Control method of energy management platform system, energy management platform system and wind power plant

Abstract

The invention discloses a control method of an energy management platform system, the energy management platform system and a wind power plant, and relates to the technical field of wind turbine generator control, and the control method comprises the steps: obtaining the required control precision of a power grid and a first target power change amplitude; according to the actual power change amplitude and the first target power change amplitude, one control period in at least one preset time window is selected to control the wind turbine generator to participate in power regulation and control; sequencing the priorities of the wind turbine generators participating in power regulation and control according to the historical operation efficiency indexes of the wind turbine generators; calculating a second precision deviation between the actual control precision of the current control period and the actual control precision of the last round of control period, and adjusting the step length and frequency of the next round of control period of the energy management platform system according to the second precision deviation, so that the absolute value of the first precision deviation is not greater than a set precision deviation threshold value; the invention aims to maximize the generating capacity of the wind power plant on the premise of meeting the test standard.

Description

Technical Field

[0001] This application relates to the field of wind turbine control technology, and in particular to a control method for an energy management platform system, an energy management platform system, and a wind farm. Background Technology

[0002] For a wind farm to successfully connect to the main power grid and generate electricity, it needs to pass the AGC (Automatic Generation Control) full-field power control test required by the State Grid Corporation of China (GBT 19963-2021 Technical Regulations for Wind Farm Connection to Power Systems and NBT 31078-2022 Performance Evaluation Methods for Wind Farm Grid Connection) and local power grid standards. When wind turbines participate in AGC full-field power control, they often fail to maximize power generation, mainly for the following reasons: 1. When the wind farm power control algorithm executes the received AGC full-field power control command, in order to ensure the test passes, it does not fully consider the constraints of the number of power-limited shutdowns, yaw and pitch operations. As the number of shutdowns, yaw and pitch operations during the power-limited process increases, the corresponding power generation time and resources consumed by the unit also increase, resulting in the power generation being consumed and unable to be maximized. 2. AGC testing generally has control index requirements such as control accuracy and response time. During the test, all indicators must be equal to or less than the control index to pass the test. Currently, to ensure compliance with the above-mentioned national and local power grid standards, wind turbine manufacturers typically follow these practices: (1) Limiting the response time to less than or equal to the test standard requirement means that the control time is not minimized while meeting the standard. However, the more the actual control time is less than the standard control time, the more time can be left for power generation, thus maximizing the power generation. (2) Ensure that the control precision is minimized while meeting the test standards. However, excessively low control precision will inevitably lead to power over-adjustment, resulting in unnecessary power limitation and ultimately affecting the overall power generation efficiency of the system. This will prevent the power generation from being maximized and lead to a waste of power generation. Summary of the Invention

[0003] The main purpose of this application is to provide a control method for an energy management platform system, an energy management platform system, and a wind farm, with the aim of maximizing the power generation of the wind farm while meeting testing standards.

[0004] This application proposes a control method for an energy management platform system applied to a wind farm, which includes multiple wind turbine units. The energy management platform system is used to control the wind turbine units to participate in power regulation upon receiving a power control command for the entire wind farm. The control method of the energy management platform system includes: Obtain the power control standard requirements of the power grid, which include the required control accuracy of the power grid and the first target power change amplitude; The actual power change amplitude of the wind farm within a preset time window is obtained, and the wind turbine is controlled to participate in power regulation in one of the control cycles within at least one preset time window based on the actual power change amplitude and the first target power change amplitude. Obtain the historical operating performance indicators of each wind turbine, and rank the wind turbines for power regulation based on the historical operating performance indicators. The actual control accuracy of the energy management platform system in the current control cycle is obtained, and the first accuracy deviation between the actual control accuracy in the current control cycle and the control accuracy required by the power grid is calculated. The absolute value of the first accuracy deviation in the current control cycle is compared with a set accuracy deviation threshold. If the absolute value of the first precision deviation in the current control cycle is greater than the set precision deviation threshold, the second precision deviation between the actual control precision in the current control cycle and the actual control precision in the previous control cycle is calculated. Based on the second precision deviation, the control step size and control frequency of the energy management platform system in the next control cycle are adjusted so that the absolute value of the first precision deviation in the next control cycle is not greater than the set precision deviation threshold.

[0005] In one embodiment, adjusting the control step size and control frequency of the energy management platform system for the next control cycle based on the second accuracy deviation, so that the absolute value of the first accuracy deviation in the next control cycle is not greater than a set accuracy deviation threshold, includes: In one embodiment, adjusting the control step size and control frequency of the energy management platform system in the next control cycle based on the second accuracy deviation, so that the absolute value of the first accuracy deviation in the next control cycle is not greater than a set accuracy deviation threshold, includes: When the second accuracy deviation is greater than zero, the control step size and control frequency of the energy management platform system in the next control cycle are increased based on the first formula, which is: ; ; When the second accuracy deviation is less than zero, the control step size and control frequency of the energy management platform system in the next control cycle are increased based on the second formula, which is: ; ; in, The control step size for the next control cycle. This represents the control step size for the current control cycle. The step size for adjusting the control power is determined for each round. The control frequency for the next control cycle. The control frequency for the current control cycle. The step size for adjusting the control frequency is determined for each round. This is for adjusting the coefficient.

[0006] In one embodiment, the adjustment coefficient is defined as the quotient obtained by dividing the required control accuracy of the power grid by the deviation threshold of the set accuracy, as shown in the formula:

[0007] Among them, e demand To meet the control precision requirements of the power grid, To set a precision deviation threshold.

[0008] In one embodiment, the power control standard requirements also include grid-required response time and grid-required control rate, and the control method of the energy management platform system further includes: Construct a system that includes control step size With control frequency Multiple initial value vector functions ; Multiple initial value vector functions Decoupling and filtering are performed according to three dimensions: response time, control accuracy, and control rate, to determine the first vector function corresponding to the shortest response time under the condition of meeting the upper limit of the power grid's required response time in each of the three dimensions. The second vector function that best approximates the required control accuracy of the power grid. And the third vector function that is closest to the required control rate of the power grid. ; Calculate the first vector function according to the error coefficient formula. Second vector function and the third vector function and multiple initial value vector functions error coefficient The formula for the error coefficient is:

[0009] Selecting the error coefficient Minimum initial value vector function The optimal empirical initial value vector ; in, To control the step size, To control the frequency.

[0010] In one embodiment, after obtaining the power control standard requirements of the power grid, the method further includes: Obtain the sum of the available active power values ​​of multiple wind turbine units, and compare the sum of the available active power values ​​of multiple wind turbine units with the power control target value; If the sum of the available active power values ​​of multiple wind turbine units is less than the power control target value, the energy management platform system is determined to operate in free generation mode; if the sum of the available active power values ​​of multiple wind turbine units is not less than the power control target value, the energy management platform system is determined to operate in dispatch mode.

[0011] In one embodiment, when the energy management platform system operates in free power generation mode, selecting one of the control cycles within at least one preset time window to participate in power regulation based on the actual power change amplitude and the first target power change amplitude includes: Obtain the required rate of power change of the power grid, and compare the actual power change amplitude with the first target power change amplitude; If the absolute value of the actual power change amplitude is not greater than the first target power change amplitude, and the power grid does not have a power change rate detection requirement, the power of the wind farm is adjusted based on the actual power change amplitude during one of the control cycles within a preset time window. If the absolute value of the actual power change amplitude is greater than the first target power change amplitude, and the power grid does not have a power change rate detection requirement, calculate the quotient N and the remainder M of the actual power change amplitude and the first target power change amplitude. The power of the wind farm is adjusted based on the target power change amplitude during one of the N preset control time periods. If the remainder M is greater than zero, the power of the wind farm is also adjusted based on the second target power change amplitude during one of the control periods during the (N+1)th preset control time period. Wherein, the magnitude of the change in the first target power is greater than the magnitude of the change in the second target power; When the power grid has a power change rate detection requirement, the actual power change amplitude of the wind farm in each preset time window is controlled to be less than the first target power change threshold.

[0012] In one embodiment, after obtaining the power control standard requirements of the power grid, the method further includes: Select the wind turbine units that can participate in power regulation to form a list of controllable wind turbines; Based on the operating parameters of the controllable fans in the controllable fan list, power increase and power decrease adjustment priority weights are set for each of the controllable fans. The power regulation of each controllable fan is controlled according to the adjustment priority weight of the power increase and decrease of each controllable fan.

[0013] In one embodiment, the historical operating performance indicators include the historical maximum power generation ratio, the historical minimum number of outages, the historical minimum number of yaws, and the historical minimum number of pitch changes. The priority ranking of wind turbine units for power regulation based on these historical operating performance indicators includes: The scores of each wind turbine are determined by assigning corresponding weights to the percentage of the highest historical power generation, the lowest number of historical outages, the lowest number of historical yaws, and the lowest number of historical pitches. The priority of wind turbines in power regulation is determined by their scores, with the scores of each wind turbine inversely proportional to its priority in power regulation.

[0014] Furthermore, to achieve the above objectives, this application also proposes an energy management platform system, which includes an improved PID controller and an energy management platform. The improved PID controller is used to control the power of multiple wind turbine units; the energy management platform includes: The data acquisition module is used to acquire the power control standard requirements of the power grid; The power regulation module is used to acquire the actual power change amplitude of the wind farm within a preset time window, select one of the control cycles within at least one preset time window to control the wind turbine to participate in power regulation based on the actual power change amplitude and the first target power change amplitude; and to acquire the historical operating efficiency index of each wind turbine, and sort the priority of the wind turbine to participate in power regulation based on the historical operating efficiency index. The improved PID controller includes: The control accuracy consistency algorithm module is used to obtain the actual control accuracy of the PID controller in the current control cycle, calculate the first accuracy deviation between the actual control accuracy of the current control cycle and the control accuracy required by the power grid, and compare the absolute value of the first accuracy deviation in the current control cycle with a set accuracy deviation threshold. The inverse step size sensing algorithm module is used to calculate the second precision deviation between the actual control precision of the current control cycle and the actual control precision of the previous control cycle when the absolute value of the first precision deviation in the current control cycle is greater than a set precision deviation threshold. The step size and frequency real-time adjustment module is used to adjust the control step size and control frequency of the PID controller in the next control cycle according to the second accuracy deviation, so that the absolute value of the first accuracy deviation in the next control cycle is not greater than the set accuracy deviation threshold.

[0015] In addition, to achieve the above objectives, this application also proposes a wind farm, comprising: a plurality of wind turbine units, a memory, an energy management platform system, and a computer program stored on the memory and executable on the energy management platform system, the computer program being configured to implement the steps of the control method of the energy management platform system described in any of the above claims.

[0016] The control method of the energy management platform system of this application includes: acquiring the power control standard requirements of the power grid, which include the grid control accuracy and the first target power change amplitude; acquiring the actual power change amplitude of the wind farm within a preset time window, and selecting one control cycle within at least one preset time window to control the wind turbines to participate in power regulation based on the actual power change amplitude and the first target power change amplitude, so as to shorten the power limiting process of the wind turbines; acquiring the historical operating efficiency index of each wind turbine, and prioritizing the wind turbines to participate in power regulation based on the historical operating efficiency index, so that wind turbines with higher power generation efficiency participate in power generation instead of power regulation, thereby increasing the power generation of the wind farm under the premise of meeting the test standards; acquiring the actual control accuracy of the energy management platform system in the current control cycle, and calculating... The first precision deviation between the actual control precision of the current control cycle and the grid-required control precision is calculated. The absolute value of this first precision deviation is compared with a set precision deviation threshold to determine the extent to which the actual control precision deviates from the grid-required control precision. If the absolute value of the actual precision deviation exceeds the set precision deviation threshold, the deviation is considered significant. In this case, the second precision deviation between the actual control precision of the current control cycle and the actual control precision of the previous control cycle is calculated to determine the direction of the first precision deviation. Based on the second precision deviation, the control step size and control frequency of the energy management platform system for the next control cycle are adjusted to ensure that the absolute value of the first precision deviation in the next control cycle does not exceed the set precision deviation threshold. This configuration allows the energy management platform system to maintain real-time control precision that is essentially consistent with the grid-required control precision, avoiding excessively low control precision that could lead to over-adjustment of power and unnecessary power limitations, thus maximizing wind farm power generation while meeting testing standards. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating an embodiment of this application; Figure 2 This is a schematic diagram of the overall power regulation of a wind farm according to an embodiment of this application; Figure 3 This is a flowchart illustrating another embodiment of this application; Figure 4 This is a flowchart illustrating yet another embodiment of this application; Figure 5 This is a flowchart illustrating another embodiment of the present application; Figure 6 This application also provides a schematic flowchart of an embodiment; Figure 7 This application also provides a schematic flowchart of an embodiment; Figure 8 A schematic diagram of an existing PID controller; Figure 9 This is a schematic diagram of an improved PID controller according to an embodiment of this application.

[0020] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0021] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0022] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0023] For a wind farm to successfully connect to the main power grid and generate electricity, it needs to pass the AGC (Automatic Generation Control) full-field power control test required by the State Grid Corporation of China (GBT 19963-2021 Technical Regulations for Wind Farm Connection to Power Systems and NBT 31078-2022 Performance Evaluation Methods for Wind Farm Grid Connection) and local power grid standards. When wind turbines participate in AGC full-field power control, they often fail to maximize power generation, mainly for the following reasons: 1. When the wind farm power control algorithm executes the received AGC full-field power control command, in order to ensure the test passes, it does not fully consider the constraints of the number of power-limited shutdowns, yaw and pitch operations. As the number of shutdowns, yaw and pitch operations during the power-limited process increases, the corresponding power generation time and resources consumed by the unit also increase, resulting in the power generation being consumed and unable to be maximized. 2. AGC testing generally has control index requirements such as control accuracy and response time. During the test, all indicators must be equal to or less than the control index to pass the test. Currently, to ensure compliance with the above-mentioned national and local power grid standards, wind turbine manufacturers typically follow these practices: (1) Limiting the response time to less than or equal to the test standard requirement means that the control time is not minimized while meeting the standard. However, the more the actual control time is less than the standard control time, the more time can be left for power generation, thus maximizing the power generation. (2) Ensure that the control precision is minimized while meeting the test standards. However, excessively low control precision will inevitably lead to power over-adjustment, resulting in unnecessary power limitation and ultimately affecting the overall power generation efficiency of the system. This will prevent the power generation from being maximized and lead to a waste of power generation.

[0024] To address the aforementioned problems, this application proposes a control method for an energy management platform system applied to a wind farm. The wind farm includes multiple wind turbines. The energy management platform system is used to control the wind turbines to participate in power regulation upon receiving a power control command for the entire wind farm. In one embodiment, referring to… Figure 1 The control method of the energy management platform system includes: Step S100: Obtain the power control standard requirements of the power grid, which include the power grid control accuracy and the first target power change amplitude of the power grid; Step S200: Obtain the actual power change amplitude of the wind farm within a preset time window, and select one of the control cycles within at least one preset time window to control the wind turbine to participate in power regulation based on the actual power change amplitude and the first target power change amplitude; Step S300: Obtain the historical operating performance index of each wind turbine, and sort the priority of wind turbines participating in power regulation according to the historical operating performance index; Step S400: Obtain the actual control accuracy of the energy management platform system in the current control cycle, and calculate the first accuracy deviation between the actual control accuracy of the current control cycle and the control accuracy required by the power grid, and compare the absolute value of the first accuracy deviation in the current control cycle with a set accuracy deviation threshold. Step S500: If the absolute value of the first precision deviation in the current control cycle is greater than the set precision deviation threshold, calculate the second precision deviation between the actual control precision in the current control cycle and the actual control precision in the previous control cycle. Step S600: Adjust the control step size and control frequency of the energy management platform system in the next control cycle according to the second accuracy deviation, so that the absolute value of the first accuracy deviation in the next control cycle is not greater than the set accuracy deviation threshold.

[0025] It should be noted that the overall power regulation (secondary power control) control method of the wind farm is as follows: Figure 2 As shown. The entire power regulation system consists of three main parts: provincial dispatch equipment, booster station equipment, and wind farm power execution equipment. The provincial dispatch equipment includes an AGC (Automatic Generation Control) master station (grid dispatch command server) and network security equipment. The booster station equipment mainly includes booster station switches, remote control devices, AGC substations, an EMS (Energy Management System), and a SCADA (Supervisory Control and Data Acquisition) server. The wind farm power execution equipment mainly includes a PLC for each wind turbine generator, a ring network switch, and a fiber optic ring network. The power control target value is sent from the AGC master station to the AGC substation in the booster station equipment via the network security equipment. The two communicate in real time through a dedicated power security network established by the network security equipment and the booster station switch to keep the power control target value synchronized. The AGC substation forwards the received power control target value to the energy management platform system. The energy management platform system uses the control method of the energy management platform system in this application to distribute the value to each wind turbine generator in the wind turbine group through the wind farm intranet. The substation equipment and the wind farm power execution equipment are connected to form a wind farm intranet through a fiber optic ring network, a ring network switch, and a substation switch. All devices in the intranet can communicate with each other.

[0026] It should be noted that the control accuracy is the absolute deviation between the actual total active power of all wind turbines in the wind farm and the power control target value. The overall power control command is a control command issued by the AGC master station, requiring the overall output power of the wind farm to reach the control target value. Upon receiving the overall power control command, the energy management platform system controls the wind turbines to participate in power regulation, so as to adjust the overall output power of the wind farm to be approximately equal to the power control target value.

[0027] It should be noted that the power control standards of the power grid vary from region to region. Therefore, before controlling wind turbines to participate in power regulation, this application needs to obtain the power control standards of the current regional power grid to ensure that the final calculated control accuracy meets the control accuracy requirements of the local power grid, thereby maximizing the power generation of the entire wind farm. In addition to the grid-required control accuracy and the first target power change amplitude, the power control standards may also include key parameters such as control response time, adjustment time, response rate, and control rate.

[0028] It should be noted that the power grid requires the actual power change amplitude of the wind farm within a preset time window not to exceed the first target power change amplitude. When the actual power change amplitude exceeds the first target power change amplitude, the wind turbine units need to be controlled to participate in power regulation so that the actual power change amplitude is lower than the first target power change amplitude. Traditional power limiting strategies only focus on meeting the control indicators required by the power grid. If the power grid requires the change to not exceed 1 / 10 of the wind farm's installed capacity in one minute, many wind turbine manufacturers use 1 / 10 or 1 / 20 of the installed capacity in one minute (as long as it is less than 1 / 10 of the installed capacity) as the maximum amplitude of the control change in this round. As long as the change does not exceed the maximum amplitude, it is possible that the power limiting operation is being performed upward or downward within this minute to prolong the adjustment process. In this case, the wind turbine units are in a non-full-load state for a long time.

[0029] To address this, this application can, when the actual power change amplitude within a preset time window is less than the first target power change amplitude, select one control cycle within the preset time window to control the wind turbine generators to participate in power regulation. Since the actual power change amplitude is small in this case, the adjustment of the wind farm's power can be based on the actual power change amplitude being smaller than the first target power change amplitude. This application can also, when the actual power change amplitude within a preset time window exceeds the first target power change amplitude, select one control cycle within multiple preset time windows to control the wind turbine generators to participate in power regulation. Since the actual power change amplitude is large in this case, the wind farm's power can be adjusted based on the first target power change amplitude. In an exemplary embodiment, when the preset time window is 60 seconds, the control cycle can be 5 seconds. With this setting, this application controls the wind turbine generators to participate in power regulation within one control cycle when the actual power change amplitude is small, thus shortening the entire power limiting process of traditional power limiting measurement to one control cycle, and controlling the wind turbine generators to generate electricity normally in other control cycles. When the actual power change amplitude is large, the wind turbine is controlled to participate in power regulation within one of the control cycles of multiple preset time windows. That is, the entire power limiting process of traditional power limiting measurement is shortened to controlling the power limiting of the wind turbine within one of the control cycles of multiple preset time windows, while the wind turbine is controlled to generate electricity normally in other control cycles, so that the power limiting process of the wind turbine in this application is shorter than that of traditional power regulation strategies.

[0030] It should be noted that the historical operating performance index quantifies the power generation efficiency and regulation losses of wind turbines during historical operation, and assesses their contribution to the total power generation of the wind farm. Specifically, it may include indicators such as the historical maximum power generation ratio, the historical minimum number of outages, the historical minimum number of yaws, and the historical minimum number of pitches. This application ranks the power generation efficiency of wind turbines from smallest to largest according to the historical operating performance index. The lower the power generation efficiency of a wind turbine, the higher its priority in participating in power regulation, so that wind turbines with higher power generation efficiency are given priority in participating in power generation rather than power regulation, thereby increasing the power generation of the wind farm under the premise of meeting the test standards.

[0031] It should be noted that the accuracy deviation threshold is used to determine whether the deviation between the actual control accuracy and the control accuracy required by the power grid is significant enough to require adjustment. The value can be 1%, 1.2% or 1.5%, and can be set according to the actual situation.

[0032] If the first precision deviation of the current control cycle is not greater than the set precision deviation threshold, it indicates that the deviation between the actual control precision of the current control cycle and the control precision required by the power grid is within the allowable range, and no adjustment is needed to avoid frequent start-stop adjustments near the boundary of the control precision required by the power grid. If the absolute value of the first precision deviation of the current control cycle is greater than the set precision deviation threshold, it indicates that there is a significant deviation between the actual control precision and the control precision required by the power grid. In this case, it is necessary to first calculate the second precision deviation between the actual control precision of the current control cycle and the actual control precision of the previous control cycle to determine the trend of the current control cycle deviating from the control precision required by the power grid. If the second precision deviation is greater than 0, it is determined that the first precision deviation is increasing in the positive direction. At this time, this application adjusts the control step size and control frequency of the next control cycle to increase, so that the actual control precision of the next control cycle decreases. If the second precision deviation is less than 0, it is determined that the first precision deviation is increasing in the negative direction. At this time, this application adjusts the control step size and control frequency of the next control cycle to increase, so that the actual control precision of the next control cycle increases, thereby adjusting the control precision in real time to be basically consistent with the control precision required by the power grid.

[0033] It should be noted that the first accuracy deviation is the difference between the actual control accuracy e and the power grid's required control accuracy e. demand The difference, i.e., the first precision deviation, is |ee demand The second precision deviation is the actual control precision e of the current control cycle. i Compared with the actual control accuracy e of the previous control cycle i-1 The difference, i.e., the second precision deviation, is |e i -e i-1 |

[0034] It should be noted that the control step size is the maximum allowable change in the total power of the wind farm in a single adjustment within each control cycle. The control frequency is the number of times per second that the energy management platform sends power adjustment commands to the wind turbines.

[0035] With the above settings, this application can adjust the control precision of the energy management platform system in real time to be basically consistent with the control precision required by the power grid, avoid excessive power over-adjustment due to insufficient control precision, and bring unnecessary power limitation, so as to maximize the power generation of the wind farm under the premise of meeting the test standards.

[0036] In one embodiment of this application, adjusting the control step size and control frequency of the energy management platform system in the next control cycle based on the second accuracy deviation, so that the absolute value of the first accuracy deviation in the next control cycle is not greater than a set accuracy deviation threshold, includes: When the second accuracy deviation is greater than zero, the control step size and control frequency of the energy management platform system in the next control cycle are increased based on the first formula, which is: ; ; When the second accuracy deviation is less than zero, the control step size and control frequency of the energy management platform system in the next control cycle are increased based on the second formula, which is: ; ; in, The control step size for the next control cycle. This represents the control step size for the current control cycle. The step size for adjusting the control power is determined for each round. The control frequency for the next control cycle. The control frequency for the current control cycle. The step size for adjusting the control frequency is determined for each round. This is for adjusting the coefficient.

[0037] In this embodiment, the adjustment coefficient n can be the quotient obtained by dividing the required control accuracy of the power grid by the set accuracy deviation threshold. This scales the adjustment step size and frequency of the control step size in the next control cycle proportionally to the allowable error scale of the power grid, automatically adapting the strength of the adjustment step size and frequency to the local power grid standard without manual parameter readjustment. For example, if the local power grid requires strict control accuracy, the required accuracy is relatively low, and the adjustment coefficient is also relatively small. This ensures a smaller adjustment step size and frequency in the next control cycle, avoiding unnecessary repeated adjustments due to power overshoot or oscillation caused by excessively large adjustment steps or frequencies, thus saving power generation time. Conversely, if the local power grid requires more lenient control accuracy, the adjustment coefficient is relatively large, ensuring a larger adjustment step size and frequency in the next control cycle. This avoids slow response and excessively long power limiting times due to overly conservative adjustments, which could affect the maximization of overall power generation.

[0038] Specifically, the formula for calculating the adjustment coefficient n is as follows:

[0039] Among them, e demand To meet the control precision requirements of the power grid, To set a precision deviation threshold.

[0040] In this embodiment, the step size of each round of control power adjustment can be 0.1% to 0.5% of the installed capacity, and the step size of each round of control frequency adjustment can be 5% to 20% of the current control frequency. The specific values ​​can be set according to the wind farm operating conditions or project requirements, and are not limited here.

[0041] It should be noted that when the second precision deviation is greater than 0, it indicates that the first precision deviation is increasing and the future trend of the deviation is also increasing synchronously. At this time, it is necessary to dynamically increase the control step size and control frequency in reverse according to the first formula to ensure that the first precision deviation does not continue to increase. When the absolute value of the first precision deviation is greater than the set precision deviation threshold, while the second precision deviation is less than 0, it indicates that the first precision deviation is increasing but the future trend of the deviation is decreasing. At this time, it is necessary to dynamically increase the control step size and control frequency in reverse according to the second formula. However, since the future trend of the first precision deviation is decreasing, the magnitude of the reverse adjustment needs to be smaller than that of the first formula. The step size of each round of control power adjustment change multiplied by the adjustment coefficient n is changed to the step size of each round of control power adjustment change divided by the adjustment coefficient n. The step size of each round of control frequency adjustment change multiplied by the adjustment coefficient n is also changed to divided by the adjustment coefficient n, so that the step size and frequency increase calculated by the second formula are smaller than those of the first formula. This ensures that the first precision deviation does not continue to increase and also does not need to be adjusted according to the future trend. The ultimate goal of this algorithm is to ensure that the real-time control accuracy in each round is dynamically consistent with the control accuracy required by the power grid. That is, the real-time control accuracy is dynamically consistent with the standard requirements of the power grid, just meeting the requirements. It will not waste power generation time and control response process due to positive or negative deviations in control accuracy, thereby increasing power generation.

[0042] Under different wind farms, wind conditions, and power grid standards, the optimal control step size and control frequency vary greatly. Traditional methods rely on human experience or default values, which often lead to solutions that are far from optimal and result in excessively long modulation cycles for PID controllers.

[0043] In this regard, in one embodiment of this application, reference is made to Figure 3 The power control standard requirements also include grid response time and grid control rate requirements, and the control method of the energy management platform system further includes: Step S010: Construct a system containing control step size With control frequency Multiple initial value vector functions ; Step S020: Convert the multiple initial value vector functions Decoupling and filtering are performed according to three dimensions: response time, control accuracy, and control rate, to determine the first vector function corresponding to the shortest response time under the condition of meeting the upper limit of the power grid's required response time in each of the three dimensions. The second vector function that best approximates the required control accuracy of the power grid. And the third vector function that is closest to the required control rate of the power grid. ; Step S030: Calculate the first vector function according to the error coefficient formula. Second vector function and the third vector function and multiple initial value vector functions error coefficient The formula for the error coefficient is:

[0044] Step S040: Select the error coefficient Minimum initial value vector function The optimal empirical initial value vector ; in, To control the step size, To control the frequency.

[0045] It should be noted that this application generates a large number of vector functions containing control step size and control frequency by utilizing previously stored historical operating data. For each vector function Under the same operating conditions, the power control algorithm is run, and its three corresponding performance indicators are recorded: response time, control accuracy, and control rate.

[0046] It should be noted that grid AGC testing imposes several stringent requirements on wind farm power control, primarily including response time, control accuracy, and control rate. Response time is the time from the issuance of the overall power control command to the point where the total output power of the wind farm is approximately equal to the power control target value. Control accuracy is the absolute deviation between the actual total active power of all wind turbines in the wind farm and the power control target value. Control rate is the change in the total active power of the wind farm per unit time. Therefore, when initializing the optimal empirical initial value vectors for the control step size and control frequency, it is essential to ensure that all three requirements are met; optimization of only one aspect at the expense of the others is not advisable.

[0047] In this embodiment, the vector function The specific steps for decoupling and filtering based on response time are as follows: First, from all vector functions... In the middle, retain vector functions whose response time is no greater than the grid's required response time. and retain the vector function Sort the vector functions in ascending order and select the smallest one as the first vector function. Vector functions The specific steps for decoupling and screening based on control precision are as follows: First, from all vector functions... In this context, a vector function is used to ensure that the absolute value of the accuracy deviation between the control accuracy and the grid-required control accuracy is not greater than a set control accuracy threshold. and retain the vector function Sort the vector functions in ascending order of their control accuracy, and select the vector function whose control accuracy is closest to the power grid's required control accuracy as the second vector function. Vector functions The specific steps for decoupling and filtering according to the control rate dimension are as follows: First, from all vector functions... In this context, a vector function is used to ensure that the absolute value of the rate deviation between the retained control rate and the grid-required control rate is not greater than a set control rate threshold. and retain the vector function Sort the vector functions in ascending order of their control rates, and select the vector function whose control rate is closest to the grid's required control rate as the third vector function. .

[0048] In multiple vector functions After decoupling and filtering, the first vector function is calculated according to the error coefficient formula. Second vector function and the third vector function and initial value vector function error coefficient It should be noted that the smaller the error coefficient in the formula, the greater the proportion of the product of data frequencies that the initial value vector function just meets the requirements of response time, control accuracy, and control rate. This indicates that the selected vector is more scientific. Therefore, this application selects the initial value vector function that minimizes the error coefficient as the optimal empirical initial value vector.

[0049] Compared to traditional methods that rely on manual trial and error or default parameters and require repeated adjustments to meet the grid AGC assessment, this application processes historical operating data in conjunction with power control standard requirements to automatically select the optimal initial value vector function that closely meets the three indicators of grid response time, grid control accuracy, and grid control rate. This can greatly save the time required for the PID controller to go from initial adjustment to meeting all grid requirements.

[0050] In one embodiment of this application, reference is made to Figure 4 After obtaining the power control standard requirements of the power grid, the following is also included: Step S120: Obtain the sum of the available active power values ​​of multiple wind turbine units, and compare the sum of the available active power values ​​of multiple wind turbine units with the power control target value; Step S121: If the sum of the available active power values ​​of multiple wind turbine units is less than the power control target value, determine that the energy management platform system is operating in free generation mode; if the sum of the available active power values ​​of multiple wind turbine units is not less than the power control target value, determine that the energy management platform system is operating in dispatch mode.

[0051] It should be noted that in the free control mode, the energy management platform system controls the wind turbines to participate in power regulation upon receiving a power control command for the entire wind farm, but does not restrict the power operation of the wind farm. In the dispatch mode, the energy management platform system controls the wind turbines to participate in power regulation upon receiving a power control command for the entire wind farm, and restricts the power operation of the wind farm.

[0052] It should be noted that if the sum of the available active power values ​​of multiple wind turbines is less than the power control target value, it indicates that the sum of the available active power values ​​of the multiple wind turbines is not up to standard. In this case, the energy management platform system needs to control the multiple wind turbines to operate in free generation mode to stop limiting their active power and to control them to generate power at full capacity, so that the sum of the available active power values ​​of the multiple wind turbines can further approach the power control target value. Therefore, in this case, this application can determine that the multiple wind turbines are operating in free generation mode. Conversely, if the sum of the available active power values ​​of multiple wind turbines is not less than the power control target value, it indicates that the sum of the available active power values ​​of the multiple wind turbines is too large. In this case, the energy management platform system needs to control the multiple wind turbines to operate in dispatch mode to limit their operating power, so that the sum of the available active power values ​​of the multiple wind turbines decreases to the power control target. Therefore, in this case, this application can determine that the multiple wind turbines are operating in dispatch mode.

[0053] In one embodiment of this application, reference is made to Figure 5 When the energy management platform system operates in free power generation mode, the step of selecting one of the control cycles within at least one preset time window to participate in power regulation based on the actual power change amplitude and the first target power change amplitude includes: Step S210: Obtain the required power change rate of the power grid, and compare the actual power change amplitude with the first target power change amplitude; Step S211: When the absolute value of the actual power change amplitude is not greater than the first target power change amplitude, and the power grid does not have a power change rate detection requirement, select one of the control cycles within the preset time window to adjust the power of the wind farm based on the actual power change amplitude; Step S212: When the absolute value of the actual power change amplitude is greater than the first target power change amplitude, and the power grid does not have a power change rate detection requirement, calculate the quotient N and the remainder M of the actual power change amplitude and the first target power change amplitude; Step S213: Select one of the control cycles within N preset control time periods to adjust the power of the wind farm based on the target power change amplitude. If the remainder M is greater than zero, adjust the power of the wind farm based on the second target power change amplitude within one of the control cycles within the (N+1)th preset control time period. Wherein, the first target power change amplitude is greater than the second target power change amplitude. Step S214: When the power grid has a power change rate detection requirement, control the actual power change amplitude of the wind farm within each preset time window to be less than the first target power change threshold.

[0054] It should be noted that the specific method of regulating the power of a wind farm based on the actual power change amplitude is as follows: when it is necessary to increase or decrease the power of the wind farm, the increase or decrease amplitude is adjusted according to the actual power change amplitude.

[0055] It should be noted that when the power grid has a power change rate requirement, the grid will randomly check the real-time power change rate of the wind turbines within a continuous preset time period. When the grid does not have a power change rate requirement, the grid will randomly check the real-time power change rate for any preset time period. The first target power change amplitude is the percentage of the total installed capacity of the wind farm that the grid requires to be within the preset time period when the energy management platform system is operating in free generation mode. The specific value is determined based on the actual requirements of the grid.

[0056] In this embodiment, where the power grid does not have a power change rate detection requirement, this application ensures that the absolute value of the actual power change amplitude is not greater than the first target power change amplitude. Since the actual power change amplitude is small, the power of the wind farm can be adjusted based on the current actual power change amplitude in one control cycle within the preset time window to complete all adjustments at once. Other control cycles within the preset time window are used for normal power generation to minimize the power limiting process. Compared to distributing the power change amplitude over the entire preset time window, the power generation of the wind farm can be maximized.

[0057] In one exemplary embodiment, assuming a preset time window of 1 minute, a first target power change amplitude of 1 / 10 of the wind farm's installed capacity as required by the power grid within 1 minute, and a control cycle of 5 seconds per round, the 1 minute is divided into 12 5-second intervals. When the absolute value of the actual power change amplitude of multiple wind turbine units is not greater than the first target power change amplitude, and the power grid does not have a power change rate detection requirement, this application can select any 5-second interval within the 12 5-second intervals to adjust the power of the wind turbine units upwards or downwards based on the current actual power change amplitude, completing all adjustments at once. The remaining 5 seconds are used for normal power generation, minimizing the power limiting process and maximizing the wind farm's power generation.

[0058] When the absolute value of the actual power change amplitude of multiple wind turbines exceeds the first target power change amplitude, and the power grid does not have a power change rate detection requirement, the actual power change amplitude is relatively large. Therefore, the actual power change amplitude can be decomposed into multiple preset time windows for execution. The specific number of preset time windows can be N, which is the quotient of the absolute value of the actual power change amplitude divided by the first target power change amplitude. The power control process of the wind turbines is decomposed into N preset time windows. Within any control cycle of any of the N preset time windows, the power of multiple wind turbines is adjusted upwards or downwards according to the first target power change amplitude. If there is a remainder in the division result, the power of multiple wind turbines is further adjusted upwards or downwards according to a smaller second target power change amplitude within any control cycle of the (N+1)th preset time window to complete the power limiting process. With this setup, upon receiving a full-field power control command, this application decomposes the power limiting process into multiple control processes to minimize the power limiting process. Since the power limiting time is minimized, the normal unconstrained power generation time of the system is correspondingly maximized, thereby maximizing the system's power generation.

[0059] In an exemplary embodiment, assuming a preset time window of 1 minute, a first target power change amplitude of 1 / 10 of the wind farm's installed capacity as required by the power grid within 1 minute, and a control cycle of 5 seconds per round, the 1 minute is divided into 12 5-second intervals. When the absolute value of the actual power change amplitude of multiple wind turbine units is greater than the first target power change amplitude, and the power grid does not have a power change rate detection requirement, this application can adjust the power of multiple wind turbine units upward or downward according to the first target power change amplitude within any 5-second interval of the N 1-minute intervals, and further adjust the power of multiple wind turbine units upward or downward according to a smaller second target power change amplitude within any 5-second interval of the N+1 1-minute interval, thereby completing the power limiting process.

[0060] When the power grid has rolling rate detection requirements (i.e., real-time monitoring of the power change rate within any consecutive 1-minute window), this application smoothly distributes the total power change to multiple control cycles and controls it according to the power change slope to ensure that the actual power change within each preset time window is less than the first target power change threshold, thereby avoiding violation of the power grid rate requirements due to excessive instantaneous changes.

[0061] In one embodiment of this application, reference is made to Figure 6 The historical operating performance indicators include the historical maximum power generation ratio, the historical minimum number of outages, the historical minimum number of yaws, and the historical minimum number of pitch changes. The priority of wind turbine units participating in power regulation based on these historical operating performance indicators includes: Step S310: Assign corresponding weights and scores to the historical maximum power generation ratio, historical minimum number of outages, historical minimum number of yaws, and historical minimum number of pitch changes to determine the score of each wind turbine. Step S311: Sort the priority of wind turbine units participating in power regulation according to the score of each wind turbine unit. The score of the wind turbine unit is inversely proportional to the priority of the wind turbine unit participating in power regulation.

[0062] It should be noted that a higher percentage of historical maximum power generation indicates higher power generation efficiency of the wind turbine; fewer historical minimum shutdowns indicate better operational continuity; fewer historical minimum yaws indicate better wind direction adaptability or less regulation interference; and fewer historical minimum pitch changes indicate more stable power control. Weighting can be set according to the importance of the percentage of historical maximum power generation, the number of historical minimum shutdowns, the number of historical minimum yaws, and the number of historical minimum pitch changes. In an exemplary embodiment, the weighting coefficients for the percentage of historical maximum power generation, the number of historical minimum shutdowns, the number of historical minimum yaws, and the number of historical minimum pitch changes are all 0.25. The wind turbine score = 0.25 × normalized value of historical maximum power generation percentage + 0.25 × (1 - normalized value of historical minimum shutdowns) + 0.25 × (1 - normalized value of historical minimum yaws) + 0.25 × (1 - normalized value of historical minimum pitch changes).

[0063] It is understandable that the higher the score of a wind turbine, the better its power generation performance. Therefore, this application prioritizes wind turbines for power regulation based on their scores, with higher scores indicating lower priority. This ensures that turbines with better power generation performance are given priority for power regulation tasks (such as power limiting and shutdown) to maximize the power generation of the wind farm.

[0064] In one embodiment of this application, reference is made to Figure 7 After obtaining the power control standard requirements of the power grid, the following is also included: Step S110: Filter the wind turbine units that can participate in power regulation to form a list of controllable wind turbines; Step S111: Based on the operating parameters of the controllable fans in the controllable fan list, set the adjustment priority weights for power increase and power decrease for each controllable fan. Step S112: Control each controllable fan to participate in power regulation according to the adjustment priority weight of power increase and power decrease of each controllable fan.

[0065] It should be noted that the controllable wind turbine is not a prototype, and refers to wind turbines that are in remote control mode, fault-free, undergoing maintenance and repair, and with normal communication. This application can screen multiple wind turbines based on the above conditions to form a list of controllable wind turbines. With this setting, this application excludes non-controllable wind turbines when controlling wind turbines to participate in power regulation, ensuring that only controllable wind turbines can participate in power regulation, thus improving the reliability of power regulation.

[0066] The operating parameters of controllable wind turbines include important parameters such as turbine model, specific power range, wind speed, pitch angle, generator temperature, and hub speed. Since these parameters are related to the efficiency of power adjustment (both increasing and decreasing), for example, turbines in high wind speed areas and close to their rated power are easier to adjust downwards, while turbines in medium-to-low wind speed areas and far from their rated power are easier to adjust upwards. Therefore, this application assigns corresponding priority weights to these operating parameters based on their importance in power adjustment, and controls the controllable wind turbines to participate in power regulation based on these priority weights. With this setup, in practical applications, when the energy management platform system controls wind turbines to participate in power regulation, it controls the wind turbines with higher priority to participate in power regulation based on the current power regulation needs (power increase or decrease). This not only improves the wind farm's response efficiency and control accuracy to overall power control commands, but also effectively shortens adjustment time and reduces unnecessary downtime and mechanical actions. This ensures successful AGC testing while creating conditions for maximizing subsequent power generation.

[0067] It should be noted that, in the case where the control method of the energy management platform system of this application also includes steps S310 and S311 of the above embodiment, this application can first execute steps S110 to S112 of this embodiment to determine the up / down priority weight, and then execute steps S310 and S311 to jointly determine the final power regulation priority, so as to achieve intelligent power control that meets the grid AGC assessment requirements and maximizes the overall power generation of the wind farm.

[0068] After the priorities in steps S110 to S112 are determined, the priorities are fine-tuned according to the four parameters in steps S310 and S311. For example, if a wind turbine is originally determined to be priority 1 according to steps S110 to S112, and the weights calculated in steps S310 and S311 correspond one-to-one with the priority ranking method in steps S110 to S112 from high to low, but the wind turbine is calculated to be priority 1 according to steps S310 and S311, it means that this wind turbine has the best historical power generation performance. Therefore, the priority time for power control should be reduced as much as possible, and this wind turbine is adjusted to priority 2, that is, downgraded by 1 level for fine-tuning.

[0069] The specific control flow of the control method of the energy management platform system of this application is illustrated by referring to the above embodiments: Step 1: Determine whether local power control or AGC function control needs to be enabled. The energy management platform system enables AGC function control upon receiving a full-field power control command. Local power control is automatically determined based on the system's own characteristics.

[0070] Step 2: Execute step 100 to provide the current power control standard requirements of the regional power grid for the following steps; and execute steps S110 to S112 to set the priority for controlling wind turbine units to participate in power regulation; Step 3: Perform steps S120 and S121 to determine the current operating modes of the multiple wind turbine units; Step 4: If the operating mode of multiple wind turbines is determined to be free generation mode, execute steps S210 to S214 to decompose the power curtailment process into multiple control processes to minimize the power curtailment time. Since the power curtailment time is minimized, the normal unconstrained power generation time of the system is correspondingly maximized, thereby maximizing the system's power generation. It should be noted that if the operating mode is dispatch mode, directly execute step 5.

[0071] Step 5: Execute steps S310 and S311 to further determine the priority of wind turbine units participating in power regulation, so as to achieve intelligent power control that meets the grid AGC assessment requirements and maximizes the overall power generation of the wind farm.

[0072] Step 6: Execute steps S400, S500 and S600 to adjust the control accuracy of the energy management platform system in real time to be basically consistent with the control accuracy required by the power grid, so as to avoid excessive power adjustment due to insufficient control accuracy, resulting in unnecessary power limitation, and to maximize the power generation of the wind farm under the premise of meeting the test standards.

[0073] The above steps S010~S040 can be executed before step 1 to automatically select the optimal initial value vector function that is close to meeting the three indicators of power grid response time, power grid control accuracy, and power grid control rate, which greatly saves the process time of the PID controller from initial adjustment to meeting the power grid requirements in terms of performance indicators.

[0074] This application also provides an energy management platform system, which includes an improved PID controller and an energy management platform. The improved PID controller is used to control the power of multiple wind turbine units. The energy management platform includes: The data acquisition module is used to acquire the power control standard requirements of the power grid; The power regulation module is used to obtain the actual power change amplitude of the wind farm within a preset time window, select one of the control cycles within at least one preset time window to control the wind turbine to participate in power regulation based on the actual power change amplitude and the first target power change amplitude; and to obtain the historical operating efficiency index of each wind turbine, and sort the priority of the wind turbine to participate in power regulation based on the historical operating efficiency index. The improved PID controller includes: The control accuracy consistency algorithm module is used to obtain the actual control accuracy of the improved PID controller in the current control cycle, calculate the first accuracy deviation between the actual control accuracy of the current control cycle and the control accuracy required by the power grid, and compare the absolute value of the first accuracy deviation in the current control cycle with a set accuracy deviation threshold. The inverse step size sensing algorithm module is used to calculate the second precision deviation between the actual control precision of the current control cycle and the actual control precision of the previous control cycle when the absolute value of the first precision deviation in the current control cycle is greater than a set precision deviation threshold. The step size and frequency real-time adjustment module is used to adjust the control step size and control frequency of the improved PID controller in the next control cycle in real time according to the second accuracy deviation, so that the absolute value of the first accuracy deviation in the next control cycle is not greater than the set accuracy deviation threshold.

[0075] refer to Figure 8 Existing PID controller algorithms only process the difference between the actual useful power value and the power control target value of the wind turbines across the entire field using a fixed input step size and frequency. This allows the actual useful power value of the wind turbines to track the power control target value in real time, but it cannot dynamically adjust the adjustment intensity based on the real-time error trend.

[0076] Therefore, in this embodiment, the existing PID controller has been improved, referring to... Figure 9Compared to existing PID controllers that rely solely on fixed step size and frequency to regulate the power of wind turbines, the improved PID controller regulates the power of wind turbines based on a control accuracy consistency algorithm module, an inverse step size sensing algorithm module, and a real-time step size and frequency adjustment module. The specific steps are as follows: When the control accuracy consistency algorithm module detects that the first accuracy deviation between the actual control accuracy (the difference between the power control target value and the actual useful power value of the wind turbine) and the grid-required control accuracy is greater than the set accuracy deviation threshold, the inverse step size sensing algorithm module is enabled. This causes the inverse step size sensing algorithm module to execute step S500, ensuring that the actual control accuracy can track the grid-required control accuracy in real time. This avoids excessive power over-adjustment due to insufficient control accuracy, which could lead to unnecessary power limitations. The inverse step size sensing algorithm module will also output the control step size and control frequency of the next control cycle calculated by step S600 to the step size and frequency real-time adjustment module. The step size and frequency real-time adjustment module updates the control step size and control frequency of the PID controller in real time based on the output of the inverse step size sensing algorithm module. This ensures that the output of the improved PID controller—the real-time control accuracy—is dynamically consistent with the grid standard requirements, just meeting the requirements. This prevents wasting power generation time and control response process due to positive or negative deviations in control accuracy, thereby increasing power generation.

[0077] It should be noted that during the initial commissioning of each wind power project, the step size and frequency real-time adjustment module is used to execute steps S010 to S040 to automatically select the optimal initial value vector function that is close to meeting the three indicators of grid response time, grid control accuracy, and grid control rate. This greatly saves the time for the PID controller to go from initial adjustment to meeting the grid requirements in terms of performance indicators.

[0078] It is understandable that, since the above modules are all software modules, users can write the corresponding programs into the existing software when using the control method of the energy management platform system of this application, without adding any hardware devices, and can achieve the above technical effects at low cost.

[0079] This application also provides a wind farm, including: a plurality of wind turbine generators, a memory, an energy management platform system, and a computer program stored on the memory and executable on the energy management platform system, the computer program being configured to implement the steps of the control method of the energy management platform system described in any of the above claims.

[0080] The wind farm provided in this application, employing the control method of the energy management platform system described in the above embodiments, can solve the same technical problems. The specific technical problems are as follows: First, when executing AGC full-field power control commands, traditional wind farm power control algorithms, in order to ensure test pass, do not fully consider the constraints of power-limited shutdowns, yaw, and pitch counts. As the number of related actions during power-limited processes increases, the corresponding power generation time and resources consumed increase, leading to power generation loss and preventing maximization. Second, traditional methods only limit the response time to within the time required by the test standard, without further minimizing the control time. In reality, the shorter the control time, the more time can be used for power generation, which is more conducive to increasing power generation. Furthermore, under the premise of meeting the test standard, excessively pursuing the minimization of control accuracy can easily cause power over-adjustment, resulting in unnecessary power limitations, thereby affecting the overall power generation efficiency of the system and leading to wasted power generation.

[0081] The beneficial effects of this system include: while meeting grid standards, the system parameters can avoid being assessed by the grid and maximize power generation, thereby improving the wind farm's revenue; at the same time, all algorithms in this application are integrated into the original energy management platform software, requiring no additional hardware and offering a low-cost advantage; furthermore, the PID controller it employs, through the optimal initial value assignment method (steps S010 to S040), can shorten the process time from initial adjustment to meeting grid indicators, offering the advantage of fast solution speed.

[0082] Compared with the prior art, the beneficial effects of the energy management platform provided by the wind farm in this application are the same as the beneficial effects of the control method of the energy management platform system provided in the above embodiments, and the other technical features of the wind farm are the same as the features disclosed in the methods of the above embodiments, which will not be repeated here.

[0083] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A control method for an energy management platform system, applied to a wind farm, the wind farm comprising multiple wind turbine units, wherein the energy management platform system is used to control the wind turbine units to participate in power regulation upon receiving a power control command for the entire wind farm, characterized in that... The control method of the energy management platform system includes: Obtain the power control standard requirements of the power grid, which include the required control accuracy of the power grid and the first target power change amplitude; The actual power change amplitude of the wind farm within a preset time window is obtained, and the wind turbine is controlled to participate in power regulation in one of the control cycles within at least one preset time window based on the actual power change amplitude and the first target power change amplitude. Obtain the historical operating performance indicators of each wind turbine, and rank the wind turbines for power regulation based on the historical operating performance indicators; The actual control accuracy of the energy management platform system in the current control cycle is obtained, and the first accuracy deviation between the actual control accuracy in the current control cycle and the control accuracy required by the power grid is calculated. The absolute value of the first accuracy deviation in the current control cycle is compared with a set accuracy deviation threshold. If the absolute value of the first precision deviation in the current control cycle is greater than the set precision deviation threshold, calculate the second precision deviation between the actual control precision in the current control cycle and the actual control precision in the previous control cycle. The control step size and control frequency of the energy management platform system in the next control cycle are adjusted according to the second accuracy deviation, so that the absolute value of the first accuracy deviation in the next control cycle is not greater than the set accuracy deviation threshold.

2. The control method for the energy management platform system as described in claim 1, characterized in that, Adjusting the control step size and control frequency of the energy management platform system for the next control cycle based on the second accuracy deviation, so that the absolute value of the first accuracy deviation in the next control cycle is not greater than a set accuracy deviation threshold, includes: When the second accuracy deviation is greater than zero, the control step size and control frequency of the energy management platform system in the next control cycle are increased based on the first formula, which is: ； ； When the second accuracy deviation is less than zero, the control step size and control frequency of the energy management platform system in the next control cycle are increased based on the second formula, which is: ； ； in, The control step size for the next control cycle. This represents the control step size for the current control cycle. The step size for adjusting the control power is determined for each round. The control frequency for the next control cycle. The control frequency for the current control cycle. The step size for adjusting the control frequency is determined for each round. This is for adjusting the coefficient.

3. The control method for the energy management platform system as described in claim 2, characterized in that, The adjustment coefficient is defined as the quotient obtained by dividing the required control accuracy of the power grid by the set accuracy deviation threshold, and the formula is: Among them, e demand To meet the control precision requirements of the power grid, To set a precision deviation threshold.

4. The control method for the energy management platform system as described in claim 1, characterized in that, The power control standard requirements also include grid response time and grid control rate requirements, and the control method of the energy management platform system further includes: Construct a system that includes control step size With control frequency Multiple initial value vector functions ; Multiple initial value vector functions Decoupling and filtering are performed according to three dimensions: response time, control accuracy, and control rate, to determine the first vector function corresponding to the shortest response time under the condition of meeting the upper limit of the power grid's required response time in each of the three dimensions. The second vector function that best approximates the required control accuracy of the power grid. And the third vector function that is closest to the required control rate of the power grid. ; Calculate the first vector function according to the error coefficient formula. Second vector function and the third vector function and multiple initial value vector functions error coefficient The formula for the error coefficient is: Selecting the error coefficient Minimum initial value vector function The optimal empirical initial value vector ; in, To control the step size, To control the frequency.

5. The control method for the energy management platform system as described in claim 1, characterized in that, Following the acquisition of power control standard requirements for the power grid, the following is also included: Obtain the sum of the available active power values ​​of multiple wind turbine units, and compare the sum of the available active power values ​​of multiple wind turbine units with the power control target value; If the sum of the available active power values ​​of multiple wind turbine units is less than the power control target value, the energy management platform system is determined to operate in free generation mode; if the sum of the available active power values ​​of multiple wind turbine units is not less than the power control target value, the energy management platform system is determined to operate in dispatch mode.

6. The control method for the energy management platform system as described in claim 5, characterized in that, When the energy management platform system operates in free power generation mode, selecting one of the control cycles within at least one preset time window to participate in power regulation based on the actual power change amplitude and the first target power change amplitude includes: Obtain the required rate of power change of the power grid, and compare the actual power change amplitude with the first target power change amplitude; If the absolute value of the actual power change amplitude is not greater than the first target power change amplitude, and the power grid does not have a power change rate detection requirement, the power of the wind farm is adjusted based on the actual power change amplitude during one of the control cycles within a preset time window. If the absolute value of the actual power change amplitude is greater than the first target power change amplitude, and the power grid does not have a power change rate detection requirement, calculate the quotient N and the remainder M of the actual power change amplitude and the first target power change amplitude. The power of the wind farm is adjusted based on the target power change amplitude during one of the N preset control time periods. If the remainder M is greater than zero, the power of the wind farm is also adjusted based on the second target power change amplitude during one of the control periods during the (N+1)th preset control time period. Wherein, the magnitude of the change in the first target power is greater than the magnitude of the change in the second target power; When the power grid has a power change rate detection requirement, the actual power change amplitude of the wind farm is controlled to be less than the first target power change threshold in each preset time window based on the real-time slope of the actual power change amplitude.

7. The control method for the energy management platform system as described in claim 1, characterized in that, Following the acquisition of power control standard requirements for the power grid, the following is also included: Select the wind turbine units that can participate in power regulation to form a list of controllable wind turbines; Based on the operating parameters of the controllable fans in the controllable fan list, power increase and power decrease adjustment priority weights are set for each of the controllable fans. The power regulation of each controllable fan is controlled according to the adjustment priority weight of the power increase and decrease of each controllable fan.

8. The control method for the energy management platform system as described in claim 1, characterized in that, The historical operational performance indicators include the historical maximum power generation ratio, the historical minimum number of outages, the historical minimum number of yaws, and the historical minimum number of pitch changes. The priority ranking of wind turbine units for power regulation based on these historical operational performance indicators includes: Each wind turbine is assigned a corresponding weight and score based on its historical maximum power generation ratio, historical minimum number of outages, historical minimum number of yaws, and historical minimum number of pitch changes to determine the score for each wind turbine. The priority of wind turbines in power regulation is determined by their scores, with the scores of each wind turbine inversely proportional to its priority in power regulation.

9. An energy management platform system, characterized in that, The energy management platform system includes an improved PID controller and an energy management platform, wherein the improved PID controller is used to control the power of multiple wind turbine units; The energy management platform includes: The data acquisition module is used to acquire the power control standard requirements of the power grid; The power regulation module is used to acquire the actual power change amplitude of the wind farm within a preset time window, select one of the control cycles within at least one preset time window to control the wind turbine to participate in power regulation based on the actual power change amplitude and the first target power change amplitude; and to acquire the historical operating efficiency index of each wind turbine, and sort the priority of the wind turbine to participate in power regulation based on the historical operating efficiency index. The improved PID controller includes: The control accuracy consistency algorithm module is used to obtain the actual control accuracy of the improved PID controller in the current control cycle, calculate the first accuracy deviation between the actual control accuracy of the current control cycle and the control accuracy required by the power grid, and compare the absolute value of the first accuracy deviation in the current control cycle with a set accuracy deviation threshold. The inverse step size sensing algorithm module is used to calculate the second precision deviation between the actual control precision of the current control cycle and the actual control precision of the previous control cycle when the absolute value of the first precision deviation in the current control cycle is greater than a set precision deviation threshold. The step size and frequency real-time adjustment module is used to adjust the control step size and control frequency of the improved PID controller in the next control cycle according to the second accuracy deviation, so that the absolute value of the first accuracy deviation in the next control cycle is not greater than the set accuracy deviation threshold.

10. A wind farm, characterized in that, include: The system comprises multiple wind turbines, a memory, an energy management platform system, and a computer program stored on the memory and executable on the energy management platform system, the computer program being configured to implement the steps of the control method for the energy management platform system as described in any one of claims 1 to 8.

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