in each time period within the target time period, and using the maximum active output value as the initial indicator of the maximum output of the wind turbine;

[0040] The initial index of the average output of the wind turbine, the initial index of the fluctuation rate of the output of the wind turbine and the initial index of the maximum output of the wind turbine are normalized to obtain the corresponding average output index of the wind turbine, the fluctuation rate index of the output of the wind turbine and the maximum output index of the wind turbine.

[0041] In a further implementation scheme, the step of calculating the comprehensive weight of each wind turbine output clustering index based on the improved three-scale analytic hierarchy process and entropy weight method includes:

[0042] The first index weight of each wind turbine output clustering index is calculated by using the improved three-scale analytic hierarchy process, and the second index weight of each wind turbine output clustering index is calculated by using the entropy weight method.

[0043] The average value of the first indicator weight and the second indicator weight is calculated to obtain the comprehensive weight of each wind turbine output clustering indicator.

[0044] In a further implementation scheme, the steps of using the typical wind turbine output scenario set to coordinate planning of offshore wind farms and energy storage systems to solve for the optimal offshore wind power storage capacity configuration and the optimal grid connection point of the offshore wind farm include:

[0045] With the goal of minimizing the construction cost, operation cost and wind curtailment cost of offshore wind farm energy storage, a collaborative planning model of offshore wind farm and energy storage system is constructed using the typical wind turbine output scenario set;

[0046] The offshore wind farm and energy storage system collaborative planning model is solved by a mixed integer programming solver to obtain the optimal offshore wind power storage capacity configuration and the optimal offshore wind farm grid connection point.

[0047] In a further implementation scheme, the construction process of the offshore wind farm and energy storage system collaborative planning model is as follows:

[0048] Taking the planned capacity of the wind power energy storage system as a decision variable, the construction cost of the offshore wind farm energy storage system is obtained according to the planned capacity of the wind power energy storage system and the unit capacity construction cost; the wind power energy storage system includes a wind turbine and an energy storage system;

[0049] The operating cost is obtained based on the maintenance cost of the wind energy storage system and the actual power generation of the traditional generator set in a typical wind turbine output scenario.

[0050] The wind curtailment cost in different scenarios is calculated based on the deviation between the actual power generation of the wind turbine in each scenario in the typical wind turbine output scenario set and the active output value;

[0051] By minimizing the sum of the construction cost, the operation cost and the wind abandonment cost, a collaborative planning model for an offshore wind farm and an energy storage system is constructed.

[0052] In a second aspect, the present invention provides an offshore wind farm energy storage collaborative planning system considering wake effect, the system comprising:

[0053] The wake effect analysis module is used to analyze the wake effect of offshore wind farms based on the original wind speed and direction data of the areas where each wind turbine generator set is located in the offshore wind farm, and to establish an incomplete sweep attenuation model for the wind turbine wake;

[0054] An active output acquisition module is used to calculate the active output value of each wind turbine generator set under the influence of the wake effect based on the wind turbine wake incomplete pass attenuation model;

[0055] An output scenario clustering module, used to construct a wind turbine output clustering index according to the active output value, and based on the wind turbine output clustering index, use a K-means clustering algorithm to cluster and obtain a typical wind turbine output scenario set;

[0056] A collaborative planning and solving module is used to use the typical wind turbine output scenario set to perform collaborative planning of offshore wind farms and energy storage systems, and solve for the optimal offshore wind power storage capacity configuration and the optimal grid connection point of the offshore wind farm;

[0057] The coordinated operation control module is used to control the offshore wind farm energy storage for coordinated and optimized operation according to the optimal offshore wind power storage capacity configuration and the optimal grid connection point of the offshore wind farm.

[0058] The present invention provides a method and system for collaborative planning of energy storage in offshore wind farms considering wake effect. The method analyzes the wake effect of offshore wind farms according to original wind speed and direction data of the areas where each wind turbine generator set in the offshore wind farm is located, and establishes an incompletely swept attenuation model for the wind turbine wake; based on the incompletely swept attenuation model for the wind turbine wake, the active output value of each wind turbine generator set under the influence of the wake effect is calculated; a wind turbine output clustering index is constructed according to the active output value, and based on the wind turbine output clustering index, a typical wind turbine output scenario set is clustered using a K-means clustering algorithm; collaborative planning of offshore wind farms and energy storage systems is performed using the typical wind turbine output scenario set, and the optimal offshore wind power storage capacity configuration and the optimal offshore wind farm grid connection point are solved; and the offshore wind farm energy storage is controlled according to the optimal offshore wind power storage capacity configuration and the optimal offshore wind farm grid connection point for coordinated and optimized operation. Compared with the existing technology, this method improves the accuracy of wind turbine output prediction by constructing an incomplete sweep attenuation model of wind turbine wake and a K-means clustering algorithm, optimizes the coordinated planning of offshore wind farms and energy storage systems, reduces wind abandonment, and realizes efficient and coordinated operation of offshore wind farms and energy storage systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 It is a schematic flow chart of a method for collaborative planning of energy storage for offshore wind farms taking into account wake effects provided by an embodiment of the present invention;

[0060] Figure 2 It is a flowchart of the offshore wind farm energy storage collaborative planning process provided by an embodiment of the present invention;

[0061] Figure 3 is a schematic diagram of incomplete sweep attenuation of wake provided by an embodiment of the present invention;

[0062] Figure 4 It is a block diagram of an offshore wind farm energy storage collaborative planning system considering wake effect provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0063] The following specifically illustrates the implementation mode of the present invention in conjunction with the accompanying drawings. The embodiments are provided for illustrative purposes only and cannot be understood as limiting the present invention. The accompanying drawings are provided for reference and illustration only and do not constitute a limitation on the scope of patent protection of the present invention, because many changes can be made to the present invention without departing from the spirit and scope of the present invention.

[0064] refer to Figure 1 , an embodiment of the present invention provides a method for collaborative planning of energy storage in offshore wind farms taking into account wake effects, such as Figure 1 As shown, the method comprises the following steps:

[0065] S1. The wake effect of offshore wind farms is analyzed based on the original wind speed and direction data of the areas where each wind turbine generator set is located, and an incomplete sweep attenuation model of wind turbine wake is established.

[0066] In some embodiments, the step of analyzing the wake effect of the offshore wind farm based on the original wind speed and direction data of the area where each wind turbine generator set in the offshore wind farm is located and establishing the incomplete sweep attenuation model of the wind turbine wake includes:

[0067] The wake effect is analyzed based on the original wind speed and direction data of the area where each wind turbine generator set is located in the offshore wind farm, and the influence range of the upstream wind turbine on the wind speed of the downstream wind turbine is quantified;

[0068] According to the influence range of the upstream fan on the wind speed of the downstream fan, determine the relative position relationship between the downstream fan and the upstream fan wake range;

[0069] When it is determined that the relative position relationship between the wake range of the downstream fan and the upstream fan is that the wake does not completely pass, the overlapping area of ​​the wake circle and the downstream fan is calculated by a geometric analysis method based on the upstream fan position, the downstream fan position and the wind direction angle;

[0070] According to the overlapping area of ​​the wake circle and the downstream wind turbine and the radius of the wind turbine blades, the actual wind speed of the wind turbine under the influence of the superposition of the wakes of multiple upstream wind turbines is calculated;

[0071] According to the actual wind speed of the wind turbine, the cut-in wind speed of the wind turbine and the cut-out wind speed of the wind turbine, an incomplete pass attenuation model of the wind turbine wake is constructed.

[0072] Specifically, Figure 2 As shown, this embodiment collects the original wind speed and direction data of the area where each wind turbine generator set in the offshore wind farm is located. The original wind speed and direction data can be obtained by anemometers and wind vanes installed on each wind turbine, and the obtained original wind speed and direction data are preprocessed. The distribution characteristics of the wind speed and direction data are analyzed according to the preprocessed original wind speed and direction data, and the main wind direction and wind speed change range are identified, so that the wind speed and wind direction changes over a period of time can be obtained, and the upstream wind turbine and the downstream wind turbine are determined. At the same time, the influence of the upstream wind turbine on the wind speed of the downstream wind turbine is considered, that is, when the upstream wind When the wind turbine is working, a wake zone with reduced wind speed and increased turbulence will be formed downstream. Therefore, this embodiment uses the Jensen wake model to analyze the expansion and attenuation of the wake generated by the upstream wind turbine in the downstream according to the collected original wind speed and wind direction data, and quantifies the influence range of the upstream wind turbine on the wind speed of the downstream wind turbine according to the simulation analysis results of the wake effect. The influence range of the upstream wind turbine on the wind speed of the downstream wind turbine mainly includes parameters such as the boundary of the wake zone, the speed and degree of wake attenuation, etc. Among them, the influence of the upstream wind turbine on the downstream wind turbine can be expressed by the following formula:

[0073]

[0074]

[0075] In the formula, is the wake wind speed of the upstream wind turbine at the downstream distance x; is the original wind speed data; is the fan thrust coefficient; is the wake radius generated by the upstream wind turbine at the downstream distance x, i.e., the wake circle radius; is the radius of the fan blade; k is the wake attenuation constant; x is the horizontal distance from point x in the downstream area of ​​the wake.

[0076] After the influence range of the upstream wind turbine on the wind speed of the downstream wind turbine is known, the present embodiment can superimpose the wind farm layout diagram with the wake influence range diagram, and determine the relative position relationship between the downstream wind turbine and the upstream wind turbine wake range according to the superposition result, wherein determining the relative position relationship between the downstream wind turbine and the upstream wind turbine wake range includes judging whether the downstream wind turbine is completely within the wake range of the upstream wind turbine, partially overlaps with the wake range, or is outside the wake range, thereby dividing the downstream wind turbine into three categories according to the determined relative position relationship, namely, completely within the wake range, partially overlaps with the wake range, and is outside the wake range for processing, and marking the downstream wind turbine whose wake is not completely swept by the downstream wind turbine and its relative position relationship with the upstream wind turbine. For the downstream wind turbine whose wake is not completely swept by the downstream wind turbine, the present embodiment needs to calculate the overlapping area of ​​the wake circle and the downstream wind turbine. In some embodiments, the step of calculating the overlapping area of ​​the wake circle and the downstream wind turbine by a geometric analysis method based on the upstream wind turbine position, the downstream wind turbine position and the wind direction angle includes:

[0077] Determine the center position and radius of the wake circle according to the upstream wind turbine position, the downstream wind turbine position and the current wind direction angle;

[0078] Based on the center position and radius of the wake circle, the center angle between the wake circle and the downstream wind turbine is calculated through geometric relationship;

[0079] According to the central angle of the circle and the radius of the downstream fan blade, the overlapping area of ​​the wake circle and the downstream fan is calculated.

[0080] like Figure 3As shown, for the calculation process of the overlapping area of ​​the wake circle and the downstream wind turbine, this embodiment needs to determine the current wind direction angle according to the wind direction data, and the wind direction angle is defined as the clockwise offset angle of the wind direction north. At the same time, the geometric parameters such as the upstream wind turbine position and the downstream wind turbine position are determined by map measurement, and the center angle of the wake circle is determined by the geometric analysis method using the determined geometric parameters, so as to calculate the overlapping area of ​​the wake circle and the downstream wind turbine according to the center angle. The calculation formula of the center angle of the wake circle is:

[0081]

[0082]

[0083]

[0084]

[0085]

[0086] In the formula, is the straight-line distance between the upstream fan i and the downstream fan m; is the horizontal coordinate of the upstream fan i; is the horizontal coordinate of the downstream fan m; is the ordinate of the upstream fan i; is the ordinate of the downstream fan m; is the straight-line distance between the upstream wind turbine i and the downstream wind turbine m, taking into account the wind direction angle and fan position angle The impact of is the wake radius of the upstream wind turbine i; is the wind direction angle, defined as the clockwise deviation angle of the wind from the north; is the fan position angle, which indicates the position angle of the fan relative to the wind direction; is the central angle, which represents the central angle corresponding to the overlap between the wake circle and the area swept by the downstream fan blades; is the center angle, which represents another center angle corresponding to the overlapping part of the wake circle and the area swept by the downstream fan blades; is the blade radius of the downstream fan at the nth row and mth column.

[0087] The overlapping area of ​​the wake circle and the downstream fan can be calculated by the following formula:

[0088]

[0089]

[0090]

[0091]

[0092]

[0093] In the formula, is the height of the overlapped area; is the blade radius of the downstream fan; is the sector area of ​​the wake circle; is the fan-shaped area swept by the downstream fan blades; is the rectangular area of ​​the overlapping region; It is the area of ​​overlap between the wake circle and the area swept by the downstream fan blades.

[0094] Since in an offshore wind farm, the downstream wind turbines will be affected by the wakes of multiple upstream wind turbines, therefore, this embodiment needs to consider the superposition effect of the wakes of multiple upstream wind turbines to correct the overlap area to determine the actual wind speed of the downstream wind turbine. Specifically, the wake effects of each upstream wind turbine are superimposed, and the interaction between the wakes is considered. According to the overlap area of ​​the wake circle and the downstream wind turbine and the radius of the wind turbine blades, combined with the analysis results of the wake superposition effect, the actual wind speed of the wind turbine under the superposition influence of the wakes of multiple upstream wind turbines is calculated. Under the superposition influence of the wakes of multiple upstream wind turbines, the specific calculation formula for the actual wind speed of the wind turbine at any position in the wind farm is:

[0095]

[0096] In the formula, is the actual wind speed of the downstream wind turbine at row n and column m in the wind farm; is the number of wind turbines in a row of wind farms; is the number of wind turbines in a row of wind farms; is the wake velocity generated by the wind turbine in the i-th row and j-th column of the wind farm.

[0097] After obtaining the actual wind speed of the wind turbine, combined with the cut-in wind speed and cut-out wind speed of the wind turbine, the wind turbine wake incomplete sweep attenuation model is constructed, so as to achieve accurate prediction of the wake effect of the offshore wind farm and provide strong support for the planning and operation of the wind farm. The mathematical expression of the wind turbine wake incomplete sweep attenuation model is:

[0098]

[0099] Where, P is the active output value of the wind turbine generator set considering the wake effect; is the rated active power of the fan. As part of the objective function in the coordinated planning model of offshore wind farms and energy storage systems, the planned capacity of wind turbines and energy storage systems is an unknown quantity that needs to be solved in the target planning. The actual rated active power of the wind turbine is obtained by the planned capacity of the wind turbine solved by the target planning, and the actual rated active power of the wind turbine is fed back to the wind turbine wake incomplete pass attenuation model for further optimization and adjustment; v is the actual wind speed of the wind turbine; is the wind turbine cut-in wind speed; is the rated wind speed of the fan; Cut out wind speed for the fan.

[0100] S2. Based on the wind turbine wake incomplete pass attenuation model, the active output value of each wind turbine generator set is calculated under the influence of the wake effect.

[0101] In some embodiments, the step of calculating the active output value of each wind turbine generator set under the influence of the wake effect based on the wind turbine wake incomplete pass attenuation model includes:

[0102] Determine the rated active power of the wind turbine according to the current capacity configuration of the wind turbine generator set;

[0103] When the actual wind speed of the wind turbine is less than the cut-in wind speed of the wind turbine or is greater than or equal to the cut-out wind speed of the wind turbine, the active output value of each wind turbine generator set is determined to be zero;

[0104] When the actual wind speed of the wind turbine is between the cut-in wind speed of the wind turbine and the rated wind speed of the wind turbine, a proportionality coefficient is calculated according to the difference between the actual wind speed of the wind turbine and the cut-in wind speed of the wind turbine, and the difference between the rated wind speed of the wind turbine and the cut-in wind speed of the wind turbine, and the active output value of each wind turbine generator set under the influence of the wake effect is calculated according to the product of the proportionality coefficient and the rated active power of the wind turbine;

[0105] When the actual wind speed of the wind turbine is between the rated wind speed of the wind turbine and the cut-out wind speed of the wind turbine, the rated active power of the wind turbine is used as the active output value of each wind turbine generator set under the influence of the wake effect.

[0106] Specifically, this embodiment collects the current capacity configuration data of each wind turbine in the offshore wind farm, determines the rated active power of the wind turbine according to the current capacity configuration of the wind turbine, so that the wind turbine can continuously and stably output the maximum power, and monitors the current actual wind speed of the wind turbine, compares the actual wind speed of the wind turbine with the cut-in wind speed and the cut-out wind speed of the wind turbine. If the current actual wind speed of the wind turbine is less than the cut-in wind speed or greater than or equal to the cut-out wind speed, the wind turbine does not generate electricity, and the active output value of the unit is set to 0; if the actual wind speed of the wind turbine is between the cut-in wind speed of the wind turbine and the rated active power of the wind turbine, the wind turbine will not generate electricity, and the active power output value of the wind turbine will be set to 0. If the wind speed is between the rated wind speed and the cut-out wind speed, the wind turbine can generate electricity normally. At this time, in this embodiment, the difference between the actual wind speed of the wind turbine and the cut-in wind speed is divided by the difference between the rated wind speed and the cut-in wind speed to obtain the proportionality coefficient, and the proportionality coefficient is multiplied by the rated active power to calculate the active output value of each wind turbine generator set under the influence of the wake effect; if the wind speed is between the rated wind speed and the cut-out wind speed, the wind turbine generator set should be able to continuously output the rated active power, but the adjustment of the wake effect must also be considered. Through different wind speed ranges, the active output value of each wind turbine generator set under the influence of the wake effect can be calculated more accurately.

[0107] S3. Construct a wind turbine output clustering index according to the active output value, and based on the wind turbine output clustering index, use a K-means clustering algorithm to cluster and obtain a typical wind turbine output scenario set.

[0108] In some implementations, the step of constructing a wind turbine output clustering index according to the active output value, and clustering a typical wind turbine output scenario set using a K-means clustering algorithm based on the wind turbine output clustering index includes:

[0109] Obtaining the active output value of each wind turbine generator set within a preset target time period, and dividing the target time period into multiple time periods;

[0110] According to the active output value of each wind turbine generator set in each time period within the target time period, a wind turbine output scenario corresponding to each time period is constructed, and the wind turbine output scenarios of all time periods are combined to form a wind turbine output scenario set;

[0111] A wind turbine output clustering index is calculated according to the active output value, and a corresponding clustering index scenario set is generated according to the wind turbine output clustering index; each clustering index scenario set includes an average wind turbine output index, a wind turbine output fluctuation rate index and a wind turbine maximum output index of the wind turbine generator set within a time period;

[0112] Determining the number of clusters, and selecting a number of cluster indicator scenarios from the cluster indicator scenario set as initial cluster centers according to the number of clusters;

[0113] Based on the improved three-scale analytic hierarchy process and entropy weight method, the comprehensive weight of each wind turbine output clustering index is calculated, and the weighted distance from each clustering index scenario to each cluster center is calculated according to the comprehensive weight of each wind turbine output clustering index.

[0114] Assign each clustering indicator scenario to the clustering cluster corresponding to the clustering center with the smallest scenario weighted distance, and iteratively update the clustering center of each clustering cluster according to the clustering indicator scenario in each clustering cluster until the clustering centers converge to obtain the optimal clustering center set;

[0115] According to the optimal cluster center set, calculating the scene weighted distance from the cluster indicator scene in each optimal cluster cluster to the cluster center;

[0116] The wind turbine output scenario corresponding to the clustering index scenario with the smallest scene weighted distance in each optimal clustering cluster is taken as the typical wind turbine output scenario to form a typical wind turbine output scenario set.

[0117] Specifically, this embodiment obtains the active output value data of each wind turbine generator set within a preset target time period, and divides the target time period into multiple time periods, each of which can be an hour, half a day or a day, etc., depending on the analysis requirements. In this embodiment, one day is used as an example for explanation. In each time period, according to the active output value data of each wind turbine generator set, a wind turbine output scenario corresponding to the time period is constructed. The wind turbine output scenario consists of the wind turbine output values ​​of 24 hours a day. The wind turbine output scenarios of all wind turbine generator sets in all time periods are combined to form a wind turbine output scenario set. The wind turbine output scenario set can be expressed as A vector, wherein each element corresponds to the active output value of a wind turbine generator set, for each wind turbine output scenario, the wind turbine average output index, the wind turbine output fluctuation index and the wind turbine maximum output index are calculated according to the active output value to form a wind turbine output clustering index, wherein the wind turbine average output index is obtained by calculating the average value of the wind turbine output in each time period within a day, the wind turbine output fluctuation index is obtained by calculating the degree of fluctuation of the wind turbine output relative to the average output within a day, and the wind turbine maximum output index is obtained by calculating the maximum output value of the wind turbine within a day. In some embodiments, the step of calculating the wind turbine output clustering index according to the active output value includes:

[0118] According to the active output value of each wind turbine generator set in each time period within the target time period, the initial index of the average output of the wind turbine is calculated;

[0119] According to the difference between the active output value of each wind turbine generator set in each time period within the target time period and the average output index of the wind turbine, the initial index of the wind turbine output fluctuation rate is calculated;

[0120] Screening out the maximum active output value from the active output values ​​of each wind turbine generator set in each time period within the target time period, and using the maximum active output value as the initial indicator of the maximum output of the wind turbine;

[0121] The initial index of the average output of the fan, the initial index of the output fluctuation rate of the fan and the initial index of the maximum output of the fan are normalized to obtain the corresponding average output index of the fan, the output fluctuation rate index of the fan and the maximum output index of the fan, wherein the calculation formula of the initial index of the average output of the fan is:

[0122]

[0123] In the formula, It is the average output index of the fan; is the average daily output of the wind turbine; is the wind turbine output in the tth time period; T is the total number of time periods, which is set to 24 hours a day in this embodiment.

[0124] The specific calculation formula for the initial index of wind turbine output fluctuation rate is:

[0125]

[0126] In the formula, It is the wind turbine output fluctuation rate indicator; is the daily output fluctuation rate of the wind turbine.

[0127] The specific calculation formula for the initial index of the maximum output of the fan is:

[0128]

[0129] In the formula, It is the maximum output index of the fan; It is the maximum daily output of the wind turbine.

[0130] In this embodiment, the initial index of the average output of the wind turbine, the initial index of the output fluctuation rate of the wind turbine, and the initial index of the maximum output of the wind turbine are normalized. The normalization formula is as follows:

[0131]

[0132] In the formula, is the normalized data; Clustering index for a wind turbine output Minimum data of Clustering index for a wind turbine output Maximum data.

[0133] In this embodiment, for each time period t, the average daily output index of the wind turbine, the daily output fluctuation rate index of the wind turbine and the daily maximum output index of the wind turbine of all wind turbines are combined into a clustering index scenario. The clustering index scenario can be represented as a vector, in which each element corresponds to the clustering index value of a wind turbine. Each clustering index scenario includes the average wind turbine output index, the wind turbine output fluctuation rate index and the wind turbine maximum output index of the wind turbine in the time period. The clustering index scenarios of all time periods are combined into a clustering index scenario set. The clustering index scenario The representation is:

[0134]

[0135] At the same time, this embodiment determines the number of clusters K of the K-means clustering algorithm by the elbow rule, randomly selects K clustering indicator scenarios from the clustering indicator scenario set as the initial clustering centers, and the number of initial clustering centers is equal to the number of clusters. Then, based on the improved three-scale analytic hierarchy process and the entropy weight method, the comprehensive weight of each wind turbine output clustering index is calculated, wherein the improved three-scale analytic hierarchy process is used to construct a comparison matrix and obtain subjective weights; the entropy weight method is used to calculate objective weights, and the final comprehensive weight is a combination of subjective weights and objective weights. In some implementations, the step of calculating the comprehensive weight of each wind turbine output clustering index based on the improved three-scale analytic hierarchy process and the entropy weight method includes:

[0136] The first index weight of each wind turbine output clustering index is calculated by using the improved three-scale analytic hierarchy process, and the second index weight of each wind turbine output clustering index is calculated by using the entropy weight method.

[0137] The average value of the first indicator weight and the second indicator weight is calculated to obtain the comprehensive weight of each wind turbine output clustering indicator.

[0138] For each clustering index scenario and each clustering center, this embodiment calculates the scene weighted distance from each clustering index scenario to each clustering center according to the comprehensive weight of each wind turbine output clustering index. The calculation of the weighted distance takes into account the weight of each index and the difference between the scene and the clustering center. Each clustering index scenario is assigned to the cluster corresponding to the clustering center with the smallest weighted distance, so that similar wind turbine output scenarios are classified into one category. The calculation formula of the scene weighted distance is:

[0139]

[0140] In the formula, Clustering indicator scenario To cluster center The scene weighted distance; is the subjective weight obtained by improving the three-scale analytic hierarchy process; is the objective weight obtained by the entropy weight method; is the value of the kth clustering index in time period t; is the kth clustering index value of the jth cluster center.

[0141] In this embodiment, the center point of each cluster is updated according to the data in each cluster. The new cluster center is the average value or weighted average value of all the scenes in the cluster. The above allocation and updating steps are repeated until the cluster center converges (that is, the change of the cluster center is less than a preset threshold) or the preset number of iterations is reached. At this time, a cluster center set is obtained. For each scene in each cluster, its weighted distance to the cluster center is calculated. The wind turbine output scene corresponding to the scene with the smallest weighted distance to the cluster center in each cluster is used as the typical wind turbine output scene of the cluster. The typical wind turbine output scenes of all clusters are combined to form a typical wind turbine output scene set. These scenes represent the typical output conditions of wind farms in different time periods and can provide a scientific basis for subsequent operation optimization and planning of offshore wind farms.

[0142] S4. Use the typical wind turbine output scenario set to coordinate planning of offshore wind farms and energy storage systems, and solve for the optimal offshore wind power storage capacity configuration and the optimal grid connection point for the offshore wind farm.

[0143] S5. Control the offshore wind farm energy storage for coordinated and optimized operation based on the optimal offshore wind power energy storage capacity configuration and the optimal offshore wind farm grid connection point.

[0144] In some embodiments, the step of using the typical wind turbine output scenario set to perform collaborative planning of an offshore wind farm and an energy storage system to solve for an optimal offshore wind power storage capacity configuration and an optimal grid connection point for the offshore wind farm includes:

[0145] With the goal of minimizing the construction cost, operation cost and wind curtailment cost of offshore wind farm energy storage, a collaborative planning model of offshore wind farm and energy storage system is constructed using the typical wind turbine output scenario set;

[0146] The offshore wind farm and energy storage system collaborative planning model is solved by a mixed integer programming solver to obtain the optimal offshore wind power storage capacity configuration and the optimal offshore wind farm grid connection point; wherein the construction process of the offshore wind farm and energy storage system collaborative planning model is:

[0147] Taking the planned capacity of the wind power energy storage system as a decision variable, the construction cost of the offshore wind farm energy storage system is obtained according to the planned capacity of the wind power energy storage system and the unit capacity construction cost; the wind power energy storage system includes a wind turbine and an energy storage system;

[0148] The operating cost is obtained based on the maintenance cost of the wind energy storage system and the actual power generation of the traditional generator set in a typical wind turbine output scenario.

[0149] The wind curtailment cost in different scenarios is calculated based on the deviation between the actual power generation of the wind turbine in each scenario in the typical wind turbine output scenario set and the active output value;

[0150] By minimizing the sum of the construction cost, the operation cost and the wind abandonment cost, a collaborative planning model for an offshore wind farm and an energy storage system is constructed.

[0151] Specifically, this embodiment aims to minimize the construction cost, operation cost and wind abandonment cost of offshore wind farm energy storage, takes the planned capacity of the wind power storage system (including the planned capacity of wind turbines and energy storage systems) as the decision variable, and uses a typical wind turbine output scenario set to build a collaborative planning model for offshore wind farms and energy storage systems. The model will consider multiple factors such as the planned capacity of the wind power storage system, maintenance costs, the power generation of traditional generators, and the wind abandonment cost. Specifically, this embodiment calculates the construction cost of the offshore wind farm energy storage system based on the planned capacity and unit capacity construction cost of the wind power storage system. The construction cost of the offshore wind farm energy storage system includes the wind turbines. The construction cost of the wind turbine group, the construction cost of the energy storage system and the construction cost of the submarine cable are calculated. At the same time, this embodiment calculates the operating cost based on the maintenance cost of the wind power energy storage system and the actual power generation of the traditional generator set in the typical wind turbine output scenario. The operating cost includes the maintenance cost of the wind turbine, the maintenance cost of the energy storage and the operating cost of the traditional generator set. According to the deviation between the actual power generation of the wind turbine and the predicted power generation (or system demand) in each scenario of the typical wind turbine output scenario, the cost of wind abandonment in different scenarios is calculated, which reflects the waste of resources caused by the uncertainty of wind power generation. The objective function of the collaborative planning model of offshore wind farms and energy storage systems is specifically:

[0152]

[0153] in,

[0154]

[0155]

[0156]

[0157]

[0158]

[0159]

[0160] In the formula, For construction costs; For operating costs; The cost of wind curtailment; is the unit capacity construction cost of wind turbines; The unit capacity construction cost of the energy storage system; The construction cost of submarine cables; is the wind turbine construction coefficient; is the energy storage system construction coefficient; Plan the capacity for wind turbines, where: , The number of wind turbines in the wind farm is a known quantity; Planning capacity for energy storage systems; It is a binary variable, 0 means that there is no plan to lay submarine cables. , 1 means planning to lay submarine cables ; A collection of routes to be selected; To mark submarine cables; Cost function for laying submarine cables; The maintenance cost of the fan; The maintenance cost of energy storage; is the operating cost of traditional generator sets; is the maintenance cost discount rate of wind turbines; is the maintenance cost discount rate of the energy storage system; is the number of traditional generators; K is the number of scenarios; T is the total number of time periods; is the power generation of the kth traditional generator set at time t in scenario d; is the power generation cost function of the traditional generator set; It is the cost of opening once; is the number of times the kth traditional unit is turned on; is the single stop cost; is the number of stops of the kth traditional unit; is the wind curtailment cost per unit of electricity; The predicted power generation (active output value) of the wind turbine generator set; is the actual power generation of the wind turbine.

[0161] In this embodiment, the constraints of the offshore wind farm and energy storage system collaborative planning model include system flow constraints, planning capacity constraints, energy storage system constraints, traditional unit output constraints and wind power generation utilization constraints. Among them, the system flow constraint adopts a DC flow model. The DC flow ignores the line resistance and the reactive power flow of the branch in the AC flow model to solve the linear equation. It is considered that the power transmission between nodes is only related to the phase difference between the nodes and the reactance of the line. At the same time, due to the influence of factors such as geographical location and actual construction, there are upper and lower limits on the planning capacity of wind turbines and energy storage systems. Therefore, this embodiment sets the planning capacity constraint:

[0162]

[0163]

[0164] In the formula, A lower limit for planning capacity of wind turbines; Planning capacity for wind turbines; capping the planned capacity of wind turbines; Plan a lower capacity limit for energy storage systems; Planning capacity for energy storage systems; Plan an upper limit on the capacity of the energy storage system.

[0165] In order to prevent deep charging and discharging from damaging the energy storage system, the state of charge and charging and discharging power of the energy storage system need to be limited within a certain range. The specific constraints of the energy storage system are:

[0166]

[0167]

[0168]

[0169]

[0170]

[0171] In the formula, is the state of charge of the energy storage system at time t in scenario d; is the charge and discharge coefficient of the energy storage system; is the charging and discharging power of the energy storage system at time t in scenario d, where a negative number indicates charging and a positive number indicates discharging; is the time difference; The charging efficiency of the energy storage system; is the discharge efficiency of the energy storage system; The lower limit of the state of charge of the energy storage system; The upper limit of the state of charge of the energy storage system; It is the lower limit of the charging and discharging power of the energy storage system; The upper limit of the charging and discharging power of the energy storage system; is the charging and discharging power of the energy storage system at time T in scenario d; is the charge state of the energy storage system at the initial moment of scenario d.

[0172] The power generation capacity of traditional thermal power generating units has upper and lower limits, and the power change is limited by the power ramp constraint of the unit. At the same time, the number of unit starts and stops is also constrained. The output constraint form of traditional units is:

[0173]

[0174]

[0175] In the formula, is a binary variable, indicating the operating status of the traditional unit k at time t in scenario d, where 1 indicates that the unit is running and 0 indicates that the unit is out of operation; is the lower limit of power generation of the traditional unit k; is the power generation of the traditional unit k at time t in scenario d; is the upper limit of the power generation capacity of the traditional unit k; is the maximum down-climbing rate of the conventional unit k; is the maximum climbing rate of the traditional unit k.

[0176] The start and stop times of the unit are constrained as follows:

[0177]

[0178]

[0179]

[0180] In the formula, is the maximum number of starts and stops of the traditional unit k.

[0181] At the same time, in order to avoid waste of resources, it is necessary to limit the utilization rate of wind power generation. The wind power generation utilization rate constraint is expressed as:

[0182]

[0183] At the same time, in order to avoid wasting resources, this embodiment needs to limit the utilization rate of wind power generation:

[0184]

[0185] In the formula, The per-unit value of the wind turbine output at time t in the wind turbine output scenario d; K is the total number of scenarios; T is the total time period; is the actual power generation of the wind turbine; Forecasted power generation for wind turbines; It is the lower limit of wind power utilization rate.

[0186] In this embodiment, a mixed integer programming solver is used to solve the collaborative planning model of offshore wind farms and energy storage systems. The mixed integer programming solver can handle optimization problems involving integer variables and continuous variables, and can solve binary variables (such as whether submarine cables are laid and whether traditional units are in operation) and continuous variables (such as the planned capacity of wind power energy storage systems) in the collaborative planning model of offshore wind farms and energy storage systems. The solution process will take all constraints into consideration and seek a solution that minimizes the sum of construction costs, operating costs and wind abandonment costs while satisfying these constraints. Ultimately, the mixed integer programming solver will output the optimal offshore wind power storage capacity configuration and the optimal grid connection point for offshore wind farms. These results will serve as the basis for collaborative planning and guide the construction and operation of actual offshore wind farms and energy storage systems.

[0187] An embodiment of the present invention provides an offshore wind farm energy storage collaborative planning method considering wake effect, the method comprising: analyzing the offshore wind farm wake effect according to original wind speed and direction data of the area where each wind turbine generator set in the offshore wind farm is located, and establishing an incompletely swept attenuation model for the wind turbine wake; based on the incompletely swept attenuation model for the wind turbine wake, calculating the active output value of each wind turbine generator set under the influence of the wake effect; constructing a wind turbine output clustering index according to the active output value, and based on the wind turbine output clustering index, clustering using a K-means clustering algorithm to obtain a typical wind turbine output scenario set; using the typical wind turbine output scenario set to perform collaborative planning of the offshore wind farm and the energy storage system, and obtaining an optimal offshore wind power storage capacity configuration and an optimal grid connection point for the offshore wind farm; and controlling the offshore wind farm energy storage to coordinate and optimize operation according to the optimal offshore wind power storage capacity configuration and the optimal grid connection point for the offshore wind farm. Compared with the existing technology, this method improves the accuracy of wind turbine output prediction by constructing an incomplete sweep attenuation model of wind turbine wake and a K-means clustering algorithm, optimizes the coordinated planning of offshore wind farms and energy storage systems, reduces wind abandonment, and realizes efficient and coordinated operation of offshore wind farms and energy storage systems.

[0188] It should be noted that the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0189] In one embodiment, Figure 4As shown, an embodiment of the present invention provides an offshore wind farm energy storage collaborative planning system considering wake effect, the system comprising:

[0190] The wake effect analysis module 101 is used to analyze the wake effect of the offshore wind farm according to the original wind speed and direction data of the area where each wind turbine generator set is located in the offshore wind farm, and establish an incomplete sweep attenuation model of the wind turbine wake;

[0191] An active output acquisition module 102 is used to calculate the active output value of each wind turbine generator set under the influence of the wake effect based on the wind turbine wake incomplete pass attenuation model;

[0192] The output scenario clustering module 103 is used to construct a wind turbine output clustering index according to the active output value, and based on the wind turbine output clustering index, use a K-means clustering algorithm to cluster and obtain a typical wind turbine output scenario set;

[0193] The collaborative planning solution module 104 is used to use the typical wind turbine output scenario set to perform collaborative planning of offshore wind farms and energy storage systems, and solve for the optimal offshore wind power storage capacity configuration and the optimal grid connection point of the offshore wind farm;

[0194] The coordinated operation control module 105 is used to control the offshore wind farm energy storage to coordinate and optimize the operation according to the optimal offshore wind power storage capacity configuration and the optimal grid connection point of the offshore wind farm.

[0195] For the specific definition of an offshore wind farm energy storage collaborative planning system considering the wake effect, please refer to the above-mentioned definition of an offshore wind farm energy storage collaborative planning method considering the wake effect, which will not be repeated here. A person of ordinary skill in the art will appreciate that the various modules and steps described in conjunction with the embodiments disclosed in this application can be implemented in hardware, software, or a combination of both. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0196] The embodiment of the present invention provides an offshore wind farm energy storage collaborative planning system considering wake effect, wherein the wake effect analysis module of the system analyzes the offshore wind farm wake effect according to the original wind speed and direction data of the area where each wind turbine generator set is located in the offshore wind farm, and establishes an incompletely swept attenuation model of the wind turbine wake; the active output acquisition module calculates the active output value of each wind turbine generator set under the influence of the wake effect based on the incompletely swept attenuation model of the wind turbine wake; the output scenario clustering module constructs a wind turbine output clustering index according to the active output value, and based on the wind turbine output clustering index, uses the K-means clustering algorithm to cluster to obtain a typical wind turbine output scenario set; the collaborative planning solution module uses the typical wind turbine output scenario set to perform collaborative planning of the offshore wind farm and the energy storage system, and obtains the optimal offshore wind power storage capacity configuration and the optimal grid connection point of the offshore wind farm; the collaborative operation control module controls the offshore wind farm energy storage to coordinate and optimize operation according to the optimal offshore wind power storage capacity configuration and the optimal grid connection point of the offshore wind farm. Compared with the existing technology, the system improves the accuracy of wind turbine output prediction by constructing an incomplete sweep attenuation model of wind turbine wake and a K-means clustering algorithm, optimizes the coordinated planning of offshore wind farms and energy storage systems, reduces wind abandonment, and realizes efficient and coordinated operation of offshore wind farms and energy storage systems.

[0197] The above-mentioned embodiments only express several preferred implementation modes of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in the technical field, several improvements and substitutions can be made without departing from the technical principles of the present invention, and these improvements and substitutions should also be regarded as the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be based on the protection scope of the claims.

Claims

1. A method for collaborative planning of energy storage for offshore wind farms considering wake effect, characterized in that: The following steps are involved: The wake effect of offshore wind farms is analyzed based on the original wind speed and direction data of the areas where each wind turbine generator set is located, and an incomplete sweep attenuation model of wind turbine wake is established. Based on the wind turbine wake incomplete pass attenuation model, the active output value of each wind turbine generator set is calculated under the influence of the wake effect; Constructing a fan output clustering index according to the active output value, and based on the fan output clustering index, using a K-means clustering algorithm to cluster and obtain a typical fan output scenario set; The typical wind turbine output scenario set is used to coordinate the planning of offshore wind farms and energy storage systems, and the optimal offshore wind power storage capacity configuration and the optimal grid connection point of the offshore wind farm are obtained; Control the offshore wind farm energy storage for coordinated and optimized operation based on the optimal offshore wind power storage capacity configuration and the optimal grid connection point of the offshore wind farm; The step of analyzing the wake effect of the offshore wind farm according to the original wind speed and direction data of the area where each wind turbine generator set in the offshore wind farm is located and establishing the incomplete sweep attenuation model of the wind turbine wake includes: The wake effect is analyzed based on the original wind speed and direction data of the area where each wind turbine generator set is located in the offshore wind farm, and the influence range of the upstream wind turbine on the wind speed of the downstream wind turbine is quantified; According to the influence range of the upstream fan on the wind speed of the downstream fan, determine the relative position relationship between the downstream fan and the upstream fan wake range; When it is determined that the relative position relationship between the wake range of the downstream fan and the upstream fan is that the wake does not completely pass, the overlapping area of ​​the wake circle and the downstream fan is calculated by a geometric analysis method based on the upstream fan position, the downstream fan position and the wind direction angle; According to the overlapping area of ​​the wake circle and the downstream wind turbine and the radius of the wind turbine blades, the actual wind speed of the wind turbine under the influence of the superposition of the wakes of multiple upstream wind turbines is calculated; According to the actual wind speed of the wind turbine, the cut-in wind speed of the wind turbine and the cut-out wind speed of the wind turbine, an incomplete pass attenuation model of the wind turbine wake is constructed.

2. A method for collaborative planning of offshore wind farm energy storage considering wake effect as claimed in claim 1, characterized in that: The step of calculating the overlapping area of ​​the wake circle and the downstream fan by a geometric analysis method based on the upstream fan position, the downstream fan position and the wind direction angle comprises: Determine the center position and radius of the wake circle according to the upstream wind turbine position, the downstream wind turbine position and the current wind direction angle; Based on the center position and radius of the wake circle, the center angle between the wake circle and the downstream wind turbine is calculated through geometric relationship; According to the central angle of the circle and the radius of the downstream fan blade, the overlapping area of ​​the wake circle and the downstream fan is calculated.

3. The offshore wind farm energy storage collaborative planning method considering wake effect according to claim 1, characterized in that: The step of calculating the active output value of each wind turbine generator set under the influence of the wake effect based on the wind turbine wake incomplete pass attenuation model comprises: Determine the rated active power of the wind turbine according to the current capacity configuration of the wind turbine generator set; When the actual wind speed of the wind turbine is less than the cut-in wind speed of the wind turbine or is greater than or equal to the cut-out wind speed of the wind turbine, the active output value of each wind turbine generator set is determined to be zero; When the actual wind speed of the wind turbine is between the cut-in wind speed of the wind turbine and the rated wind speed of the wind turbine, a proportionality coefficient is calculated according to the difference between the actual wind speed of the wind turbine and the cut-in wind speed of the wind turbine, and the difference between the rated wind speed of the wind turbine and the cut-in wind speed of the wind turbine, and the active output value of each wind turbine generator set under the influence of the wake effect is calculated according to the product of the proportionality coefficient and the rated active power of the wind turbine; When the actual wind speed of the wind turbine is between the rated wind speed of the wind turbine and the cut-out wind speed of the wind turbine, the rated active power of the wind turbine is used as the active output value of each wind turbine generator set under the influence of the wake effect.

4. The offshore wind farm energy storage collaborative planning method considering wake effect according to claim 1, characterized in that: The step of constructing a fan output clustering index according to the active output value, and clustering a typical fan output scene set using a K-means clustering algorithm based on the fan output clustering index includes: Obtaining the active output value of each wind turbine generator set within a preset target time period, and dividing the target time period into multiple time periods; According to the active output value of each wind turbine generator set in each time period within the target time period, a wind turbine output scenario corresponding to each time period is constructed, and the wind turbine output scenarios of all time periods are combined to form a wind turbine output scenario set; A wind turbine output clustering index is calculated according to the active output value, and a corresponding clustering index scenario set is generated according to the wind turbine output clustering index; each clustering index scenario set includes an average wind turbine output index, a wind turbine output fluctuation rate index and a wind turbine maximum output index of the wind turbine generator set within a time period; Determining the number of clusters, and selecting a number of cluster indicator scenarios from the cluster indicator scenario set as initial cluster centers according to the number of clusters; Based on the improved three-scale analytic hierarchy process and entropy weight method, the comprehensive weight of each wind turbine output clustering index is calculated, and the weighted distance from each clustering index scenario to each cluster center is calculated according to the comprehensive weight of each wind turbine output clustering index. Assign each clustering indicator scenario to the clustering cluster corresponding to the clustering center with the smallest scenario weighted distance, and iteratively update the clustering center of each clustering cluster according to the clustering indicator scenario in each clustering cluster until the clustering centers converge to obtain the optimal clustering center set; According to the optimal cluster center set, calculating the scene weighted distance from the cluster indicator scene in each optimal cluster cluster to the cluster center; The wind turbine output scenario corresponding to the clustering index scenario with the smallest scene weighted distance in each optimal clustering cluster is taken as the typical wind turbine output scenario to form a typical wind turbine output scenario set.

5. A method for collaborative planning of offshore wind farm energy storage considering wake effect as claimed in claim 4, characterized in that: The step of calculating the fan output clustering index according to the active output value comprises: According to the active output value of each wind turbine generator set in each time period within the target time period, the initial index of the average output of the wind turbine is calculated; According to the difference between the active output value of each wind turbine generator set in each time period within the target time period and the average output index of the wind turbine, the initial index of the wind turbine output fluctuation rate is calculated; Screening out the maximum active output value from the active output values ​​of each wind turbine generator set in each time period within the target time period, and using the maximum active output value as the initial indicator of the maximum output of the wind turbine; The initial index of the average output of the wind turbine, the initial index of the fluctuation rate of the output of the wind turbine and the initial index of the maximum output of the wind turbine are normalized to obtain the corresponding average output index of the wind turbine, the fluctuation rate index of the output of the wind turbine and the maximum output index of the wind turbine.

6. A method for collaborative planning of offshore wind farm energy storage considering wake effect as claimed in claim 4, characterized in that: The step of calculating the comprehensive weight of each wind turbine output clustering index based on the improved three-scale analytic hierarchy process and entropy weight method includes: The first index weight of each wind turbine output clustering index is calculated by using the improved three-scale analytic hierarchy process, and the second index weight of each wind turbine output clustering index is calculated by using the entropy weight method. The average value of the first indicator weight and the second indicator weight is calculated to obtain the comprehensive weight of each wind turbine output clustering indicator.

7. The offshore wind farm energy storage collaborative planning method considering wake effect according to claim 1, characterized in that: The steps of using the typical wind turbine output scenario set to coordinate planning of offshore wind farms and energy storage systems to obtain the optimal offshore wind power storage capacity configuration and the optimal offshore wind farm grid connection point include: With the goal of minimizing the construction cost, operation cost and wind curtailment cost of offshore wind farm energy storage, a collaborative planning model of offshore wind farm and energy storage system is constructed using the typical wind turbine output scenario set; The offshore wind farm and energy storage system collaborative planning model is solved by a mixed integer programming solver to obtain the optimal offshore wind power storage capacity configuration and the optimal offshore wind farm grid connection point.

8. A method for collaborative planning of offshore wind farm energy storage considering wake effect as claimed in claim 7, characterized in that: The construction process of the offshore wind farm and energy storage system collaborative planning model is as follows: Taking the planned capacity of the wind power energy storage system as a decision variable, the construction cost of the offshore wind farm energy storage system is obtained according to the planned capacity of the wind power energy storage system and the unit capacity construction cost; the wind power energy storage system includes a wind turbine and an energy storage system; The operating cost is obtained based on the maintenance cost of the wind energy storage system and the actual power generation of the traditional generator set in a typical wind turbine output scenario. The wind curtailment cost in different scenarios is calculated based on the deviation between the actual power generation of the wind turbine in each scenario in the typical wind turbine output scenario set and the active output value; By minimizing the sum of the construction cost, the operation cost and the wind abandonment cost, a collaborative planning model for an offshore wind farm and an energy storage system is constructed.

9. An offshore wind farm energy storage collaborative planning system considering wake effect, characterized in that: The system comprises: The wake effect analysis module is used to analyze the wake effect of offshore wind farms based on the original wind speed and direction data of the areas where each wind turbine generator set is located in the offshore wind farm, and to establish an incomplete sweep attenuation model for the wind turbine wake; An active output acquisition module is used to calculate the active output value of each wind turbine generator set under the influence of the wake effect based on the wind turbine wake incomplete pass attenuation model; An output scenario clustering module, used to construct a wind turbine output clustering index according to the active output value, and based on the wind turbine output clustering index, use a K-means clustering algorithm to cluster and obtain a typical wind turbine output scenario set; A collaborative planning and solving module is used to use the typical wind turbine output scenario set to perform collaborative planning of offshore wind farms and energy storage systems, and solve for the optimal offshore wind power storage capacity configuration and the optimal grid connection point of the offshore wind farm; A coordinated operation control module is used to control the offshore wind farm energy storage for coordinated and optimized operation according to the optimal offshore wind power storage capacity configuration and the optimal grid connection point of the offshore wind farm; Wherein, the wake effect analysis module is specifically used for: The wake effect is analyzed based on the original wind speed and direction data of the area where each wind turbine generator set is located in the offshore wind farm, and the influence range of the upstream wind turbine on the wind speed of the downstream wind turbine is quantified; According to the influence range of the upstream fan on the wind speed of the downstream fan, determine the relative position relationship between the downstream fan and the upstream fan wake range; When it is determined that the relative position relationship between the wake range of the downstream fan and the upstream fan is that the wake does not completely pass, the overlapping area of ​​the wake circle and the downstream fan is calculated by a geometric analysis method based on the upstream fan position, the downstream fan position and the wind direction angle; According to the overlapping area of ​​the wake circle and the downstream wind turbine and the radius of the wind turbine blades, the actual wind speed of the wind turbine under the influence of the superposition of the wakes of multiple upstream wind turbines is calculated; According to the actual wind speed of the wind turbine, the cut-in wind speed of the wind turbine and the cut-out wind speed of the wind turbine, an incomplete pass attenuation model of the wind turbine wake is constructed.

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