A new energy high penetration rate power grid wind farm coordinated frequency modulation control method

Abstract

The present application relates to the technical field of power grid regulation, and particularly relates to a wind farm coordinated frequency regulation control method for a new energy high-penetration power grid, which comprises: obtaining power demand prediction information of wind turbines, wherein the power demand prediction information comprises a predicted power change amount of wind turbines in the power grid at each future time of a target prediction period; determining a frequency regulation priority value of each wind turbine according to maximum power prediction information and pitch angle redundancy information of each wind turbine in the wind turbines, wherein the maximum power prediction information comprises a predicted power maximum value of the corresponding wind turbine at each future time of the target prediction period; and determining a power adjustment value of each wind turbine in the target prediction period according to the power demand prediction information and the frequency regulation priority value of each wind turbine. The present application can dynamically match the power demand of the wind power part of the power grid in combination with the actual regulation response capability of each wind turbine, and improve the power regulation effect of the wind turbines in the power grid.

Description

Technical Field

[0001] This invention relates to the technical field of power grid regulation, specifically to a wind farm coordinated frequency regulation control method for power grids with high penetration of new energy sources. Background Technology

[0002] With the increasing penetration of new energy sources in the power system, wind farms are playing an increasingly important role in the power grid. Due to the advantages of wind energy, such as its cleanliness, renewability, and wide distribution, wind power installed capacity is growing rapidly worldwide. At the same time, the power system is gradually shifting from a high-inertia system dominated by traditional thermal power to a low-inertia system primarily based on new energy sources. To ensure the safe and stable operation of the power grid, new energy units need to play an active role in primary and secondary frequency regulation, providing rapid support and dynamic adjustment to the grid frequency. Therefore, how to leverage the collective effect of wind farms and enhance their flexibility and synergy in grid operation has become one of the key directions for the current development of the power industry.

[0003] Currently, in the actual process of wind turbines participating in grid frequency regulation, due to the inherent instability of wind energy resources, the output power of a single wind turbine often fluctuates, making it difficult to stably support grid frequency regulation. Existing wind farm frequency regulation methods fail to fully consider the differences between wind turbines and future wind speed trends, resulting in uneven load distribution among different wind turbines during regulation: some wind turbines experience increased losses due to over-response, while others remain idle, leading to poor overall regulation performance.

[0004] In other words, existing technologies are not very effective at regulating the power of wind turbines in the power grid. Summary of the Invention

[0005] The purpose of this invention is to provide a wind farm coordinated frequency regulation control method for power grids with high penetration of new energy sources, which solves the technical problem of poor power regulation effect of wind turbines in the power grid in the existing technology.

[0006] In a first aspect, one embodiment of the present invention provides a wind farm coordinated frequency regulation control method for a power grid with high penetration of new energy sources, the method comprising:

[0007] Obtain power demand forecast information for wind turbines, wherein the power demand forecast information includes the predicted power change of wind turbines in the power grid at each future moment in the target forecast period;

[0008] Based on the maximum power prediction information and pitch angle redundancy information of each wind turbine in the wind turbine group, the frequency regulation priority value of each wind turbine is determined. The maximum power prediction information includes the predicted maximum power of the corresponding wind turbine at each future moment in the target prediction period, and the pitch angle redundancy information is used to indicate the angle difference between the pitch angle of the corresponding wind turbine at the current moment and the optimal pitch angle of the corresponding wind turbine.

[0009] Based on the power demand forecast information and the frequency regulation priority value of each wind turbine, the power adjustment value of each wind turbine in the target forecast period is determined.

[0010] In one embodiment, obtaining the power demand forecast information of the wind turbine includes:

[0011] Obtain frequency prediction information of the power grid, wherein the frequency prediction information includes the predicted frequency change of the power grid at each future moment in the target prediction period;

[0012] Based on the power output ratio of wind turbines in the power generation equipment group, the frequency power mapping coefficient, and the frequency prediction information, the power demand prediction information of wind turbines is determined.

[0013] In one embodiment, determining the power demand forecast information of the wind turbines based on the power output ratio of the wind turbines in the power generation equipment group, the frequency power mapping coefficient, and the frequency forecast information includes:

[0014] Calculate the ratio of the frequency power mapping coefficient to the predicted frequency change of the power grid at each future moment in the target prediction period, so as to obtain the predicted total power change of the power generation group at each future moment in the target prediction period.

[0015] The predicted total power change of the power generation equipment group at each future moment in the target prediction period is calculated by multiplying the power output ratio of the wind turbine in the power generation equipment group, so as to obtain the predicted power change of the wind turbine at each future moment in the target prediction period.

[0016] In one embodiment, the step of obtaining the power output percentage of the wind turbine in the power generation equipment group includes:

[0017] At each historical moment, the ratio of wind power generation of the wind turbine to the total power generation of the power generation equipment group is calculated to obtain multiple historical power ratios.

[0018] The average of the multiple historical power ratios is determined as the power output ratio of the wind turbine in the power generation equipment group.

[0019] In one embodiment, when the power demand forecast information indicates an increase in wind power, the frequency regulation priority value is positively correlated with the angle difference indicated by the pitch angle redundancy information; when the power demand forecast information indicates a decrease in wind power, the frequency regulation priority value is negatively correlated with the angle difference indicated by the pitch angle redundancy information; the frequency regulation priority value is positively correlated with the upper quartile of the plurality of predicted power maxima included in the maximum power forecast information.

[0020] In one embodiment, the maximum power prediction information includes the prediction variance of the corresponding wind turbine at each future moment in the target prediction period;

[0021] The step of determining the frequency regulation priority value for each wind turbine based on the maximum power prediction information and pitch angle redundancy information of each wind turbine in the wind turbine unit includes:

[0022] Calculate the average of multiple prediction variances for each wind turbine to obtain the prediction risk index for each wind turbine.

[0023] Calculate the ratio of the upper quartile of the multiple predicted power maxima corresponding to each wind turbine to the predicted risk index to obtain the power priority index corresponding to each wind turbine.

[0024] The frequency regulation priority value of each wind turbine is determined based on the power priority index corresponding to each wind turbine and the angle difference.

[0025] In one embodiment, determining the frequency regulation priority value of each wind turbine based on the power priority index corresponding to each wind turbine and the angle difference includes:

[0026] Calculate the angle difference between the pitch angle of each wind turbine at the current moment and the corresponding optimal pitch angle to obtain the angle difference value of each wind turbine;

[0027] Calculate the angle difference between the maximum and minimum pitch angles of each wind turbine to obtain a reference difference value for each wind turbine;

[0028] Calculate the ratio of the angle difference of each fan to the reference difference to obtain the angle normalization value corresponding to each fan.

[0029] When the power demand forecast information indicates an increase in wind power, the product of the power priority index and the angle normalization value corresponding to each wind turbine is calculated to obtain the frequency regulation priority value of each wind turbine.

[0030] When the power demand forecast information indicates a reduction in wind power, the ratio of the power priority index to the angle normalization value for each wind turbine is calculated to obtain the frequency regulation priority value for each wind turbine.

[0031] In one embodiment, determining the power adjustment value for each wind turbine in the target forecast period based on the power demand forecast information and the frequency regulation priority value of each wind turbine includes:

[0032] Based on the power demand forecast information, the average value of the predicted power change of the wind turbine at multiple future times is calculated to obtain the power change target value.

[0033] The frequency regulation priority values ​​of multiple wind turbines are summed to obtain the total priority value;

[0034] Calculate the ratio of the frequency regulation priority value of each fan to the total priority value to obtain the frequency regulation coefficient of each fan;

[0035] Based on the frequency regulation coefficient of each wind turbine and the target power change value, the power adjustment value of each wind turbine in the target prediction period is determined.

[0036] In one embodiment, the power adjustment value is the product of the power change target value and the frequency regulation coefficient of the corresponding fan.

[0037] In one embodiment, the sum of the power adjustment value and the power value of the corresponding fan at the current moment is less than or equal to the maximum rated power of the corresponding fan, and the sum of the power adjustment value and the power value of the corresponding fan at the current moment is greater than or equal to the minimum rated power of the corresponding fan.

[0038] In a second aspect, an embodiment of the present invention provides a wind farm coordinated frequency regulation control system for a power grid with high penetration of new energy sources, the system comprising:

[0039] The demand forecasting module is used to obtain power demand forecasting information for wind turbines, wherein the power demand forecasting information includes the predicted power change of wind turbines in the grid at each future moment in the target forecasting period.

[0040] The priority identification module is used to determine the frequency regulation priority value of each wind turbine based on the maximum power prediction information and pitch angle redundancy information of each wind turbine in the wind turbine group. The maximum power prediction information includes the predicted maximum power value of the corresponding wind turbine at each future moment in the target prediction period, and the pitch angle redundancy information is used to indicate the angle difference between the pitch angle of the corresponding wind turbine at the current moment and the optimal pitch angle of the corresponding wind turbine.

[0041] The power adjustment module is used to determine the power adjustment value of each wind turbine in the target prediction period based on the power demand prediction information and the frequency regulation priority value of each wind turbine.

[0042] Thirdly, in another embodiment of the present invention, an electronic device is provided, including a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the method described in the first aspect.

[0043] Fourthly, in another embodiment of the present invention, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the method described in the first aspect.

[0044] The present invention has the following beneficial effects:

[0045] This invention determines the total power variation of a wind turbine during a target prediction period by predicting its power change. It then further predicts the maximum power of each individual turbine within the wind turbine during the target prediction period to assess the power output capability of each turbine. Combining the pitch angle difference between the turbine's current moment and its optimal pitch angle, the invention comprehensively determines the control response capability of each turbine from both power output capability and angle adjustment capability perspectives, quantifying this as a frequency regulation priority value. Finally, based on the frequency regulation priority value of each turbine, the aforementioned total power variation is decomposed to determine the power adjustment value for each turbine during the target prediction period. Based on this, power pre-adjustment operations are performed on each turbine. This allows for dynamic matching of the power demand of the power grid in the wind power sector with the actual control response capability of each turbine, improving the stability of the wind turbine's output power and enhancing the power regulation effect of wind turbines in the power grid. Attached Figure Description

[0046] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 This is a flowchart illustrating a wind farm coordinated frequency regulation control method for a high-penetration power grid of new energy provided by an embodiment of the present invention;

[0048] Figure 2 This is a schematic diagram of the structure of a wind farm coordinated frequency regulation control system for a new energy high-penetration power grid provided in an embodiment of the present invention;

[0049] Figure 3 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0050] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a wind farm coordinated frequency regulation control method for a high-penetration renewable energy power grid proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0051] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0052] The following description, in conjunction with the accompanying drawings, details the specific scheme of a wind farm coordinated frequency regulation control method for a new energy high-penetration power grid provided by the present invention.

[0053] This invention proposes a wind farm coordinated frequency regulation control method for power grids with high penetration of new energy sources. Please refer to [link / reference]. Figure 1 The diagram illustrates a flowchart of a wind farm coordinated frequency regulation control method for a high-penetration renewable energy power grid, according to an embodiment of the present invention. The method includes:

[0054] Step S1: Obtain power demand forecast information for wind turbine units.

[0055] The power demand forecast information includes the predicted power change of wind turbines in the power grid at each future moment in the target forecast period.

[0056] In applications, continuous time can be evenly divided based on a pre-defined time period value (such as 20 seconds, 60 seconds, 120 seconds, etc.) to obtain multiple continuous time periods. The aforementioned target prediction period can be understood as the next time period of the current time period.

[0057] Specifically, obtaining the power demand forecast information for wind turbine generators includes:

[0058] Obtain frequency prediction information of the power grid, wherein the frequency prediction information includes the predicted frequency change of the power grid at each future moment in the target prediction period;

[0059] Based on the power output ratio of wind turbines in the power generation equipment group, the frequency power mapping coefficient, and the frequency prediction information, the power demand prediction information of wind turbines is determined.

[0060] The aforementioned power generation equipment group is a cluster of all power generation equipment (such as thermal power generation equipment, photovoltaic power generation equipment, wind power generation equipment, nuclear power generation equipment, etc.) used to provide electricity in the power grid.

[0061] The aforementioned frequency-power mapping coefficients are used to perform numerical conversion between grid frequency and the power generation capacity of a group of power generation equipment.

[0062] In a power grid, when the power generation of a group of generating units is balanced with the power consumption of the load, the grid frequency remains constant. When the power generation exceeds the power consumption, the grid frequency tends to increase due to excess energy. Conversely, when the power generation is less than the power consumption, the grid frequency tends to decrease due to insufficient energy supply. In other words, changes in the grid frequency can reflect, to some extent, the grid's demand on the power generation of the generating units. An increase in grid frequency means that the power generation of the generating units needs to decrease; conversely, a decrease in grid frequency means that the power generation of the generating units needs to increase.

[0063] Based on this, the present invention proposes to indirectly determine the power demand change of the power generation equipment group at each future moment of the target prediction period by predicting the frequency change of the power grid at each future moment of the target prediction period and combining it with the pre-acquired frequency power mapping coefficient. Then, based on the power output ratio of the wind turbine in the power generation equipment group, the power demand change of the wind turbine at each future moment of the target prediction period (i.e., power demand prediction information) is determined.

[0064] In applications, multiple historical frequency fluctuation values ​​(i.e., the difference between two historical frequencies at adjacent historical moments) of the power grid can be obtained at multiple historical moments within a reference time period. Curve fitting (e.g., fitting using the least squares method) can be performed on these multiple historical frequency fluctuation values ​​to obtain a fitting curve indicating the change of power grid frequency fluctuation values ​​over time. Then, based on this fitting curve, prediction can be made to obtain the frequency prediction information of the power grid. The reference time period can be a time period that traces back a certain duration (e.g., 1 day, 7 days, etc.) from the current moment.

[0065] Similarly, the frequency-power mapping coefficient can be obtained by acquiring multiple historical frequencies of the power grid at multiple historical moments within the reference time period and multiple historical total power generation of the power generation equipment group at multiple historical moments within the reference time period, calculating the sum of multiple historical frequencies to obtain the frequency sum, and calculating the sum of multiple historical total power generation to obtain the total power generation sum, and then calculating the product of the total power generation sum and the frequency sum.

[0066] The steps for obtaining the power output percentage of wind turbines in the power generation equipment group include:

[0067] At each historical moment (within the reference time period), the ratio of wind power generation of the wind turbine to the total power generation of the power generation equipment group is calculated to obtain multiple historical power ratios.

[0068] The average of the multiple historical power ratios is determined as the power output ratio of the wind turbine in the power generation equipment group.

[0069] It should be noted that in this invention, time periods or time cycles are divided into 1-second intervals. For example, when the total duration of a certain time period is 60 seconds, the time period can be divided into 60 intervals.

[0070] Further, determining the power demand forecast information for the wind turbines based on the power output ratio of the wind turbines in the power generation equipment group, the frequency power mapping coefficient, and the frequency forecast information includes:

[0071] Calculate the ratio of the frequency power mapping coefficient to the predicted frequency change of the power grid at each future moment in the target prediction period, so as to obtain the predicted total power change of the power generation group at each future moment in the target prediction period.

[0072] The predicted total power change of the power generation equipment group at each future moment in the target prediction period is calculated by multiplying the power output ratio of the wind turbine in the power generation equipment group, so as to obtain the predicted power change of the wind turbine at each future moment in the target prediction period.

[0073] For example, the predicted power change of the wind turbine at the t-th future time in the target prediction period. It can be represented as:

[0074]

[0075] in, This indicates the proportion of power output of wind turbines in the group of power generation equipment. This represents the aforementioned frequency power mapping coefficient. The predicted frequency change of the power grid at the t-th future time in the target prediction period.

[0076] In this invention, the wind turbine unit is a cluster of equipment consisting of multiple wind power generation devices (also known as wind turbines).

[0077] Step S2: Determine the frequency regulation priority value of each wind turbine based on the maximum power prediction information and pitch angle redundancy information of each wind turbine in the wind turbine group.

[0078] The maximum power prediction information includes the predicted maximum power of the corresponding wind turbine at each future moment in the target prediction period, and the pitch angle redundancy information is used to indicate the angle difference between the pitch angle of the corresponding wind turbine at the current moment and the optimal pitch angle of the corresponding wind turbine.

[0079] When the power demand forecast information indicates an increase in wind power, the frequency regulation priority value is positively correlated with the angle difference indicated by the pitch angle redundancy information; when the power demand forecast information indicates a decrease in wind power, the frequency regulation priority value is negatively correlated with the angle difference indicated by the pitch angle redundancy information; the frequency regulation priority value is positively correlated with the upper quartile of the multiple predicted power maxima included in the maximum power forecast information.

[0080] The above-mentioned optimal pitch angle can be understood as the minimum pitch angle of the corresponding wind turbine (considering only the normal power generation situation below the rated wind speed).

[0081] After determining the predicted power change of the wind turbine at each future moment in the target prediction period, the upper quartiles of multiple predicted power maxima for each turbine in the target prediction period, as well as the angle difference between the pitch angle of each turbine at the current moment and the optimal pitch angle of the corresponding turbine, are analyzed. The control response capability of each turbine is comprehensively evaluated from the aspects of limit power and power adjustable range. This allows for adaptive control of each turbine under actual operating conditions, making the operating state of each turbine more consistent after power control, avoiding overload or idle conditions of some turbines, and achieving effective utilization of the power output capacity of the wind turbine.

[0082] The larger the upper quartile of the maximum predicted power values ​​of the wind turbine in the target prediction period, the higher the limit power of the wind turbine, and the greater the power variation it can theoretically withstand. Therefore, it has a higher priority in subsequent power regulation (i.e., a higher frequency regulation priority value).

[0083] In this invention, when the average value of the predicted power change of the wind turbine at each future moment in the target prediction period is greater than zero, it indicates that the power demand prediction information indicates an increase in wind power; and when the average value of the predicted power change of the wind turbine at each future moment in the target prediction period is less than zero, it indicates that the power demand prediction information indicates a decrease in wind power.

[0084] When power demand forecast information indicates an increase in wind power, the smaller the angle difference between the pitch angle of the wind turbine at the current moment and the optimal pitch angle of the corresponding wind turbine, the smaller the extent to which the wind turbine can increase its power generation through pitch angle adjustment. The smaller the actual power variation that the wind turbine can bear through pitch angle adjustment, the lower its priority in subsequent power regulation (i.e., the lower the frequency regulation priority).

[0085] When power demand forecast information indicates a reduction in wind power, the smaller the angle difference between the pitch angle of the wind turbine at the current moment and the optimal pitch angle of the corresponding wind turbine, the greater the reduction in power generation that the wind turbine can achieve by adjusting the pitch angle. The wind turbine can also handle a greater power change through pitch angle adjustment. Therefore, it has a higher priority in subsequent power regulation (i.e., a higher frequency regulation priority).

[0086] In one example, the maximum power prediction information for each wind turbine in a wind turbine system can be obtained using an ARIMA model. In this example, the predicted maximum power of a wind turbine at a future time can be understood as the maximum power generation that the wind turbine can achieve at that future time, under a set confidence level (such as a 95% confidence level). Furthermore, each wind turbine corresponds to an ARIMA model (the ARIMA model corresponding to a wind turbine is trained using the power data of that wind turbine over a historical time period).

[0087] Furthermore, the maximum power prediction information includes the prediction variance of the corresponding wind turbine at each future moment in the target prediction period;

[0088] The step of determining the frequency regulation priority value for each wind turbine based on the maximum power prediction information and pitch angle redundancy information of each wind turbine in the wind turbine unit includes:

[0089] Calculate the average of multiple prediction variances for each wind turbine to obtain the prediction risk index for each wind turbine.

[0090] Calculate the ratio of the upper quartile of the multiple predicted power maxima corresponding to each wind turbine to the predicted risk index to obtain the power priority index corresponding to each wind turbine.

[0091] The frequency regulation priority value of each wind turbine is determined based on the power priority index corresponding to each wind turbine and the angle difference.

[0092] In the above setup, the prediction reliability of the maximum power prediction information of each wind turbine is evaluated by calculating the average of the multiple prediction variances corresponding to each wind turbine. Then, by combining the maximum power prediction information of each wind turbine, the power priority index of each wind turbine in the power prediction dimension is determined, and the priority of each wind turbine in the subsequent adjustment process is determined by combining the angle difference.

[0093] For example, the power priority index of the i-th wind turbine in a wind turbine generator set. It can be represented as:

[0094]

[0095] in, This represents the upper quartile of the multiple predicted power maxima corresponding to the i-th wind turbine. This represents the predicted risk index corresponding to the i-th wind turbine. This represents the normalization function.

[0096] Specifically, determining the frequency regulation priority value of each wind turbine based on its power priority index and the angle difference includes:

[0097] Calculate the angle difference between the pitch angle of each wind turbine at the current moment and the corresponding optimal pitch angle to obtain the angle difference value of each wind turbine;

[0098] Calculate the angle difference between the maximum and minimum pitch angles of each wind turbine to obtain a reference difference value for each wind turbine;

[0099] Calculate the ratio of the angle difference of each fan to the reference difference to obtain the angle normalization value corresponding to each fan.

[0100] When the power demand forecast information indicates an increase in wind power, the product of the power priority index and the angle normalization value corresponding to each wind turbine is calculated to obtain the frequency regulation priority value of each wind turbine.

[0101] When the power demand forecast information indicates a reduction in wind power, the ratio of the power priority index to the angle normalization value for each wind turbine is calculated to obtain the frequency regulation priority value for each wind turbine.

[0102] For example, when the power demand forecast information indicates a reduction in wind power, the frequency regulation priority value of the i-th wind turbine in the wind turbine group... It can be represented as:

[0103]

[0104] in, This represents the power priority index of the i-th wind turbine. This represents the pitch angle of the i-th wind turbine at the current moment. This represents the minimum pitch angle (also known as the optimal pitch angle) of the i-th wind turbine. This represents the maximum pitch angle of the i-th wind turbine. This represents the angle normalization value corresponding to the i-th wind turbine. +0.01 is to avoid the denominator being 0.

[0105] When the power demand forecast information indicates an increase in wind power, the formula for calculating the frequency regulation priority value of the i-th wind turbine in the wind turbine group is similar to the above formula (the difference is that division is replaced by multiplication), and will not be repeated here to avoid repetition.

[0106] Step S3: Based on the power demand forecast information and the frequency regulation priority value of each wind turbine, determine the power adjustment value of each wind turbine in the target forecast period.

[0107] The step of determining the power adjustment value for each wind turbine in the target prediction period based on the power demand forecast information and the frequency regulation priority value of each wind turbine includes:

[0108] Based on the power demand forecast information, the average value of the predicted power change of the wind turbine at multiple future times is calculated to obtain the power change target value.

[0109] The frequency regulation priority values ​​of multiple wind turbines are summed to obtain the total priority value;

[0110] Calculate the ratio of the frequency regulation priority value of each fan to the total priority value to obtain the frequency regulation coefficient of each fan;

[0111] Based on the frequency regulation coefficient of each wind turbine and the target power change value, the power adjustment value of each wind turbine in the target prediction period is determined.

[0112] The power adjustment value is the product of the power change target value and the frequency regulation coefficient of the corresponding fan.

[0113] For example, the power adjustment value of the i-th wind turbine in the target prediction period. It can be represented as:

[0114]

[0115] in, This represents the aforementioned target value for power conversion, where W represents the total priority value. This represents the frequency regulation priority value of the i-th fan.

[0116] In the above settings, the contribution of each wind turbine to power adjustment within the target prediction period is represented by the frequency regulation priority value. The higher the frequency regulation priority value, the higher the contribution of the corresponding wind turbine, that is, the higher the amount of power adjustment the corresponding wind turbine undertakes within the target prediction period.

[0117] After determining the power adjustment value for each wind turbine in the target prediction period, the power generation is adjusted according to the corresponding power adjustment value of each wind turbine at the beginning of the target prediction period. When all wind turbines have completed the corresponding power generation adjustment, the power pre-adjustment operation of the wind turbine units in the target prediction period can be regarded as completed.

[0118] In application, the sum of the power adjustment value and the power value of the corresponding fan at the current moment is less than or equal to the maximum rated power of the corresponding fan, and the sum of the power adjustment value and the power value of the corresponding fan at the current moment is greater than or equal to the minimum rated power of the corresponding fan.

[0119] Based on the above settings, in order to avoid the power adjustment value of the wind turbine exceeding its actual power adjustment capacity, the maximum and minimum rated power of the wind turbine are used as real constraints to avoid the problem of power adjustment value distortion of the wind turbine within the target prediction period, and to ensure that the power pre-adjustment operation of the wind turbine within the target prediction period is successfully completed.

[0120] In this invention, the maximum rated power can be the product of the maximum theoretical power generation of the corresponding wind turbine (provided by the wind turbine manufacturer) and the safety operation coefficient (with a value of 0-1, used to avoid the wind turbine's service life from rapidly declining due to long-term high power generation, which can be set by wind turbine operation and maintenance personnel based on experience, for example, 0.8); the minimum rated power is the power generation of the wind turbine when it is in standby mode (referring to the wind turbine generating power only to maintain its own operation, neither drawing power from the grid nor supplying power to the grid).

[0121] It should be noted that in practical applications, after introducing the above-mentioned maximum and minimum rated power as realistic constraints, if the sum of the power adjustment values ​​of each wind turbine within the target prediction period cannot completely cover the power change target value, then the absolute difference between the power change target value and the sum of the power adjustment values ​​of each wind turbine within the target prediction period is calculated, and this absolute difference is reported to the power grid (the reporting time is within the time period of the current moment to ensure that the power grid has relatively sufficient time to carry out power difference compensation and control operations). The power grid then controls the traditional power generation equipment (i.e., thermal power generation equipment) to compensate for this power difference, thereby ensuring the stable operation of the power grid.

[0122] In summary, this invention determines the total power variation of a wind turbine during a target prediction period by predicting its power change. It then further predicts the maximum power of each individual turbine within the wind turbine during the target prediction period to assess the power output capability of each turbine. By combining the pitch angle difference between the turbine's current moment and its optimal pitch angle, the control response capability of each turbine is comprehensively determined from both power output capability and angle adjustment capability perspectives, and quantified as a frequency regulation priority value. Finally, based on the frequency regulation priority value of each turbine, the aforementioned total power variation is decomposed to determine the power adjustment value for each turbine during the target prediction period. Based on this, power pre-adjustment operations are performed on each turbine. This allows for dynamic matching of the power demand of the power grid in the wind power sector with the actual control response capability of each turbine, improving the stability of the wind turbine's output power and enhancing the power regulation effect of wind turbines in the power grid.

[0123] This invention proposes a wind farm coordinated frequency regulation control system for a power grid with high penetration of new energy sources. Please refer to [link / reference]. Figure 2The diagram illustrates a structural schematic of a wind farm coordinated frequency regulation control system 200 for a high-penetration renewable energy power grid, according to an embodiment of the present invention. The system includes:

[0124] The demand forecasting module 201 is used to obtain power demand forecasting information of wind turbine units, wherein the power demand forecasting information includes the predicted power change of wind turbine units in the grid at each future moment in the target forecasting period.

[0125] The priority identification module 202 is used to determine the frequency regulation priority value of each wind turbine based on the maximum power prediction information and pitch angle redundancy information of each wind turbine in the wind turbine group. The maximum power prediction information includes the predicted maximum power value of the corresponding wind turbine at each future moment in the target prediction period, and the pitch angle redundancy information is used to indicate the angle difference between the pitch angle of the corresponding wind turbine at the current moment and the optimal pitch angle of the corresponding wind turbine.

[0126] The power adjustment module 203 is used to determine the power adjustment value of each wind turbine in the target prediction period based on the power demand prediction information and the frequency regulation priority value of each wind turbine.

[0127] It should be noted that the system provided in the above embodiments is only an example of the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer equipment can be divided into different functional modules to complete all or part of the functions described above. In addition, the wind farm coordinated frequency regulation control system and the wind farm coordinated frequency regulation control method embodiment of the new energy high penetration grid provided in the above embodiments belong to the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0128] This invention also provides an electronic device. Please refer to [link to relevant documentation]. Figure 3 The electronic device may include a processor 301, a memory 302, and a program 3021 stored in the memory 302 and capable of running on the processor 301.

[0129] When program 3021 is executed by processor 301, it can achieve the following: Figure 1 Any steps in the corresponding method embodiments and the achievement of the same beneficial effects will not be repeated here.

[0130] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by hardware related to program instructions, and the program can be stored in a readable medium.

[0131] This invention also provides a readable storage medium storing a computer program, which, when executed by a processor, can perform the above-described functions. Figure 1 Any step in the corresponding method embodiment can achieve the same technical effect, and will not be repeated here to avoid repetition.

[0132] The computer-readable storage medium of this invention can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. For example, a computer-readable storage medium can be an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0133] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0134] The program code contained on the storage medium can be transmitted using any suitable medium, including but not limited to wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0135] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or terminal. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0136] This invention also provides a computer program product that, when run on a computer, causes the computer to perform the aforementioned steps to implement the wind farm coordinated frequency regulation control method for a new energy high-penetration power grid provided in the above embodiments.

[0137] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0138] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A wind farm coordinated frequency regulation control method for a power grid with high penetration of new energy sources, characterized in that, The method includes: Obtain power demand forecast information for wind turbines, wherein the power demand forecast information includes the predicted power change of wind turbines in the power grid at each future moment in the target forecast period; Based on the maximum power prediction information and pitch angle redundancy information of each wind turbine in the wind turbine group, the frequency regulation priority value of each wind turbine is determined. The maximum power prediction information includes the predicted maximum power of the corresponding wind turbine at each future moment in the target prediction period, and the pitch angle redundancy information is used to indicate the angle difference between the pitch angle of the corresponding wind turbine at the current moment and the optimal pitch angle of the corresponding wind turbine. Based on the power demand forecast information and the frequency regulation priority value of each wind turbine, the power adjustment value of each wind turbine in the target forecast period is determined. Specifically, when the power demand forecast information indicates an increase in wind power, the frequency regulation priority value is positively correlated with the angle difference indicated by the pitch angle redundancy information; when the power demand forecast information indicates a decrease in wind power, the frequency regulation priority value is negatively correlated with the angle difference indicated by the pitch angle redundancy information; and the frequency regulation priority value is positively correlated with the upper quartile of the multiple predicted power maxima included in the maximum power forecast information. The maximum power prediction information includes the prediction variance of the corresponding wind turbine at each future moment in the target prediction period; The step of determining the frequency regulation priority value for each wind turbine based on the maximum power prediction information and pitch angle redundancy information of each wind turbine in the wind turbine unit includes: Calculate the average of multiple prediction variances for each wind turbine to obtain the prediction risk index for each wind turbine. Calculate the ratio of the upper quartile of the multiple predicted power maxima corresponding to each wind turbine to the predicted risk index to obtain the power priority index corresponding to each wind turbine. The frequency regulation priority value of each wind turbine is determined based on the power priority index corresponding to each wind turbine and the angle difference. The step of determining the frequency regulation priority value of each wind turbine based on the power priority index corresponding to each wind turbine and the angle difference includes: Calculate the angle difference between the pitch angle of each wind turbine at the current moment and the corresponding optimal pitch angle to obtain the angle difference value of each wind turbine; Calculate the angle difference between the maximum and minimum pitch angles of each wind turbine to obtain a reference difference value for each wind turbine; Calculate the ratio of the angle difference of each fan to the reference difference to obtain the angle normalization value corresponding to each fan. When the power demand forecast information indicates an increase in wind power, the product of the power priority index and the angle normalization value corresponding to each wind turbine is calculated to obtain the frequency regulation priority value of each wind turbine. When the power demand forecast information indicates a reduction in wind power, the ratio of the power priority index to the angle normalization value for each wind turbine is calculated to obtain the frequency regulation priority value for each wind turbine.

2. The wind farm coordinated frequency regulation control method for a high-penetration renewable energy power grid according to claim 1, characterized in that, The acquisition of power demand forecast information for wind turbine generators includes: Obtain frequency prediction information of the power grid, wherein the frequency prediction information includes the predicted frequency change of the power grid at each future moment in the target prediction period; Based on the power output ratio of wind turbines in the power generation equipment group, the frequency power mapping coefficient, and the frequency prediction information, the power demand prediction information of wind turbines is determined.

3. The wind farm coordinated frequency regulation control method for a high-penetration renewable energy power grid according to claim 2, characterized in that, The step of determining the power demand forecast information for wind turbines based on the power output ratio of wind turbines in the power generation equipment group, the frequency power mapping coefficient, and the frequency forecast information includes: Calculate the ratio of the frequency power mapping coefficient to the predicted frequency change of the power grid at each future moment in the target prediction period, so as to obtain the predicted total power change of the power generation group at each future moment in the target prediction period. The predicted total power change of the power generation equipment group at each future moment in the target prediction period is calculated by multiplying the power output ratio of the wind turbine in the power generation equipment group, so as to obtain the predicted power change of the wind turbine at each future moment in the target prediction period.

4. The wind farm coordinated frequency regulation control method for a high-penetration renewable energy power grid according to claim 2, characterized in that, The steps for obtaining the power output percentage of wind turbines in a power generation system include: At each historical moment, the ratio of wind power generation of the wind turbine to the total power generation of the power generation equipment group is calculated to obtain multiple historical power ratios. The average of the multiple historical power ratios is determined as the power output ratio of the wind turbine in the power generation equipment group.

5. The wind farm coordinated frequency regulation control method for a high-penetration renewable energy power grid according to claim 1, characterized in that, The step of determining the power adjustment value for each wind turbine in the target prediction period based on the power demand forecast information and the frequency regulation priority value of each wind turbine includes: Based on the power demand forecast information, the average value of the predicted power change of the wind turbine at multiple future times is calculated to obtain the power change target value. The frequency regulation priority values ​​of multiple wind turbines are summed to obtain the total priority value; Calculate the ratio of the frequency regulation priority value of each fan to the total priority value to obtain the frequency regulation coefficient of each fan; Based on the frequency regulation coefficient of each wind turbine and the target power change value, the power adjustment value of each wind turbine in the target prediction period is determined.

6. The wind farm coordinated frequency regulation control method for a high-penetration renewable energy power grid according to claim 5, characterized in that, The power adjustment value is the product of the power change target value and the frequency regulation coefficient of the corresponding fan.

7. The wind farm coordinated frequency regulation control method for a high-penetration renewable energy power grid according to claim 1, characterized in that, The sum of the power adjustment value and the power value of the corresponding fan at the current moment is less than or equal to the maximum rated power of the corresponding fan, and the sum of the power adjustment value and the power value of the corresponding fan at the current moment is greater than or equal to the minimum rated power of the corresponding fan.

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